The Local Benefits of Global Air
Pollution Control in Mexico City
Final Report of the Second Phase of the
Integrated Environmental Strategies
Program in Mexico
by
Galen McKinley, Miriam Zuk, Morten Hojer,
Monserrat Avalos, Isabel González, Mauricio Hernández,
Rodolfo Iniestra, Israel Laguna, Miguel Ángel Martínez, Patricia Osnaya,
Luz Miriam Reynales, Raydel Valdés and Julia Martínez
Instituto Nacional de Ecología, México
Instituto Nacional de Salud Publica, México
August 2003
Table of Contents
I.
Executive Summary
McKinley
II.
Project Summary
III.
Emission Reductions and Costs
III.1. General Methodology
III.2. Renovation of the taxi fleet
III.3. Extension of the Metro
III.4. Hybrid buses
III.5. Measures to reduce leaks of Liquefied Petroleum Gas
III.6. Co-generation
McKinley and Zuk
McKinley
Hojer
Osnaya
McKinley
McKinley
Laguna
IV.
Air quality modeling
McKinley and Iniestra
V.
Health impacts analysis
Zuk with Avalos, Martínez, Hernández,
González, Reynales and Valdés
VI.
Valuation
Zuk with Avalos, Martínez, Hernández,
González, Reynales and Valdés
VII.
Integration: The Co-Benefits model
VIII.
Results
McKinley
IX.
Conclusions
McKinley
McKinley and Zuk
Appendix A. Air Quality Modeling
McKinley and Iniestra
Appendix B. Capacity Building
Zuk and McKinley
Appendix C. Basic User’s Guide for the Co-Benefits Model
McKinley and Zuk
Acknowledgements:
We thank the U.S. Environmental Protection Agency (EPA) and the U.S.-Mexico
Foundation for Science (FUMEC) for their support of the project. We appreciate the input
of Dr. Adrián Fernandez of INE. We also thank Dr. Jason West of the US EPA for his
attention to the project.
ii
Contact Information:
Consultants to Instituto Nacional de Ecología:
Galen McKinley
Miriam Zuk
Morten Hojer
[email protected]
[email protected]
[email protected]
Instituto Nacional de Ecología:
Julia Martínez
Montserrat Avalos
Isabel González
Rodolfo Iniestra
Miguel Ángel Martínez
Israel Laguna
Patricia Osnaya
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
Instituto Nacional de Salud Publica:
Mauricio Hernández
Luz Miriam Reynales
Raydel Valdes
[email protected]
[email protected]
[email protected]
iii
Chapter I. Executive Summary
From September 2002 to August 2003, the Second Phase of the Integrated Environmental
Strategies Program in Mexico was undertaken at the Instituto Nacional de Ecología (INE;
National Institute of Ecology) of Mexico. In this report, activities and findings are
summarized. During this project, the following goals have been achieved:
•
•
•
•
Estimate cost savings due to health improvements related to air pollution reductions
occurring simultaneously with greenhouse gas (GHG) emissions reductions,
Compare costs and benefits for the specific policy measures,
Build capacity in the Mexican government for integrated, quantitative environmental
and economic assessment, and
Provide results and tools with relevance to emission control decision- making process in
Mexico City.
We produce estimates of annualized reductions of emissions of local and global air
pollutants and program costs for three transportation measures (taxi fleet renovation, metro
expansion, and hybrid buses), one residential measure to reduce leaks of liquefied
petroleum gas (LPG) from stoves, and one industrial measure for cogeneration for the
periods 2003-2010 and 2003-2020 at several discount rates. Using reduced-form air quality
modeling techniques, the impacts of changed emissions on exposure are calculated. Then
using dose-response methodology, public health improvements due to reduced exposure are
estimated. Finally, various valuation metrics are applied to determine the monetized health
benefits to society of the control measure.
We find that the 5 measures considered in this study could reduce annualized exposure to
particulate air pollution by 1% and to maximum daily ozone by 3%, and also reduce
greenho use gas emissions by 2% (more than 300,000 tons C equivalent per year) for both
the time periods. We estimate that for both time horizons, over 4400 quality-adjusted lifeyears (QALYs) per year could be saved, with monetized public health benefits on the order
of $200 million USD per year. In contrast, total costs are under $70 million USD per year.
The mean cost per QALY is estimated to be under $40,000 for the 5 measures. Of the
measures considered, transportation measures are most promising for simultaneous
reductions of both local and global pollution in Mexico City.
This analysis has been integrated in to an user-friendly modeling tool using Analytica
software. The Co-Benefits Model has been made available to decision- makers and their
staffs in Mexico City. There is interest from these groups in applying the model to their
work and in modifying it for use in other regions of Mexico, particularly the City of Toluca
in the State of Mexico.
Capacity building has been a major part of this project. A large group of INE staff have
actively contributed to the research effort. Regular meetings and training sessions have
been held with members of the Metropolitan Environmental Commission (CAM) and other
environmental agencies in the Mexico City. These meetings have encouraged active
4
participation in this project and aided the integration of this work with other air pollution
control efforts in the region.
5
Chapter II. Project Summary
II.1. Introduction
Due to complex socio-political, economic and geographical realities, Mexico City suffers
from one of the worst air pollution problems in the world. Greenhouse gas emissions from
the City are also substantial. In this study, we compare the costs and benefits of a set of
politically- relevant air pollution control measures for the City and simultaneously consider
the greenhouse gas emission impacts of these measures. We find that with 5 control
measures, it would be possible to reduce annualized exposure to particulate air pollution by
1% and to peak ozone by 3%, and also to reduce greenhouse gas emissions by 2% (more
than 300,000 tons C equivalent per year) for the time periods 2003-2010 and 2003-2020.
We estimate that for both time horizons, over 4400 quality-adjusted life- years (QALYs) per
year could be saved, with monetized public health benefits on the order of $200 million
USD per year. In contrast, total costs are under $70 million USD per year. The mean cost
per QALY is estimated to be under $40,000 for the 5 measures. We find that transportation
measures are likely to be the most promising for simultaneous reductions of both local and
global pollution in Mexico City.
II.2. Motivation
With nearly 20 million inhabitants, 3.5 million vehicles, and 35,000 industries, Mexico City
consumes more than 40 million liters of fuel each day. It is also located in a closed basin
with a mean altitude of 2240m. The combination of these and other factors has led to a
serious air quality problem. In 2002, Mexico City air quality exceeded local standards for
ozone (110 ppb for 1 hour) on 80% of the days of the year. Particulate 24- hour standards
were exceeded on 5% of the days (SMA, 2002).
Greenhouse gas (GHG) emissions from Mexico City are also significant. In 1998, Mexico
ranked as the 13th largest GHG producing nation. Mexico City emits approximately 13% of
the national total (Sheinbaum et al., 2000). Using a 3.3% annual growth rate (West et al.,
2003) and a 1996 base year estimate of 45,585,000 tons of CO2 (Sheinbaum et al., 2000),
we estimate that the annualized GHG emission of Mexico City for the period 2003-2010
and 2003-2020 will be 17 million tons of C equivalent per year and 20 million tons C
equivalent per year, respectively.
As emissions of GHG and local air pollutants are often generated from the same sources,
there may exist opportunities for their joint control. In this study, we have developed a
cost-benefit analysis framework to analyze the trade-offs between costs, public health
benefits, and GHG emission reductions for a select set of control measures. In an effort to
disseminate the knowledge collected in this work, we have also created a reduced- form
analysis tool for use by policy makers.
This study fits into an ongoing process of analysis and action regarding Mexico City air
quality. At present, Mexico City government is currently in the process of implementing
6
its third air quality management plan. The first plan, PICCA (Programa Integral para el
Control de la Contaminación Atmosférica) was initiated in 1990 and had several major
accomplishments, including the introduction of two way catalytic converters, the phase out
of leaded gasoline, and establishment of vehicle emissions standards. The second program,
PROAIRE (Programa para Mejorar la Calidad del Aire en el Valle de México 1995-2000)
achieved the introduction of MTBE, restrictions on the aromatic content of fuels and
reduction of sulfur content in industrial fuel. While significant improvements in ambient
air quality have improved, levels remain dangerously high, therefore the government has
recently initiated the third plan, PROAIRE 2002-2010, as an extension of previous plans.
PROAIRE 2002-2010 includes 89 control measures targeting emissions reductions from
mobile, point and area sources, as well as proposing education and institutional
strengthening measures to combat the air pollution that afflicts the city. While some of
these measures are slowly being implemented, little quantitative analysis has been done
prior to designing this plan. Decision makers are now faced with the difficulty in setting
priorities when presented with a such a large range of control options. Several studies are
currently quantitatively analyzing these issue (Molina et al., 2002). A recent study by West
et al. (2003) aimed to analyze a large number of PROAIRE and climate change control
measures to determine the least cost set of options for joint control. This study builds on
these works, by simplifying and integrating the analysis to provide real time answers to
policy makers.
II.3. Methodology
Emissions Reductions and Costs for Specific Control Measures
We estimate the time profiles of local pollutant (PM10 , SO2 , CO, NOx , and HC) and global
pollutant (CO2 , CH4 , and N2 O) emission reductions, and costs for 5 control measures that
address transportation, residential and industrial emission sources. We estimate emissions
reductions and costs for each year from 2003 to 2020 such that the different time-profiles of
the programs’ costs and impacts can be studied. These two time horizons were chosen to
allow us to analyze the short term on the time frame of the plan itself, and a longer term
analysis on the scale of the project implementation. For incorporation into the cost – benefit
analysis, results are annualized using several discount rates. In this Project Summary, we
present results using a 5% discount rate only.
Below, key aspects of the control measures analyzed in this study are outlined. In Tables
II.1 and II.2, the estimated emissions reductions and costs of these measures are presented.
Taxi fleet renovation
• 80% of old taxis are replaced by 2010
• Fuel efficiency increases from 6.7 km/L to 9 km/L
• Tier I technology is assumed in 1999 and newer models
• Changes in emissions of primary particulate matter are not estimated
7
Metro expansion
• 76 km of new construction by 2020 (5 km between 2003 and 2010, 71 km
from 2011 to 2020)
• Riders assumed to come from microbuses and combis
• Recuperation value of capital is included, using a 30 year useful life
Hybrid buses
• 1029 hybrid buses are brought into circulation, replacing diesel buses, by
2006
• Emissions factors from detailed study for New York City (MJ Bradley and
Associates, 2000)
LPG leaks
•
•
Stove maintenance is performed in 1 million households to eliminate leaks
This is a combination of 4 measures that each address a specific part of LPG
stove systems (TUV, 2000)
Cogeneration
• Installation of 160 MW of capacity by 2010
• Recuperation value of capital is included, using a 20 year useful life
Table II.1. Annualized emissions reductions (tons / year)
Control Measure
PM 10
SO2
CO
NOx
HC
CO2
CH4
N2 O
Time horizon 2003-2010
Taxi Renovation
0
64
165,483
5,135
16,863
275,007
64
498
Metro Expansion
1
4
3,518
155
324
19,567
5
1
Hybrid Buses
73
14
566
-119
274
54,063
2
0
LPG Leaks
0
0
0
9
0
75
2,480
0
7,475
590,080
0
10
0
Cogeneration
0
0
1
Time horizon 2003-2020
Taxi Renovation
0
59
146,380
3,060
12,811
257,542
60
466
Metro Expansion
9
65
28,835
1,271
2,653
160,368
39
9
Hybrid Buses
82
16
635
-134
307
60,656
2
0
LPG Leaks
0
0
0
0
1,954
5,888
0
0
0
0
13
110
0
856,031
15
1
Cogeneration
8
Table II.2. Annualized abatement costs (2003 million US$ / year)
Control Measure
Public Investment
Taxi Renovation
Metro Expansion
Hybrid Buses
LPG Leaks
Cogeneration
16.10
5.37
54.33
1.31
0
Taxi Renovation
Metro Expansion
Hybrid Buses
LPG Leaks
Cogeneration
8.90
44.05
30.04
0.73
0
Private Investment
Time Horizon 2003-2010
53.66
0
0
1.81
4.83
Time Horizon 2003-2020
29.67
0
0
1.00
7.33
Fuel, Operations,
Maintenance
Total Cost
-61.16
-0.01
-9.10
-1.39
-4.33
8.59
5.37
45.24
1.74
0.49
-57.33
-0.02
-10.21
-0.84
-6.40
-18.76
44.03
19.84
0.89
0.92
Exposure Modeling
For the estimation of the impacts of emission reduction on ambient concentrations and
population exposures, we have developed a range of reduced-form modeling approaches.
Results from a source apportionment study are used to estimate changes in primary and
secondary PM10 . Ozone isopleths from Salcido et al. (2001) are used to estimate peak O3
changes occurring with changes in hydrocarbon and NOx emissions.
In order to account for the spatial relationship of population and pollution concentrations,
as well as to account for annual exposures, we use reduced form models to provide a
reduction fraction (RF) of pollutant concentration (Cesar et al., 2002; USEPA, 1999). This
reduction fraction is then multiplied by projected population-weighted concentrations for
the appropriate time horizon. These projected concentrations use as a baseline the mean
1995-1999 observed, population-weighted (1995 census) 24- hour mean PM10 (64.06 ug/m3 )
or O3 maximum concentration (0.114 ppm), from Cesar et al. (2000). The projection to
future population-weighted concentrations is achieved by a linear interpolation of mean
concentration results from the Multiscale Climate Chemistry Model (MCCM) model for
1998 and 2010 based on the emissions inventory for 1998 and emissions inventory
projection for 2010 of the CAM (PROAIRE, 2002; Salcido et al. 2001).
To estimate changes in PM10 concentrations, the chemical species in the observed
particulate matter are attributed to primary pollutants based on chemical analyses of the
composition of particulate matter in the MCMA (Chow et al. 2002). Fractional changes in
the emission inventories of primary pollutants can then be related to fractional reductions in
particulate concentrations. Results of chemical analyses of the composition of particulate
matter from 6 sampling sites during the IMADA campaign of March 1997 (Chow et al.
2002) are averaged, with weighting based on the total mass of each sample. In order to
attribute organic carbon to its primary (combustion) and secondary (hydrocarbon) sources,
observed organic carbon is disaggregated into its primary and secondary contributions.
Following Turpin et al. (1991), we estimate the primary organic contribution to total
organic carbon based on a fixed ratio to elemental carbon mass of 1.9, a mean value for the
9
Los Angeles basin. The mass of secondary organic carbon is then the difference of the total
organic carbon mass and the mass of primary organic carbon. Total primary particulate
mass from combustion sources (25%) is the sum of primary organic and elemental carbon.
Secondary organic carbon mass (2%) is attributed to hydrocarbon emissions. Additionally,
the mass of particles associated with geological sources (45%) is attributed to primary PM10
emissions from geologic sources; the mass of particles associated with total particulate
ammonium nitrate (7%) is attributed to NOx emissions; and the mass of particles associated
ammonium sulfate (11%) is attributed to SO2 emissions.
The peak mean O3 reduction fraction (RO 3 max) is estimated from the fractional reductions
in hydrocarbon (RHC) and NOx (RNOx ) by:
RO3 max = 0.5353*RNOx - 0.2082*(RNOx )2 + 0.1112*RHC
This equation is derived from a series of runs of the MCCM for Mexico City (Salcido et al.,
2001) where HC and NOx emissions are varied in equal proportion from all sources and O3
concentration changes were recorded. The above equation results from a polynomial
regression fit to the results of Salcido et al. (2001).
These reduced- form air quality modeling approaches are limited by the still large
uncertainty about fundamental processes responsible for ozone and particulate formation in
the Mexico City Valley. Further, the approaches have uncertainty due to the lack of spatial
and temporal resolution and imperfections in the modeling and measurement techniques on
which the approaches are based. An exact quantification of the uncertainty is beyond the
scope of this analysis. Based on the work of Cohen et al. (2003) and comparisons made
during this study, we make a conservative estimate of 30% uncertainty on primary
particulate results, and 50% uncertainty on the secondary particulate and maximum ozone
results.
In Table II.3, concentration change estimates based on Source Apportionment and the
Ozone Isopleth methods are shown for each of the control measures.
Table II.3. Annual particulate and maximum ozone exposure changes (ìg/m3 )
Taxi Renovation
Metro Expansion
Hybrid Buses
LPG Leaks
Cogeneration
Taxi Renovation
Metro Expansion
Hybrid Buses
LPG Leaks
Cogeneration
Particulates (PM 10 )
Mean
95% CI
Time Horizon 2003-2010
0.36
(0.17 : 0.58)
0.01
(0.01 : 0.02)
0.14
(0.06 : 0.23)
0.07
(0.02 : 0.28)
0
(0 : 0)
Time Horizon 2003-2020
0.24
(0.12 : 0.38)
0.12
(0.07 : 0.18)
0.15
(0.07 : 0.25)
0.06
(0.02 : 0.12)
0
(0 : 0.01)
10
Maximum Daily O3
Mean
95% CI
5.13
0.14
-0.07
0.91
0.06
(1.59 : 9.97)
(0.04 : 0.28)
(-0.14 : -0.02)
(0.14 : 1.76)
(0.02 : 0.11)
3.02
1.07
-0.07
0.74
0.08
(0.94 : 5.87)
(0.33 : 2.08)
(-0.14 : -0.02)
(0.23 : 1.43)
(0.02 : 0.15)
Health Impacts Analysis
Results from epidemiological studies are used to estimate avoided cases of mortality and
morbidity (Hij ) due to reductions in ambient concentrations of ozone and PM10 . A standard
dose response equations with the following form is used:
H ij = βij × Ri × C j × N
Where âij is the dose-response coefficient for the ith effect from the j th pollutant (% increase
in cases/year/person/ ìg/m3 ), Ri is the background rate of the effect of interest
(cases/year/person), Cj is the change ambient concentration of pollutant j (µg/m3 ) averaged
across the entire population as determined by the air quality module, and N is the
population at risk (persons).
A set of 19 health impacts, including premature mortality, chronic bronchitis, medical
attention for cardiovascular and respiratory disease, and work loss days are analyzed in this
study. Dose response coefficients for each outcome are gathered from three main metaanalyses (USEPA, 1999; Cesar et al., 2002; Evans et al., 2002), with supplementary studies
for information on select outcomes. Greater weight is placed on evidence originating from
Mexico. Uncertainty in epidemiological evidence is included in our modeling, by including
a distribution of possible dose response values. A detailed description of the sources for
each coefficient and a summary table are included in Chapter V.
Information on rates of hospitalizations and emergency room visits for respiratory and
cardiovascular diseases were gathered in a co-study conducted by the National Institute of
Public Health (INSP) using the database from the IMSS social security system. This
system covers approximately 80% of the population of the Federal District and nearly 30%
of the state of Mexico. This database was chosen due to its data quality and availability.
While it does not represent the entire Mexico city population, it accurately captures the
trends in the city. Furthermore, the data gathered from this database account for less than
10% of the total monetary impacts. Tables II.4a and b summarize results of the health
impacts for the two time horizons.
11
Table II.4a Annual mean health impacts (cases/year)
Time horizon 2003-2010
Taxi
Renovation
1.1 Acute Mortality
Total mortality
Infant mortality
1.2 Chronic Mortality
Total
Cardio-respiratory
Lung Cancer
1.3 Chronic Bronchitis
1.4 Hospital admissions
All Respiratory
COPD
All Cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
Pneumonia
Asthma
1.5. Emergency room visits (ERVs)
Respiratory Causes
Asthma
1.6. Restricted Activity Days
1.7 Minor Restricted Activity Days
1.8 School Absenteeism
Metro
Expansion
Hybrid
Buses
LPG Leaks Cogeneration
57
29
2
1
9
11
11
6
1
0
6
1
7
0
0
0
2
0
2
1
0
1
0
0
0
448
16
171
89
4
223
38
1
1
0
49
21
6
1
0
0
0
1
1
1
0
0
0
0
0
1
39
7
0
0
0
9
4
2
0
0
0
0
1
0
1,065
990
13,326
495,076
218,384
30
28
476
14,660
6,458
17
14
5,103
44,611
17,303
190
176
2,663
90,682
39,723
12
11
123
5,207
2,336
12
Table II.4b Annual mean health impacts (cases/year)
Time horizon 2003-2020
Taxi
Renovation
1.1 Acute Mortality
Total mortality
Infant mortality
1.2 Chronic Mortality
Total
Cardio-respiratory
Lung Cancer
1.3 Chronic Bronchitis
1.4 Hospital admissions
All Respiratory
COPD
All Cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
Pneumonia
Asthma
1.5. Emergency room visits (ERVs)
Respiratory Causes
Asthma
1.6. Restricted Activity Days
1.7 Minor Restricted Activity Days
1.8 School Absenteeism
Metro
Expansion
Hybrid
Buses
LPG Leaks Cogeneration
36
19
15
10
10
12
9
5
1
0
4
0
4
2
0
2
3
0
3
1
0
1
0
0
0
295
152
184
76
6
134
22
0
0
0
29
12
49
8
0
0
0
10
5
1
0
0
0
0
0
1
33
5
0
0
0
7
3
3
1
0
0
0
1
0
632
583
8,908
296,928
132,439
232
215
4,584
119,279
52,346
19
15
5,575
48,591
18,814
154
144
2,320
73,350
32,756
16
15
176
7,190
3,174
Valuation
Here we evaluate the benefits of reduced health impacts by economic valuation and in
terms of the quality-adjusted life- years (QALYs) saved. The economic valuation allows us
to compare the costs with the benefits using the same metric. QALYs, on the other hand,
allow comparisons of benefits to costs without putting monetary values on public health.
This provides us with an alternative means of measuring control effectiveness, and allows
us to calculate cost per QALY ratios.
For the economic valuation we use three methodologies to determine the total social benefit
due to reductions in health impacts: 1. Direct health costs 2. Productivity loss and 3.
Willingness to pay (WTP). These three methods are combined to give the total social
benefits from reductions in health impacts, removing some impacts to avoid overlap. Direct
health costs were derived from an analysis by the Mexican National Institute of Public
Health (INSP) of costs of hospitalizations and emergency room visits. Productivity loss is
calculated by the salary loss over the duration of an illness or years lost due to premature
mortality.
Finally, for WTP, we use results from a study conducted in Mexico (Ibarrarán
et al., 2002) as well as those from the international body of literature adjusted to Mexican
income, placing more weight on the Mexican study.
13
Table II.5. Monetary benefits (2003 million US$ / year)
Taxi Renovation
Metro Expansion
Hybrid Buses
LPG Leaks
Cogeneration
Taxi Renovation
Metro Expansion
Hybrid Buses
LPG Leaks
Cogeneration
Mean
Time Horizon 2003-2010
152
4.97
38.4
28.7
1.46
Time Horizon 2003-2020
96.0
44.7
41.6
24.4
2.03
95% CI
(57.3 : 293)
(2.08 : 9.07)
(12.3 : 80.2)
(9.11 : 59.9)
(0.48 : 2.95)
(37.7 : 182)
(19.2 : 83.3)
(13.5 : 88.1)
(8.05 : 52.1)
(0.68 : 4.09)
Finally, in order to provide an alternative valuation method that does not apply a dollar
value to health, we also perform a QALY analysis. QALYs account for both duration and
quality of life in each health state when calculating health benefits. The QALYs gained by
an intervention are simply the sum of quality-adjusted life years gained by avoiding
premature mortality and disease. QALYs are calculated by the following equation:
QALY = u ( H i ) × Ti
Where u(Hi) is a utility weight assigned to a given health outcome (zero to one), and Ti is
the duration of that health outcome. The utility weights we use here are from several
international studies (Fryback et al., 1993; Liu et al., 2000; Stouthard et al., 2000), as none
have yet been done in Mexico. The duration of illnesses are obtained from the IMSS
databases, whereas the life years lost per premature mortality are calculated from a separate
INSP study.
Table II.6. Total QALYs saved per year
Taxi Renovation
Metro Expansion
Hybrid Buses
LPG Leaks
Cogeneration
Taxi Renovation
Metro Expansion
Hybrid Buses
LPG Leaks
Cogeneration
Mean
Time Horizon 2003-2010
2935
102
972
574
28
Time Horizon 2003-2020
1914
946
1050
493
39
14
95% CI
(1543 : 4694)
(57 : 159)
(415 : 1718)
(209 : 1110)
(10 : 52)
(1009 : 3003)
(554 : 1425)
(440 : 1902)
(175 : 944)
(15 : 74)
II.4. Results
We find that the combination of these 5 measures will substantially reduce emissions of
local air pollutants, as well as GHG. These measures will reduce PM10 exposure by
approximately 1% (0.6 ìg/m3 ) for both time horizons; and will reduce maximum ozone
concentrations by approximately 3% (6.2 ìg/m3 and 4.8 ìg/m3 , respectively for 2003-2010
and 2003-2020), while eliminating emissions of more than 300,000 tons C equivalent per
year and 400,000 tons C equivalent per year, respectively. Together, these reductions will
save more than 4,600 and 4,400 QALYs per year, respectively. Monetized benefits are
estimated to be $225 million USD per year and $210 million USD per year, respectively,
for the combined 5 controls. Total annualized costs are less than 30% of the estimated
benefits: we estimate costs to be $66 million per year for 2003-2010 and $50 million USD
per year for 2003-2020.
Each measure contributes uniquely to these results. The impact of each individual measure
is discussed below.
For the 2003-2010 time horizon, the benefits of the Taxi Fleet Renovation are far greater
than the costs (Table II.2. and II.5). Costs are small for this measure because of significant
fuel efficiency gains realized with newer vehicles. Benefits are high because of large ozone
reductions, and also because of significant reductions in secondary particulate
concentrations reductions (Table II.3). We estimate that approximately 3,000 QALYs per
year could be saved with the measure (Table II.6), at mean cost of approximately $3,000
per QALY. On the longer time horizon, net costs turn into net savings as the fuel cost
savings continue to accumulate without additional investment costs. Annualized benefits
are still large, though less so, for the long time horizon because there is deterioration in
emissions among aging vehicles that gradually increases local emissions, and thus
decreases local benefits with time. For 2003-2020, we estimate that approximately 2,000
QALYs per year could be saved (Table II.6) at the same time as cost savings are realized.
Consistent with existing government proposals, this analysis assumes that only 5 km of
Metro would be built from 2003-2010, and an additional 71 km from 2011-2020. For this
reason, it appears as to be a relatively small, inexpensive measure on the short time horizon,
but much larger undertaking on the long horizon (Table II.2). Because Metro Expansion
involves significant capital investment, the inclusion of the recuperation value for the
Metro (30 year useful life) offsets a significant portion of these initial costs. We find that
the local emission reduction benefits (Table II.5, II.6) can also be large and compensate for
a majority, if not all, of the net costs for both time horizons. For example, for 2003-2020,
we estimate that approximately 950 QALYs per year could be saved (Table II.6) at a cost
of approximately $50,000 per QALY by the expansion of the Metro. This analysis assumes
that the extension of the Metro causes a significant reduction in the use of on-road public
bus transportation, which means local emissions are significantly reduced. However,
increase in Metro length requires more electricity and increases emissions from power
plants that are primarily located outside the valley. Thus, the Metro Expansion causes a net
transfer of local emissions from inside to outside the valley. We assume that population
density is substantially lower where the electricity is generated than in Mexico City, and for
this reason, public health impacts will be negligible from increased power generation. This
15
transfer of local emission helps to make local benefits large enough to offset much, if not
all, of the costs for this measure.
The Hybrid Buses measure has large upfront investment costs due to the expensive nature
of the technology, but also generates significant cost savings on the long term due to greatly
enhanced fuel efficiency (Table II.2). Benefits are large for both time horizons primarily
because of large reductions in primary particulate emissions. For both time horizons, we
find that approximately 1,000 QALYs per year could be saved (Table II.6). This measure is
implemented between 2003 and 2006. Annualized costs are, therefore, lower and benefits
higher for the long time horizon than for the short time horizon; thus the cost per QALY
reduces from approximately $60,000 for 2003-2010 to $20,000 for 2003-2010.
The LPG leaks reduction measure, on the other hand, has low costs because of the low unit
costs for each stove repair. Benefits are much larger than the costs because of the
significant reduction in hydrocarbon emissions that reduces both ozone and secondary
organic particulate exposure. For both time horizons, approximately 500 QALYs per year
could be saved (Table II.6) at a cost of approximately $50,000 per QALY.
For Cogeneration, net costs are low due to the significant gains in fuel efficiency and the
inclusion of the recuperation value of the equipment at the end of each time horizon (20
year useful life). Local benefits are not very large for this measure because the gains in
efficiency derive from simultaneous on-site production of thermal and electrical energy that
replaces off-site electricity generation and on-site thermal energy production. As explained
above, only a small portion (3.1%) of the electricity consumed in Mexico City is generated
in the valley. Though Cogeneration significantly reduces the total emissions by
substantially increasing efficiency, the measure moves emissions of local pollutants into the
valley, and thus local benefits are small. QALYs saved are on the order of 30 per year for
both time horizons (Table II.6) at a cost of approximately $25,000 per QALY.
In Figure II.1, we compare local and global net benefits. The local net benefits are defined
as the Monetized Health Benefits (Table II.5) minus Costs (Table II.2), while the global net
benefit is the reduction in GHG emission. Figure II.1 illustrates that the Taxi Fleet
Renovation measure is clearly the best measure from the joint local – global perspective.
The Hybrid Bus measure for 2003-2020 and the LPG Leak measure on both time horizons
are the next- most promising for joint local / global control. The Metro Expansion, in large
part because of its very high costs, is less promising from the joint perspective.
Cogeneration also does not have sufficient local benefits to make it interesting for joint
local – global control.
16
Figure II.1: Net Health Benefits vs. C equivalent Reduction
II.5. Discussion and Conclusions
Taxi fleet renovation offers the most promising opportunity for the joint control of local
and global pollution of the measures studied here. Furthe r, benefits might be found to be
significantly larger than estimated here if changes in primary particulate matter emissions
could be estimated. The large potential benefits of this measure have already been
recognized by decision- makers in Mexico City, and the implementation of this measure has
begun as of 2002-2003 with public funding for the replacement of 3,000 taxis.
The LPG leak measure also provides benefits than are much larger than the total costs.
Emissions reductions and local benefits from this measure are small compared to the taxi
fleet renovation, but investment costs are quite small, making implementation of the LPG
leak measure relatively feasible from a decision- making standpoint.
Cogeneration provides more than 50% of the GHG benefits from this set of measures, but
essentially no local benefit because it moves emissions of local pollutants into the valley,
and health benefits from the reduced emissions at power plants located outside the valley
are assumed to be negligibly small. Were a similar study conducted at the national level,
Cogeneration may turn out to be a promising joint local / global option because health
benefits derived in populations living near to power plants could be considered. This will
depend, of course, on populatio n exposure to emissions generated by electricity production
across the country.
17
Metro Expansion has large local benefits, particularly for the long time horizon when the
measure has been fully implemented. However, the extremely high initial investment costs
required for the measure make its implementation unlikely.
Finally, the Hybrid Bus measure may have positive net benefits if the long time horizon is
considered. However, the analysis of this measure has large uncertainty because the
emission factors used were derived for the altitude, driving conditions, and fuel mix of New
York City, not for Mexico City. Altitude has been shown (Yanowitz et al. 2000) to
significantly impact emissions behavior from heavy-duty vehicle technology, but these
impacts ha ve not been specifically calculated for the technologies under consideration here.
We recommend that a better understanding of emissions factors be obtained and also that
the cost-effectiveness of other types of advanced technologies (e.g. Cohen et al., 2003) also
be considered in order to determine what would be the best advanced bus technology to
introduce in Mexico City.
This work indicates that measures to improve the efficiency of transportation are key to
joint local / global air pollution control in Mexico City. The three measures in this category
that are analyzed here all have monetized public health benefits that are larger than their
costs when the appropriate time horizon is considered. Global benefits, due to improved
fuel efficiency, are also large. In contrast, we find that traditional “no-regrets” electricity
efficiency do provide large GHG emission reductions, but do not provide local benefits to
Mexico City because the majority of electricity is produced outside of the valley in which
Mexico City is located.
Further work is needed to analyze more measures that cover a wider range of opportunities
for joint local / global air pollution control. Also very important is to quantify the air
pollution improvements and cost savings that could be acquired from reduced congestion
in the MCMA. Such an analysis may indicate that the benefits from transportation
efficiency improvement are, in fact, much larger than estimates here. Improved
understanding of emission factors from new and old vehicles under Mexico City driving
conditions is also greatly needed, and could significantly impact results.
II.6. References
CAM, Comisión Ambiental Metropolitana (2002) “Programa para Mejorar la Calidad del
Aire de la Zona Metropolitana del Valle de México, 2002-2010” (PROAIRE), Comisión
Ambiental Metropolitana, México City.
Cesar, H., et al. (2000) “Economic valuation of Improvement of Air Quality in the
Metropolitan Area of Mexico City,” Institute for Environmental Studies (IVM)
Cesar, H., et al. (2002) “Air pollution abatement in Mexico City: an economic valuation,”
World Bank Report
18
Chow, J.C., J.G. Watson, S.A. Edgerton, and E. Vega (2002) “Chemical composition of
PM2.5 and PM10 in Mexico City during winter 1997,” The Science of the Total Environment
287, p.177-201.
Cohen, J.T., J.K. Hammitt, and J.I. Levy (2003) Fuels for urban transit buses: A costeffectiveness analysis. Environ. Sci. Technol 37. 1477-1484.
Evans et al. (2002) “Health benefits of air pollution control,” in Air Quality in the Mexico
Megacity: An Integrated Assessment, Kluwer Academic Publishers, Boston, 384 pp.
Fryback, D., E. Dasbach, R. Klein, B. Klein, N. Dorn, K. Peterson, and P. Martin (1993)
"The beaver dam health outcomes study: initial catalog of health-state quality factors,"
Medical Decision Making, 13: 89-102.
Ibarrarán, M., E. Guillomen, Y. Zepeda, and J. Hammit (2002) “Estimate the economic
value of reducing health risks by improving air quality in Mexico City,” preliminary
results.
Liu, J., J. Hammitt, J. Wang, and J. Liu (2000) “Mother’s willingness to pay for her own
and her child’s health: a contingent valuation study in Taiwan,” Health Economics, 9: 319326.
M.J. Bradley & Associates, Inc. (2000) “Hybrid-electric drive heavy-duty vehicle testing
project: Final emissions report.” http://www.navc.org/Navc9837.pdf
Salcido et al. (2001) “MCCM Parametric Studies: Estimation of the NOx , HC and PM10
emission reductions required to produce a 10% reduction in the Ozone and PM10 surface
concentrations and compliance with the MCMA air quality standards, with reference to the
2010 MCMA Emission Inventory,” Grupo de Modelación de la Comisión Ambiental
Metropolitan (CAM), 42 pp.
Sheinbaum P., C., L. Ozawa, O. Vázquez, and G. Robles (2000) “Inventario de emisiones
de gases de efecto invernadero asociados a la producción y uso de la energía en la Zona
Metropolitana del Valle de México: Informe final.” Grupo de Energía y Ambiente, Instituto
de Ingeniería, UNAM, report to the CAM and the World Bank.
SMA, Secretaria del Medio Ambiente del Distrito Federal (2002) Red Automática de
Monitoreo Atmosférico (RAMA).
Stouthard, M., M. Essink-Bot and G. Bonsel (2000) “Disability weights for disease: a
modified protocol and results for a western European region,” European Journal of Public
Health, 10: 24-30.
Turpin, B.J., J.J. Huntzicker, S.M. Larson and G.R. Cass (1991) “Los Angeles summer
midday particulate carbon: Primary and secondary aerosol,” Envi. Sci. Technol., 25(10)
1788-1793.
19
TUV Rheinland de Mexico, S. A. de C. V. (2000) “Programa para la reducción y
eliminación de fugas de Gas LP, en las instalaciones domésticas de la Zona Metropolitana
del Valle de México.”
U.S. Environmental Protection Agency (1999) "The Benefits and Costs of the Clean Air
Act 1990-2010," Washington, D.C., Office of Air and Radiation, EPA report no. 410/R99/001.
West, J.J., P. Osnaya, I. Laguna, J. Martínez, A. Fernández (2003) “Co-control of urban air
pollutants and greenhouse gases in México City.” Final report to US National Renewable
Energy Laboratory, subcontract ADC-2-32409-01.
Yanowitz, J., R.L. McCormick and M.S. Graboski (2000) “In-use emissions from HeavyDuty diesel vehicles.” Environ. Sci. Technol. 3, p 729-740.
20
III.1 General Methodology for Estimating Emissions Reductions and Costs
III.1.1. Introduction
We estimate the time profiles of local pollutant (PM10 , SO2 , CO, NOX, and HC) and global
pollutant (CO2 , CH4 , and N2 O) emission reductions, and direct costs for 5 control measures
that address transportation, residential and industrial sources of local and global air
pollution emissions. Detailed descriptions of each measure is outlined in sections III.2
through III.6. We also report emission reductions of PM2.5, calculated as a fraction of PM10
emissions (US EPA, 2000) for illustrative purposes, but do not use these estimates of
emission reduction in the rest of the analysis.
As described below, for each measure an emissions baseline is defined given currently
measured or otherwise determined emissions factors and activity levels, combined with
reasonable future predictions regarding their behavior without intervention. Control
measures cause a change from this baseline by altering future activity levels and / or
emissions factors. While emissions factors used in the study are meant to capture current
driving conditions, the cost savings and changes in emissions due to reduced congestion
could not be calculated because this was far beyond the scope of this study. We encourage
the pursuit of improved understanding of congestion impacts in future work since these
impact may, in fact, be large.
Our objective is to estimate emissions reductions and costs for each year from 2003 to
2020. In this way, the different time-profiles of the programs costs and impacts can be
studied. For incorporation into the cost – benefit and ancillary benefits analyses that are the
goal of this study, we annualize the results obtained over these time horizons using several
different discount rates. Annualized costs and emissions reduction can be considered as a
constant annual flux of costs or emission reductions over the time-period that gives an
equivalent net present value to the net present value estimated from the actual time-profile
of the program. In this way, annualized results allow direct comparisons between measures
with different time-profiles.
Further, annualized results allow cost-benefit and ancillary benefit calculations to be much
simplified since it is only necessary to calculate air quality changes and health impacts
based on a single set of emissions reductions that appropriately represent the entire time
horizon, as opposed to having to do such calculations for each year. The fact that our
reduced-form air quality models (see Chapter IV) are essentially linear facilitates the use of
annualized emissions reductions.
III.1.2. Choice of Time Horizon
We study both a short time horizon (2003 through 2010) that is consistent with Mexico
City’s Program for Improved Air Quality in the Valley of Mexico (Programa para Mejorar
la Calidad de Aire en el Valle de Mexico, PROAIRE) 2002-2010. We also study a long
time horizon (2003 through 2020) that allows consideration of the lasting effects of control
21
measures implemented up to 2010, and also allows consideration of realistic long-term
implementation plans for the Metro Expansion control measure.
III.1.3. Choice of Discount Rate
We calculate costs and emissions reductions using 3 discount rates, 3%, 5% and 7%. We
also present results when discounting is ignored, or 0%. Our benchmark scenario, for
which results are considered in Chapters IV to IX, uses a discount rate of 5%.
III.1.4. Equations used for Discounting and Annualization
Discounting to estimate the Net Present Value (NPV) in 2003 (where j is the year from
2003, “value” is the emission reduction or cost in that year, and dr is the discount rate) uses
Equation III.1.1.
n
value j
j =1
(1 + dr ) j
NPV = ∑
Equation III.1.1
Annualization (where Nyr is the number of years over which to annualize) uses Equation
III.1.2.
annualized _ value =
dr
⋅ NPV
1 − (1 + dr ) − Nyr
[
]
Equation III.1.2
III.1.5. References
U.S. Environmental Protection Agency (2000) "National Air Pollutant Emission Trends:
1900 - 1998," Washington, D.C., EPA report no. 454/R-00-002.
22
III.2. Renovation of the Taxi Fleet
III.2.1. Introduction
In 1998 approximately 109,400 taxis were circulating in the Mexico City Metropolitan
Area (MCMA); 103,298 in the Federal District and the rest in the State of Mexico.
According to official figures, the total number of taxis accounted for 3.4 percent of the
entire vehicle fleet in the metropolitan area that year (CAM, 2002a, Table 5.2.2.2). In the
Federal District alone, taxis accounted for about 5 percent of the vehicle fleet and about 20
percent of the total vehicle kilometers traveled (CAM, 2002a, Table A.2.6). The emissions
from these activities are estimated at 188 tons per year of PM10 ; 535 tons of SO2 ; 115,200
tons of CO; 10,366 tons of NOX; and 13,733 tons of HC, respectively (CAM, 2002a, Table
5.2.2.8).
By their nature taxis are high- use vehicles. Over time their emission control systems would
be expected to deteriorate more rapidly than those of other vehicles used less intensively
(however, see Kojima and Bacon, 2001). This is one reason why taxis are sometimes
subject to more frequent tests in vehicle inspection and maintenance (I/M) programs. Highuse vehicles also consume more fuel, which contributes particularly to emissions of
greenhouse gases (GHG), and which makes up an important part of the vehicle operating
costs. The problems associated with emissions from taxis are thus similar to the ones of the
private car fleet, but they tend to be exacerbated by a more intense use of taxi vehicles.
The weighted average age of taxis in the Federal District was 5.7 years in 1998. Four years
later, this number had grown considerably and, according to some estimates, 49% of the
fleet was more than 10 years old and should have been taken off the road in order to
comply with existing regulations (Gonzalez, 2002). However, there are large uncertainties
associated with these estimates. A reliable vehicle registration database does not exist, and
it is difficult to obtain time-series data. While new vehicle sales are added to the existing
population every year, vehicle retirement is often not captured. As a result, large
differences have been measured when the official figures are compared with data from
extensive field surveys (Kojima and Bacon, 2001).
The inconsistencies observed in the official records of the overall fleet size and
composition are recognized by the Metropolitan Commission for Transport and Roadways
(COMETRAVI, 1999a), and are similar to problems encountered in other parts of Latin
America (for a discussion in the context of the MCMA, see Gakkenheimer et al. 2002).
Modeling the evolution of the taxi fleet is also complicated by the fact that most taxis are
traded on the market for used vehicles, and that an unknown number of vehicles have been
turned into taxis illegally.
Yet, despite these challenges there seems to be a consens us within the local governments of
the MCMA that something needs to be done about the emissions from the existing taxi
fleet. High- use vehicles (i.e., taxis and microbuses) are currently required to be renewed
after a certain number of years, but the restrictions are not effectively enforced and the age
of an increasing number of these vehicles is higher than their age limit.
23
Apart from their impact on air quality and human health, there are also other problems
related to the taxis. In particular, 60-70 percent of the taxi owners have only one vehicle as
their main source of household income (Gonzalez, 2002). As a consequence, these owners
work between 8 and 12 hours a day and typically they do not have any kind of social
security. Public policies to reduce emissions from taxis ought to be sensitive to this fact. In
the present analysis, however, we shall focus on the total emission reductions and the direct
costs of such policies, while ignoring their implications for the distribution across
individuals and households.
III.2.2. Description of the Measure
In response to growing concerns about the emissions from taxis, an ambitious program has
been designed to scrap 80,099 old taxis in the Federal District, and to replace them by
vehicles that comply with more stringent emissions standards. The program is being
implemented over a four year period, provided sufficient public funds are available. There
are four overall goals of the program (Gonzalez, 2002).
First, in order to reduce emissions of local air pollutants, such as CO, NOX and HC, old taxi
vehicles will be replaced by newer vehicles that comply with at least Tier 1 emission
standards. The replacement is facilitated by an incentive for present taxi owners to scrap
their old vehicle in exchange for a premium of 1,500 U.S. dollars. In addition, subsidies are
given to owners of new taxis in terms of reduced purchase prices from the automobile
industry, a special tax relief from the government of the Federal District, interest rate
subsidies from credit institutions, and subsidies on spare parts and services.
Second, a requirement is included in the program that new vehicle engines must comply
with a minimum fuel economy of 12.6 km per liter. Compared with the existing taxi fleet,
the requirement would imply not only considerable savings in fuel cost, but also a reduction
in GHG emissions. Note, however, that this is based on the assumption of no “rebound
effect” from an improvement in the fuel economy of new vehicles (NRC, 2002; Portney,
2002).
Third, as emphasized above a number of other problems surround the organization of the
taxi fleet. About 90 percent of the vehicles in service are so-called “free” taxis that
circulate the streets empty looking for passengers. In contrast with fixed-site taxis, which
typically operate from a coordinated taxi stand, free taxis are not formally organized. They
produce more emissions per passenger kilometer traveled and are generally considered to
be less safe. In the taxi renewal program, provisions are therefore included to increase the
share of fixed-site taxis as a means to reduce the emissions and improve the safety of the
passengers simultaneously. However, it remains an open question to what extent the
operators of free taxis will have sufficient incentives to join a taxi stand, or another form of
coordinated operation. Consequently, we shall not consider this element of the program in
the analysis.
The fourth goal of the program is to improve the income of the taxi owners through public
and private subsidies and through increased social security. Financial support is thus
provided, not only for the scrappage of old and the purchase of new taxis, but also for
24
recurring expenditures on vehicle operation and maintenance (i.e., interest rate subsidies
and subsidies on spare parts and services). In addition, since taxi credits are generally
considered by the commercial banks to be a risky asset leading to a prohibitively large risk
premium on private commercial loans, a mechanism has been designed between the private
financial sector, the government of the Federal District, and the National Development
Bank (Nacional Financiera) to provide guaranteed loans at fixed interest rates. An
insurance scheme for taxi owners is also being considered jointly with the loan for the
purchase of a new vehicle (Gonzalez, 2002; SETRAVI, 2002a).
According to the announced plan, the taxi renewal program is being implemented from
2002 to 2006 as part of an overall effort to integrate transport and environmental policies in
the Federal Dis trict (CAM, 2002b; SETRAVI, 2002b). However, the financial viability of
the program remains insecure. Not only are the financial resources of the Federal District
scarce, but there are also large imbalances in the public finances of the transport sector.
These imbalances stem in part from a massive underpricing of public transport and
infrastructure, such as the metro system and the road network, and in part from the inability
of the local Secretariat of Transport and Roadways to raise public revenues. For the fiscal
year of 2002, it is estimated that only 37% of the total expenditures in the transport sector
are covered by the revenues raised (Gakkenheimer et al., 2002; SETRAVI, 2002b).
From the documents available it is difficult to get a clear picture of the current state of the
taxi substitution program. In the preliminary Integrated Transport and Roadways Program
(Programa Integral de Transporte y Vialidad, PITV) for 2002-2006, a total amount of 10
million US dollars has been designated to a fund for the substitution of 10,000 free taxis
(SETRAVI, 2002b). In the Program for Improved Air Quality in the Valley of Mexico
(Programa para Mejorar la Calidad de Aire en el Valle de Mexico, PROAIRE) 2002-2010,
about 80,000 of the oldest taxis are expected to be gradually replaced at a total cost of 800
million U.S. dollars, of which 80 million dollars would be financed by the public sector and
720 million dollars by the private sector (CAM, 2002b). Finally, in a brief summary of the
progress of PROAIRE, Paramo (2003) comments on the availability of funds for the
substitution of only 3,000 taxis for the fiscal year 2002.
These discrepancies are probably a reflection of the financial insecurity of the program. It is
also a fact that the fiscal budget covers expenditures only one year ahead, while the
scrappage and replacement of taxis is a multi- year effort that cuts across institutional
boundaries within and outside the government of the Federal District. In this respect, the
program should be contrasted with the only other known scrappage program of a
comparable magnitude, which was considered for almost a decade in California to improve
air quality in the greater Los Angeles area, but which was subsequently abandoned by
policy makers (Dixon and Garber, 2001a, 2001b; Dixon, Garber, and Porche, 2002).
Some taxis in the Federal District have already been scrapped and replaced. Information
about these experiences would be useful for the evaluation of the program. Yet, data on the
costs and emissions characteristics of both the old taxis that are scrapped and the new
vehicles introduced have not been available for the purpose of the analysis. We therefore
conduct a prospective analysis of the program, based on our expectations about its likely
impacts, and assume a period of implementation from 2003 to 2007.
25
In the analysis, we focus on the real social costs, as opposed to the financial costs,
associated with the scrappage and replacement of taxis, the implications for the emissions
reductions, and the human health impacts in the Federal District. In particular, we are
interested in the question of whether the taxi renewal program is desirable from an overall
societal perspective, taking into account only the allocative efficiency of the measure. This
means that we include the real resource costs associated with the scrappage and
replacement of taxis (i.e., scrappage subsidy, vehicle replacement cost, and fuel cost), while
we omit the financial costs associated with a loss to some and a gain to other agents of the
economy (i.e., public and private transfers).
III.2.3. Data Requirements
For the purpose of the evaluation, a wide range of data is needed to estimate the baseline
emissions trajectory for the taxi fleet without the control measure. These data include the
base year (1998) emissions inventory for the MCMA, distributed between the Federal
District and the State of Mexico (CAM, 2002a).
Data is also needed to extend the inventory with estimates of PM2.5 and GHG emissions.
For this extension, we introduce a number of simplifying assumptions. In particular, we are
interested in an explicit calculation of the average annual fuel consumption of taxis, which
at the same time can be used to estimate fuel consumption in the future. Emissions of GHG
are then straightforward to calculate on the basis of emissions factors (in grams per
kilogram of fuel consumed) reported by the Intergovernmental Panel on Climate Change
(IPCC, 1997).
Finally, the baseline emissions for the period 1998 to 2020 are estimated on the basis of
expectations about the annual rates of change in both the size and composition of the taxi
fleet and its emissions characteristics. To the extent possible, these data are obtained from
publicly available documents. Where such data are unavailable, alternative assumptions are
discussed and justified explicitly.
Once the baseline scenario has been specified, it is an easy matter to impose the control
measure according to the number of taxis to be replaced and the time period of
implementation described above. The annual emissions are then re-calculated in the control
scenario for the time period of analysis, and the emissions reductions derived as the
difference between the two scenarios. Great care needs to be taken in order to ensure that
relevant parameters in the control scenario are correctly adjusted. If, for example, emissions
standards are introduced in the control scenario that do not already exist in the baseline
scenario, such as more stringent tailpipe emissions or fuel economy standards, this change
needs to be reflected in the parameter values (i.e., the emissions factors and the fuel
economy of the new vehicles).
On the basis of the changes introduced in the control scenario, data are needed on the
incremental capital costs and operation and maintena nce (O&M) costs of each new taxi.
The capital cost include the initial scrappage subsidy (1,500 USD) and the incremental cost
to the taxi owner from the purchase of a new vehicle. The O&M costs include the value of
26
changes in fuel consumption, valued at constant real prices over time. We assume that
government administration costs of the program are negligible, since no emissions testing is
associated with the scrapping of old vehicles. Also, monitoring and enforcement costs are
not included. During the first years of the program, when the oldest vehicles are replaced,
we believe that these costs can be ignored, because incentives are provided for taxi owners
to join the program, in part, through the scrappage subsidies and, in part, through the
subsidies for new vehicles. However, during later years of the program, when younger
vintages of vehicles are retired, participation in the program will eventually become
unattractive as the used car prices of younger vehicles raise above the scrappage subsidy.
This is clearly in opposition with the objectives of the program, and requires more careful
consideration of the enforcement mechanisms needed to replace almost 80% of the taxi
fleet in the Federal District.
Yet, there is some confusion in the official perception of how the taxi substitution program
is enforced. In the Secretariat of Transport and Roadways (SETRAVI, 2002a), the program
is viewed as voluntary. This means that the decision to scrap an old taxi and replace it by a
new one is left entirely to the owner. Incentives therefore need to be put in place for the
program to take effect (see, for example, Dixon and Garber, 2002a).
In the economics literature, these incentives are typically analyzed in models of so-called
“rational scrappage”, where the optimal decision of the owner to keep or scrap the vehicle
is based on the minimization of the present value of the costs from the two alternatives,
with all the relevant costs included (e.g., Hahn, 1995; Alberini et al., 1995, 1998). Vehicle
scrappage rates above the natural rate of retirement are then achieved by policies (e.g.,
scrappage subsidies, emission fees, differentiated ownership taxes, or more stringent
inspection and maintenance) that change the relative costs in favor of scrappage. Public
policies based on this type of scrappage is also sometimes referred to as “voluntary
accelerated vehicle retirement” (VAVR) programs (U.S. EPA, 1998; ESMAP, 2002).
In contrast with this viewpoint of the taxi substitution program as voluntary, the
Metropolitan Environmental Commission describes it in PROAIRE as mandatory (CAM,
2002b). Since taxi owners in the Federal District are allowed to operate only under a system
of public concessions, compliance with the taxi substitution program in ensured in
PROAIRE by making the renewal of the concession for each taxi owner dependent on
participation in the program. Failure to participate in the program by not scrapping the old
taxi means that the license of the owner to own and operate a taxi expires.
For convenience, we adopt the latter viewpoint. Estimating a model of rational scrappage is
beyond the scope of the present analysis. Given the available time and data, we therefore
assume that the incentives provided by the program are sufficient to make the taxi owners
comp ly. If they are not sufficient, we assume that compliance can be enforced through the
system of concessions currently in place in the Federal District. This greatly simplifies the
analysis. However, the assumption of compliance is questionable given a past history of
problems with concessions in the MCMA, particularly with respect to the operation of
urban buses (Estache, 2001; Gakkenheimer et al., 2002). One should therefore be cautious
in the interpretation of the results from the present analysis.
27
III.2.4. Determining baseline emissions
Following the emissions inventory (CAM, 2002a), a bottom- up approach is used to
estimate the total emissions in 1998 by multiplying the level of activities (i.e., the number
of taxis and their annual vehicle kilometers traveled) with the level of emissions per unit of
activity (i.e., the emissions factors in grams per kilometer). First, we describe the data used
in this approach. We then turn to the projection of the activities and the emissions
characteristics of the taxi fleet.
The base year (1998) emissions
A vehicle registration database is not available for the MCMA. In its place, data on the size
and composition of the vehicle fleet, as well as its emissions characteristics, can be
obtained from the vehicle verification program. The program requires an emissions test to
be performed every six months on vehicles circulating in the Federal District and the State
of Mexico (see Gakkenheimer et al., 2002).
The activity data for the base year emissions inventory are specific to each model year
vehicle in 1998 and spans a total of 25 model year vintages. Figure III.2.1. shows the age
distribution of taxis and private cars in the Federal District (CAM, 2002a, Table A.2.2).
The age distribution of taxis is from the vehicle verification program in the second semester
of 1999. The figure illustrates that the taxi fleet is not very old, compared with the private
car fleet, and that taxis appear to be retired faster than private cars. This is probably because
taxis travel more kilometers every year, and therefore deteriorate more rapidly due to wear
and tear. Other interpretations are also possible related to factors external to the vehicles
themselves, such as differences in the price of maintenance and repair (Hamilton and
Macauley, 1998).
Figure III.2.1. Age distribution of taxis and private cars in the Federal District (1998)
30%
25%
% of total
20%
Taxis
Private cars
15%
10%
5%
0%
0
2
4
6
8
10
12
14
16
Vehicle age (in years)
28
18
20
22
24
The taxis are assumed to travel 200 kilometers per day during 6 days a week. This yields a
total distance traveled of 62,600 kilometers per year (COMETRAVI, 1999). The estimate
represents an upper bound of the annual vehicle kilometers traveled (VKT) per taxi,
compared with other estimates of odometer readings taken from the verification program in
the period from 1996 to 1999 (Kojima and Bacon, 2001). These estimates indicate that taxis
in the Federal District on average travel 30.000 km per year – about half the estimate we
use in the present study.
Notice that we do not differentiate across model year vehicles in terms of their annual
VKT. In agreement with most empirical observations, other studies typically assume that
old vehicles travel less than new ones (e.g., Mostashari, 2003). This pattern has also been
found in the MCMA, although at a very aggregate level (Kojima and Bacon, 2001). Here
we simplify the analysis and leave the quantitative significance of such a variation for
further study.
Given the total distance traveled for each model year, the emissions of criteria pollutants
(CO, NOX, and HC) are calculated with the emissions factors from the emissions inventory
shown in Table III.2.1 (CAM, 2002a, Annex A). In the inventory, emissions factors for
diesel fueled vehicles and motorcycles are estimated through the MOBILE5-MCMA
model. The MOBILE model was originally developed by the U.S. EPA, and has
subsequently been adjusted for use in Mexico, including the Mexico City Metropolitan
Area (Radian International, 1997; ERG and Radian International, 2000; Burnette et al.,
2001). The model is part of a larger, on-going effort to improve the capacity within Mexico
for the development of emissions inventories. However, the MOBILE model has been
subject to critical scrutiny in the U.S. in recent years, particularly as a means to estimate the
expected emissions reductions from mobile source control measures (Harrington et al.,
1998; NRC, 2000). A new generation of the model has therefore been developed to address
some of its limitations (U.S. EPA, 2001, 2002).
For gasoline fueled vehicles in Mexico City, including taxis, emissions data have been
obtained from tunnel studies and measurement campaigns conducted, in part, by the
Mexican Petroleum Institute (IMP) during the 1990s. Focusing on the emissions of
hydrocarbons, two tunnel studies report results on the measurement of exhaust emissions
profiles for motor vehicles in operation, as well as hot soak emissions from vehicles in a
parking garage (Mugica et al., 1998; Vega et al., 2000). These results were then combined
with ambient air quality measurements to develop a source apportionment model, which
shows that somewhere between 55% and 64% of the ambient concentrations of nonmethane hydrocarbons (NMHC) can be attributed to the emissions from motor vehicles.
However, despite these and other efforts, it is a very complicated and time consuming task
to develop a comprehensive emissions inventory, which at the same time can be validated
through the use of various independent methods (for an excellent discussion, see Molina et
al., 2002). Estimating the emissions of taxis in the MCMA is no exception, and it is not
clear from the 1998 emissions inventory what are the sources of the emissions factors for
taxis (CAM, 2002a, Annex A). The data are shown in Table III.2.1, but measurement
results have been obtained only for model years 1991 to 1998. For all the previous model
years, the emissions factors of private cars were used instead.
29
Table III.2.1. Emissions factors of local air pollutants from taxis in the MCMA (g/km)
Model
year
PM 10
PM 2.5
CO
NO x
HC
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
76.40
76.40
76.40
76.40
76.40
76.40
76.40
55.60
55.60
55.60
55.60
55.60
39.60
39.60
39.60
31.40
31.40
15.20
15.20
15.20
15.20
15.20
15.20
15.20
15.20
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.40
2.40
1.48
1.48
1.48
1.48
1.48
1.48
1.48
1.48
6.26
6.26
6.26
6.26
6.26
6.26
6.26
5.68
5.68
5.68
5.68
5.68
4.55
4.55
4.55
3.59
3.59
1.84
1.84
1.84
1.84
1.84
1.84
1.84
1.84
The emissions factors for PM10 are obtained from a measurement study conducted in the
Denver (Colorado) area in the U.S. during 1996 and 1997 (Cadle et al., 1999, 2001). The
study offers a useful point of reference for the MCMA, because both locations are at high
altitudes, which is known to have an important influence on emissions. Obviously, a
number of other factors that are not accounted for might lead to differences in the emissions
of PM in the two areas. These factors include differences in temperatures, the urban driving
cycles used to test emissions, characteristics of the vehicle fleet and the sample of vehicles
in the test. For example, under the driving cycle defined by the Federal Test Procedure
(FTP), the Denver study finds considerable differences between the emissions of PM10
from new vehicles (2.82 mg per mile, model years 1991-1996) and the emissions from
older vehicles (95.5 mg per mile, model years 1971-1980) during the summer. During the
winter, this difference is narrowed somewhat (Cadle et al., 1999, Table 4).
In the emissions inventory for the MCMA, no distinction is made between model years
with respect to the emissions of PM10 . This is unfortunate for the evaluation of the taxi
substitution program, because the emissions reductions from this program are obtained
precisely from the differences between old and new vehicles. Failure to take these
30
differences into account in the emissions inventory therefore means that the emission
reductions are at best underestimated, at worst they are completely ignored.
Reductions of PM10 can still be obtained from the taxi substitution program, if the new
vehicles in the control scenario comply with more strict exhaust emissions standards than
new vehicles in the baseline scenario. This is in fact the source of PM10 reductions in
PROAIRE (CAM, 2002b), where new vehicles in the taxi substitution program are
assumed to comply with the strict Tier 2 emissions standards, while new taxis in the
baseline are assumed to comply with the 1998 model year emissions reported in Table
III.2.1. We believe that such estimates are unfounded, because they are based on a
comparison of certification standards for Tier 2 vehicles, when they leave the factory (as
stated in the U.S. regulations), with the measurement of in-use emissions from new
vehicles in Denver. Moreover, the estimates tend to obscure the fact that there might be
important PM emissions reductions from a difference between model years that is not taken
into account. An effort should therefore be made to remedy this in the emissions inventory.
Emissions of PM2.5 are assumed to be a constant fraction of PM10 . The fraction is obtained
from the national emissions trends observed in the U.S. over the last decade (U.S. EPA,
2000). Estimates of PM2.5 are not included in the official emissions inventory for the
MCMA, and those presented here must be considered rather hypothetical. Since the fine
particulate fraction is used as an all purpose estimate, the qualifications above for PM10 also
applies to PM2.5.
Emissions of GHG (CO2 , CH4 , and N2 O) are estimated from emissions factors reported in
IPCC (1997, Table 1-27) in grams per kilogram of fuel consumed. The total annual fuel
consumption is in turn estimated on the basis of assumptions about the average fuel
economy of taxis model year 1998 and earlier on the one hand (i.e., the existing fleet), and
model year 1999 and later on the other hand (i.e., the new fleet). The fuel economy, the
aggregate fuel consumption in 1998, and the emissions factors are shown in Table III.2.2.
The estimate of the fuel economy of the existing fleet is calculated backwards, given data
from Mexican Petroleum (PEMEX) on total gasoline consumption in the MCMA in 1998,
and given the total VKT of taxis. A density of 0.736 kg/liter for PEMEX Premium and 0.73
kg/liter for PEMEX Magna is used to convert fuel consumption in liters to kilograms,
before applying the emissions factors.
Table III.2.2. GHG emissions factors for taxis in the MCMA (g/kg)
Model
Year
1998
and before
1999
and later
Fuel
economy
(km/liter)
Consumption Consumption
Premium 1998 Magna 1998
(liter)
(liter)
CO2
(g/kg)
CH4
(g/kg)
N2 O
(g/kg)
6.7
61,332,677
960,878,607
3,172.31
0.43
1.81
9.0
0
0
3,172.31
0.32
0.46
The average fuel economy of the existing fleet at 6.7 km per liter compares reasonably well
with the fuel economy assumptions adopted in IPCC (1997, Table 1-27). For the future
vehicle fleet, starting with model year 1999, we assume that all new vehicles will leave the
31
automobile manufacturer with an average fuel economy of 12.6 km per liter. Adjusted for
the urban driving cycle and an observed bias in laboratory measurements, this reduces to
9.0 km per liter (U.S. EPA, 2001).
The data applied so far in the analysis on vehicle activities and emissions characteristics for
the local criteria pollutants coincide with the data used in the 1998 emissions inventory
(CAM, 2002a). The total emissions in 1998 are therefore identical, except for a small
difference in rounding. For the projection of the baseline activities and emissions, however,
there are differences in both the methodology and the data used. For the purpose of
comparison, the reader is referred to the calculation of projections and emissions reductions
in PROAIRE until 2010 (CAM, 2002b).
Vehicle fleet and travel demand projections
In the evaluation of measures to control emissions from mobile sources, it is customary to
develop models that are able to generate forecasts of future travel demand. These models
are based on expectations about the growth in income per capita and other socio-economic
characteristics of the population, which may serve as explanatory variables. Some models
generate simple estimates of changes in the number and distance of trips, distributed over
different modal alternatives (i.e., private cars, taxis, microbuses, etc.). Other models
involve more complex econometric estimation. There are also models which include
changes in land use among the driving forces behind vehicle ownership and use
(Harrington and McConnell, 2003).
To some extent, all these different alternatives are relevant to the estimation of the future
emissions from taxis in the MCMA, given changes in the number of vehicles, their age
distribution, and the total distance traveled (Mostashari, 2003). In the present analysis,
however, we side-step the issue of travel demand modeling for two reasons.
First, although the taxis in the MCMA are privately owned, the ownership and use of taxis
is conditioned on public concessions issued by the government of the Federal District and
the State of Mexico. These concessions, if effectively monitored and enforced, act like a
constraint on the expansion of the number of taxis. Rather than being a variable in need of
explanation, the growth in the taxi fleet thereby becomes a parameter over which the policy
makers assume direct control.
In the Federal District, no new concessions are currently issued as the result of an explicit
political choice (SETRAVI, 2002b). This is seen as a means to reduce the share of taxis in
the vehicle fleet over time, since they are generally considered to be in oversupply. In the
analysis, we therefore assume a zero percent growth rate of new taxis in the Federal
District. Obviously, this parameter can be changed in order to see the implications from the
choice of different alternatives. In the State of Mexico, an annual growth rate of 2 percent is
expected according to SENER (2000).
Second, the demand for vehicles and their use is on occasion seen as a derived demand for
transport services with certain characteristics. What is demanded is not the vehicle per se,
but rather the mobility it provides under specified conditions, such as size, speed, and
32
safety. But, although taxi owners have preferences over these alternatives, the essential
decision with respect to travel seems to be one of supply, not demand. From the viewpoint
of the taxi owner, assuming he is also the driver, the problem can therefore be stated as one
of choosing how many kilometers to supply, given alternative prices (i.e., the taxi fare),
capital and labor costs, and a labor- leisure trade off. In other words, whereas the private car
is most easily seen as a durable consumer good, the taxi is more like a producer good. This
ought to lead to differences in the modeling strategy of future travel behavior.
Vehicle fleet turnover
Given the growth rates of new taxis, the total size of the taxi fleet until 2020 is determined.
Since we do not discriminate between model years in terms of annual distance traveled, the
total VKT of taxis is also determined. If old vehicles are assumed to be driven less than
new vehicles, as the evidence seems to indicate, the total VKT depends not only on the total
number of vehicles, but also on the age distribution.
In order to determine the age distribution of the taxi fleet over time, we develop a simple
model of the fleet turnover, which consists of two basic elements; a natural rate of
retirement and a rate of replacement. We assume that the two rates are identical each year,
so that the old taxis retired are automatically replaced by new ones. This means that the
turnover of the taxi fleet is independent of the overall fleet size, a fact which helps us
interpret the results. The natural rate of retirement (or the natural scrappage rate) is
determined through the specification of age specific “death” probabilities, with the property
that old vehicles are more likely to be retired than new vehicles. This property is supported
by the empirical literature. The retirement rates of taxis are calculated on the basis of a
linear function in 1999, which produces a total retirement of taxis that year equal to 4% of
the fleet. This function is kept constant during all the subsequent years. The retirement rates
are shown in Figure III.2.2.
Figure III.2.2. Age specific retirement rates for taxis in the MCMA
0.20
Retirement rate
0.16
0.12
0.08
0.04
0.00
0
2
4
6
8
10
12
14
16
Vehicle age (in years)
33
18
20
22
24
From the cumulated retirement rates, a survival function is derived in Figure III.2.3. The
figure shows, in percentage terms, how many taxis of each model year would be expected
to survive in comparison with the number of taxis in the fleet from the beginning. Given the
retirement and survival rates, it is easy to confirm that 17 years is the maximum age all
taxis in the MCMA. This is deliberately a conservative estimate.
Figure III.2.3. Age specific survival rates for taxis in the MCMA
1.00
Survival rate
0.80
0.60
0.40
0.20
0.00
0
2
4
6
8
10
12
14
16
18
20
22
24
Vehicle age (in years)
III.2.5. Estimating emissions reductions and costs for the measure
The previous section has described in considerable detail the development of the baseline
scenario and the choice of parameter values for that purpose. In this section, we merge the
baseline with the specification of a benchmark control scenario. The benchmark refers to a
choice for the analysis of what we believe are moderate parameter values, as well as a
control scenario that is not too stringent. The combination for this analysis is shown in
Table III.2.3.
Table III.2.3 is a “policy analysis matrix” which illustrates the key assumptions and policy
variables in the analysis. The policy variables are those over which policy makers and
others exercise control in the taxi renewal program, such as the number of old taxis to be
replaced, and the standards to be required from new taxis. Although the control scenario is
quite ambitious in the number of taxis to be scrapped and replaced, it is not very stringent
in the other policy variables. This is easily observed in the table for the tailpipe emissions
and the fuel economy standards of new vehicles in the control scenario, which are assumed
to be identical with the standards of new vehicles in the baseline scenario. Since there are
no additional requirements associated with these standards, they have no incremental costs
either.
34
Table III.2.3. Benchmark control scenario for renovation of the taxi fleet
Baseline
Parameters
Parameter
values
Taxi
ownership
concessions
Benchmark control scenario
Accelerated
Fuel
turnover/
economy
scrappage
standards
Tailpipe
emission
standards
Vehicle ownership growth rate
Federal District
0%
State of Mexico
2%
Concessions
issued by the
government
Vehicle replacement rate
Maximum age
17 years
80,099 taxis
Vehicle kilometers traveled (VKT)
VKT per day
200 km
Days per year
313 days
“No Driving
Day” not
included
Fuel economy and fuel economy standards
Model year 1998
Model year 1999
6.7
km/liter
9.0
km/liter
EPA adjusted
9.0 km/liter
Tier 1
Tier 1
Tailpipe emissions standards
Model year 1999
Emissions deterioration and durability requirements
Model year 1998
Model year 1999
IMP emis.
factors
Adjusted
IMP factors
No durability
requirements
The policy matrix is easy to extend in both the vertical and horizontal dimensions. With
respect to the baseline parameter assumptions, it may serve as a useful tool for identifying
possible variations in the parameter values, with the aim of conducting a sensitivity analysis
and, eventually, include the control measure in the Analytica software. With respect to the
benchmark control scenario, one can imagine a number of other, more stringent control
measures, such as larger improvements in the fuel economy of new vehicles, Tier 2
emission standards, and standards for the durability of the emission control technology. If
combined with estimates of the associated incremental costs, an increasingly more strict
control scenario can be subject to an incremental cost-effectiveness analysis.
From Table III.2.3 it appears that an important caveat apply to the analysis. Thus, it should
be noted that the No Driving Day program (Hoy No Circula) has been ignored in the
estimation of the total distance traveled. The program restricts vehicles from circulating in
the MCMA for one or two days during a week, if the vehicle exceeds certain in- use
emission standards in the vehicle verification test. Assuming that No Driving Day remains
unchanged over the time period of the analysis, including the program would restrict a
growing number of vehicles in the baseline according to an increasing emissions
35
deterioration. By contrast, when these vehicles are replaced with new ones, the restrictions
from the No Driving Day would be suspended due to the improved emissions control. The
net effect of this change would therefore be to reduce the emissions reductions obtained in
the present analysis.
III.2.6. Costs and Emissions Reductions with the Measure
The undiscounted emissions reductions from the benchmark control scenario are shown in
Table III.2.4. As explained above, there are no reductions of PM emissions, since no
distinction is made between the emissions factors of old and new vehicles in the emissions
inventory. By contrast, the emissions reductions of criteria pollutants and GHG are quite
substantial. The time profile shows that the emissions reductions peak in 2007, the last year
when taxis are scrapped and replaced through the program. The emissions reductions then
start to decline. This observation is a logical consequence of the scrappage program as a
measure that produces emissions reductions only temporarily. Since none of the
fundamental parameters behind the turnover of the taxi fleet are affected (i.e., growth and
retirement rates), the size and composition of the fleet in the control scenario will
automatically converge to the original size and composition of the fleet in the baseline
scenario. And since no permanent changes are assumed in the emissions characteristics of
the new taxis, over and above the new vehicles in the baseline scenario, the total emissions
in the control scenario will eventually return to the baseline level.
Table III.2.4. Emission reductions, renovation of the taxi fleet, without discounting
(tons/yr)
Year
PM 10
PM 2.5
SO2
CO
NO x
HC
CO2
CH4
N2 O
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
21
41
61
80
88
83
78
73
69
65
61
58
54
50
47
44
41
38
48,819
98,180
149,710
199,910
224,295
221,387
215,184
216,217
205,229
188,932
161,978
138,049
114,409
95,596
78,851
68,477
56,915
44,830
1,857
3,698
5,263
6,776
7,649
6,815
5,482
4,386
2,688
1,515
555
31
-490
-254
-21
-18
-15
-12
5,190
10,391
15,702
20,841
23,586
22,901
21,135
19,686
16,951
14,340
11,010
8,654
6,294
4,768
3,315
2,594
1,840
1,071
88,969
177,159
263,476
345,917
377,458
357,732
338,532
319,927
301,826
284,296
267,350
250,998
235,252
218,906
204,468
190,789
177,886
166,711
21
42
62
81
89
84
79
75
71
67
63
59
55
51
48
45
42
39
161
321
477
626
683
648
613
579
546
515
484
454
426
396
370
345
322
302
36
Table III.2.5. Annualized emissions reductions from the renovation of the taxi fleet
(ton/yr)
Discount
rate
PM 10
PM 2.5
SO2
CO
NO x
HC
CO2
CH4
N2 O
Time horizon 2003-2010
0%
0
0
66
171,713
5,241
17,429
283,646
67
514
3%
0
0
65
167,970
5,178
17,090
278,478
65
504
5%
0
0
64
165,483
5,135
16,863
275,007
64
498
7%
0
0
63
163,010
5,090
16,636
271,528
64
492
Time horizon 2003-2020
0%
0
0
59
140,387
2,550
11,682
253,758
60
459
3%
0
0
59
144,397
2,866
12,403
256,479
60
464
5%
0
0
59
146,380
3,060
12,811
257,542
60
466
7%
0
0
60
147,818
3,239
13,159
258,018
61
467
The annualized emissions reductions are shown in Table III.2.5 for a plausible range of
discount rates. The table shows that the results are not very sensitive, neither to changes in
the discount rate, nor to changes in the time horizon of the analysis. With the exception of
the NOX reductions, the changes are quite small. The larger change in NOX reductions from
2010 to 2020 is due to a sharp decline in NOX emissions reductions, which even become
negative from 2015 and onwards. This seeming paradox is explained by the emissions
factors for NOX in the emissions inventory, which show a remarkable jump in the
emissions for the 1989 and 1990 model year vehicles. However, while this jump had a
justification in the past, as a technical artifact of the existing taxi fleet, it is unlikely to be
replicated in the future. A more careful modeling of the future emissions of taxis, including
the (NOX) emissions deterioration rates, might show a less drastic return to the underlying
baseline trend. Such a modeling exercise could be conducted on the MOBILE5 or
MOBILE6 models.
The time profile of the undiscounted cost estimates and the annualized costs are shown in
Table III.2.6 and Table III.2.7. It is clear from the undiscounted costs that although both the
public and private costs of capital are substantial, there are also considerable savings in fuel
costs due to an improved fuel economy of new vehicles. Note that the fuel savings peak in
the year 2007 for the same reasons as the emissions reductions. Similarly, the annual
savings will eventually become zero at some point beyond the time horizon of the analysis.
Given the time profile of the costs, with large up- front capital costs and fuel savings
distributed over the entire time period of analysis, it is expected that the total cost estimates
are sensitive to both the choice of discount rate and the choice of time horizon. A lower
discount rate leads to much lower total costs, because the future savings are discounted less
compared to the initial capital costs. Likewise, when the time horizon of the analysis is
extended from 2010 to 2020, the fuel savings are included over a longer period, while the
capital costs remain unchanged. This leads to negative total costs for the 2003 to 2020
period, independently of the discount rate applied in the analysis.
37
Table III.2.6. Costs of renovation of the taxi fleet without discounting
(millions US$/yr)
Year
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Public
Investment
Private
Investment
O&M
Cost
Total
Cost
24.03
24.03
24.03
24.03
24.03
80.10
80.10
80.10
80.10
80.10
-19.74
-39.31
-58.60
-76.93
-83.95
-79.56
-75.29
-71.31
-67.28
-63.37
-59.59
-55.95
-52.44
-48.79
-45.58
-42.53
-39.65
-37.16
84.39
64.82
45.53
27.20
20.18
-79.56
-75.29
-71.31
-67.28
-63.37
-59.59
-55.95
-52.44
-48.79
-45.58
-42.53
-39.65
-37.16
Table III.2.7. Annualized costs of renovation of the taxi fleet (millions US$/yr)
Discount rate
Public
Investment
0%
3%
5%
7%
15.02
15.68
16.10
16.50
0%
3%
5%
7%
6.68
8.00
8.90
9.79
Compliance Cost (2003 millons US$/ yr)
O&M
Private Investment
Cost
Time Horizon 2003-2010
50.06
-63.09
52.26
-61.94
53.66
-61.16
55.00
-60.39
Time Horizon 2003-2020
22.25
-56.50
26.67
-57.10
29.67
-57.33
32.65
-57.43
Total
Cost
1.99
6.00
8.59
11.12
-27.58
-22.43
-18.76
-14.99
III.2.7. Uncertainty
As emphasized already, there are many unquantified uncertainties associated with the wide
range of parameter values included in the present analysis. It is therefore recommendable
that a careful sensitivity analysis is performed, in order to check to what extent the results
obtained are robust. Such an analysis can also be used to identify those parameters that
contribute most to the overall uncertainty, and provide a basis for future improvements.
38
Among the parameters in the present analysis, some can be identified as playing a key role
for the final results. They include the emissions factors of PM10 , the emissions of old highuse vehicles, emissions deterioration rates for the future taxi fleet, and the age specific rate
of retirement. These variables, in particular, should be subject to more careful analysis of
their quantitative impact on the results. In addition, we have adopted a moderate control
scenario in the present analysis. If cost estimates for Tier 2 vehicles can be obtained, it
would be interesting to see how large further emissions reductions can be achieved. Since
this would imply a permanent shift in the emission rates of new vehicles, the emission
reductions would also be more permanent.
Considerable uncertainty also exists with respect to the implementation of the taxi
substitution program and its expected level of compliance. Since this has been conveniently
assumed away in the present analysis, the question of the enforcement of scrappage and
replacement does not arise. This is a form of model uncertainty that should be taken
seriously.
III.2.8. Discussion and Next Steps
On the basis of the present analysis, the scrappage and replacement of taxis seems to be a
worthwhile measure to adopt in order to control the large share of emissions from mobile
sources in the MCMA. However, this conclusion depends on a number of uncertain factors,
whose influence has not been formally analyzed. A careful sensitivity analysis could
provide a more firm ground upon which the taxi substitution program is recommended to
policy makers and communicated to the public. In this respect, an effort should be made to
explain, to what extent the results obtained from the scrappage and replacement of taxis can
be expected to carry over to the in- use private car fleet.
A fundamental source of conflict exists in the current design of the program. This conflict
means that, under the worst circumstances, an over- valued price of the oldest taxis is being
used to achieve emissions reductions which are highly uncertain, and which we have been
unable to verify and document within the time available for this study. The uncertainty
derives from a poor understanding of the average emissions of old taxis, for which there are
no directly estimated emissions factors in the emissions inventory. It also derives from a
supposedly large distribution over this average, which has been observed in numerous
measurement studies of private cars. If these observations hold also for taxis, an improved
cost-effectiveness of the taxi substitution program might be achieved by an improved
targeting of the program. As a thought experiment, a better targeting is associated with
improved knowledge of high-emitting taxis and their costs in the used car market. This
calls for an increased testing of the taxis in the program, possibly at the expense of
scrapping 80% of the fleet.
III.2.9. References
Alberini, A., W. Harrington, and V. McConnell (1995), “Determinants of Participation in
Accelerated Vehicle Retirement Programs”, The RAND Journal of Economics, 26(1), 93112.
39
Alberini, A., W. Harrington, and V. McConnell (1998), “Fleet Turnover and Old Car Scrap
Policies”, Discus sion Paper 98-23, Resources for the Future, Washington, D.C.
Burnette, A.D., S. Kishan, and M.E. Wolf (2001), “MOBILE5-Mexico: An Emission
Factor Model for On-Road Vehicles in Mexico”, Paper presented at the 10th Annual
Emission Inventory Conference, May 2, 2001, Denver, Colorado.
Cadle, S.H, P. Mulawa, E.C. Hunsanger, K. Nelson, R.A. Ragazzi, R. Barrett, G.
Gallagher, D.R. Lawson, K.T. Knapp, and R. Snow (1999), “Light-Duty Motor Vehicle
Exhaust Particulate Matter Measurements in the Denver, Colorado Area”, Journal of the
Air and Waste Management Associaction, 49, 164-74.
Cadle, S.H., P. Mulawa, P. Groblicki, C. Laroo, R.A. Ragazzi, K. Nelson, G. Gallagher,
and B. Zielinska (2001), “In-Use Light-Duty Gasoline Vehicle Particulate Matter
Emissions on Three Driving Cycles”, Environmental Science and Technology, 35, 26-32.
CAM (Comisión Ambiental Metropolitana) (2002a), Inventario de Emisiones de la Zona
Metropolitana del Valle de México, 1998 (Mexico, D.F.: CAM).
CAM (Comisión Ambiental Metropolitana) (2002b), Programa para Mejorar la Calidad
del Aire de la Zona Metropolitana del Valle de México, 2002-2010 (Mexico, D.F.: CAM).
COMETRAVI (Comisión Metropolitana de Transporte y Vialidad) (1999), “Diagnóstico de
las Condiciones del Transporte y sus Implicaciones sobre la Calidad del Aire”, in Estudio
Integral de Transporte y Calidad del Aire para la Zona Metropolitana del Valle de México,
Vol. 1 (Mexico, D.F.: COMETRAVI and CAM).
Dixon, L. and S. Garber (2001a), Fighting Air Pollution in Southern California by
Scrapping Old Vehicles (Santa Monica, CA.: RAND Institute for Civil Justice).
Dixon, L. and S. Garber (2001b), “Scrapping Old Vehicles Would Improve Southern
California Air Quality at Reasonable Cost”, Research Brief, RB-9033, RAND Institute for
Civil Justice, Santa Monica, CA.
Dixon, L., S. Garber, and I. Porche (2002), “Driven Into a Corner”, RAND Review, 26(3),
10-15.
ERG (Eastern Research Group) and Radian International (2000), MOBILE5-Mexico:
Documentation and User’s Guide (Denver, CO.: Western Governors’ Association and
Binational Advisory Committee).
ESMAP (UNDP/World Bank Energy Sector Management and Assistance Program) (2002),
“Can Vehicle Scrappage Programs Be Successful?”, South Asia Urban Air Quality
Management Briefing Note No. 8, World Bank, Washington, D.C.
Estache, A. (2001), “Privatization and Regulation of Transport Infrastructure in the 1990s”,
The World Bank Research Observer, 16 (1), 85-109.
40
Gakenheimer, R., L.T. Molina, J. Sussman, C. Zegras, A. Howitt, J. Makler, R. Lacy, R.
Slott, and A. Villegas, with contributions from M.J. Molina and S. Sanchez (2002), “The
MCMA Transportation System: Mobility and Air Pollution”, in L.T. Molina and M.J.
Molina, eds. (2002), Air Quality in the Mexico Megacity: An Integrated Assessment
(Dordrecht: Kluwer Academic Publishers).
Gonzalez, L.E. (2002), “Design of the Taxi Renewal Program”, Presentation at the Fifth
Workshop on Mexico City Air Quality, January 21.-24., 2002, Ixtapan de la Sal, State of
México (MIT Integrated Program on Urban, Re gional and Global Air Pollution).
Hahn, R.W. (1995), “An Economic Analysis of Scrappage”, The RAND Journal of
Economics, 26(2), 222-42.
Hamilton, B.W. and M.K. Macauley (1998), “Competition and Car Longevity”, Discussion
Paper 98-20, Resources for the Future, Washington, D.C.
Harrington, W., V.D. McConnell, and M. Cannon (1998), “A Behavioural Analysis of
EPA’s MOBILE Emission Factor Model”, Discussion Paper 98-47, Resources for the
Future, Washington, D.C.
Harrington, W. and V.D. McConnell (2003), Motor Vehicles and the Environment
(Washington, D.C.: Resources for the Future).
IPCC (Intergovernmental Panel on Climate Change) (1997), Revised 1996 IPCC
Guidelines for National Greenhouse Gas Inventories, Vols. 1-3 (Bracknell, UK: IPCC).
Kojima, M. and R. Bacon (2001), Mexico Energy Environment Review, May 2001
(Washington, D.C.: Joint UNDP/World Bank Energy Sector Management Assistance
Programme).
Molina, M.J., L.T. Molina, J. West, G. Sosa, and C. Sheinbaum, with contributions from
F.C. Martini, M.A. Za vala, and G. McRae (2002), “Air Pollution Science in the MCMA:
Understanding Source-Receptor Relationships Through Emissions Inventories,
Measurements, and Modeling”, in L.T. Molina and M.J. Molina, eds. (2002), Air Quality in
the Mexico Megacity: An Integrated Assessment (Dordrecht: Kluwer Academic Publishers).
Mostashari, A. (2003), “Design of Robust Air Quality Measures for the Road-Based Public
Transportation Sector in Megacities: The Case of the Mexico City Metropolitan Area
(MCMA)”, M.Sc. Dissertatio n, Massachusetts Institute of Technology, Boston.
Mugica, A.V., R.E. Vega, J.L. Arriaga, and M.E. Ruiz (1998), “Determination of Motor
Vehicle Profiles for Non-Methane Organic Compounds in the Mexico City Metropolitan
Area”, Journal of the Air and Waste Management Association, 48, 1060-68.
NRC (National Research Council, Transportation Research Board) (2000), Modeling
Mobile Source Emissions (Washington, D.C.: National Academy Press).
41
NRC (National Research Council, Transportation Research Board) (2002), Effectiveness
and Impact of Corporate Average Fuel Economy (CAFE) Standards (Washington, D.C.:
National Academy Press).
Paramo, V.H. (2003), “Mexico City Metropolitan Area Report”, Presentation at Diesel
Days, January 16-17, 2003, World Bank Clean Air Initiative and World Resources Institute,
Washington, D.C..
Portney, P. (2002), “Penny-Wise and Pound-Fuelish? New Car Mileage Standards in the
United States”, Resources, 147, 10-15.
Radian International (1997), Mexico Emissions Inventory Program Manuals, Vol. 6: Motor
Vehicle Inventory Development (Denver, CO.: Western Governors’ Association and
Binational Advisory Committee).
SENER (Secretaría de Energía) (2000), Prospectiva de Mercado de Gas Natural
Comprimido, 2000-2010 (Mexico, D.F.: SENER)
SETRAVI (Secretaría de Transporte y Vialidad) (2002a), Programa de Financiamiento
para la Sustitución de Taxis en el Distrito Federal (Mexico, D.F.: SETRAVI).
SETRAVI (Secretaría de Transporte y Vialidad) (2002b), Primera Versión del Programa
Integral de Transporte y Vialidad, 2002-2006 (Mexico, D.F.: SETRAVI).
U.S. EPA (Environmental Protection Agency) (1998), “Transportation Control Measures:
Accelerated Vehicle Retirement”, TRAQ Technical Overview, EPA420-S-98-001.
U.S. EPA (Environmental Protection Agency) (2000), Guidelines for Preparing Economic
Analyses (Washington, D.C.: EPA 240-R-00-003).
U.S. EPA (Environmental Protection Agency) (2001a) Light-Duty Automotive Technology
and Fuel Economy Trends: 1975 Through 2001 (Washington, D.C.: EPA420-R-01-008)
U.S. EPA (Environmental Protection Agency) (2001b), EPA’s New Generation Mobile
Source Emissions Model: Initial Proposal and Issues (Washington, D.C.: EPA).
U.S. EPA (Environmental Protection Agency) (2002), “Official Release of the MOBILE6
Motor Vehicle Emissions Factor Model”, Federal Register, 67(19), 4254-57.
Vega, E., V. Mugica, R. Carmona, and E. Valencia (2000), “Hydrocarbon Source
Apportionment in Mexico City Using the Chemical Mass Balance Receptor Model”,
Atmospheric Environment, 34, 4121-29.
42
III.3. Expansion of the Metro
III.3.1. Introduction
Economic growth is the fundamental force that promotes road congestion and air pollution
because it influences rates of motorization, generation of trips and urban growth.
The origin-destination (O-D) survey conducted by the National Statistics Institute (INEGI)
in 1994, indicates that approximately 29 million vehicle trip segments per day were made
in the MCMA. Federal District has 66.5% of these trips, whereas trips of the urbanized
municipalities of the State of Mexico represent 33.5%. This is 1.73 trip sections per capita.
The projection indicates that in 2020 the trip segments will have grown to 36 million. In
terms of overall mode share, using the data on trip segments in 1994, low and medium
occupanc y modes dominate the landscape. Colectivos account for over 50 percent of trip
segments and autos and taxis another 20 percent. Among the high occupancy modes, the
metro accounts for roughly 14 percent of all trips, followed by urban and suburban buses
with 10 percent. The new Program to Improve the Air Quality in the Valley of Mexico
(Programa para Mejorar la Calidad del Aire en el Valle de México 2002-2010 or
PROAIRE), also reports a similar tendency in 1998; the capture of passengers of the low
occupancy transport continues increasing. On basis to the 1998 emission inventory, near
2.5 million tons of local pollutants were emitted, of which 84% were generated by the
mobile sources, principally private autos, taxis, combis and microbuses. For 2010
PROAIRE projects 3.2 million tons of local pollutants, and 5.5 million of vehicles, 80%
low occupancy transport.
From a modal share perspective, the most worrying trend is the massive shift towards lower
capacity modes at the expense of Metro ridership and bus us e. This is one of the principal
policy challenges facing the city's transportation system.
On basis of previous parragraphs, the objective of the Expansion of the Metro is to give a
fast, efficient, safe and nonpolluting transport, in agreement with the future demand in the
Mexico City Metropolitan Area.
III.3.2. Description of the Measure
This control measurement is included in the Integral Strategy of Transport and Air Quality
for the MCMA, published by the Metropolitan Commission of Road and Transport
(COMETRAVI) in 1999. The Metropolitan Environmental Commission also participated in
this strategy. A document that served as base was the “Plan Maestro del Metro y Trenes
Ligeros 1996” (Master Plan of Metro and Light Trains, 1996). This Plan proposed the
addition of 166 km of new lines of metro, divided between 76 km of urban metro in central
city (Federal District) and 68 km of suburban metro or regional train (State of Mexico) for
year 2020. In this document the suburban metro was not analyzed. COMETRAVI reports
that almost 5 kilometers of metro in the DF will be constructed between 2000 and 2010,
and 70 km between 2011 and 2020. PROAIRE also includes the same total installation, but
with an implementation plan of 76 kilometros from 2002 to 2010. However, PROAIRE
43
does report the same emission reductions as COMETRAVI. In these documents, economic
indicators (Net Present Value) and the emission reductions of carbon dioxide were not
considered. In the co-control study, West et al (2003), included both indicators.
In our analysis of this measure, we consider that 76 kilometers of metro in the Federal
District will be constructed in the period from 2003 to 2020, to have a total network in the
DF of 276 km by 2020, in agreement with the COMETRAVI implementation plan. Also in
accordance with this plan, implementation is staged with 5 km being constructed between
2003 and 2010, with an additional 71 km between 2011 and 2020.
The passengers who travel by metro in 1994 (base year of COMETRAVI analysis) were
more than 4 million and for 2020 this number will be increased by 111% due to the
Expansion of the metro.
III.3.3. Data Requirements
To estimate the impact of the Expansion of Metro, we must have the future growth (2003 to
2020) of population in the MCMA; trips per capita; vehicular fleet and the mode share for
vehicle trip segments in the period. To understand current metro use, we need existing
metro kilometers, annual and daily round trips, traveled km /day, and transported
passengers. For other transport modes, we need occupancy per vehicle, traveled kilometer
per day, and the number of days in circulation. Also we need emission factors for local and
global pollutants (in g/km and kg/GWh) which are coupled with vehicle kilometers traveled
or energy consumption estimates. We need investment, operation and maintenance, and
fuel costs.
III.3.4. Determining Baseline Emissions
Our benchmark scenario for emissions reductions due to the Expansion of the metro are
estimated as a difference from the baseline emissions of the metro and medium occupancy
transport (combis and microbuses) that will be substituted or avoided by the metro and
those in the control scenario. In other scenarios not considered in detail here, we also
estimate the impacts if the substitution were to occur from only microbuses, from private
cars and taxis, and from diesel buses.
Since a baseline for this measure was not considered in previous studies, we established the
baseline. We assume that the capacity of high occupancy transport (metro, trolleybus and
light rail) is almost saturated, then the number of passengers who travel in this transport,
will remain fixed until 2020. The increase of trips from 2003 to 2020 will be absorbed by
other travel modes – low and medium occupancy modes - and that they do not require of
investment (buses, microbus, combis, private taxis, cars) in infrastructure.
With population growth data in the period of analysis and the number of trips per capita, we
considered that almost 29 million of trips were made in 1994 and more than 36 million of
trips would be made in 2020.
44
The annual increase of the low and medium capacity vehicular fleet until 2020 was
estimated on the basis of the increase trips absorbed from the massive transport and the
individual occupanc y of each vehicle (bus 350 passengers; microbus 300; combi 7; private
auto 1.7). With the annual units of each transport modes and their daily travel kilometers,
we estimated the annual kilometers. It was not possible to obtain a distribution of the
vehicular fleet per year model, we only estimated the total number of each vehicle in each
year. This explanation has importance for the definition of the emission factors that we
used.
We calculated the baseline emissions with the emission factor and activity level. In the case
of the metro , activity level was the electricity consumption (GWh) and the emission factors
were taken from the IPCC and PROAIRE. For local pollutants, the emission factors
considers the fraction of electrical energy that is generated by the power stations of the
ZMVM in relation to the interconnected system (3.1%). The emission factors for global
pollutants, consider the total of emissions due to the generation of electricity (global effect).
The emission factor for CO2 , CH4 and N2 O, was weighed according to the primary energy
consumption used (fossil fuel) to generate electricity in the interconnected system.
EFG= ∑(Ci/Cn) * EF CO2i
i
EFG = the emission factor for CO2 , CH4 or N2 O (taken from IPCC, 1997).
Ci = the consumption of fuel i, used to generate electricity [PJ] in the interconnected
system.
Cn = the total consumption of all fuels, used to generate electricity [PJ] in the
interconnected system.
Emissions of local pollutants in COMETRAVI (1999) are estimated assuming that the
Metro replaces (avoids) diesel buses but the emission factors used appear to be very large
(a factor or more than 2 compared with PROAIRE emission factors for urban buses).
Obviously, this will tend to overestimate emission reductions, and possibly by a large
amount. In our analysis, we considered four scenarios: the expansion of the Metro would
replace buses; microbuses and combis; microbuses; private autos and taxis. We assume
avoided vehicle use eliminates old vehicles. Due to social realities in Mexico City, we
consider the most feasible scenario is the sustitution of microbus and combis, and so this is
used as our benchmark.
Because we do not have an annual distribution of the vehicular fleet per year model, we
also weighed the emission factors for local pollutants reported in the emission inventory
1998 for the MCMA and global pollutants in IPCC.
45
Table III.3.1. Metodology to estimate weighted emission factors
CO2 (g/km)
Year
Number of microbuses
% of total fleet
1974
555
1.16
E.F. x model year
601
EF weighted
6.96
:
:
:
:
:
1994
Total
199
47,950
0.33
100
3.96
1.31
596
The final emission factors used, can be seen in the next table.
Table III.3.2. Emission Factors
Transport
Mode
PM 10
PM 2.5
SO2
CO
NO x
HC
CO2
CH4
N2 O
0.27
261,938
3.08
1.44
Metro (kg/GWh)
0.78
0.60
0.09
6.29
53.99
Colectivos (g/km)
Microbuses
0.03
0.02
0.12
108
4.75
9.86
596
0.13
0.03
Combis
0.03
0.02
0.12
59.4
2.70
5.65
589
0.12
0.04
III.3.5. Estimating Emissions Reductions and Costs for the Measure
In agreement with the COMETRAVI plan for the Expansion of the metro, we estimated the
annual consumption of energy (GWh) by the annual additional kilometers of metro until the
2020. With the weighted emission factor in kg/GWh, we estimated the emissions due to the
Expansion of the metro. We assumed that the additional passengers who will travel in
metro come from microbuses and combis. If each microbus and combi transport 350 and 70
passengers per day respectively, then we have more than 13,000 microbuses and 6,200
combis avoided. Both microbus and combi travelled 200 km per day and circulate 313 days
per year, then we know the total kilometers avoided due to the Expansion of the metro and
multiplying per the correponding emission factor, we have the emissions of each pollutant
in the control scenario.
Emission reductions are the difference between the baseline emissions and control scenario.
The cost for a kilometer of metro ($35 million USD) from MCMA is taken from a July
2002
article
in
Reforma
Magazine
(http://www.reforma.com/ciudaddemexico/articulo/209896/). However, COMETRAVI
(1999c) indicates a price of $52 million USD. Regarding to Operations and Maintenance,
we estimated the cost due to the incremental energy consumption of the new kilometers of
metro. The price of electricity is taken from electronic page of Mexican Energy Ministry.
Recuperation value at the end of either time period is included, considering a useful life of
30 years.
46
To consider the saved liters of gasoline by the avoided microbuses and combis, we used the
fuel efficiency reported by Sanchez et al, 1999 (2.39 km/lt for microbuses and 3 km/lt for
combis). The price of Magna gasoline liter ($5.97) is taken from the website of the
Mexican Petroleum Corporation, PEMEX. The 15% value added tax is removed, and
conversion to dollars uses an exchange rate of 10.
III.3.5. Costs and Emissions Reductions with the Measure
In Tables III.3.3 and III.3.4, it is illustrated that there are significant CO2 reductions due to
this measure across the entire time horizon to 2020. On the local side, reductions of HC and
NOx will have the most significant impact on air quality.
Table III.3.3. Emissions reductions for expansion of the metro without discounting
(tons/yr)
Year
PM 10
PM 2.5
SO2
CO
NO x
HC
CO2
CH4
N2 O
2003
0
0
1
829
37
76
4,610
1
0
2004
1
0
2
1,658
73
153
9,220
2
1
2005
1
1
3
2,487
110
229
13,830
3
1
2006
1
1
4
3,316
146
305
18,440
5
1
2007
1
1
5
4,144
183
381
23,050
6
1
2008
2
1
6
4,973
219
458
27,660
7
2
2009
2
1
7
5,802
256
534
32,270
8
2
2010
2
1
8
6,631
292
610
36,880
9
2
2011
5
3
21
17,126
755
1,575
95,245
23
5
2012
9
6
34
27,620
1,217
2,541
153,611
38
9
2013
12
8
47
38,114
1,680
3,506
211,976
52
12
2014
15
10
60
48,609
2,143
4,472
270,342
66
16
2015
18
12
74
59,103
2,605
5,437
328,707
80
19
2016
2017
22
25
14
16
87
100
69,597
80,092
3,068
3,530
6,403
7,368
387,073
445,438
95
109
22
26
2018
28
18
113
90,586
3,993
8,334
503,804
123
29
2019
2020
31
35
20
23
126
139
101,080
111,575
4,455
4,918
9,299
10,265
562,169
620,535
137
152
32
36
47
Table III.3.4. Annualized emissions reductions for expansion of the metro (tons/yr)
Discount
rate
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
Time horizon 2003-2010
0%
1
1
5
3,730
164
343
20,745
5
1
3%
1
1
4
3,602
159
331
20,030
5
1
5%
1
1
4
3,518
155
324
19,567
5
1
7%
1
1
4
316
19,115
5
1
0%
12
8
105
37,408
1,649
3,441
208,048
51
12
3%
10
6
78
32,093
1,415
2,952
178,490
44
10
5%
9
6
65
28,835
1,271
2,653
160,368
39
9
7%
8
5
54
25,828
1,138
2,376
143,648
35
8
3,437
151
Time horizon 2003-2020
Tables III.3.5 and III.3.6 indicate that significant savings are obtained due to the
substitution of microbus and combis, but the cost of constructing the metro is very high.
Table III.3.5. Costs for expansion of the metro without discounting (millions US$/yr)
Year
Public Investment
Private Investment
O&M / Fuel
Total
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
19.47
19.47
19.47
19.47
19.47
19.47
19.47
19.47
246.46
246.46
246.46
246.46
246.46
246.46
246.46
246.46
246.46
246.46
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-0.01
-0.01
-0.02
-0.02
-0.03
-0.03
-0.04
-0.05
-0.12
-0.19
-0.26
-0.33
-0.40
-0.48
-0.55
-0.62
-0.69
-0.76
19.46
19.46
19.45
19.44
19.44
19.43
19.43
19.42
246.35
246.27
246.20
246.13
246.06
245.99
245.91
245.84
245.77
245.70
48
Table III.3.6. Annualized costs for expansion of the metro (millions US$/yr)
Discount rate
0%
3%
5%
7%
0%
3%
5%
7%
Abatement Cost (2003 millons US$/ yr)
Fuel, Operations,
Public Investment
Private Investment
Maintenance
Time Horizon 2003-2010
2.92
0
-0.01
4.32
0
-0.01
5.37
0
-0.01
6.50
0
-0.01
Time Horizon 2003-2020
29.28
0
-0.04
38.45
0
-0.03
44.05
0
-0.02
48.88
0
-0.02
Total Cost
2.91
4.31
5.37
6.50
29.24
38.43
44.03
48.87
III.3.6. Uncertainty
For this measure, we have uncertainties about present and future estimated activities (e.g.,
the size, composition and age of the vehicle fleet and the activity level per vehicle). We
assumed that both the vehicle kilometers traveled and the emission factors are constant
through time in the baseline. It means the there is no tendency for an improvement either of
fuel efficiency, or of fuel quiality over time. In practice, however, technological changes
would be expected to occur in the long run as a response to environmental policies. Also It
is possible that the occupacy of the microbuses and combis that we used is slightly
underestimated.
In recent years, metro mode share has decreased. A major reason for the decline in
ridership is that the population has expanded further from the urban core. The Metro cannot
effectively serve the people living in these new developments unless it extends until the
State of Mexico. We only consider the Expansion of the metro in the Federal District.
COMETRAVI reports the lines of metro that will be constructed in the State of Mexico, but
does not estimate the emissions reductions in this area.
Costs for the Metro Expansion measure do not include potential subsidies to cover its
operating costs.
III.3.7. Discussion and Next Steps
We made the first effort to calculate a baseline for this measure, and will be necessary that
this measure is studied with new and better information (which is commented in
uncertainty) in the future.
Other work has illustrated to us that we would obtain greater emissions reductions for this
measure if we assumed substitution of private cars and taxis. Nevertheless, there are great
social barriers to be overcome before private vehicles owners accept switch to riding the
Metro or other pub lic transportation. Due to social realities in Mexico City, we consider the
49
most feasible scenario is the sustitution of microbus and combis, and so this is used as our
benchmark. Emissions reductions in this scenario are still very significant.
The implementation of this measurement is very expensive, but it has additional benefits
since it reduces the colectivos fleet, avoid the vehicular congestion and local pollutant
emissions.
It will be necessary to review the new Master Plan of Metro in the MCMA, to update this
measurement and to review if there is better information regarding the synergies between
Expansion of metro and other transport modes. The Expansion of the metro towards the
State of Mexico also will have to be analyzed, since this area will have the greater urban
growth in the next years.
III.3.8. References
CAM, Comisión Ambiental Metropolitana (2002a), “Programa para Mejorar la Calidad del
Aire de la Zona Metropolitana del Valle de México, 2002-2010” (PROAIRE), Comisión
Ambiental Metropolitana.
CAM, Comisión Ambiental Metropolitana (2002b), “Inventario de Emisiones de la Zona
Metropolitana
del
Valle
de
México,
1998”,
in
http://www.sma.gob.mx/publicaciones/aire/html
COMETRAVI, Comisión Metropolitana de Transporte y Vialidad (1999a), “Diagnóstico de
las Condiciones del Transporte y sus Implicaciones sobre la Calidad del Aire”, in “Estudio
Integral de Transporte y Calidad del Aire para la Zona Metropolitana del Valle de México,
Vol. 1” (Mexico City: COMETRAVI and CAM).
COMETRAVI, Comisión Metropolitana de Transporte y Vialidad (1999b), “Definición de
Políticas para el Metro, Tren Ligero, Trolebús Urbano y Otros Medios de Transporte
Masivo en un Nivel Metropolitano”, in Estudio Integral de Transporte y Calidad del Aire
para la Zona Metropolitana del Valle de México, Vol. 4 (Mexico City: COMETRAVI and
CAM).
COMETRAVI, Comisión Metropolitana de Transporte y Vialidad (1999c), “Definición de
Políticas para la Infraestructura del Transporte”, in Estudio Integral de Transporte y
Calidad del Aire para la Zona Metropolitana del Valle de México, Vol. 6 (Mexico City:
COMETRAVI and CAM).
Gobierno del Distrito Federal (2003). Información básica del sistema de transporte
colectivo Metro in http://www.df.gob.mx/agenda2000/transporte/9_1.html
Instituto Nacional de Estadística, Geografía e Informática, INEGI (2003), Principales
características del Sistema de Transporte Colectivo Metro, Banco de Información
Económica. In http://www.inegi.gob.mx
50
IPCC, Intergovernmental Panel on Climate Change (1997), “Revised 1996 IPCC
Guidelines for National Greenhouse Gas Inventories, Vol. 3” (Bracknell, UK: IPCC).
Molina, L.T. and M.J. Molina, eds. (2002), Air Quality in the Mexico Megacity: An
Integrated Assessment, Kluwer Academic Publishers, Boston, 384 pp.
PEMEX Refinación (2002), Anuario Estadístico 2002. México, D.F. In
http://www.pemex.gob.mx/index.cfm/action/content/sectionID/1/catID/237/subcatID/246/i
ndex.cfm?action=content&sectionID=1&catID=237&subcatID=246
Sánchez Sergio et., al (1999) "Evaluación del Gas Natural", México, D.F.
SETRAVI, Secretaría de Transporte y Vialidad (2000), Primera Versión del Programa
Integral de Transporte y Vialidad.
SENER, Secretaría de Energía (2001), Balance Nacional de Energía 2000.
West, J.J., P. Osnaya, I. Laguna, J. Martínez, A. Fernández (2003) Co-control of urban air
pollutants and greenhouse gases in México City. Final report to US National Renewable
Energy Laboratory, subcontract ADC-2-32409-01.
51
III.4. Hybrid Buses
III.4.1. Introduction
The 2000 SETRAVI inventory indicates that the RTP has 985 buses and STP has 168 units
(Molina and Molina, 2002). PROAIRE indicates that this is a small part (~10%) of the total
diesel vehicle fleet (13,067 vehicles in 2000). The concept of this measure is to replace
almost all of these publicly owned diesel transport buses (RTP and STE) with highly
efficient, low polluting hybrid buses.
III.4.2. Description of the Measure
We follow the implementation plan of PROAIRE measure 22 (introduction of compressed
natural gas (CNG) buses), simply changing the technology for this replacement to a hybrid
bus as in West et al. (2003). Thus, 1,029 buses from the public RTP and STE systems are to
be replaced, almost all of the buses from those systems. We assume 257 buses are replaced
each year for 4 years (2003 – 2006), following the implementation schedule from
PROAIRE.
III.4.3. Data Requirements
To estimate the impact of the introduction of hybrid buses, we must have emission factors
for local and global pollutants (in g/km or g/L) and couple these with vehicle kilometers
traveled or fuel usage estimates. We need investment, operation and maintenance, and fuel
costs.
III.4.4. Determining Baseline Emissions – Diesel Buses
Emissions reductions due to the introduction of the hybrid buses are estimated as a
difference from the baseline emissions of the diesel buses currently in the system and those
that would enter the fleet via fleet growth or natural replacement of old vehicles.
Baseline emissions are estimated using emission factors with age and vehicle age
distributions used for PROAIRE measure 22 by the Secretariat of the Environment for the
Federal District. However, this baseline projection for vehicle age distribution is altered
from PROAIRE so that the estimated 2% growth in the fleet each year occurs only in the
new vehicles. Additionally, a natural turnover of 2% is assumed in which the oldest buses
have the highest likelihood of being removed from the fleet and replaced with a new
vehicle, similar to that which is done for the taxi fleet projection. This is a rough model of
natural retirement that certainly could be improved, but it provides a mid-ground between a
pessimistic baseline in which no natural turnover occurs and the optimistic PROAIRE
baseline in which there is no aging of the vehicle fleet.
To calculate greenhouse gas emissions from the diesel buses, we need a fuel efficiency
which is not reported by PROAIRE since those emissions factors are based on kilometers
traveled, not fuel use. We use the M.J. Bradley and Associates (2000) estimate of fuel
52
efficiency for the NovaBUS RTS Diesel Series 50 for the mean of the two city driving
cycles (Table III.4.1). We note that this fuel efficiency with PROAIRE kilometers-traveled
emissions factors (in g/km) gives very similar fuel-based emissions factors (in g/L) as
World Bank fuel efficiency with World Bank kilometers-traveled emissions factors for CO,
HC, and PM, and NOx factors agree within a factor of 2.
III.4.5. Estimating Emissions Reductions and Costs for the Measure
Previous work on Hybrid vehicles for Mexico City consists of a study by Consultants to the
World Bank (2000) in which 4 bus technologies were compared to a diesel option.
However, emissions factors in the report have many apparent inconsistencies between the
various hybrid technologies; with IPCC emission factors for greenhouse gases; with
emission estimates from driving cycle tests in New York City (M.J. Bradley and
Associates, 2000); and with PROAIRE emissions factors for diesels. This is likely because
the study used manufacturer’s data and information from other third-party sources, not
actual driving cycle tests in Mexico City. It is likely that the information compiled in the
report came from diverse sources and that the driving cycles on which this information was
based was not consistent between technologies, or perhaps even for the same technologies.
Further, we have not been able to identify the authors of the study in order to ask
methodological questions. Finally, representatives of the World Bank have confirmed that
emission factors in this study had many problems and are difficult to interpret (J. A. Lopez
Silva, personal communication to J. West).
In this light, we decide not to use results from the Consultants to the World Bank study in
this work, except to make a rough check for internal consistency on diesel fuel efficiency.
We base our estimates on the report of M.J. Bradley and Associates for New York City
(NYC). For the Orion-LMCS VI Hybrid Diesel bus, we use the mean local emission factors
(Table III.4.2) and fuel efficiency (Table III.4.1) for the mean of two driving cycles in New
York City traffic (NYC, Manhattan). For global emissions, we use CO2 , CH4 , and N2 0
emissions factors from IPCC 1997 Manual 4, Table 1-32 with diesel density for Mexico in
2001 (Table III.4.3). Finally, since SO2 emissions factors are not available in the M.J.
Bradley and Associates results, we calculate by mass balance using a sulfur content of
diesel of 400 ppm (G. Stevens, p. communication, Table III.4.4). We assume these
emission factors and fuel efficiencies are constant with time.
Table III.4.1. Fuel efficiencies (km/L) for hybrid and diesel,
Mean of NYC and Manhattan Cycles (MJB 2000)
Orion LMCS VI – Hybrid Diesel
NovaBUS RTS Diesel Series 50
53
1.21
0.79
Table III.4.2. Local emissions factors (g/km) for hybrids, Mean of NYC and
Manhattan Cycles (MJB 2000)
PM
CO
NO x
HC
0.05
1.59
19.72
0.41
Table III.4.3. IPCC 1997 global emissions factors (g/L)
h
CH4
N2 O
2522
0.1
0.07
Table III.4.4. SO2 emission factor, from mass balance (g/L)
SO2
0.64
The cost for an Orion VI ($385,000 USD) from NYC is taken from a January 2003 news
article in Metro Magazine (http://metro- magazine.com/t_featpick.cfm?id=90504764). This
article also notes that part of the additional investment cost for a hybrid bus can be offset
because the use of regenerative braking means that brake pads need less often replacement,
and direct-drive electric motors are less costly to maintain than diesel motors. However,
another
article
(http:/acc6.its.Brooklyn.cuny.edu/~scintech/hybrid/Economical.html)
indicates that in the pilot study in NYC with hybrids, maintenance costs increased by
284%, though it notes that these costs should decrease as more buses are introduced and the
buses are better engineered. Unfortunately, neither article quantifies maintenance costs in a
way that is transferable to this work, so we only include the difference in fuel expenditure
under the Fuel, Operations and Maintenance cost category. The price of diesel is that which
was charged by PEMEX in February 2003, $4.9 MX / L, from the INEGI website (updated
17 march 2003). The 15% value added tax is removed, and conversion to dollars uses an
exchange rate of 10. Recuperation value at the end of either time period is not included.
III.4.6. Costs and Emissions Reductions with the Measure
Tables III.4.5 and III.4.6 illustrate that there are significant CO2 reductions due to this
measure across the entire time horizon to 2020. On the local side, NOx emissions increase,
while all other local pollutant emissions are reduced.
54
Table III.4.5. Emissions reductions for hybrid buses without discounting (tons/yr)
Year
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
23
47
70
93
93
93
93
93
93
93
93
93
93
93
93
93
93
93
21
41
62
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
13
27
40
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
180
360
539
720
720
720
720
720
720
720
720
720
720
720
720
720
720
720
-38
-76
-114
-152
-152
-152
-152
-152
-152
-152
-152
-152
-152
-152
-152
-152
-152
-152
87
174
261
348
348
348
348
348
348
348
348
348
348
348
348
348
348
348
17,185
34,371
51,556
68,809
68,809
68,809
68,809
68,809
68,809
68,809
68,809
68,809
68,809
68,809
68,809
68,809
68,809
68,809
1
1
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Table III.4.6. Annualized emissions reductions for hybrid buses (tons/yr)
Discount
rate
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
Time horizon 2003-2010
0%
76
67
15
585
-123
283
55,895
2
0
3%
74
65
14
573
-121
277
54,795
2
0
5%
73
65
14
566
-119
274
54,063
2
0
7%
72
64
14
558
-118
270
53,333
2
0
Time horizon 2003-2020
0%
86
75
17
660
-139
319
63,069
3
0
3%
84
74
16
645
-136
312
61,656
2
0
5%
82
72
16
635
-134
307
60,656
2
0
7%
81
71
16
624
-132
302
59,622
2
0
Tables III.4.7 and III.4.8 indicate that while up- front investment costs are large, there are
significant savings in terms of reduced fuel expenditure that offset a significant amount of
these investments when the full time period is considered.
55
Table III.4.7. Costs for hybrid buses without discounting (millions US$/yr)
Year
Public Investment
Private Investment
O&M / Fuel
Total
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
98.95
98.95
98.95
99.33
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-2.89
-5.78
-8.67
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
96.05
93.16
90.27
87.75
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
-11.58
Table III.4.8. Annualized costs for hybrid buses (millions US$/yr)
Discount rate
0%
3%
5%
7%
0%
3%
5%
7%
Abatement Cost (2003 millons US$/ yr)
Fuel, Operations,
Public Investment
Private Investment
Maintenance
Time Horizon 2003-2010
49.52
0
-9.40
52.44
0
-9.22
54.33
0
-9.10
56.18
0
-8.97
Time Horizon 2003-2020
22.01
0
-10.61
26.77
0
-10.37
30.04
0
-10.21
33.35
0
-10.03
Total Cost
40.12
43.22
45.24
47.20
11.40
16.39
19.84
23.32
III.4.7. Uncertainty
For this measure, we use emission factors from a detailed, robust study using laboratory
tests of actual hybrid and diesel bus technology for operations at sea level and under New
York City driving conditions (MJ Bradley & Associates, 2000). It is very important to
remember that emissions in Mexico City may be significantly different for both diesels and
hybrids because of the high altitude (mean altitude is 2240m). While the Consultants to the
World Bank (2000) study was an analysis for Mexico City, at no place in the document is
it indicated that the emissions factors reported therein were derived or somehow adjusted
for the altitude of Mexico City. Since the appropriate tests are difficult and expensive and
56
are not normally done in the US and Europe, it is highly unlikely that these emission factors
would be for altitudes of >2000m without there being specific mention to this effect in the
report.
III.4.8. Discussion and Next Steps
Improved understanding of emissions factors for operations at altitude for all technologies
is the key next step for improving this analysis. In a current project by World Bank and
EMBARQ for the creation of dedicated bus lanes in Mexico City, plans are being made to
test a variety of vehicle technology at altitude. Although it is not clear that hybrids will be
considered in this study, there should be at a minimum improved understanding of baseline
diesel bus emissions from this work.
We also note that Cohen et al. (2003) find that emission-controlled diesel buses would be
more cost-effective than compressed natural gas (CNG) technology in US cities. Hybrid
technologies are likely as expensive or more so than CNG. Thus, before further pursuing
the introduction of hybrid buses in Mexico City, it would be sensible to do comparable
analyses for other bus technologies.
III.4.9. References
CAM, Comisión Ambiental Metropolitana (2002), “Programa para Mejorar la Calidad del
Aire de la Zona Metropolitana del Valle de México, 2002-2010” (PROAIRE), Comisión
Ambiental Metropolitana.
Cohen, J.T., J.K. Hammitt, and J.I. Levy (2003) Fuels for urban transit buses: A costeffectiveness analysis. Environ. Sci. Technol 37. 1477-1484.
Consultants to World Bank (2000) Estudio de prefactibilidad para la introducción de
autobuses híbridos en las prestación del servicio público de transporte de pasajeros en la
ZMVM, report to the World Bank.
IPCC, Intergovernmental Panel on Climate Change (1997), “Revised 1996 IPCC
Guidelines for National Greenhouse Gas Inventories, Vols. 1-3” (Bracknell, UK: IPCC).
M.J. Bradley & Associates, Inc. (2000) “Hybrid-electric drive heavy-duty vehicle testing
project: Final emissions report.”
Molina, L.T. and M.J. Molina, eds. (2002), Air Quality in the Mexico Megacity: An
Integrated Assessment, Kluwer Academic Publishers, Boston, 384 pp.
West, J.J., P. Osnaya, I. Laguna, J. Martínez, A. Fernández (2003) Co-control of urban air
pollutants and greenhouse gases in México City. Final report to US National Renewable
Energy Laboratory, subcontract ADC-2-32409-01.
57
III.5. Measures to reduce leaks of Liquefied Petroleum Gas
III.5.1. Introduction
A majority of stoves in the residences of Mexico City use Liquefied Petroleum Gas (LPG),
supplied in portable or roof-top tanks, as their fuel source. These systems are prone to
leaks, in part because LPG is stored under significant pressure. LPG is a hydrocarbon (HC)
source that contributes to ozone and also forms CO2 in the atmosphere. The goal of this set
of measures is to perform maintenance procedures on the stove systems, replacing worn out
parts or permanently closing off stove pilots, in order to eliminate these leaks. We base our
calculation of the costs and emissions reduction potential for the four measures based on a
detailed study of LPG gas leaks by TUV (2000), as summarized in the calculations of the
first phase of the IES study for Mexico (West et al. 2003).
III.5.2. Description of the Measures
Pictels, regulators, and connections are specific pieces of the LPG stove / tank system that
wear out with time and need replacing. In a service visit to the residence, these pieces are
replaced, respectively, in the measures entitled “Change of Pictels (LPG1)”, “Change of
Regulators (LPG2)”, “Change of Connections (LPG3)”. Unlit pilots also cause LPG leaks,
thus in the “Closure of Pilots (LPG4)” measures, pilots are permanently closed in the
service visit and then disposable lighters would be used to light the stove.
Since the implementation of these measures requires in- home service visits by stove
maintenance professiona ls, it is sensible to consider that all four measures would be
implemented simultaneously in order to save on implementation costs. Thus, we also
present the sum of these four measures as a single combined LPG leak measure (LPG).
Results will be based on the combined LPG measure.
III.5.3. Data Requirements
To estimate the emissions reductions and costs to reduce LPG leakage, we need
information about rates of LPG loss from current leaks in stove systems and the costs to
repair these leaks. Further, we must understand the useful lifetimes of these repairs so that
the appropriate schedules for redoing the repairs can be included in the analysis. For this set
of measures, all this information is derived from the TUV (2000) study.
III.5.4. Determining Baseline Emissions
TUV (2000) estimated the number of household with LPG leaks from pictels, regulators,
connections and pilots via random in- home tests of stoves and their connections. Their
estimate of the total potential for LPG leak controls from each part of the stove system
forms the baselines for these measures.
58
III.5.5. Estimating Emissions Reductions and Costs for the Measures
We base our calculation of the costs and emissions reduction potential for the four
measures on the TUV (2000) study. For consistency with other studies recently done in
Mexico City (PROAIRE), we retain qualitative assumptions about implementation
schedules for the control measures as have been previously made.
The repairs to the pictels, regulators and connections in the LPG stove systems have useful
lifetimes of between 2 and 5 years. Thus, for the emission reduction benefits of these
repairs to be maintained, the repairs must be repeated after the useful lifetimes end. In our
estimates, we assume that there is a zero failure rate for these replacements. This is the
same assumption that was made by Kellyn Roth in her thesis work on residential measures
for air pollution control in Mexico City at MIT (2003). Ms. Roth does believe that some
failure rate is likely to be more realistic, however she was not able to find any literature to
support a specific choice of a failure rate (K. Roth, personal communication).
We use a maximum time horizon to 2020 in this study, in which we aim to estimate the
longer-term impacts of policies implemented up to 2010 as outlined in PROAIRE. Thus we
assume no maintenance of the repairs after 2010.
The local air quality benefit of these measures is a reduction in HC emissions. In
atmosphere, HC released through LPG leakage is transformed to CO2 . The elimination of
the leaks via this measure leads to both a HC and CO2 benefit. LPG is 61% propane
(molecular weight = 44) and 39% butane (molecular weight = 58). Thus, as in the cocontrol work, we estimate CO2 emissions by:
(tons HC)*(0.61)*(44/44)*3 + (tons HC)*(0.39)*(44/58)*4 = tons CO2
Equation III.5.1
We assume the initial investment is from the public sector, and then additional investment
in replacement occurs from the private sector. Leakages in LPG stove systems results in
significant fuel waste, which costs $4.9 MX / kg LPG. Operations and maintenance costs
are negative (savings) because this fuel wastage is reduced.
Change of Pictels (LPG1)
TUV (2000) estimates that there are 1.59 million households with LPG leaks from wornout pictels, and that from each 0.00533 tons of hydrocarbon (HC) are lost per year. To
eliminate these loses, pictel replacements can be performed at a cost of $105 MX ($10.5
US). We assume, as did PROAIRE, that pictels in a total of 1,000,000 households are
replaced by 2010. We divide this equally over the period 2003-2010 (8 years), such that
there are 125,0000 installations / year. The pictel’s useful life is 2 years, and thus
replacements must be redone each 2 years to maintain the HC loss benefit.
Change of Regulators (LPG2)
We assume, consistent with PROAIRE, that regulators in a total of 336,584 households are
replaced by 2010. TUV (2000) estimates that from each regulator, 0.01389 tons of
59
hydrocarbon (HC) is lost per year. Regulator replacements can be performed at a cost of
$190 MX ($19 US). We divide this equally over the period 2003-2010 (8 years), such that
there are 42,073 installations / year. The regulator’s useful life is 5 years, and thus
replacements must be redone each 5 years to maintain the HC loss benefit.
Change of Connections (LPG3)
We estimate that connections in a total of 961,664 households are replaced by 2010. We
divide this equally over the period 2003-2010 (8 years), such that there are 120,208
installations / year. TUV (2000) estimates that from each connection, 0.00926 tons of
hydrocarbon (HC) are lost per year. To eliminate these loses, connections could be replaced
at a cost of $240 MX ($24 US). The connection’s useful life is 5 years, and thus
replacements must be redone each 5 years to maintain the HC loss benefit.
Closure of Pilots (LPG4)
We assume, as was done in PROAIRE, that pilots in a total of 912,512 households are
closed by 2010. We divide this equally over the period 2003-2010 (8 years), such that there
are 114,064 closures / year. TUV (2000) estimates that from each pilot, 0.00482 tons of
hydrocarbon (HC) is lost per year. Closures cost $75 MX ($7.5 US). We assume this
investment comes from the public sector. Additional investment in disposable lighters (3 /
year at $64.5 MX / yr) comes from the private sector. Closure of the pilots is a permanent
fix for the leaks, so there is no repeat investment necessary.
III.5.6. Costs and emissions reductions with the measures
Emission reductions and costs are presented for each ind ividual measure and for a
combination of all four measures in Tables III.5.1 to III.5.18.
60
Change of Pictels (LPG1)
Table III.5.1. Emissions reductions for change of pictels (LPG1) without discounting
(tons/yr)
Year
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
666
1,333
1,999
2,665
3,331
3,998
4,664
5,330
2,665
0
0
0
0
0
0
0
0
0
2,008
4,015
6,023
8,031
10,039
12,046
14,054
16,062
8,031
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Table III.5.2. Annualized emissions reductions for change of pictels (LPG1)
(tons/yr)
Discount
rate
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
Time horizon 2003-2010
0%
0
0
0
0
0
2,998
9,035
0
0
3%
0
0
0
0
0
2,895
8,723
0
0
5%
0
0
0
0
0
2,828
8,522
0
0
7%
0
0
0
0
0
2,763
Time horizon 2003-2020
8,325
0
0
0%
0
0
0
0
0
1,481
4,462
0
0
3%
0
0
0
0
0
1,626
4,900
0
0
5%
0
0
0
0
0
1,711
5,155
0
0
7%
0
0
0
0
0
1,784
5,376
0
0
61
Table III.5.3. Costs for change of pictels (LPG1) without discounting
(millions US$/yr)
Year
Public Investment
Private Investment
O&M / Fuel
Total
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
0
0
0
0
0
0
0
0
0
0
0
0
1.31
1.31
2.63
2.63
3.94
3.94
0
0
0
0
0
0
0
0
0
0
-0.33
-0.65
-0.98
-1.31
-1.63
-1.96
-2.29
-2.61
-1.31
0
0
0
0
0
0
0
0
0
0.99
0.66
1.65
1.32
2.31
1.98
2.96
2.64
-1.31
0
0
0
0
0
0
0
0
0
Table III.5.4. Annualized costs for change of pictels (LPG1)
(millions US$/yr)
Discount rate
0%
3%
5%
7%
0%
3%
5%
7%
Abatement Cost (2003 millons US$/ yr)
Fuel, Operations,
Public Investment
Private Investment
Maintenance
Time Horizon 2003-2010
1.31
1.97
-1.47
1.31
1.87
-1.42
1.31
1.81
-1.39
1.31
1.75
-1.35
Time Horizon 2003-2020
0.58
0.88
-0.73
0.67
0.96
-0.80
0.73
1.00
-0.84
0.78
1.04
-0.87
62
Total Cost
1.81
1.77
1.74
1.71
0.73
0.83
0.89
0.94
Change of Regulators (LPG2)
We note that the net costs for this measure are negative (Table III.5.7 and III.5.8) due to
significant fuel savings compared to a relatively small maintenance cost.
Table III.5.5. Emissions reductions for change of regulators (LPG2) without
discounting (tons/yr)
Year
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
584
1,169
1,753
2,338
2,922
3,506
4,091
4,675
4,091
3,506
2,338
1,169
0
0
0
0
0
0
1,761
3,522
5,283
7,044
8,805
10,566
12,327
14,088
12,327
10,566
7,044
3,522
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Table III.5.6. Annualized emissions reductions for change of regulators (LPG2)
(tons/yr)
Discount
rate
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
Time horizon 2003-2010
0%
0
0
0
0
0
2,630
7,925
0
0
3%
0
0
0
0
0
2,539
7,652
0
0
5%
0
0
0
0
0
2,480
7,475
0
0
7%
0
0
0
0
0
2,423
7,302
0
0
Time horizon 2003-2020
0%
0
0
0
0
0
1,786
5,381
0
0
3%
0
0
0
0
0
1,896
5,714
0
0
5%
0
0
0
0
0
1,954
5,888
0
0
7%
0
0
0
0
0
1,999
6,023
0
0
63
Table III.5.7. Costs for change of regulators (LPG2) without discounting
(millions US$/yr)
Year
Public Investment
Private Investment
O&M / Fuel
Total
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0.80
0.80
0.80
0.80
0.80
0.80
0.80
0.80
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.80
0.80
0.80
0
0
0
0
0
0
0
0
0
0
-0.29
-0.57
-0.86
-1.15
-1.43
-1.72
-2.00
-2.29
-2.00
-1.72
-1.15
-0.57
0
0
0
0
0
0
0.51
0.23
-0.06
-0.35
-0.63
-0.12
-0.41
-0.69
-2.00
-1.72
-1.15
-0.57
0
0
0
0
0
0
Table III.5.8. Annualized costs for change of regulators (LPG2)
(millions US$/yr)
Discount rate
0%
3%
5%
7%
0%
3%
5%
7%
Abatement Cost (2003 millons US$/ yr)
Private
Fuel, Operations,
Public Investment
Investment
Maintenance
Time Horizon 2003-2010
0.80
0.30
-1.29
0.80
0.28
-1.24
0.80
0.26
-1.22
0.80
0.25
-1.19
Time Horizon 2003-2020
0.36
0.13
-0.87
0.41
0.14
-0.93
0.44
0.15
-0.96
0.47
0.15
-0.98
64
Total Cost
-0.19
-0.17
-0.15
-0.14
-0.39
-0.38
-0.37
-0.36
Change of Connections (LPG3)
Table III.5.9. Emissions reductions for change of connections (LPG3) without
discounting (tons/yr)
Year
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3,354
6,709
10,063
13,417
16,772
20,126
23,480
26,835
23,480
20,126
13,417
6,709
0
0
0
0
0
0
3,354
6,709
10,063
13,417
16,772
20,126
23,480
26,835
23,480
20,126
13,417
6,709
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Table III.5.10. Annualized emissions reductions for change of connections (LPG3)
(tons/yr)
Discount
rate
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
Time horizon 2003-2010
0%
0
0
0
0
0
5,009
15,095
0
0
3%
0
0
0
0
0
4,836
14,575
0
0
5%
0
0
0
0
0
4,725
14,238
0
0
7%
0
0
0
0
0
4,616
Time horizon 2003-2020
13,909
0
0
0%
0
0
0
0
0
3,401
10,249
0
0
3%
0
0
0
0
0
3,611
10,883
0
0
5%
0
0
0
0
0
3,721
11,214
0
0
7%
0
0
0
0
0
3,807
11,473
0
0
65
Table III.5.11. Costs for change of connections (LPG3) without discounting
(millions US$/yr)
Year
Public Investment
Private Investment
O&M / Fuel
Total
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2.88
2.88
2.88
2.88
2.88
2.88
2.88
2.88
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2.88
2.88
2.88
0
0
0
0
0
0
0
0
0
0
-0.55
-1.09
-1.64
-2.18
-2.73
-3.27
-3.82
-4.36
-3.82
-3.27
-2.18
-1.09
0
0
0
0
0
0
2.34
1.79
1.25
0.70
0.16
2.50
1.95
1.41
-3.82
-3.27
-2.18
-1.09
0
0
0
0
0
0
Table III.5.12. Annualized costs for change of connections (LPG3)
(millions US$/yr)
Discount rate
0%
3%
5%
7%
0%
3%
5%
7%
Abatement Cost (2003 millons US$/ yr)
Fuel, Operations,
Public Investment
Private Investment
Maintenance
Time Horizon 2003-2010
2.88
1.08
-2.45
2.88
1.00
-2.37
2.88
0.95
-2.32
2.88
0.90
-2.26
Time Horizon 2003-2020
1.28
0.48
-1.67
1.47
0.51
-1.77
1.60
0.53
-1.82
1.71
0.54
-1.87
66
Total Cost
1.51
1.52
1.52
1.53
0.10
0.21
0.30
0.38
Closure of Pilots (LPG4)
Table III.5.13. Emissions reductions for reductions for pilot closures (LPG4) without
discounting (tons/yr)
Year
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
550
1,100
1,649
2,199
2,749
3,299
3,849
4,398
4,398
4,398
4,398
4,398
4,398
4,398
4,398
4,398
4,398
4,398
1,657
3,314
4,970
6,627
8,284
9,941
11,597
13,254
13,254
13,254
13,254
13,254
13,254
13,254
13,254
13,254
13,254
13,254
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Table III.5.14. Annualized emissions reductions for pilot closures (LPG4)
(tons/yr)
Discount
rate
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
Time horizon 2003-2010
0%
0
0
0
0
0
2,474
7,455
0
0
3%
0
0
0
0
0
2,389
7,199
0
0
5%
0
0
0
0
0
2,334
7,032
0
0
7%
0
0
0
0
0
2,280
Time horizon 2003-2020
6,870
0
0
0%
0
0
0
0
0
3,543
10,677
0
0
3%
0
0
0
0
0
3,373
10,163
0
0
5%
0
0
0
0
0
3,257
9,814
0
0
7%
0
0
0
0
0
3,141
9,464
0
0
67
Table III.5.15. Costs for pilot closures (LPG4) without discounting
(millions US$/yr)
Year
Public Investment
Private Investment
O&M / Fuel
Total
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0.86
0.86
0.86
0.86
0.86
0.86
0.86
0.86
0
0
0
0
0
0
0
0
0
0
0.74
1.47
2.21
2.94
3.68
4.41
5.15
5.89
5.89
5.89
5.89
5.89
5.89
5.89
5.89
5.89
5.89
5.89
-0.27
-0.54
-0.81
-1.08
-1.35
-1.62
-1.89
-2.16
-2.16
-2.16
-2.16
-2.16
-2.16
-2.16
-2.16
-2.16
-2.16
-2.16
1.32
1.79
2.25
2.72
3.19
3.65
4.12
4.59
3.73
3.73
3.73
3.73
3.73
3.73
3.73
3.73
3.73
3.73
Table III.5.16. Annualized costs for pilot closures (LPG4)
(millions US$/yr)
Discount rate
0%
3%
5%
7%
0%
3%
5%
7%
Abatement Cost (2003 millons US$/ yr)
Fuel, Operations,
Public Investment
Private Investment
Maintenance
Time Horizon 2003-2010
0.86
3.31
-1.21
0.86
3.20
-1.17
0.86
3.12
-1.14
0.86
3.05
-1.12
Time Horizon 2003-2020
0.38
4.74
-1.74
0.44
4.51
-1.65
0.47
4.36
-1.60
0.51
4.20
-1.54
68
Total Cost
2.95
2.88
2.83
2.79
3.39
3.30
3.24
3.17
Combined LPG Measure (LPG)
Since the implementation of these measures would require in-home visits by stove
maintenance professionals, it is sensible to consider that all four measures would be
implemented simultaneously in order to save on implementation costs. Here, we group
these four measures into a single combined LPG leak measure (LPG, Tables III.5.17 and
III.5.18).
Table III.5.17. Annualized emissions reductions for a combined LPG measure (LPG)
(tons/yr)
Discount
rate
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
Time horizon 2003-2010
0%
0
0
0
0
0
13,111
39,509
0
0
3%
0
0
0
0
0
12,659
38,148
0
0
5%
0
0
0
0
0
12,367
37,266
0
0
7%
0
0
0
0
0
12,081
Time horizon 2003-2020
36,406
0
0
0%
0
0
0
0
0
10,211
30,769
0
0
3%
0
0
0
0
0
10,506
31,660
0
0
5%
0
0
0
0
0
10,642
32,071
0
0
7%
0
0
0
0
0
10,731
32,337
0
0
Table III.5.18. Annualized costs for a combined LPG measure (LPG)
(millions US$/yr)
Discount rate
0%
3%
5%
7%
0%
3%
5%
7%
Abatement Cost (2003 millons US$/ yr)
Fuel, Operations,
Public Investment
Private Investment
Maintenance
Time Horizon 2003-2010
5.85
6.66
-6.42
5.85
6.35
-6.20
5.85
6.15
-6.06
5.85
5.95
-5.92
Time Horizon 2003-2020
2.60
6.23
-5.00
2.99
6.12
-5.15
3.24
6.03
-5.21
3.47
5.93
-5.26
Total Cost
6.09
6.00
5.94
5.89
3.83
3.96
4.05
4.14
III.5.7. Uncertaint y
Emissions reductions per leak are likely well quantified given the through field testing
performed for the TUV (2000) study. However, emissions reduction potential for these
measures may be overestimated because it is assumed that all system replacements and
69
pilot closures occur in systems that are actually leaking. Additional uncertainty arises from
the use of a constant baseline of the number of households with leakages in their stove
systems. Thus, there is no estimation of improvements in future technology that would
reduce leakages in new systems, nor of the deterioration of existing systems that might lead
to additional future leaks, nor of the growth in the number of households with LPG stoves
in the MCMA.
Costs are likely underestimated for these measures because no public investment in
enforcement, monitoring or public information is included.
III.5.8. Discussion and Next Steps
Costs and emission reductions for these LPG leak measures are relatively small in
comparison to other measures considered in this study. However, there is the potential for
net costs to be negative (e.g. LPG2) due to significant fuel cost savings.
Further work should focus on improving cost estimates by understanding the full program
costs of these types of measures. Additional understanding of how replacements could be
focused only on stove systems that are actually leaking would also improve this set of
measures.
In the following sections, we consider only the combined LPG leak measure (LPG). We
note, however, that it is possible in the Analytica model to consider each of these four
measures individually.
III.5.9. References
CAM, Comisión Ambiental Metropolitana (2002), “Programa para Mejorar la Calidad del
Aire de la Zona Metropolitana del Valle de México, 2002-2010” (PROAIRE), Comisión
Ambiental Metropolitana.
TUV Rheinland de Mexico, S. A. de C. V. (2000) “Programa para la reducción y
eliminación de fugas de Gas LP, en las instalaciones domésticas de la Zona Metropolitana
del Valle de México.”
West, J.J., P. Osnaya, I. Laguna, J. Martínez, A. Fernández (2003) Co-control of urban air
pollutants and greenhouse gases in México City. Final report to US National Renewable
Energy Laboratory, subcontract ADC-2-32409-01.
70
III.6. Cogeneration
III.6.1. Introduction
Cogeneration is defined as the simultaneous generation of heat and power, both of which
are used to satisfy the energy requirements of industrial, commercial and/or residential
sector. Cogeneration is also known as Combined Heat and Power (CHP) or Total energy.
Cogeneration plants are available to provide outputs from 1 kWe to 500 MWe. Plants for
industrial applications typically fall into the range from 1 to 50 MWe.
Cogeneration is one of the strategies of the Mexican Government to promote energy
efficiency. The organism in charge of this effort is the National Commission for Energy
Savings (CONAE), a decentralized organism of the Ministry of Energy.
CONAE, through its Direction of Cogeneration and non-Conventional Energy Sources,
carried out in 1995 the cogeneration potential of the industry & commercial sectors and the
petrochemical facilities of the Mexican Oil Company (PEMEX) at a national level.
CONAE estimates that Mexico’s cogeneration potential is 7,586 MWe for installations
with additional combustion and 14,229 MWe for installations without additional
combustion. Around 68% of total cogeneration potential corresponds to industry sector, of
which 16% is located at the Federal District & the State of Mexico.
Table III.6.1. National cogeneration po tential by sector
Sector
Additional combustion
(MWe)
5,200
1,613
773
7,586
Industry
Petrochemical
Commercial
Total
Without additional combustion
(MWe)
9,750
3,026
1,453
14,229
Table III.6.2. Industrial cogeneration potential by state
X.
State of Mexico
Federal District
TOTAL
MCMA
State
Additional combustion
(MWe)
324
530
5,200
854
16.42 %
Without additional combustion.
(MWe)
605
994
9,750
1,599
16.4%
According to the Energy Regulatory Commission (CRE), the office in charge of
authorizing cogeneration permits in the State of Mexico, there are two projects currently
being installed. These are included in this study.
71
Table III.6.3. Cogeneration permits at State of Mexico (MW)
Energía Bidarena
Location:
Capacity
Energy
Fuel
Technology
Industry
Resolution
State of Mexico
2.1
14.12
Natural gas
Internal Combustion Engine
Paper
RES:055-1996
Becton Dickinson de México
Location:
State of Mexico, municipality of Cuatitlan Izcalli
Capacity
6.54
Energy
40.87
Fuel
Natural gas
Technology
Internal Combustion Engine
Industry
Resolution
RES:014-2001
MW
GWh/ year
MW
GWh/ year
III.6.2. Description of the Measure
In this measure, we assume that 10% of the cogeneration potential of the industrial sector in
the Mexico City Metropolitan Area (MCMA) is implemented. We assume that all
installations have no additional combustion requirement. This means around 160 MWe,
which are installed in the period 2004 to 2010. Annually, that represents a capacity of ∼23
MWe.
To estimate the replaced thermal energy consumed by industries at MCMA, we assume
three heat to electricity ratios (Q/E), which represent three scenarios of thermal requirement
of industries.
The installation of cogeneration systems replace the electricity supplied by the grid,
produced by the Federal Electricity Commission (CFE), and the thermal energy supplied by
boilers at industrial facilities. In the three scenarios, it is supposed that there is no need for
additional electricity and heat energy.
The costs calculations are based on data of the Cogeneration Direction at CONAE, and the
local and global emissions on factors of the Environmental Protection Agency (EPA) for
criteria pollutants and the Intergovernmental Panel on Climate Change (IPCC) for GHG.
To calculate the local emissions, we consider the percentage of local generation at MCMA,
considering it’s contribution to the interconnected grid of CFE. For GHG emissions we
assume the total interconnected grid, considering the mix of fuels and the prospective of
new generation plants of the Ministry of Energy (SENER). According to SENER, the
electricity generation of MCMA power plants is approximately 3.1% of electricity
consumed at MCMA. This is important because it means that the reductions in emissio ns
72
from power plants associated with reductions electricity use have little impact on local air
quality in the MCMA.
We include the recuperation value of capital for both time horizons, considering the useful
life of cogeneration systems of 20 years.
III.6.3. Data Requirements
To estimate the impact of the introduction of cogeneration systems, it is necessary to
determine the electricity and thermal requirements in terms of energy units for each
industry. For our study we assume an average value, due to the lack of specific information,
assuming three Q/E scenarios (Low Scenario=0.8, Medium Scenario=3, High Scenario=4).
To calculate the fuel consumption, we must know the efficiency of power plants and the
structure of interconnected system in order to determine the fuels that cogeneration systems
replace. Additionally, the efficiency of industrial boilers is required in order to estimate the
replaced fuel consumption.
To determine the costs it is required the investment per installed cogeneration capacity
(US$ / MWe), the costs of electricity and fuel, and the O&M costs (US$/MWh).
III.6.4. Determining Baseline Emissions
Emissions reductions due to the introduction of cogeneration systems are estimated as a
difference from the baseline emissions of the electricity produced by CFE and the thermal
energy produced by industrial boilers (CFE+ boilers) and those that would be produced if
all the electricity and thermal energy were produced by cogeneration systems.
Baseline emissions are estimated using emission factors of EPA for criteria pollutants and
IPCC’s emission factors for GHG.
As stated in the definition of the measure, it is assumed that 160 MWe are replaced from
2004 to 2010. Considering an operation factor of industry process (cogeneration system),
we can estimate the total electricity that power plants should supply to industrial devices.
The fuel consumption is calculated considering the mix of power plants (fuel-oil, natural
gas, coal, nuclear, hydro, etc) of the interconnected grid, the transmission losses and their
conversion efficiencies for GHG emissions, and the percentage of local power plants at
MCMA related to the interconnected grid for local pollutants. This information is based on
the “Energy Balance 2002” and “The Electricity Sector Prospective, 2002-2012”, both
edited by the Ministry of Energy. (Secretaría de Energía, http://www.energia.gob.mx).
73
Table III.6.4. Percentage of generation at MCMA
Power plant
Valle de México
Jorge Luque
Nonoalco
1
2
3
Total MCMA
Total Interconnected
system (IS)
% (MCMA / IS)
Capacity (MW)
838
362
148
1,348
Generation (GWh)
4,677
778
39
5,494
176,710
3.1
The thermal energy is determined for the three Q/E scenarios. To estimate the fuel
consumption it is considered the efficiency of industrial boilers.
Table III.6.5. Heat and electricity requirements of industry (Energy units in MW)
Year
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Permit titles
(MWe)
8.64
Cogeneration
Potential (MWe)
Accumulated
Capacity (MWe)
23
23
23
23
23
23
23
31
54
77
100
123
146
169
169
169
169
169
169
169
169
169
169
169
74
Thermal Energy Supply
(Q/E) (MW)
Low
Medium
High
Scenario
Scenario Scenario
0.80
3
4
25
43
62
80
98
117
135
135
135
135
135
135
135
135
135
135
135
94
163
232
300
369
437
506
506
506
506
506
506
506
506
506
506
506
126
217
309
400
491
583
674
674
674
674
674
674
674
674
674
674
674
Table III.6.6. Heat and electricity supplied by cogeneration systems
(Power units in MW-h)
Year
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Electricity consumption
(MW-h)
Thermal Energy Supply (Q/E) (MW-h)
Low Scenario
Medium Scenario
High Scenario
0.80
176,505
304,572
432,638
560,704
688,770
816,836
944,903
944,903
944,903
944,903
944,903
944,903
944,903
944,903
944,903
944,903
944,903
3
661,896
1,142,144
1,622,392
2,102,640
2,582,889
3,063,137
3,543,385
3,543,385
3,543,385
3,543,385
3,543,385
3,543,385
3,543,385
3,543,385
3,543,385
3,543,385
3,543,385
4
882,527
1,522,858
2,163,189
2,803,520
3,443,851
4,084,182
4,724,513
4,724,513
4,724,513
4,724,513
4,724,513
4,724,513
4,724,513
4,724,513
4,724,513
4,724,513
4,724,513
220,632
380,715
540,797
700,880
860,963
1,021,046
1,181,128
1,181,128
1,181,128
1,181,128
1,181,128
1,181,128
1,181,128
1,181,128
1,181,128
1,181,128
1,181,128
To estimate the baseline of fuel consumption we consider that the electricity generation of
MCMA power plant stations and the thermal energy produced by industrial boiler at
industry facilities have to satisfy the same electricity and thermal requirements of
cogeneration systems. To calculate incremental fuel consumption we assume the following
assumptions.
Table III.6.7. Considerations for fuel calculation
Value
0.90
0.80
0.35
0.15
0.7
3.11
40,112
Cogeneration efficiency
Operation Factor (Op. F)
Power plant efficiency
Transmission losses
Industrial boiler efficiency
% of MCMA power plants production
Natural Gas Heat Value Content
75
Notes
0.8 < Eff. < 1
0 < Op F < 1
0 < Eff < 0.4
0 < T. Losses < 0.15
0 < Eff < 0.9
%
(KJ / m3)
Table III.6.8. Total fuel consumption baseline at MCMA
CURRENT SYSTEM (CFE + INDUSTRIAL BOILER)
Natural Gas Consumption (1,000 m3 / year)
(Q/E)
Year
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Low Scenario (0.8)
24,700
42,622
60,544
78,465
96,387
114,309
132,231
132,231
132,231
132,231
132,231
132,231
132,231
132,231
132,231
132,231
132,231
Medium Scenario (3)
86,934
150,010
213,086
276,162
339,238
402,314
465,390
465,390
465,390
465,390
465,390
465,390
465,390
465,390
465,390
465,390
465,390
High Scenario (4)
115,222
198,822
282,423
366,024
449,625
533,225
616,826
616,826
616,826
616,826
616,826
616,826
616,826
616,826
616,826
616,826
616,826
III.6.5. Estimating emissions reductions and costs for the measure
As stated in the definition of cogeneration, the co-production of electricity and heat is
defined by the Heat/Electricity ratio (Q/E), for our analyzes we consider three scenarios for
the Q/E ratio. This value could be used to represent the thermal demand of industries
according to their energy demand.
76
Table III.6.9. Thermal energy demand (MW)
Year
Permit
titles
(MWe)
Cogeneration
Accumulated
Potential (MWe) Capacity (MWe)
Thermal Energy Supply (Q/ E)
(MW)
Low Scenario
(0.8)
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
8.64
TOTAL
23
23
23
23
23
23
23
Medium Scenario
High
(3)
Scenario (4)
31
54
77
100
123
146
169
169
169
169
169
169
169
169
169
169
169
25
43
62
80
98
117
135
135
135
135
135
135
135
135
135
135
135
94
163
232
300
369
437
506
506
506
506
506
506
506
506
506
506
506
126
217
309
400
491
583
674
674
674
674
674
674
674
674
674
674
674
169
135
506
674
Fuel consumption
Theoretically, almost any fuel is suitable for cogeneration. In practice, fossil fuels,
especially natural gas (for economical as well as for environmental reasons) predominate,
but municipal solid waste, certain industrial gases and biomass are also important.
For our analyses we consider natural gas as fuel to be consistent to the Fuel Regulation at
MCMA. To calculate the fuel consumption, we consider the energy efficiency of
cogeneration systems. The energy efficiency of cogeneration systems can reach 90% or
more. For our benchmark scenario we assume an efficiency of 90% for cogeneration
systems.
77
Table III.6.10. Total energy consumption of cogeneration systems
Year
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Accumulated Capacity
(MWe)
31
54
77
100
123
146
169
169
169
169
169
169
169
169
169
169
169
Total Energy Consumption of Cogeneration System (MW)
Low Scenario
(0.8)
63
109
154
200
246
291
337
337
337
337
337
337
337
337
337
337
337
Q/E
Medium Scenario
(3)
140
241
343
444
546
648
749
749
749
749
749
749
749
749
749
749
749
High Scenario
(4)
175
302
429
556
683
809
936
936
936
936
936
936
936
936
936
936
936
Table III.6.11. Fuel consumption of cogeneration systems
Year
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Natural Gas Consumption (m3 / s)
Natural Gas Consumption (1,000 m3 / year)
Q/E
Q/E
Low Scenario
Medium
High Scenario Low Scenario
Medium
High Scenario
(0.8)
Scenario
(4)
(0.8)
Scenario
(4)
(3)
(3)
2
3
4
39,603
88,007
110,009
3
6
8
68,338
151,861
189,827
4
9
11
97,072
215,716
269,645
5
11
14
125,807
279,571
349,463
6
14
17
154,541
343,425
429,282
7
16
20
183,276
407,280
509,100
8
19
23
212,011
471135
588,918
8
19
23
212,011
471,135
588,918
8
19
23
212,011
471,135
588,918
8
19
23
212,011
471,135
588,918
8
19
23
212,011
471,135
588,918
8
19
23
212,011
471,135
588,918
8
19
23
212,011
471135
588918
8
19
23
212,011
471,135
588,918
8
19
23
212,011
471,135
588,918
8
19
23
212,011
471,135
588,918
8
19
23
212,011
471,135
588,918
78
Costs
The costs associated to cogeneration installations can be classified into two groups: capital
costs & operating costs. The capital costs depend of the main components. Cogeneration
plant consists of four basic elements:
1. A prime mover: Steam turbine, reciprocating engine, gas turbine, micro-turbines,
stirling engines and fuel cells.
2. An electricity generator.
3. A heat recovery system.
4. A control system.
For our study we consider the average costs reported by CONAE. Tables III.6.12 and 13.
Table III.6.12. Investment costs
Prime mover
Gas turbine
(US$ / KW)
850-270
Steam turbine
Internal combustion engine (ICE)
Heat recovery equipments (HRU)
400-150
650-300
(US$ / KW)
Average Costs
700
275
475
Average costs
Diesel or gas engine
700-600
650
Heat recovery for steam turbines
Heat recovery for exhaust gases
400-200
150-75
300
112.5
Table III.6.13. Operation and Maintenance Cost
Prime mover
Internal Combustion Engine (diesel)
(US$ / MWh)
7.28
Internal Combustion Engine (natural gas)
Steam turbine
Gas turbine (natural gas)
5.18
1.63
3.08
For the benchmark scenario, we only assume the costs for the Medium Scenario Q/E = 3.
79
Table III.6.14. Total costs for benchmark scenario
Year
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Cogeneration Accumulated
Potential
Capacity
(MWe)
(MWe)
0
23
23
23
23
23
23
23
0
31
54
77
100
123
146
169
169
169
169
169
169
169
169
169
Total Investment Cost
(Million US$)
Internal
Steam
Gas turbine + Combustion
Turbine +
HRU
Engines +
HRU
HRU
0
0
0
18.6
25.7
13.1
18.6
25.7
13.1
18.6
25.7
13.1
18.6
25.7
13.1
18.6
25.7
13.1
18.6
25.7
13.1
18.6
25.7
13.1
169
169
Operation & Maintenance
Costs (Million US$)
Internal
Gas
Steam
Combustion
turbine
Turbine
Engines
0
0.7
1.2
1.7
2.2
2.7
3.1
3.6
3.6
3.6
3.6
3.6
3.6
3.6
3.6
3.6
0
1.1
2.0
2.8
3.6
4.5
5.3
6.1
6.1
6.1
6.1
6.1
6.1
6.1
6.1
6.1
0
0.4
0.6
0.9
1.1
1.4
1.7
1.9
1.9
1.9
1.9
1.9
1.9
1.9
1.9
1.9
3.6
3.6
6.1
6.1
1.9
1.9
For the cost analyzes, it is considered the cost of avoided electricity at 0.04 US$/ KWh and
the costs of incremental natural gas = 0.065 US$ / m3 .
As stated in the description of the measure, the cost analyses consider the recuperation
value of investment to 2010, considering the lifetime of cogeneration systems of 20 years.
This consideration is important in the cost-effectiveness of the measure, due to the high
investments required and it’s large useful life.
III.6.5. Costs and emissions reductions with the measure
As previously stated, we use emission factors of IPCC for GHG at a national level for the
baseline, considering the mix of fuels and power plants of CFE (fuel oil, natural gas, coal,
diesel). To calculate the emissions of cogeneration systems, we use IPCC factors for natural
gas for industrial sector . For local pollutants we use EPA’s factor to estimate the baseline
and impacts of cogeneration at ZMVM. (Table III.6.15) To estimate the reduction tons of
pollutants and GHG we require the fuel consumption (m3 /year), the emission factor (Kg /
TJ), and the Heat Content Value (HCV) of each fuel consumed. For local impacts we only
considered the HC V of natural gas.
80
Table III.6.15. Emission factors
IPCC
Fuel
CO2
CO NOx SO2
EPA ( AP – 42)
CH4 NMVOC N2 O
CO2
CO NOx SO2 CH4 NMVOC N2 O
Kg / TJ
Kg / TJ
Electricity Generation
Natural Gas 56,100
18
250
Heavy Fuel 77,367
Oil
Diesel
74,067
15
Coal
94,600
-----
1
5
0.1 49,716 14.8 116
200 995.3 0.9
5
16
220 230.8 0.9
-----
0.9
0.3 74,595 14.9 140.2 936.9 0.8
-----
0.3
5
0.4
236.7 -----
-----
0.4
9
380 1,045.2 0.7
5
1.6 145,137 13.7 316.11045.2 1.1
-----
0.8
-----
0.9
16.7
80
0.2
1
Industrial sector
Natural gas 56,100
16
64
----
1.4
5
0.1 49,716 34.8 41.4
0.2
1
Sources:Revised IPCC Guidelines for national Greenhouse Gas Inventories, Workbook, volume 2
AP-42 Compilation of Air Pollutant Emissions Factors AP-42, 5th Edition, Volume I, Stationary Point and
Area Sources
Emission factor
(Kg/106 m3 )
PM (total)
121.6
VOC
88
TOC
176
Source: Air Pollution Engineering manual. Second Edition, Air & Waste Management Association
Natural Gas
Tables III.6.16 and III.6.17, illustrate that there are very large CO2 reductions due to this
measure across the entire time horizon to 2020. On the local side, NOx emissions reduce,
while there is no effect other local pollutants except for CO. This is because the fuel source
considered in both the baseline and control measure is primarily natural gas, which is
approximately 90% methane (CH4 ).
81
Table III.6.16. Emissions reductions for cogeneration systems without discounting
(tons/yr)
Year
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
6
9
11
14
16
19
19
19
19
19
19
19
19
19
19
19
0
29
50
71
92
112
133
154
154
154
154
154
154
154
154
154
154
154
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
235,481
405,117
570,125
734,075
884,295
1,033,945
1,198,778
1,218,795
1,207,823
1,198,252
1,189,828
1,182,358
1,175,687
1,169,695
1,164,283
1,159,369
1,154,890
0
4
7
10
12
15
18
21
21
21
21
21
21
21
21
21
21
21
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Table III.6.17. Annualized emissions reductions for cogeneration systems (tons/yr)
Discount
rate
PM 10
PM 2.5
SO2
CO
NOx
HC
CO2
CH4
N2 O
Time horizon 2003-2010
0%
0
0
0
10
80
0
632,727
11
1
3%
0
0
0
9
77
0
606,875
10
1
5%
0
0
0
9
75
0
590,080
10
1
7%
0
0
0
9
72
0
573,667
10
1
0%
0
0
0
15
121
0
937,933
16
1
3%
0
0
0
14
115
0
889,344
16
1
5%
0
0
0
13
110
0
856,031
15
1
7%
0
0
0
13
106
0
822,510
14
1
Time horizon 2003-2020
82
Tables III.6.18 and III.6.19 indicate that while up- front investment costs are large, there are
significant savings in terms of avoided electricity use from 2010 to 2020.
Table III.6.18. Costs for cogeneration without discounting (million US$/year)
Year
Public
Investment
Private
Investment
Electricity costs
Incremental
O&M costs
Incremental
Fuel cost
Total
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
18.56
18.56
18.56
18.56
18.56
18.56
18.56
0
0
0
0
0
0
0
0
0
0
0
-8.8
-15.2
-21.6
-28.0
-34.4
-40.8
-47.2
-47.2
-47.2
-47.2
-47.2
-47.2
-47.2
-47.2
-47.2
-47.2
-47.2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7.2
12.3
17.5
22.7
27.9
33.1
38.3
38.3
38.3
38.3
38.3
38.3
38.3
38.3
38.3
38.3
38.3
0
16.9
15.7
14.5
13.2
12.0
10.8
9.6
-9.0
-9.0
-9.0
-9.0
-9.0
-9.0
-9.0
-9.0
-9.0
-9.0
Table III.6.19. Annualized costs for cogeneration systems (millions US$/year)
Discount rate
0%
3%
5%
7%
0%
3%
5%
7%
Abatement Cost (2003 millions US$/ year)
Fuel, Operations,
Public Investment
Private Investment
Maintenance
Time Horizon 2003-2010
0
3.24
-4.65
0
4.17
-4.46
0
4.83
-4.33
0
5.51
-4.21
Time Horizon 2003-2020
0
5.05
-7.05
0
6.40
-6.66
0
7.33
-6.40
0
8.25
-6.14
Total Cost
-1.40
-0.28
0.49
1.30
-1.99
-0.26
0.92
2.11
III.6.6. Uncertainty
For this measure, several factors determine cost–effectiveness, however the are two that
are most important. The first is the high investments required for its installation and
second, the balance of electricity and fuel costs. As stated in the definition of the measure,
we assume only 10% of total cogeneration potential at MCMA, however that figure could
83
be higher if there were incentives in the next years that promote the use of cogeneration
installations.
It is important to mention that the size of cogeneration installations depends of the ratio of
electricity and fuel consumption of each industry (Q/E ratio). This is an important issue due
the required cogeneration scheme (prime mover), is defined by this ratio, and therefore its
capital costs.
III.6.7. Discussion and Next Steps
As stated in the definition of the measure, the heat demand was carried out according to the
Q/E ratio scenarios, nevertheless this value depends on the energy and electricity
requirements of each industry, based on it’s production. Future work should be done to
determine the Q/E ratio for each industry.
It is important to note that the low local benefits of cogeneration at MCMA are due to the
small regional generation, which is around 3.1% of interconnected grid. On the other side,
the results show that cogeneration is an effective measure to reduce GHG to Mexico. This
is due mainly to the high contribution of fossil fuels to national electricity generation.
As stated in previous sections, future work should be focused in the cost analyses of each
industry. Some of the items that should be included in future works for specific installations
are: Installation costs (e.g. materials, labour, etc. ), fuel cost chain (e.g. installation of
ducts, labour, prices of alternatives fuels, etc), detailed maintenance costs (e.g. labour,
spare parts, etc.) One of the main issues that determine the cost-effectiveness of
cogeneration is the recuperation value of investment. As mentioned before, it was
considered the useful life of 20 years for all installations; at this point, it is suggested to
consider the evolution of cogeneration technology (e.g. efficiency, availability,
performance, etc) that will be reflected in the capital and operation and maintenance costs
(O&M).
Finally, it is important to mention that the introduction of cogeneration systems at MCMA
depends on several factors, some of the most important are: Investment cost per KWh
installed, O&M cost, availability of natural gas, natural gas cost and electricity cost. Future
work should be done considering this issues.
III.6.8. References
Comisión Nacional para el Ahorro de Energía (2003), Potencial Nacional de Cogeneración
1995,
Davis, W., eds. (2000), Air Pollution Engineering Manual. Second Edition, Air & Waste
Management Association, Wiley & Sons, US.
Fisk, R., VanHousen, R., eds. (1998), Cogeneration Application Considerations, GE Power
Systems, Schenectady, NY.
84
IPCC, Intergovernmental Panel on Climate Change (1997), “Revised 1996 IPCC
Guidelines for National Greenhouse Gas Inventories, Vols. 1-3” (Bracknell, UK: IPCC).
Secretaría de Energía (2002), Balance Nacional de Energía 2001.
Secretaría de Energía (2002), Prospectiva del Sector Eléctrico 2002-2011.
The European Association for the Promotion of Cogeneration - COGEN Europe, (2001), A
Guide to Cogeneration. Guide produced under the auspices of EDUCOGEN, CONTRACT
N° XVII/4.1031/P/99-159.
West, J.J., P. Osnaya, I. Laguna, J. Martínez, A. Fernández (2003) Co-control of urban air
pollutants and greenhouse gases in México City. Final report to US National Renewable
Energy Laboratory, subcontract ADC-2-32409-01.
85
Chapter IV. Air Quality Modeling
IV.1. Introduction
Air quality modeling is an important tool for environmental assessment and is a key in the
support decions made by policy makers on air quality issues.
A simulation model is a mathematical specification of a system that depicts quantitatively
how the system behaves. It is a set of equations, with estimated coefficients and parameters,
that depict the relationships among the variables represented in the model. The equations
can be solved for values of the variables determined within the model (the endogenous
variables) given the values of the other variables determined outside the model (the
exogenous variables) and the parameters.
Simulation can be used for policy analysis. Alternative policies can be simulated to predict
how the system would behave if those policies had been impleme nted and to estimate their
effects. Simulation models have been used to evaluate alternative fiscal, monetary and
environmental policies.
Models can be classified according to several aspects (including subject matter, objective,
structure, degree of aggregation, time horizon and basic approach). A general classification
includes two kinds of models: structural models and reduced form models. The equations
of a structural model depict what the model builder believes are basic causal relationships.
A structural model often depicts the interactions among several endogenous variables of the
model. To understand the underlying determinants of a system’s behaviour, one needs a
structural model. Often the equations of a structural model can be solved to provide an
expression for each endogenous variables in terms of exogenous variables only. These
equations are called the reduced form of the model. A reduced form does not depict the
actual causal relationships of the system. However, a set of reduced form equatio ns can be
used to predict how the endogenous variables respond to the exogenous variables.
IV.2. Reduced-Form Atmospheric Modeling Methodology
For the estimation of the impacts of emission reduction on ambient concentrations and
population exposures, we developed a range of reduced- form modeling approaches. We
focused on the estimation of particle concentrations because previous studies have shown
that health impacts from particle concentrations overwhelmingly dominate health benefit
calculations (Cesar et al, 2000; Cesar et al. 2002; Zuk, 2002; Evans et al. 2002).
Our estimate of PM10 is derived from the source apportionment results of Chow et al.
(2002). We estimate primary concentrations resulting from direct PM10 emissions, and
secondary PM10 concentrations from hydrocarbon, NOx, and SO2 emissions. Ozone
isopleths from the Salcido et al. (2001) are used to estimate peak O3 changes occurring with
changes in hydrocarbon and NOx emissions.
86
We also developed a box model to estimate primary PM10 concentrations from direct PM10
emissions; and the Marginal PM method of West and San Martini (2001) to estimate
secondary sulfate and nitrate particle concentrations (Appendix A). However, following
commentary from technical staff of the government agencies attending our regular
meetings, we determined that the box model and Marginal PM methods have large
uncertainty, and that the use of the source apportionment results is a better way to estimate
both primary and secondary concentration changes due to emission changes. Thus, we have
eliminated the box model and use Marginal PM as explicit components of the analysis. We
use results from our primary reduced- form models, source apportionment and Ozone
Isopleths, to give central estimates for the reduction fractions in pollutants derived from
emissions changes. Uncertainty is determined for these estimates as described in section
IV.6.
IV.3. Use of Reduced-Form Models with Observations: Including Spatial and
Temporal Distribution
In order to account for the spatial distribution of population and of pollutant concentrations,
we use the reduced form models to provide a reduction fraction (RF) of pollutant
concentration (Cesar et al. 2002, USEPA, 1999). This reduction fraction is then multiplied
by projected future population-weighted concentrations for the appropriate time horizon.
These projected concentration are based on the mean 1995-1999 observed, populationweighted (1995 census) 24-hour mean PM (64.06 ug/m3 ) or O3 maximum concentration
(0.114 ppm), from Cesar et al. (2000). The projection to future population-weighted
concentrations is achieved by a linear interpolation or extrapolation of mean concentration
results from the CAM’s MCCM model for 1998 and 2010 based on the emissions inventory
for 1998 and emission inventory projection for 2010 of the CAM (PROAIRE, 2002). This
method was described in the first progress report. Projection factors are presented in Table
IV.1.
Table IV.1. Projection factors used to scale 1995-1999 observed concentration
to future timeframes. 24 hour and maximum O3 concentrations
use the same scaling factor
O3
PM 10
Ratio
2010 / 1998
1.075
1.280
Ratio
2002-2010 / 1998
1.050
1.186
Ratio
2002-2020 / 1998
1.081
1.303
IV.4. Using Source Apportionment Results for Primary and Secondary PM
Source apportionment is a chemical analyses of the composition of particulate matter in the
region of interest. To use these results as a reduced- form model, the chemical species in the
observed particulate matter are attributed to primary pollutants. Then, fractional changes in
the emission of the primary pollutants can be related to fractional reductions in particulate
concentrations via the chemical apportionment. The governing equation is:
RFPM 10 = ∑ Fi ⋅ RFi
Equation IV.1
87
Where RF PM10 is the reduction fraction of the PM concentration, Fi is the fraction of PM
mass due to a primary pollutant i and RF i is the reduction fraction of the emission of the
primary pollutant.
We use the results for chemical composition of PM10 in Mexico City of Chow et al. (2002)
to estimate the fractions of PM mass due to each primary pollutant. Chow et al. (2002)
present results at 6 sites distributed across Mexico City. We average these results to arrive
at a single fractionation estimate for the entire MCMA.
The mean mass of PM10 due to each primary pollutant i is estimated by summing the results
presented in terms of fractions of PM2.5 and the coarse fraction, and the total mass of PM2.5
and the coarse fraction at each of the six sites.
Mi =
1 6
⋅ M kCoarse )
∑ (Fi2,k.5 ⋅ M k2.5 + FiCoarse
,k
6 k =1
Equation IV.2
In order to attribute organic carbon to its primary (combustion) and secondary
(hydrocarbon) sources, we must disaggregate the observed organic carbon into primary and
secondary contributions. Following Turpin et al. (1991), we estimate the primary organic
contribution to total organic carbon based on a fixed ratio to elemental carbon mass of 1.9,
a mean value for the Los Angeles basin (range 1.4-2.4). Thus the mass of primary organic
carbon (MOC1 ) is: MOC1 = MEC*1.9. The mass of secondary organic carbon (MOC2 ) is then
the difference of the total organic carbon mass (MOC_TOT ) and the mass of primary organic
carbon: MOC2 = MOC_TOT – MOC1. We then estimate that MOC1 is due to the same primary
particle combustion sources that produce elemental carbon. Therefore, total primary
particulate mass from combustion sources is MPRI_COMB = MOC1 + MEC. Secondary organic
carbon mass (MOC2 ) is attributed to hydrocarbon emissions (MHC = MOC2 ).
The mass of particles associated with geological sources by Chow et al. (2002) (MPRI_GEO)
is attributed to primary PM10 emissions from geologic sources. The mass of particles
associated with Total Particulate Ammonium Nitrate (MNO3) is attributed to NOx emissions
(MNOx =MNO3 ); and the mass of particles associated Ammonium Sulfate (M SO4) is attributed
to SO2 emissions (M SO2 = MSO4).
Fractions are then calculated by dividing the mass of each attributed primary pollutant by
the mean mass across the 6 stations. Results are presented in Table IV.2
Fi =
Mi
1
(M k2.5 + M kCoarse )
∑
6 k =1
6
⋅
Equation IV.3
88
Table IV.2. Apportionment fractions relating primary pollutant emissions to observed
PM 10
Primary pollutant
FPRI_GEO
FPRI_COMB
FHC
FNOX
FSO2
Fraction
0.45
0.25
0.02
0.07
0.11
We find that 90% of the particulate mass is accounted for by primary pollutants under
considered in this study. Chow et al. (2002) find that, on average for Mexico City, 10% of
particle mass is salt, non-crustal and unidentified material; this fraction is implicitly
assumed to be constant in this source apportionment analysis.
Using the apportionment fractions in Table IV.2, the total reduction fraction for PM10
concentration is then:
5
RFPM 10 = ∑ Fi ⋅ RFi
Equation IV.4
i =1
where: RFi =
∆Ei
Ei
Equation IV.5
ÄEi is the change in emissions of each primary pollutant due to a control measure. Ei is total
emission inventory for each pollutant, from either 2010 (PROAIRE), or 2002-2010 or
2002-2020 depending upon time horizon. 2002-2010 inventory is a simple mean based on a
linear interpolation between 1998 and 2010 PROAIRE inventories, and 2002-2020 is a
simple mean based on linear interpolation and linear extrapolation to 2020 from the 1998
and 2010 PROAIRE inventories. Once the baseline emissions inventories are prepared for
all control measures (see chapter III. Emissions and control strategy costs), as part of the
process of estimating emission reductions for each measure, then these inventory estimates
will be revised.
IV.5. Ozone Isopleths for Peak Ozone
The peak mean O3 reduction fraction (RO 3 max) is estimated from the fractional reductions
in hydrocarbon (RHC) and NOx (RNOx) by:
RO3 max = 0.5353*RNOx - 0.2082*(RNOx)2 + 0.1112*RHC
Equation IV.6
These are estimated from a series of MCCM model runs (Salcido et al., 2001) where HC
and NOx emissions are varied in equal proportion from all sources and O3 concentration
changes are recorded. The above equation results from multiple linear regression fits to the
results of Salcido et al. (2001) using the Analyse-It package for Microsoft Excel.
89
The use of Ozone Isopleths (Chapter IV) to estimate air quality changes due to emission
changes requires this analysis to consider all hydrocarbons to be equivalent in terms of their
impact on ozone formation. In fact, all hydrocarbons are not equivalently reactive. For
example, the hydrocarbons in LPG are of a low reactivity that might mean that the impact
on ozone concentration from reductions in LPG leaks may be overestimated using this
methodology (Molina et al. 2002).
IV.6. Applying Uncertainty Bounds
Comparisons using 6 control measures in the version 5.3 of the model (Co-Beneficios-5.3)
indicate that the use of source apportionment results (SA) estimates 1/3 the 1o particulate
change as does the Box Model. For secondary particulates, SA estimates approximately 20
times the sulfate change and 1.5 times the nitrate change as does the Marginal PM method
(Table IV.3). These ratios are the same for all the measures analyzed.
Table IV.3. Comparing results of reduced-form air quality mode ls (Co-Beneficios-5.3)
SA / Box
1º PM 10
0.35
SA / Marginal
2º Sulfate PM 10
17
2º Nitrate PM 10
1.6
There is large uncertainty in the Box Model – it is generally not considered appropriate in a
region of variable wind and complex topography. Further, to get reasonable results in
comparison to observations, we have used very low winds. In summary, the Box Model is
likely far too simplified to give good quantitative estimates. There is also large uncertainty
in the Marginal PM method, in part because of problems of temporal and spatial
representativity of the data used in the derivation of the coefficients. SA has more certainty
that these two methods, and it also provides a consistent method to estimate both primary
and secondary particulate change based on observations of the MCMA. Thus, we use SA as
our central estimate in this study. We apply error estimates to these estimates using the
results from the other two methods, and also other efforts to quantify uncertainty in the
relationship between emissions, concentrations, and population exposure. The application
of these uncertainty ranges is described below.
Cohen et al. (2003) use intake fractions (Levy et al., 2002) in their cost-effectiveness
comparison of public bus technologies in the US. They estimate the ranges of uncertainty
as presented in Table IV.4. Ranges are presented as a multiplier of the Central estimate.
Table IV.4. Low and high bound multipliers giving intake fraction uncertainty in
Cohen et al. (2003)
1o PM (immediate)
1 o PM (after transport)
NOx to 2 o PM
NOx to O3
SO2 to 2 o PM
Low
1
½
1/5
1/5
1/3
90
High
5
2
6
5
3
At the 6th Workshop of the Integrated Program on Urban, Regional and Global Air
Pollution, in January 2003, Hammitt et al. (2003) indicated that their study of the benefit
and costs of diesel particle filters for the MCMA, they use a multipliers of 1/5 and 5 in a
triangular distribution for all PM intake fractions in the MCMA.
Given the above evidence, we choose to apply low and high multipliers to the central
estimates from the SA. We use 1/3 and 3 for 1o PM because this is both consistent with our
comparison between the box model and the source apportionment, and also with a midvalue between those for immediate 1o PM exposure and exposure after transport that were
used with intake fractions in Cohen et al. (2003) (Table IV.4) and similar to the value of
Hammitt et al. (2003). Our choice for 2o PM is a subjective balance between the large range
indicated by the SA / Marginal PM comparison, the fact that the Marginal method is
considered very uncertain, and the values used in Cohen et al. (2003) and Hammitt et al.
(2003). For uncertainty in the ozone calculation, we also apply multipliers to the Ozone
Isopleths, basing our values on Cohen et al. 2003. These changes are implemented in
version 5.4 of the model and above. The low and high multipliers used in the model are
indicated in Table IV.5.
Table IV.5. Low and high bound uncertainty multipliers
Low
1/3
1/5
1/5
1 o PM
All 2o PM
O3
High
3
5
5
IV. 7. Results and Uncertainty
In this section, concentration changes, calculated via the above-described methodology, are
presented for each measure. We present mean annualized concentration reductions derived
from our benchmark scenario, which uses a 5% discount rate, for both the time periods
2003-2010 (Table IV.6) and 2003-2020 (Table IV.7). In Tables IV.6 and Table IV.7,
negative values indicate an increase in concentration.
Table IV.6. Mean concentration reductions from annualized emissions reductions for
2003-2010 at 5% (ug/m3 )
Primary
Taxi
renovation
Metro
expansion
Hybrid
Buses
Combined LPG
Leak
Cogeneration
Particulates (PM 10 )
Nitrates
Sulfates
Organics
Maximum O3
0
0.222
0.038
0.096
5.13
0.002
0.007
0.002
0.002
0.142
0.131
-0.005
0.008
0.002
-0.071
0
0
0
0.070
0.907
0
0.003
0
0
0.057
91
Table IV.7. Mean concentration reductions from annualized emissions reductions for
2003-2020 at 5% (ug/m3 )
Particulates (PM 10 )
Nitrates
Sulfates
Primary
Taxi
Renovation
Metro
expansion
Hybrid
Buses
Combined LPG
Leak
Cogeneration
Organics
Maximum O3
0
0.129
0.033
0.073
3.02
0.016
0.054
0.036
0.015
1.071
0.142
-0.006
0.009
0.002
-0.073
0
0
0
0.061
0.737
0
0.005
0
0
0.077
According to this analysis, the Taxi renovation, Metro expansion (for 2003-2020), and the
Combined LPG Leak measure would create significant reductions in maximum ozone
concentrations. Primary particulate reductions are substantial only for the Hybrid Bus
measure. Reductions in nitrate and organic secondary particulates are large for the Taxi
renovation and Metro measures. The Combined LPG measure reduces secondary organic
particles. Changes in secondary sulfates are smaller, though not insignificant for the Taxi
renovation and Metro measures.
In Table IV.8 and IV.9, results for total PM10 and ozone change are presented along with
the uncertainty associated with these estimates. Uncertainty is generally large.
Table IV.8. Total particulate and maximum ozone change from annualized emissions
reductions for 2003-2010 at 5%, with uncertainty (ug/m3 )
Particulates (PM 10 )
Mean
95% CI
Taxi
Renovation
Metro expansion
Hybrid
Buses
Combined
LPG Leak
Cogeneration
Maximum O3
Mean
95% CI
0.36
(0.17 : 0.58)
5.13
(1.59 : 9.97)
0.01
(0.01 : 0.02)
0.14
(0.04 : 0.28)
0.14
(0.06 : 0.23)
-0.07
(-0.14 : -0.02)
0.07
(0.02 : 0.28)
0.91
(0.14 : 1.76)
0
(0 : 0)
0.06
(0.02 : 0.11)
92
Table IV.9. Total particulate and maximum ozone change from annualized emissions
reductions for 2003-2020 at 5%, with uncertainty (ug/m3 )
Particulates (PM 10 )
Mean
95% CI
Taxi
Renovation
Metro expansion
Hybrid
Buses
Combined
LPG Leak
Cogeneration
Maximum O3
Mean
95% CI
0.24
(0.12 : 0.38)
3.02
(0.94 : 5.87)
0.12
(0.07 : 0.18)
1.07
(0.33 : 2.08)
0.15
(0.07 : 0.25)
-0.07
(-0.14 : -0.02)
0.06
(0.02 : 0.12)
0.74
(0.23 : 1.43)
0
(0 : 0.01)
0.08
(0.02 : 0.15)
The inclusion of spatial distribution with Ozone Isopleths and or the application of the
source apportionment results should be considered as a way to improve these
methodologies.
IV.8. References
Cesar, H., K. Dorly, X. Olsthoorn, L. Brander, P. V. Beukering, V. Borja-Aburto, V. Torres
Meza, A. Rosales Castillo, G. Oliaz Fernandez, R. Muñoz Cruz, G. Soto Montes de Oca, R.
Uribe Ceron, E. Vega López, P. Cicero Fernández, A. Cilalic González Martinez, MM
Niño Zarazua and MA Niño Zarazua (2000) “Economic valuation of Improvement of Air
Quality in the Metropolitan Area of Mexico City,” Institute for Environmental Studies
(IVM)
Cesar, H., G. Schadler, M. Hojer, P. Cicero-Fernandez, L. Brander, T. Buhl, A. C.
Villagomez, K. Dorland, A. C. G. Martinez, H. Hasselknippe, P. M. Oritz, A. V. Montero,
A. Salcido, J. Sarmiento, and P. V. Beukering (2002) “Air pollution abatement in Mexico
City: an economic valuation,” World Bank Report
Chow, J.C., J.G. Watson, S.A. Edgerton, and E. Vega (2002) “Chemical composition of
PM2.5 and PM10 in Mexico City during winter 1997,” The Science of the Total Environment
287, p.177-201.
Cohen, J.T., J.K. Hammitt, and J.I. Levy. 2003. Fuels for urban transit buses: A costeffectiveness analysis. Environ. Sci. Technol 37. 1477-1484.
Evans, J., J. Spengler, J. Levy, J. Hammitt, H. Suh, P. Serrano, L. Rojas-Bracho, C. SantosBurgoa, H. Rojas-Rodriguez, M. Caballero-Ramirez and M. Castillejos (2000)
“Contaminación atmosférica y salud humana en la Ciudad de México,” MIT-IPURGAP
Report No. 10.
93
Hammitt, J.K., G. Stevens, and A. Wilson. 2003. Benefit-cost analysis of diesel particulate
filters: preliminary results. Presentation at 6th Workshop of the Integrated Program on
Urban, Regional and Global Air Pollution, Mexico City, January 2003.
Levy, J.I., S.K. Wolff, and J.S. Evans. 2002. A regression-based approach for estimating
primary and secondary particulate matter intake fractions. Risk Analysis 22. 895-904.
Molina, M.J., L.T. Molina, J. West, G. Sosa, and C. Sheinbaum Pardo (2002) “Air
pollution science in the MCMA: Understanding source-receptor relationships through
emissions inventories, measurements, and moideling,” in Air Quality in the Mexico
Megacity: An Integrated Assessment, Kluwer Academic Pub lishers, Boston, 384 pp.
Salcido et al. (2001) “MCCM Parametric Studies: Estimation of the NOx, HC and PM10
emission reductions required to produce a 10% reduction in the Ozone and PM10 surface
concentrations and compliance with the MCMA air quality standards, with reference to the
2010 MCMA Emission Inventory,” Grupo de Modelación de la Comisión Ambiental
Metropolitan (CAM), 42 pp.
Turpin, B.J., J.J. Huntzicker, S.M. Larson and G.R. Cass (1991) “Los Angeles summer
midday particulate carbon: Primary and secondary aerosol,” Envi. Sci. Technol., 25(10)
1788-1793.
U.S. Environmental Protection Agency (1999) "The Benefits and Costs of the Clean Air
Act 1990-2010," Washington, D.C., Office of Air and Radiation, EPA report no. 410/R99/001.
West, J. and I. San Martini (2001) Report of the Fourth Workshop on Mexico City Air
Quality, March 8-10, 2001, El Colegio de Mexico, Mexico. MIT-Integrated Program on
Urban, Regional and Global Air Pollution Report No. 25, November 2001.
Zuk, M (2002) “Evaluating the benefits of reducing uncertainty in air quality management:
the case of Mexico City,” Masters thesis, Massachusetts Institute of Technology.
94
Chapter V.
V.1.
Health Impacts Analysis
Introduction
It has been well established that exposure to outdoor air pollutants can cause several
cardiovascular and respiratory outcome, and even may cause premature mortality. As the
primary motivation for air quality management is to reduce the public health burden of
pollution, it is important to evaluate how measures aimed at reducing air pollution will
affect human health. In this section we expand on the methodology outlined previously to
explain some of the basic theory behind our calculations and data sources. In part I, we
describe the sources of the dose response coefficients used in the analysis, followed by an
explanation of the sources of data on morbidity and mortality rates in Mexico City,
concluding with a description of the methodology and results of the Year of Potential Life
Lost (YPLL) analysis and final results.
Before documenting each part of the analysis, we must first review the basic calculations
being made, so that it is clear where the sections fit in. As explained in the main report, we
use a general dose response model to estimate the changes in health outcomes due to
changes in ambient exposures of ozone and PM10 . Equation V.1 describes the general form
of this equation.
H ij = βij × Ri × C j × N
Equation V.1
Where âi is the dose-response coefficient for the ith effect from the pollutant of interest (%
increase cases/year/person/unit exposure), Ri is the background rate of the effect of interest
(cases/year per person), C is the ambient concentration of pollutant (µg/m3 ) averaged across
the entire population, and N is the population at risk (number of people). The dose
response coefficients come from epidemiological studies and are described in section 2 and
the background rates of disease and mortality in section 3. Concentrations come from the
air quality module, and population exposed is the population of Mexico City in the year
2000. Since our analysis of measures finds annualized emissions, the concentration input
to this module is annualized concentrations reductions for a stream of 8 or 18 years of
emissions. Therefore this value does not represent any single year, but rather an average
year in this time period. We therefore use the year 2000 as our base year, although perhaps
it would be better to use the projected population in the middle of the period.
Table V.1 lists the set of 19 health outcomes analyzed here. These outcomes were chosen
based on epidemiological evidence of their association with air pollution. To avoid overlap
in the valuation section, we do not include all of these outcomes when calculating total
monetary benefits.
95
Table V1. Outcomes evaluated
Mortality due to Acute Exposure
All Causes
Infant Mortality
Mortality due to Chronic Exposure
All Causes
Cardio-respiratory Causes
Lung Cancer
Chronic Bronchitis
Hospital Admissions
All Respitaroty Causes
Asthma
COPD
Pneumonia
All Cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
Emergency Room Visits (ERVs)
All Respiratory Causes
Asthma
Restricted Activity Days
Minor Restricted Activity Days
School Absenteeism
V.2.
Dose Response Analysis
There is an abundance of literature on the health impacts of air pollution. For certain
outcomes there is a plethora of evidence, whereas for others there have been only a few key
studies. The purpose of this analysis was to gather information from 3 principal metaanalyses, (USEPA, 1999; Cesar et al. 2002; and Evans et al. 2000), as well as other
epidemiological literature to determine the appropriate dose response values to use for our
analysis. It was beyond the scope of this study to conduct an original meta-analysis, nor
did we find it necessary since there have already been a number of such summary studies
conducted for Mexico. The reason for using the 3 meta-analyses is that they include
different studies in their analysis and have therefore found different results. Due to the
wide range of dose response results from the various studies, we have decided to include a
range of possible dose response values, rather than a single number. In this section, we first
present the sources of data and the ranges chosen for each health outcome, followed by a
summary table.
When analyzing various sources of epidemiological studies, it became evident that many
different metrics were used. First, it was necessary to ensure that the outcomes of interest
were the same as those in the studies. This was guaranteed by comparing ICD codes for the
96
various outcomes. Secondly, we needed to determine the air quality metric we would use
in the model. We decided that for particulates we would use µg/m3 for 24- hour average
PM10 . This was chosen since many of the studies were done with these units, and since the
emissions were calculated in PM10 . Here we use the standard set of conversion factors to
convert results in to PM10 shown in Table V.2a.
Table V.2a Ratios to convert to PM 10
PM 10 ≅ PM 15
PM 10 ≅ PM 13
PM 10 ≅ TSP * 0.55
PM 10 ≅ PM 2.5 / 0.6
PM 10 ≅ CoH / 0.55
PM 10 ≅ BS
For ozone, we use one hour maximum concentrations in µg/m3 units. Most studies are
reported in ppb, however. To convert units, we assume that at the elevation of Mexico
City, where the typical atmospheric pressure is 580 mm Hg and the temperature is 16 C, 1
ppb of ozone is approximately equal to 1.5 µg/m3 . While most epidemiological studies are
conducted using one hour maximum concentrations of ozone, there are several that use 24hour averages as well. To convert these results to one hour maximums, we use data from 5
central monitoring stations from ’98-’02 to calculate the 24hr to 1 hour maximum ratio.
Table V.2b Ozone 24 average and 1 hour maximum data
Year
1998
1999
2000
2001
2002
24hr Average
(ppm)
0.037
0.034
0.037
0.033
0.032
One hour maximum
(ppm)
0.179
0.166
0.168
0.154
0.149
Ratio
0.207
0.205
0.220
0.214
0.215
Averaging the above information we found a ratio of 0.2 for 24-hour to 1 hour maximum
ozone concentrations, and multiplied the dose response values by this ratio to convert
coefficients.
Mortality due to Chronic Exposure in Adult (35+) Populations
As of date, no cohort mortality studies have been conducted in Mexico City; however,
within the United States several studies have been carried out to examine the relationship
between premature mortality and long-term exposure to particulate matter. The most
relevant studies are the Six U.S. Cities study (Dockery et al., 1993) and the American
Cancer Society study (Pope et al., 1995 and 2002).
The approaches used in these studies were to estimate separately for three cause-of-death
categories: all-cause, lung cancer and cardiopulmonary mortality. The analyses included
97
controls for several risk factors, including age, gender, race, smoking education, body mass
index, alcohol consumption, occupational exposure, diet indices, and others.
The Six Cities cohort study, which lasted 16 years, was restricted to 8,111 white subjects
who were 25 through 74 years of age. For all cause mortality they found a 14% (4.3% to
25%) increase in mortality per 10 µg/m3 increase in fine particles (PM2.5), for lung cancer
20% (NS to 70%) and for cardiopulmonary mortality 20% (6% to 37%). Translated to PM10
(considering a PM2.5/PM10 ratio of approximately 0.6), this respectively corresponds to
8.4% (2.6% to 15%), 12% (NS to 42%) and 12% (3.6% to 22%).
The ACS cohort study of 2002, extended the prior 1995 study until December of 1998.
This study associated PM2.5 with all-cause, cardiopulmonary and lung cancer mortality. For
all-cause mortality they found a 6% (2% to 11%) increase in mortality per 10 µg/m3
increase in PM2.5, for lung cancer 14% (4% to 23%) and for cardiopulmonary mortality 9%
(3% to 16%).Translated to PM10 respectively, 3.6%( 1.2% to 7%), 8.4% (2.4% to 14%) and
5.4% (2% to 10%).
From these studies, it can be concluded that there is strong evidence that long-term
exposure to fine particulate air pollution is an important risk factor for lung cancer and
cardiopulmonary mortality. This holds true even after controlling for cigarette smoking,
BMI, diet, occupational exposure, other individual risk factors and after controlling for
regional and other spatial differences.
Table V.3 compares percent increase in mortality risk obtained in these studies associated
with the increase in PM2.5 and PM10 concentration.
Table V.3 Results from 2 Cohort Mortality Studies
PM 10
All
Lung cancer
Cardiopulmonary
Six Cities (Dockery, 1993)
ACS (Pope, 2002)
% increased risk (95% CI) associated with a 10 µg/m3 increase
in PM 10
8.4% (2.6% – 15%)
3.6% (1.2% –7%)
12% (NS – 42%)
8.4% (2.4% – 14%)
12% (3.6% – 22%)
5.4% (2% – 10%)
In the Analytica model, the dose-response coefficients used for mortality considered allcause mortality. Given that there have been no studies on chronic mortality in Mexico and
limited evidence from the U.S., a value of 0 was chosen as a plausible minimum, the ACS
2002 study as a mid-value and the Six Cities study as a maximum to represent the plausible
range of dose response values for mortality.
Mortality due to Acute Exposure
The evidence from time series studies around the world has been substantially consistent.
A recent analysis, the National Morbidity, Mortality and Air Pollution Study (NMMAPS)
by Samet et al. (2000), looked at death counts and air pollution levels in 90 cities across the
U.S. and attempted to account for differences in methodology, geographic conditions and
the effects of gaseous air pollutants in a systematic manner. They found an average
98
increase of 0.5% risk of mortality per 10µg/m3 of PM10 , which was greater for mortality
due to heart and lung disease than total deaths. A recent meta-analysis by Levy et al.
(2000) considered nearly 30 international studies and using a random effects model, found
a pooled result of 0.7% increase per 10 µg/m3 increase in PM10 concentrations (95% CI:
0.6%, 0.8%).
A number of time series studies have been conducted in Mexico City all of which appear to
show a significant relationship between mortality and particulates. An earlier study by
Borja Aburto et al. (1997) found the effect of same-day 24-hour average TSP
concentrations resulted in a 5% increase in premature mortality (95% CI: 3.0%, 6.7%) for a
100 µg/m3 increase. Three recent studies (Borja-Aburto et al., 1998; Loomis et al., 1999;
and Castillejos et al., 2000) considered various particle size fractions and were conducted
by a single research team over the same geographic area and time period. The two studies
by Borja-Aburto have similar estimates for total mortality (1.1% and 1.2% increase per 10
µg/m3 ), while the Castillejos study has a higher estimate of 2.5%, potentially associated
with the longer exposure window. A number of other time series studies have been
conducted analyzing the relationship between pollution exposure and mortality for specific
causes.
The Harvard risk assessment (Evans et al., 2000) indicated that if the Mexican studies were
pooled, the risk of mortality in Mexico City would be around 1.4% increase per 10µg/m3 .
This seems well within the range of findings from international studies. Given the
relatively narrow range of results from these 3 sources, we choose to use the NMMAPS
dose response coefficient as the lower bound, the pooled international coefficient from
Levy et al. (2000) for the central estimate Mexican value as an upper bound to account for
variability in findings across the studies.
In the analysis of ozone’s affect on daily mortality, there is some evidence, from both the
Mexican and worldwide literature of an association between ozone and premature
mortality. Only one of the Mexican studies found a statistically significant association
between ozone and cardiovascular mortality (Borja-Aburto et al., 1998). The recent metaanalysis by Levy et al. (2000), found ozone to be a significant predictor of mortality, even
when particulate matter was included in the model. Out of 50 studies, only four met their
strict inclusion criteria. The pooled estimate from these four studies indicated a 0.4%
increase in premature mortality per 10µg/m3 increase in 24-hour average ozone
concentrations. In this study we use the estimates of relative risk from Levy et al. (2000),
with a lower bound of zero effect and upper bound of 0.7% per 10µg/m3 of 24-hour average
ozone concentrations. Translated to 1 hour maximum, this is equivalent to 0, 0.08% and
0.14% per 10µg/m3 of 1-hour maximum ozone levels.
Infant Mortality
There have been several studies on the association between exposure to air pollution and
infant mortality around the world including the Czech republic, Mexico City, Sao Paulo,
Beijing and the U.S. The two most relevant studies are the U.S. (Woodruff et al. 1997) and
Mexico City (Loomis et al., 1999) studies. The Mexico City study found that a 10 µg/m3
99
increase in PM2.5 was associated with a 6.3% increase in infant mortality (95% CI: NS,
13.2%). Translated to PM10 , this corresponds to 3.9% (95% CI: -.3%, 8.2%) per 10µg/m3 .
The U.S. study by Woodruff et al. (1997) involved an analysis of approximately 4 million
infants born between 1989 and 1991. They analyzed all cause and respiratory related postneonatal mortality and found that exposure to 10 µg/m3 of PM10 corresponded to 1.04%
(95% CI: 1.02%, 1.07%) changes in death rates.
Given the limited availability of infant mortality studies, we choose to use 0 as a plausible
minimum, the U.S. study as a mid-value and the Mexican study as a maximum (0, 1.04%,
3.9%) to represent the plausible range of dose response values.
Chronic Bronchitis
No large-scale epidemiological studies of chronic bronchitis have been conducted within
Mexico; however, there are two smaller studies in Mexico City (Santos-Burgoa et al., 1998;
Romano, 2000) which address chronic bronchitis to some extent, nevertheless the evidence
linking air pollution to this ailment is relatively weak. Although these studies provide some
evidence that chronic bronchitis prevalence may be linked to air pollution in Mexico City,
definitive conclusions cannot be reached given the sample size and study population
selected, the study design, and lack of direct concentration measures across the entire
sample.
In the worldwide literature, there are three primary studies that evaluate links between longterm exposure to air pollution and development of bronchitis (Abbey et al., 1993; Schwartz,
1993; Abbey et al., 1995). The Schwartz study in 1993 consisted of a cross sectional
analysis of bronchitis prevalence and mean levels of total suspended particles (TSP) in 53
urban area in the U.S. In this study concentration-response relationships were estimated,
finding a 7% (95% CI: 2%, 12%) increase in chronic bronchitis rates for a 10 µg/m3
increase in TSP.
Both of the Abbey et al. studies were prospective cohort studies of a Seventh Day Adventist
population in California. The 1993 study estimated a 36% increase in chronic bronchitis
associated with ten years of exposure to a 60 µg/m3 increment of TSP. Therefore an
increase in chronic exposure of 10 µg/m3 of TSP would increase chronic bronchitis
incidence by 5%. In the 1995 study, PM2.5 was examined. It was estimated that a 45 µg/m3
increase in PM2.5 was associated with a relative risk of 1.81 (95% CI: 0.98%, 3.25%) for
chronic bronchitis development, meaning that a 10 µg/m3 increase in PM2.5 would increase
chronic bronchitis by 18%.
Assuming that PM2.5 comprises 60% of PM10 (PM2.5 /PM10 ratio or approximately 0.6 used
in Mexico and similar to de default ratio used within the U.S.), and that PM10 comprises
55% of TSP (standard conversion assumptions); the estimates from these three studies
correspond to 12% (Schwartz et al. indicate a 7% increase due to TSP), 9% (Abbey et al.
(1993) indicate a 5% increase due to TSP) and 8% (Abbey et al. (1995) indicate a 14%
increase due to PM2.5) increase of chronic bronchitis for a 10 µg/m3 increase in chronic
PM10 exposure.
100
Due to the lack of strong estimates within Mexico City, a chronic bronchitis dose-response
coefficient needs to be derived from the U.S. studies. Pooling these three studies (assuming
p = 0.05 (Abbey et al. 1993), it can be estimated that chronic bronchitis incidence increases
by 10% (95% CI: 5%, 15%) for every 10 µg/m3 increase in long-term exposure to PM10
exposure (Evans et al., 2000).
Hospital Admissions for Cardiovascular Disease in Elderly (65+) Populations
There have been several studies analyzing the cardiovascular effects of air pollution
exposure. Here we consider 2 meta-analyses and the NMMAPS study to synthesize
available studies. The Harvard white paper (Evans et al., 2000) summarized findings by
pooling four studies on cardiovascular hospital admissions in elderly populations. Their
analysis found that a 10 µg/m3 increase in 24- hour average PM10 resulted in a 0.6% (95%
CI: 0.4%, 0.8%) increase of hospitalizations due to cardiovascular causes
Results form the World Bank study on the other hand found an increase of 1.22% (95% CI:
0.94%, 1.5%) for every 10 µg/m3 increase in 24-hour average PM10 for cardio and cerebrovascular admissions
The NMMAPS study (unconstrained distributed lag, random effects model) found that for
every 10 µg/m3 increase in PM10 , there is a 1.07% (CI: .67%, 1.46%) increase in
cardiovascular disease in elderly.
For the purposes of this study we use the NMMAPS results as the central estimates, with
the MIT and World bank estimates as lower and upper bounds respectively (0.6%, 1.07%,
1.22%).
Acute Myocardial Infarction Hospital Admissions in Elderly (65+) Populations
There have been a number of studies on hospitalizations for Ischemic Heart Disease, of
which Acute Myocardial Infarction (heart attack) is a subset. The Schwartz and Morris
(1995) study in Detroit found that a 10µg/m3 change in PM10 resulted in a 0.6% (0.2%, 1%)
change in hospitalizations of people 65 and older. The study by Lipmann et al. (2000) also
in Detroit found an increase in Ischemic heart disease per 10µg/m3 of PM10 of 1.78% (95%
CI: 0.1%,3.6%). Finally a study by Linn et al. (2000) in Loss Angeles found a 0.6%
increase (CI: 0.3%, 0.8%).
Using these three studies, we estimate a range of 0.2% as the lower bound, 1.78% as the
upper bound and 0.6% as the middle for our study.
Hospital Admissions for Congestive Heart Failure in Elderly (65+) Populations
There exist several studies on hospitalizations for congestive heart failure, of which we
restrict our analysis to those which analyzed PM10 and elderly patients. Under this
101
restriction, we encountered 3 studies including: Schwartz and Morris (1995) which found a
1.0% (95% CI: 0.4, 1.6) increase in hospitalizations, the study by Morris and Naumova
(1998) for Chicago found a 0.8% (95% CI: 0.2, 1.4) increase, and finally a study by
Lipmann et al. (2000) which estimated a 1.9% (95% CI: 0.0, 3.7) increase in
hospitalizations from congestive heart failure per 10 ug/m3 PM10 . For our analysis we use
the central estimate from these three studies for our range of values.
Hospital Admissions for Respiratory Disease
The meta-analysis from the World Bank (Cesar et al. 2002) considered 4 studies for the
impact of exposure to ozone on respiratory hospital admissions. The weighted average
increase in specific hospitalizations for respiratory diseases 3.76% (CI 95% 0.45 - 7.05) for
10 ppb of ozone. For PM10 they considered 12 studies and found a pooled estimate
increase was 1.39 (CI 95% 1.18-1.60) per 10 ug/m3 .
In the meta-analysis of Levy et al. (1999), they considered 6 studies and found a 1% (95%
CI: 0, 1.9%) increase for every 10 ug/m3 PM10 , and 0.4% (95% CI: 0, 0.8%) for every
10ug/m3 of ozone.
In our analysis, we combine the results from the 2 meta-analyses. For PM10 we use a range
of 0%, 1%, and 2%. For ozone, converting units, we find 2.5% from the World Bank and
0.4% for Levy et al. (2000). So we use a range of (0%, 0.4%, 2.5%) increase in respiratory
hospital admissions for every 10 ug/m3 ozone.
Hospital Admissions for COPD in Elderly (65+) Populations
Results from the NMMAPS study (unconstrained distributed lag, random effects model)
indicate that for every 10 µg/m3 increase in PM10 , there is a 2.88% (CI: 0.19%, 5.64%)
increase in COPD admissions in elderly populations.
The World bank study pooled 11 studies on COPD admissions and found a pooled result of
2.34% (CI 95% 1.80 - 2.89) for 10 ug/m3 of PM10 of the general population. For the ozone
effect they used results from only 2 studies with a pooled effect of 5.5% (0, 7.5%) per 10
ppb of ozone, which when translated to ug/m3 would be (3.6%, CI: 0, 5%).
Since the results of the NMMAPS and World Bank are quite similar, here we use the
results of the World Bank for ozone and PM10 .
Hospital Admissions for Pneumonia in Elderly (65+) Populations
In the World Bank report, they pooled 4 studies from the U.S. and found a pooled estimated
they found was an increase of (1.40% CI 95% 1.05%, 1.75%) in pneumonia hospital
admissions for each 10 ug/m3 of PM10 . For the ozone effect they again used just 2 studies
which found 5.2% and 5.7% increase in pneumonia hospitalizations per 10 ppb of ozone
(older than 65). They use a range of 5.2% (2%, 8%) to capture variability in study results.
Translated to ug/m3 this would be 3.5% (CI: 1.3%, 5.3%).
102
Results from the NMMAPS study (unconstrained distributed lag, random effects model)
indicate that for every 10 µg/m3 increase in PM10 , there is a 2.07% (CI: 0.94%, 3.22%)
increase in pneumonia admissions in elderly populations.
For PM10 we choose to use 1 % as a lower bound, the World Bank estimate (1.4%) as a
central estimate and NMMAPS (2.07%) as an upper limit. For ozone we use only the WB
results.
Hospital Admissions for Asthma
The world bank report considered 11 studies on PM10 impact on asthma hospitalizations
and found a pooled estimate of 3.02% (CI 95% 2.05 - 4.00) increase in hospitalizations for
asthma
per
every
10
ug/m3
of
PM10 .
For ozone, the World Bank (Cesar et al. 2002) report pooled results from 4 studies.
Pooling these results they estimated an increase of 1.47% (CI 95% 0.41 - 2.53) per 10 ppb
of ozone and in ug/m3 terms (1% CI: 0, 1.7%)
Asthma Emergency Room Visits (AERV)
Several studies on relating asthma emergency room visits to pollution exposure have been
conducted in Mexico city, including the study by Romieu et al. (1995) in which they
measured ozone, SO2 , NO2 and PST, and found that a 50ppb increase in peak hour ozone
was associated in a 43% (95% CI: 23%,65%) rise in AERVs. This implies that an increase
of 10µg/m3 in ozone would raise AERVs by 5% (95% CI:3%, 6%).
Another Mexican study is Damokosh et al. (2000) in which they found that a 10 ppb
increase in 5 day mean ozone concentrations was associated with a 15% rise in AERVs
(95% CI: 0%, 40%), while a 10 ppb rise in 11 day mean concentrations resulted in a 33%
(95% CI: 0%, 73%) rise in AERVs. This implies that a 10ug/m3 rise in peak hour ozone is
associated with 4.4% (0%, 9.7%) rise in AERVs. In a Canadian study (Stieb et al., 1996)
they found that a 10 ppb rise in ozone caused 3.5% (95% CI: 1.7%, 5.3%) rise in AERVs,
or 2.3% (95% CI: 1.1%, 3.5) per 10 ug/m3 of ozone.
In this study, we use the result of Damokosh et al. (2000) (4.4%) as the central estimate, the
Canadian study (2.3%) as the lower limit and results from Romieu et al. (1995) as the upper
limit (5%).
We consider two studies for AERV association to PM10 exposure. The study by Schwartz et
al. (1993) found that an increase of 10µg/m3 in PM10 produced a rise of 4% in AERVs
(95% CI: 1%, 7%), with insignificant effects from ozone and SO2 . In the study by Lipset et
al. (1997) it was found that a rise 10µg/m3 in PM10 was associated with a 4.50% (95% CI:
2.16, 7.0) of AERVs in children.
Since these two studies have very similar results, we choose to use those of the Schwartz et
al. (1993) study for our analysis.
103
Emergency Room Visits for All Respiratory Causes
Two studies from Mexico City exist, in which they have found an association between
Respiratory ERVs and pollution exposure. The study by Damokosh et al. (2000), in which
they found that a 14 µg/m3 rise in 6 day mean PM2.5 concentrations was associated with a
rise of 10% (95% CI: 0%, 30%) in respiratory ERVs. In terms of PM10 , this translates to
4% (0, 12%). A study for the same causes by Samet et al., 1981, found a rise of 10µg/m3
of PM10 was associated with an increase of 0.8% (95% CI: 0.2%, 1.4%) in respiratory
ERVs.
In the World Bank Report, they pooled several studies and found an association of 3.11%
(95% CI: 2.35, 3.88) for every 10µg/m3 PM10 and 2% (95% CI: 1%, 3%) for every 10
ug/m3 of ozone. Finally a study by Schwartz et al. (1993) found a 3.4% (95% CI: 1%, 6%)
for every10ug/m3 de PM10 .
In our ana lysis we use the results from the Samet study (1%) as the lower limit, the World
Bank (Cesar et al., 2002) (2%) as the central estimate, and the result from Damokosh et al.
(2000) (4%) as the upper limit. For ozone, we simply use the results from the World Bank
study.
Restricted Activity Days (RAD) and Minor Restricted Activity (MRAD) in Adults (18+)
Restricted activity days (RAD) refer to days when individuals are forced to reduce their
normal activity due to acute or chronic conditions, including bed days, work- loss days,
school loss days and cut down days. This term was coined by the U.S. Health Interview
Survey and is an indicator of morbidity outcomes. A related outcome is minor restricted
activity days (MRAD), in which individuals neither miss work nor spend the day in bed,
but do have some reduction in their daily activities. For the effects of pollution exposure
on restricted activity in adults, there have been only two studies, both of which were
conducted in the US. Evidence from these studies (Ostro 1987, Ostro and Rothchild, 1989)
indicates that there is a 0.5% (95% CI: 0, 2%) increase in risk of minor restricted activity
days per 10 µg/m3 in peak hour ozone and a 7% (95% CI: 5%, 9%) increase per 10 µg/m3
of PM2.5. Results from the analysis on RADS from the first study indicated a 5% (95% CI:
3%, 7%) increase in RAD from 10µg/m3 in 24-hour average PM2.5 in the population of 1865.
We use the results from these two studies in the current analysis, adjusting for PM2.5/PM10
ratio (0.62), which gives coefficients for RAD of 0.3% ( 95% CI: 0, 1.3%) and for MRAD
of 4.3% (95% CI: 3.1%, 5.6%) for every 10µg/m3 in 24-hour PM10 and 0.5% (95% CI: 0,
2%) for 10µg/m3 peak hour ozone.
104
School Absenteeism
A final parameter, which has been associated directly with high levels of PM10 pollution
and indirectly with the toxic effects resulting from exposure, is child absenteeism from
school. There has been only one study in Mexico on absenteeism in preschool children
(Romieu et al., 1992). This study found a 0.9% increase in elementary school absenteeism
per 10 µg/m3 increase in 1-hour maximum ozone concentrations. While this study did
control for age, temperature, sex and tobacco exposure, it did not account for PM10
exposures, and it is therefore difficult to draw conclusions from its results.
Within the worldwide literature, there are three primary studies addressing school
absenteeism, one conducted in recent years (Ransom and Pope, 1992) and two older studies
from the 1970s. Neither of the older studies found a significant relationship between air
pollution and school absenteeism. The recent study was conducted in Utah between 1985
and 1990, in an area with very little ozone or SO2 . Absenteeism was found to be
significantly associated with PM10 in regression models controlling for weather and time,
with a 10 µg/m3 increase in 28-day average PM10 associated with a 4% increase in absences
(95% CI: 2%, 6%). While there isn’t a wealth on information on this outcome, we choose
to use the results from the se two studies directly to estimate changes in school absenteeism
in children from exposure to PM10 and ozone.
Table V.4 Dose Response Coefficients (% /10 ug/m3 ) used in our analysis
Mean1
1.1 Acute Mortality
Total mortality
Infant mortality
1.2 Chronic Mortality
Total
Cardio-respiratory
Lung Cancer
1.3 Chronic Bronchitis
1.4 Hospital admissions
All Respiratory
Asthma
COPD
Pneumonia
All Cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
1.5. Emergency room visits (ERVs)
Respiratory Causes
Asthma
1.6. Restricted Activity Days
1.7 Minor Restricted Activity Days
1.8 School Absenteeism
PM 10
Ozone
IC 95%
Mean2
IC 95%
0.7
1.04
0.5 – 1.4
0 – 3.9
0.08
0 – 0.14
3.6
5.4
8.4
10
0 – 8.4
0 – 12
0 – 12
5 – 15
1
3
2.3
1.4
1.1
1
0.6
0– 2
2– 4
1.8 – 2.9
1 – 2.1
0.6 – 1.2
0.8 – 1.9
0.2 – 1.8
0.4
1
3.6
3.5
0 – 2.5
0 – 1.7
0– 5
1.3 – 5.3
2
4
0.3
4.3
4
1– 4
1 –7
0 – 1.3
3.1 – 5.6
2– 6
2
4
1– 3
2.3 – 4.5
0.5
0.9
0– 2
105
V.3.
Mortality and Medical Attention Frequency
In this section we describe the sources and methodology used to determine background
rates of morbidity and mortality outcomes. Before doing so, it is first necessary to explain
the population and databases used for this analysis.
V.3.1. Study Population
It was established at an early stage of the project, that the target population from which
information on hospitalization frequency and health costs would be derived, would be the
group affiliated with the Mexican Institute of Social Security (IMSS) in the federal entities
(Mexico City and the State of Mexico). This was decided for reasons of data availability
and quality. According to the structure of the IMSS, we selected zones 15 and 16 from the
State of Mexico and zones 35, 36, 37 and 38 from the Mexico City.
Population information for the MCMA was obtained from the National Population Council
(CONAPO) of Mexico. We have decided to use the year 2000 as the year of reference for
two reasons: a) This was the year that the population census was conducted, b) The data for
mortality and health attention, specifically that which refers to the codification of the causes
of death and diseases, would be more precise for this year, considering that the codification
was updated from the International Classification of Diseases Version 9 (ICD 9) to Version
10 (ICD) in 1998.
The following tables describe the age structure of the total populations of the DF and the
state of Mexico as well as the population in each that is covered by IMSS. It should be
noted that while the total population of the state of Mexico is considered here, only part of
that total population lives in the greater metropolitan area (roughly 60%). We have
included the entire population from the state of Mexico covered by IMSS, under the
assumption that the majority of this population lives in the industrial zone of the state,
which is part of the Metropolitan Area.
Table V.5. MCMA population for year 2000
Age groups
State
Total
<1
1- 4
5- 9
10 - 14
15 - 19
20 - 29
30 - 49
50 - 64
65+
Federal
8,796,861 149,119 601,675
784,996
814,951
835,777 1,732,950 2,484,302 861,198 531,893
District
State
of
13,107,252 268,592 1,089,242 1,384,175 1,403,539 1,372,352 2,625,938 3,477,731 1,004,202 481,481
Mexico
Source: Population Estimated at Mid Year. CONAPO. Proyecciones de Población, 1995-2050.
106
Table V.6 IMSS population in the MCMA for the year 2000
State
Total
Federal District
Males
3,327,130
Females 3,639,200
Total
6,966,330
State of Mexico
Males
1,907,809
Females 1,984,720
Total
3,892,529
<1
1- 4
5- 9
10 - 14
15 - 19
20 - 29
30 - 49
50 - 64
36,364
34,359
70,723
156,149
147,603
303,752
291,512
280,414
571,926
265,682
256,178
521,860
141,125
619,770
973,932
157,409
677,698 1,102,354
298,534 1,297,468 2,076,286
396,865
519,120
915,985
32,900
31,153
64,053
127,051
120,590
247,641
203,893
197,092
400,985
181,390
176,245
357,635
98,461
109,597
208,058
189,314
216,048
405,362
388,183
529,811
411,251
569,065
799,434 1,098,876
The IMSS population represents approximately 30% of the population of the state of
Mexico and 80% from the Federal District. As is evident from figure V.1, this distribution
is not even across age groups. For instance, there seems to be a larger representation of
elderly in the IMSS population than of children and infants. This could bias the frequency
data in 2 ways. Either is could bias the data upwards, as elderly tend to have higher
hospitalization rates than the average population or downwards, since infants who may be
acutely susceptible to illness from air pollution are less represented. We are managing the
data under the assumption that these affects will weigh each other out and that the
frequency data gives us a pretty good idea of the distribution and rates of medical attention
in the Mexico City population.
Figure V.1
Population of IMSS and MCMA
Comparison between IMSS and the Total MCMA Populations YEAR 2000
65 +
90%
50-64
49%
30-49
AG
E
GR
OU
P
35%
20-29
30%
15 a 19
14%
10 a 14
24%
5a9
26%
1a4
18%
<1
17%
TOTAL
32%
0
5,000,000
10,000,000
15,000,000
POPULATION
IMSS
107
MCMA
20,000,000
25,000,000
While the IMSS by no means represents the entire Mexico City population, we feel that it is
sufficient for our calculations. In our final workshop it was suggested that we conduct the
analysis for all sectors of the populations (public, private and non-covered). We have
considered this option, however given the fact that the information gathered from the IMSS
database accounts for less than 10% of the total impact when calculating the monetary
value of the health impacts. Therefore, while not perfect, we find it adequate enough for its
uses.
V.3.2. Mortality
The information on mortality in the year 2000 was obtained from the Mortality Databases
published by the National Institute of Statistics, Geography and Informatics and the
Ministry of Health of Mexico (INEGI/SSA) in the publication Estadísticas de Mortalidad
Año 2000 - 2001. This information was obtained for mortality due to all causes as well as
for specific diseases such as:
ICD- 10°
I 21
I 50
C 33 – C34
J 12 – J 18
J20
J 40 – J 44
J 45
Cause
CARDIOVASCULAR
Acute Myocardial Infarction (AMI)
Congestive Heart Failure (CHF)
RESPIRATORY
Tumor in trachea, bronchioles and lung
Pneumonia
Acute bronchitis
Bronchitis, Emphysema, (CPOD)
Asthma
Considering the suggestions of the Department of Quality of Information and Evaluation of
the Performance of the SSA, a first approximation was done on the behavior of mortality in
the year 2000 which involved consulting the previously mentioned databases for the years
2000 and 2001. The former was consulted to identify the deaths that occurred in Mexico in
the year 2000, and to thereby eliminate the deaths reported in the year 2000 that actually
occurred in other years. The 2001 database was used to include those deaths that occurred
in 2000 but that were not reported until 2001. Finally, prorating we estimated the
undeclared deaths by sex and age.
The sub-registry of deaths greater than 30 years of age was corrected using the
methodology proposed by Brass (1975). Figures V.2 and V.3 show the assumptions of
required alignment pertaining to the consideration of adults for these ages, by representing
the pairs graphically (ny / ny+ ; dy+ / ny+). Using the “least squares” method, we obtained the
parameters α and β, the first being an indicator of the implicit rate of growth in this
population group and the second being a correction factor of the sub-registry – for men and
women.
108
Figure V.2 Correction of mortality re gistry. Mexico 2000 (Men)
ANEXO A1
Corrección del registro de defunciones. México 2000 (hombres)
los puntos se refieren a las edades promedio 5,10,15,20,30,40,50,60 y 70 [alpha=0.034 beta=1.05]
0.07
0.06
0.05
ny / 0.04
ny
+
0.03
0.02
0.01
0
0
0.02
0.04
0.06
0.08
0.1
0.12
dy+ / ny+
Figure V.3 Correction of mortality registry. Mexico 2000 (Women)
ANEXO A2
Corrección del registro de defunciones. México 2000 (mujeres)
los puntos se refieren a las edades promedio 5,10,15,20,30,40,50,60 y 70
[alpha=0.034 beta=1.09]
0.06
0.05
0.04
ny
/
ny 0.03
+
0.02
0.01
0
0
0.02
0.04
0.06
dy / ny+
109
0.08
0.1
0.12
The sub-registry correction factor (f) is obtained by dividing expected by observed deaths,
and gives us an indication if observed deaths need to be increased. For the estimation of
the sub-registry (f) in the younger age groups, given that the linearity criteria mentioned
previously did not apply, expected deaths was used as a reference, which were obtained by
applying the mortality quotients proposed by the National Population Council (CONAPO)
for the Mexican population in the year of interest. The major deficiency of the report is in
the age group from 1-4 years, both in males and females, and the age groups of 0 years and
5-9 years also showed difficulties. In the adolescent and young adult groups, there is less
discrepancy between expected and observed deaths.
Once the correction of the sub-registry for deaths was finished and the causes considered in
this exercise were identified, we evaluated the proportional distribution by sex and fiveyear age groups. The results by gender and age group for the MCMA and the IMSS
population are displayed in Tables V.4 to V.9.
Table V.4 Total mortality for the MCMA population in 2000
Mortality by all causes (except accidents), MCMA population, 2000
Mexico City
Age group
Male
Female
0
2,212
1,668
1a4
288
215
5a9
77
90
10 a 14
84
68
15 a 19
117
119
20 a 24
206
144
25 a 29
400
206
30 a 34
566
252
35 a 39
722
376
40 a 44
863
554
45 a 49
1,023
765
50 a 54
1,228
1,054
55 a 59
1,511
1,257
60 a 64
1,723
1,669
65 a 69
2,115
2,161
70 a 74
2,241
2,669
75 a 79
2,483
2,850
80+
4,679
7,904
Reference: Mortality DB INEGI/SSA, 2000-2001.
110
Male
State of Mexico
Female
5,135
639
175
149
209
282
501
643
871
1,083
1,329
1,499
1,760
1,955
2,108
2,097
2,257
4,002
3,939
509
139
130
189
264
319
425
575
737
1,005
1,187
1,493
1,778
2,105
2,147
2,220
5,760
Table V.5 Total Mortality for the IMSS, MCMA population in 2000
Mortality by all causes (except accidents), IMSS population in 2000
Mexico City
Age group
Male
Female
0
496
404
1a4
53
53
5a9
31
32
10 a 14
38
24
15 a 19
49
50
20 a 24
77
56
25 a 29
136
78
30 a 34
171
96
35 a 39
238
130
40 a 44
262
236
45 a 49
362
348
50 a 54
502
491
55 a 59
636
585
60 a 64
822
834
65 a 69
1,078
1,125
70 a 74
1,186
1,322
75 a 79
1,250
1,315
80+
2,100
2,944
Reference: Mortality DB INEGI/SSA, 2000-2001.
Male
State of Mexico
Female
833
654
85
69
61
44
43
41
71
59
83
84
151
123
175
156
199
184
270
250
418
372
563
500
678
626
853
785
940
931
968
883
957
843
1,341
1,537
Table V.6 Mortality by cause for the total D.F. population in 2000
Mortality by causes of interest, total population, Mexico City
Cause of death
Group age
AMI
CHF
A. Bronchitis Pneumonia
0
0
5
60
114
1
0
3
3
17
5
0
1
0
1
10
0
1
1
6
15
6
0
0
2
20
8
0
0
9
25
16
1
1
12
30
36
3
0
23
35
62
8
0
30
40
108
7
0
35
45
143
11
0
19
50
196
18
0
20
55
292
16
1
20
60
367
32
2
27
65
506
57
0
51
70
626
69
3
65
75
699
83
4
110
80
2,245
381
34
470
Total
5,310
697
111
1,033
Reference: Mortality DB INEGI/SSA, 2000-2001.
111
Asthma
6
3
0
1
1
3
1
0
2
3
2
1
4
10
3
8
12
28
90
COPD Lung Cancer
14
0
10
0
1
0
1
1
2
0
0
2
2
4
5
3
2
4
12
19
14
16
33
38
57
51
82
58
153
99
240
107
321
104
902
96
1,852
604
Total
200
37
4
11
11
22
37
71
108
185
206
307
442
578
870
1,119
1,334
4,155
9,696
Table V.7 Mortality by cause for the total State of Mexico population in 2000
Mortality by causes of interest, total population, State of Mexico, 2000
Cause of death
Group age
AMI
CHF
A. Bronchitis Pneumonia Asthma
0
0
13
261
355
12
1
0
8
24
52
10
5
0
5
7
2
4
10
0
3
1
9
2
15
1
4
2
14
0
20
7
7
3
17
5
25
15
4
0
19
5
30
28
5
0
24
9
35
62
8
3
21
2
40
97
18
2
29
6
45
156
21
3
25
9
50
180
24
3
28
8
55
240
34
5
29
9
60
344
48
4
49
9
65
407
69
1
38
14
70
422
79
8
75
26
75
458
104
12
79
24
80
1,311
410
48
321
68
Total
3,729
862
388
1,187
220
Reference: Mortality DB INEGI/SSA, 2000-2001.
COPD Lung Cancer
27
0
5
0
0
0
2
0
4
2
1
0
7
0
5
4
13
11
20
15
32
26
39
41
76
52
118
55
220
63
294
62
377
69
1,011
76
2,252
476
Total
668
99
18
18
27
40
50
75
119
188
272
322
445
628
812
965
1,123
3,246
9,115
Table V.8 Mortality by cause for the IMSS population in the D.F. in 2000
Mortality by causes of interest, total population, IMSS, Mexico City, 2000
Cause of death
Group age
AMI
CHF
A. Bronchitis Pneumonia Asthma
0
0
0
5
17
0
1
0
2
0
1
0
5
0
0
0
0
0
10
0
0
0
2
1
15
2
0
0
0
0
20
3
0
0
4
2
25
4
1
0
4
0
30
6
0
0
11
0
35
16
2
0
12
0
40
40
3
0
3
0
45
56
4
0
8
1
50
72
6
0
4
0
55
138
7
0
10
0
60
180
15
1
11
4
65
254
24
0
23
2
70
310
27
1
23
3
75
342
42
2
46
6
80
480
75
3
104
2
Total
1,903
210
12
284
21
Reference: Mortality DB INEGI/SSA, 2000-2001.
112
COPD
1
0
0
0
2
0
0
0
1
3
5
16
27
44
75
146
170
288
778
Lung Cancer Total
0
23
0
3
2
2
0
3
1
5
0
9
3
13
1
18
2
33
6
56
5
79
22
121
29
212
30
285
46
424
62
572
48
657
32
982
291 3,500
Table V.9 Mortality by cause for the IMSS population in the State of Mexico in 2000
Mortality by causes of interest, total population, IMSS, State of Mexico, 2000
Cause of death
Group age
AMI
CHF
A. Bronchitis Pneumonia Asthma
COPD Lung Cancer Total
0
0
2
22
50
0
4
0
78
1
0
2
2
2
1
1
0
8
5
0
0
2
0
0
0
0
2
10
0
1
0
0
0
1
0
2
15
0
1
0
3
0
1
1
6
20
3
3
0
4
1
1
0
13
25
4
2
0
2
1
0
0
9
30
7
1
0
7
4
1
0
21
35
19
2
0
3
0
2
5
31
40
34
2
0
5
0
8
8
58
45
53
8
0
1
1
13
8
85
50
71
8
2
7
2
20
14
125
55
94
11
1
14
3
41
31
194
60
149
20
0
14
4
52
24
264
65
177
24
0
16
2
100
27
347
70
185
23
1
21
6
135
26
398
75
192
21
2
23
4
151
33
427
80
377
75
8
74
13
349
28
924
Total
1,365
208
40
249
43
881
207 2,994
Reference: Mortality DB INEGI/SSA, 2000-2001.
To include this information in our model, we divided the mortality counts for the entire
MCMA (for all cause, cardio-respiratory and lung cancer) by the MCMA population to
provide us with the mortality rate. Data on the IMSS population is used in the YPLL
analysis described later. Therefore for the mortality analysis we are using city wide rates,
and not just the IMSS population.
V.3.3. Medical Attention
To calculate the frequency of utilization of medical attention for the major diseases
associated with environmental air contaminants, we selected a medical attention units
databases from the zones of the IMSS that correspond to the MCMA and obtained the
frequency of hospitalization admissions, emergency room visits and length-of-stay.This
information was collected for each of the federal entities and was grouped into the two
categories of pathologies of the study: cardiovascular diseases and respiratory diseases.
This data was corrected by a factor of 25%, to account for the observed underutilization by
people health care coverage. This underutilization is due to the fact that people receive
IMSS coverage from work, but either choose to use private health care or other family
providers. Tables V.10 - V.12 describe the results of this analysis.
113
Table V.10 Emergency room visits for the IMSS populations in 2000
Emergency room visits by gender, MCMA IMSS, year 2000
Mexico City
State of Mexico
Frequency
Frequency
Disease
Male
Female
Total
Male
Female
AMI*
1,230
654
1,884
693
353
CHF*
1,219
1,778
2,996
904
1,351
Total cardiovascular disease
2,449
2,431
4,880
1,596
1,704
Acute bronchitis
2,555
2,711
5,266
587
625
Pneumonia
1,947
1,713
3,660
680
455
Asthma
6,291
9,185
15,476
5,128
7,464
COPD*
4,830
4,926
9,756
3,991
3,391
Lung cancer
514
368
881
150
83
Total respiratory disease
16,137
18,903
35,040
10,536
12,018
Reference: DB Ambulatory Attention, IMSS, 2000.
Correct by utilization 25%
* Population over 15 years
Total
1,045
2,255
3,300
1,212
1,135
12,591
7,383
233
22,321
Table V.11 Hospital admissions for the IMSS population in the D.F. in 2000
Hospital admissions by gender, MCMA IMSS, year 2000
Mexico City
Frequency
Hospital Days
Disease
Male
Female
Total
Male
Female
AMI*
1,085
516
1,601
10,738
4,911
CHF*
556
748
1304
4,438
6,38
Total cardiovascular disease
1,641
1,264
2,905
15,175
11,293
Acute bronchitis
134
115
249
711,
646
Pneumonia
1,298
1,141
2,439
13,781
11,918
Asthma
875
658
1,533
2,690
2,749
COPD*
959
1,093
2,051
7,861
9,706
Lung cancer
411
234
645
3,005
2,041
Total respiratory disease
3,676
3,240
6,916
28,049
27,060
Reference: DB Hospital Attention, IMSS, 2000.
Ibid.
Total
15,649
10,819
26,468
1,358
25,699
5,439
17,568
5,046
55,109
Table V.12 Hospital admissions for the IMSS population in the State of Mexico in 2000
Hospital admissions by gender, MCMA IMSS, year 2000
State of Mexico
Frequency
Hospital Days
Disease
Male
Female
Total
Male
Female
AMI*
288
126
414
1,936
770
CHF*
229
355
584
1,598
2,683
Total cardiovascular disease
516
481
998
3,534
3,453
Acute bronchitis
56
69
125
261
363
Pneumonia
543
434
976
4,335
3,209
Asthma
315
330
645
1,093
1,324
COPD*
614
621
1,235
4,528
4,470
Lung cancer
43
21
64
326
130
Total respiratory disease
1,570
1,475
3,045
10,543
9,495
Reference: DB Hospital Attention, IMSS, 2000.
114
Total
2,706
4,280
6,986
624
7,544
2,416
8,998
456
20,038
The admittances to emergency and hospitalization services for cardiovascular diseases are
primarily for Congestive Heart Failure (CHF), occurring primarily in women, whereas
utilization due to AMI occurs primarily among men. It is important to clarify that the
differences observed in the two federal entities is due to the fact that the majority of
specialized medical center are geographically located in the Federal District, and they
possibly receive patients referred from the State of Mexico and other areas. With regard to
respiratory diseases, we observe that the utilization of emergency services is primarily for
asthma and then for chronic bronchitis. As for hospitalizations, pneumonia and chronic
bronchitis are the causes with the highest frequency.
To include this frequency data in our model, we divide the frequency of ERVs and
hospitalizations by the IMSS study population as described earlier. This allows us to
understand the rate of medical attention , which is used in the dose response calculation.
These rates can be multiplied by the total population of Mexico city to determine the total
frequency of medical attention and mortality, assuming that the rate for IMSS is
generalizable to the entire MCMA population.
V.4.
Potential Years of Life Lost
There has been widespread debate on the issue of the quantity of life lost per premature
mortality. This concern arises from the need to value health impacts, and the question
about whether we should be putting the same value on saving 1 year of life as saving 10
years of life or more. While the evidence is inconclusive if individuals place more value
on saving lives with more life expectancy than less, we choose to quantify the potential life
years saved per case of avoided premature mortality here, allowing the user to decide later
if it is more appropriate to value life years saved or cases of premature mortality avoided.
In order to calculate potential life years saved due to avoided premature mortality, we must
first construct life tables using the corrected mortality data from section V.3.2.
V.4.1. Life Table Analysis
Before reviewing the methodology for constructing life tables, here we define certain
variables:
Table V.13. Variable definitions for Life Tables Calculations
x
mx
qx
lx
dx
Lx
Tx
ex
ax
w
Age interval
Mortality rates
Probability of death
# of Survivors at the beginning of period X
Decrement Function - # people dying during period x
Stationary Population or Person-Years lived at age x
Person-Years lived at and over age x
Life Expectancy at age x
Fraction of the last year (or period) lived
last age considered (here 85+)
115
To construct life tables, it is first necessary to divide mortality registries in age groups: 0 ,
1-4, 5-9,..., 80+. To achieve this, we use the corrected death registry described in Section
V.3.2, grouped by 5 year age groups. From this information we can calculate mortality rates
(mx ), and mortality quotient (qx ) to achieve the calculation of life expectancy for the
different age groups (ex ).
The definition of specific mortality rates (mx ) in a defined population is expressed as: mx =
Dx / Px , where Dx is the number of corrected deaths in an age group and Px is the
population of that age group.
After calculating the mortality rates (nmx ) for each 5 year age group by sex and IMSS
delegation, we calculate the corresponding mortality quotient (nqx ). To calculate the
quotients from the mortality rates, we use the methodology proposed by Chiang (1993),
that uses the value ax which is the fraction of the five year period that on average the
deceaced lived. For the age group 0 (less than 1 year old) ax, is taken as the desegregation
factor k calculated for the deceased less than 1 year old, from the detailed information of
age at the time of death in hours, days, weeks and months. For the rest of the age groups, ax
is calculated as the weighted mean of the age of death divided by the size of the age interval
considered.
The general equation to obtain (qx ) is:
nmx
qx =
1 + (1- ax ) nmx
Once the mortality quotients have been determined from actual data, a fictitious cohort is
applied. The series lx is the number of survivors at the beginning of each age group. We
apply q0 to a population of 100,000 (l0 = 100,000), and successively reduce the number of
survivors in each age group by applying the probability of mortality (qx ), until the entire
population is extinguished for deaths (dx ) after the group 80+. Given that mortality is a
demographic phenomenon with a intensity of 1, in other words that everyone is exposed to
this risk, for survivors in the last age group (l80 ) we apply a mortality quotient (q80+) of 1.
Where the number of deceased in each age group is found by:
d(x,x+n ) = l x – nq,x
With the series of survivors (lx ) at each age completed, we can calculate the series of
person-years lived by the generation until age x (Lx ). The value Lx is interpreted as the total
years lived by the survivors until year x+1, in addition to a contribution of ax years for
those who died between x and x+1. Continuing with the methodology of Chiang, Lx is
obtained by the following equation:
nLx = n( l x – dx,x+n ) + ax dx, x+n
For the analysis of the group 80+, Lx is calculated by:
116
L 8 0+ = 3.725 * l80+ + 0.000062 5 * (l80+)2
Or if the group were 85+, the calculation would be:
L85+ = l85+ * log(l85+)
The log function expresses the notion of the progressive deterioration of human beings,
clearly manifested in age groups where the mortality quotient are increasing. This is
particularly valid for age groups where the predominant cause of mortality is due to old
age.
We also calculate the probability of surviving age x and to arrive at the age x+n. The is
calculated in the function Sx from Lx , by:
S x = L x, x + n
Lx
We also calculated the accumulated years lived (Tx ), which is the total number of years left
to live by survivors lx from the xth year, until the complete extinction of the fictitious
generation. The value T0 is the total number of life years for the generation from birth until
death of the last survivor and is calculated by:
Tx = Lx + Lx+1 +… + Lù-1
The last calculation of the life table is that of life expectancy (ex ), that represents the
median number of life years left for survivors at age X. The life expectancy at birth (e0 ), is
the most used indicator of the life table and refers to the median number of years a
generation of newborns will live.
Life expectancy (ex ) is calculated as follows
ex = Tx / lx
Tables V.14 and V.15 summarize the results for our life table calculations for the Federal
District and the State of Mexico respectively.
117
Table V.14. Abridged IMSS Mexico City Life Table for both sexes from 2000
(Zones IMSS 35, 36, 37 y 38)
nA(x,
Age
nmx
nq x
l(x,x+n)
d(x,x+n)
nL(x, x+n)
nS(x, x+n)
nT(x, x+n)
ex
0
1
5
10
15
20
25
30
35
40
45
50
0.01296112
0.00039102
0.00012777
0.00015102
0.00054142
0.00042856
0.00046511
0.00054364
0.00076213
0.00119505
0.00191936
0.00306231
0.0128165
0.00156259
0.00063866
0.00075484
0.00270391
0.00214058
0.00232281
0.00271459
0.00380416
0.00595798
0.00955276
0.01520277
100000
98718
98564
98501
98427
98161
97951
97723
97458
97087
96509
95587
1282
154
63
74
266
210
228
265
371
578
922
1453
98884
394503
492686
492346
491558
490290
489179
487969
486456
484033
480328
474537
0.99738635
0.99910229
0.99930844
0.99839991
0.99742119
0.99773309
0.99752649
0.99689972
0.99502012
0.99234512
0.98794304
0.98189042
7888894
7790009
7395506
6902820
6410474
5918917
5428626
4939448
4451479
3965023
3480990
3000662
78.9
78.9
75.0
70.1
65.1
60.3
55.4
50.5
45.7
40.8
36.1
31.4
0.13
0.40
0.57
0.57
0.57
0.51
0.50
0.51
0.55
0.52
0.52
0.53
55
60
65
70
75
80 +
0.00433285
0.00602409
0.00839685
0.01187124
0.0158282
0.01957057
0.02144681
0.02970431
0.04115153
0.05764708
0.07608184
1
94133
92115
89378
85700
80760
74616
2019
2736
3678
4940
6144
74616
465943
454210
438028
416163
388191
363589
0.9748184
0.96437369
0.95008358
0.93278537
0.93662379
2526125
2060182
1605972
1167944
751780
363589
26.8
22.4
18.0
13.6
9.3
4.9
0.53
0.53
0.52
0.50
0.49
0.12
x+n)
Table V.15 Abridged IMSS State of Mexico Life Table for both sexes from 2000
(Zones IMSS 15 y 16)
Age
nmx
nq x
l(x,x+n)
d(x,x+n)
nL(x, x+n)
nS(x, x+n)
nT(x, x+n)
ex
0
1
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80 +
0.02350367
0.00084216
0.00033934
0.00037086
0.00119943
0.0009829
0.00107853
0.00138097
0.00174134
0.00263988
0.0045406
0.00716824
0.01049464
0.01519356
0.02111308
0.02335173
0.03410986
0.0351982
0.02303331
0.00336168
0.00169529
0.0018527
0.00598043
0.0049038
0.00537813
0.00688158
0.00867196
0.01311851
0.02245614
0.03525767
0.05115774
0.07323356
0.10041848
0.11029742
0.15669044
1
100000
97697
97368
97203
97023
96442
95969
95454
94797
93975
92742
90659
87463
82989
76911
69188
61556
51911
2303
328
165
180
580
473
516
657
822
1233
2083
3196
4474
6078
7723
7631
9645
51911
97999
389979
486435
485597
483762
481163
478560
475659
472092
466994
458668
445917
426351
400008
365806
326795
282772
244775
3.97942314
1.24733762
0.99827794
0.9962192
0.99462836
0.99458972
0.99393787
0.99250186
0.98920057
0.98217086
0.97220052
0.95612304
0.93821178
0.91449626
0.89335649
0.86528861
0.86562583
7269330
7171332
6781353
6294918
5809320
5325559
4844396
4365836
3890177
3418085
2951091
2492424
2046507
1620155
1220147
854342
527547
244775
72.7
73.4
69.6
64.8
59.9
55.2
50.5
45.7
41.0
36.4
31.8
27.5
23.4
19.5
15.9
12.3
8.6
4.7
118
nA(x,
x+n)
0.13
0.39
0.51
0.54
0.53
0.56
0.50
0.51
0.54
0.53
0.52
0.54
0.51
0.51
0.51
0.50
0.48
0.11
V.4.2. Years of Potential Life Lost (YPLL) for Mortality due to Chronic Exposure
Using the results on life expectancy from the life tables, we are able to calculate the total
YPLL using the following expression:
x=w
∑d
x ex
x =0
Where ex is the life expectancy at each age, and dx is the number of deaths at each age. To
find the life years lost for each age group, you must only multiply ex by dx . This is done for
mortality due to the specific set of diseases described in section V.3.2. Using results from
each age group we are able to calculate the average number of YPLL for a given disease.
Results from this calculation are presented in Tables V.16 and V.17 for the Federal District
and the State of Mexico respectively.
Table V.16. YPLL by cause for the IMSS population in the Federal District (2000)
Years of potential life lost (YPLL) by causes of interest, total population, IMSS, Mexico City, 2000
YPLL by cause of death según causa de muerte
Group age
AMI
CHF
A. Bronchitis Pneumonia Asthma
COPD
Lung Cancer Total
0
0
0
411
1,313
0
82
0 1,805
1
0
161
0
81
0
0
0
242
5
0
0
0
0
0
0
150
150
10
0
0
0
148
73
0
0
221
15
133
0
0
0
0
133
65
331
20
190
0
0
251
126
0
0
567
25
234
58
0
234
0
0
175
701
30
320
0
0
533
0
0
54
906
35
720
96
0
526
0
48
95 1,486
40
1,638
125
0
125
0
126
251 2,265
45
2,022
147
0
294
37
183
184 2,866
50
2,260
191
0
127
0
509
700 3,788
55
3,696
190
0
272
0
734
788 5,680
60
4,034
340
23
249
91
975
680 6,392
65
4,570
435
0
417
36
1,342
834 7,635
70
4,215
371
14
316
41
1,990
837 7,784
75
3,176
395
19
432
56
1,579
451 6,109
80
2,350
370
15
512
10
1,413
158 4,827
Total
29,558
2,880
481
5,830
470
9,115
5,422 48,333
Mean
16
14
39
21
22
12
19
14
Reference: Mortality DB INEGI/SSA, 2000-2001.
119
Table V.16. YPLL by cause for the IMSS population in the State of Mexico (2000)
Years of potential life lost (YPLL) by causes of interest, total population, IMSS, State of Mexico, 2000
YPLL by cause of death según causa de muerte
Group age
AMI
CHF
A. Bronchitis Pneumonia Asthma
COPD
Lung Cancer Total
0
0
155
1,554
3,550
0
313
0 5,572
1
0
153
151
153
76
76
0
609
5
0
0
165
0
0
0
0
165
10
0
68
0
0
0
67
0
135
15
0
63
0
189
0
63
63
315
20
172
171
0
229
57
57
0
686
25
208
104
0
104
52
0
0
468
30
336
48
0
333
189
48
0
955
35
745
84
0
131
0
87
218 1,047
40
1,206
73
0
189
0
300
298 1,767
45
1,647
256
0
32
32
417
259 2,385
50
1,921
219
55
192
55
548
384 2,989
55
2,153
258
23
328
70
936
702 3,768
60
2,879
392
0
274
78
999
470 4,622
65
2,780
381
0
254
32
1,572
429 5,019
70
2,254
283
12
259
74
1,650
320 4,532
75
1,634
181
17
198
34
1,281
283 3,345
80
1,773
352
38
347
62
1,638
133 4,210
Total
19,705
3,242
2,016
6,760
812
10,053
3,558 42,588
Mean
14
16
50
27
19
11
17
14
Reference: Mortality DB INEGI/SSA, 2000-2001.
We include this information in our analysis by assuming that mortality due to chronic
exposure is due to one of the above causes. Without any prior knowledge of the
distribution of these deaths, we include a range of life years lost for chronic mortality using
the above numbers with a triangular distribution of 10, 15 and 23.
V.4.3. Years of Potential Life Lost (YPLL) for Mortality due to Acute Exposure
For the case of mortality due to acute exposure, there is little known about the actual
number of years that mortality due to air pollution advances death. There is some evidence
that only very sick individuals are effected by air pollution episodes, thereby advancing
death by only a couple of months (Schwartz, 2000). On the other hand, evidence from the
recent cohort studies appears to indicate that air pollution can cause mortality to occur even
more prematurely.
To determine the number of years lost to premature mortality from short term air pollution
fluctuations, the methodology used by Carrothers (2000) was adopted for this analysis. In
his study an upper bound was chosen that accounted for the possibility that some persons of
average life-expectancy may be affected by air pollution episodes and estimated that fourfifths of those dying from short term exposures to air pollution have life-expectancies
equivalent to a patient coronary heart disease, using Kuntz's CHD cohort, and one-fifth
have average life expectancy; thus yielding a value of 6.5 years. For a middle estimate, he
assumed a life expectancy of people suffering from coronary heart failure, estimated at 2.5
120
years. Finally, since it is possible that people dying from air pollution only had a few
months of life left, a plausible lower-bound was estimated at 0.5 years.
V.5.
Discounting
When evaluating the avoided mortality impacts due to reductions in air pollution, itis
important to take into account the value of time. In the case of mortality due to chronic
exposure to air pollution, it is commonly assumed that there is a latency of the effect, or
that mortality will occur a number of years after the exposure. This is not the case,
however, with mortality due to acute exposure, as it is assumed that these deaths are
occurring within days of exposure.
The timing of health benefits is important because people exhibit a non- zero real rate of
time preference for both money and health, and would therefore value reduced risk today
more than risk reduced a year from now. There are a number of ethical reasons why people
would prefer to reduce their risks today rather than in the future beyond the time value of
money, including such considerations as uncertainty about the future, ideas of techno logical
progress and attitudes towards living in the present (Cropper et al., 1994). Furthermore,
when deciding about controls that will have an effect in the future, we typically discount
costs incurred in the future to present values. The same must be done with health benefits
in order to compare the costs to the benefits, otherwise it will always look more
advantageous to save lives in the future rather then today. We must therefore account for
this latency in effect by discounting the health effects occurring in the future relative to
today.
It is important therefore that we account for the fact that the avoided mortalities occur a
number of years after exposure. To account for the value of time for future benefits, the
number of lives saved in the future were discounted to the present using the following
equation:
dl = d × (1 + r )−l
Where dl is the number of discounted lives saved, d is the number of lives saved, l is
latency of the mortality effect and r is the social discount rate. Latency of health effects
range from 10 to 20 years for risks from smoking and up to 40 years for cancer outcomes.
Standard cost effectiveness analysis uses a range of 5 to 15 years, but even longer periods
could be plausible, based on the general pattern of chronic diseases such as COPD and
heart disease (Carrothers, 2000). For the purposes of this analysis, we adopted the
assumptions of Carrothers (2000), where he modeled the average latency period for
mortality from chronic exposure as ten years with an upper- and lower-bound of twenty and
five years, respectively.
V.5. Results
Here we present the results for the reductions in health impact for the 5 measures. We are
able to distinguish not only the magnitude of the health impacts, but from which pollutant
the impacts are derived. In general we find that the Taxi measure has the greatest health
121
impacts and that most of these benefits result from ozone reductions and secondary PM10 .
The Metro expansion measure results in significant health benefits, also due mostly to
ozone and secondary PM, most of which appears in the time horizon 2003-2020 as most of
the construction occurs after the year 2010. The measure of Hybrid buses, which reduces
mostly primary particulates results in significant mortality and bronchitis benefits. The
measure for reducing LPG leaks results in significant ozone reductions and therefore
impacts acute mortality and respiratory morbidity. Finally, since the measure of Cogeneration reduces electricity generation outside the valley and brings fuel consumption
into the city, it does not provide substantial health benefits.
The following 10 tables summarize the results of the health impacts analysis and describe
the number of cases of morbidity and mortality effects that the controls could prevent
described by the expected value and the range of possibilities in the 95% confidence
interval. These results are found using a discount rate of 5%.
122
Table V.17 Avoided mortality and morbidity cases for the measure of
Taxi Renovation 2003-2010
Mean
1.1 Acute Mortality
Total mortality
Infant mortality
12 Chronic Mortality
Total
Cardio-respiratory
Lung Cancer
1.3 Chronic Bronchitis
1.4 Hospital Admissions
All respiratory
COPD
All cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
Pneumonia
Asthma
1.5 Emergency Room Visits (ERVs)
Respiratory Causes
Asthma
1.6 Restricted Activity Days
1.7 Minor Restricted Activity Days
1.8 School Absenteeism
123
95% CI
57
29
(26:102)
(10:59)
6
1
7
448
(2:13)
(0:1)
(2:13)
(210:787)
223
38
1
1
0
49
21
(43:567)
(9:86)
(0:1)
(0:1)
(0:1)
(15:103)
(6:43)
1,065
990
13,326
495,076
218,384
(352:2,129)
(335:1,955)
(4,654:27,050)
(176,731:1,106,344)
(96,752:376,902)
Table V.18 Avoided mortality and morbidity cases for the measure of
Taxi Renovation 2003-2020
Mean
1.1 Acute Mortality
Total mortality
Infant mortality
12 Chronic Mortality
Total
Cardio-respiratory
Lung Cancer
1.3 Chronic Bronchitis
1.4 Hospital Admissions
All respiratory
COPD
All cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
Pneumonia
Asthma
1.5 Emergency Room Visits (ERVs)
Respiratory Causes
Asthma
1.6 Restricted Activity Days
1.7 Minor Restricted Activity Days
1.8 School Absenteeism
124
95% CI
36
19
(16:63)
(7:39)
4
0
4
295
(2:8)
(0:1)
(2:9)
(147:474)
134
22
0
0
0
29
12
(26:366)
(5:49)
(0:1)
(0:1)
(0:1)
(9:59)
(4:27)
632
583
8,908
296,928
132,439
(211:1,236)
(203:1,132)
(3,323:17,859)
(113,153:599,831)
(61,258:227,615)
Table V.19 Avoided mortality and morbidity cases for the measure of
Metro Expansion 2003-2010
Mean
1.1 Acute Mortality
Total mortality
Infant mortality
12 Chronic Mortality
Total
Cardio-respiratory
Lung Cancer
1.3 Chronic Bronchitis
1.4 Hospital Admissions
All respiratory
COPD
All cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
Pneumonia
Asthma
1.5 Emergency Room Visits (ERVs)
Respiratory Causes
Asthma
1.6 Restricted Activity Days
1.7 Minor Restricted Activity Days
1.8 School Absenteeism
125
95% CI
2
1
(1:3)
(0:2)
0
0
0
16
(0:0)
(0:0)
(0:0)
(9:25)
6
1
0
0
0
1
1
(1:15)
(0:2)
(0:0)
(0:0)
(0:0)
(0:3)
(0:1)
30
28
476
14,660
6,458
(11:59)
(10:54)
(207:886)
(5,922:30,866)
(3,150:11,034)
Table V.20 Avoided mortality and morbidity cases for the measure of
Metro Expansion 2003-2020
Mean
1.1 Acute Mortality
Total mortality
Infant mortality
12 Chronic Mortality
Total
Cardio-respiratory
Lung Cancer
1.3 Chronic Bronchitis
1.4 Hospital Admissions
All respiratory
COPD
All cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
Pneumonia
Asthma
1.5 Emergency Room Visits (ERVs)
Respiratory Causes
Asthma
1.6 Restricted Activity Days
1.7 Minor Restricted Activity Days
1.8 School Absenteeism
126
95% CI
15
10
(8:25)
(4:18)
20
0
2
152
(1:4)
(0:0)
(1:4)
(83:241)
49
8
0
0
0
10
5
(11:125)
(2:19)
(0:0)
(0:0)
(0:0)
(3:21)
(2:9)
232
215
4,584
119,279
52,346
(86:457)
(77:416)
(1,951:8,459)
(50,667:232,700)
(26,219:85,139)
Table V.21 Avoided mortality and morbidity cases for the measure of
Hybrid Buses 2003-2010
Mean
1.1 Acute Mortality
Total mortality
Infant mortality
12 Chronic Mortality
Total
Cardio-respiratory
Lung Cancer
1.3 Chronic Bronchitis
1.4 Hospital Admissions
All respiratory
COPD
All cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
Pneumonia
Asthma
1.5 Emergency Room Visits (ERVs)
Respiratory Causes
Asthma
1.6 Restricted Activity Days
1.7 Minor Restricted Activity Days
1.8 School Absenteeism
127
95% CI
9
11
(4:17)
(3:26)
2
0
2
171
(1:5)
(0:0)
(1:5)
(68:320)
1
0
0
0
0
0
1
(0:3)
(0:0)
(0:0)
(0:0)
(0:0)
(0:0)
(0:2)
17
14
5,103
44,611
17,303
(0:45)
(0:42)
(1,106:11,430)
(16,295:81,640)
(5,657:33,909)
Table V.22 Avoided mortality and morbidity cases for the measure of
Hybrid Buses 2003-2020
Mean
1.1 Acute Mortality
Total mortality
Infant mortality
12 Chronic Mortality
Total
Cardio-respiratory
Lung Cancer
1.3 Chronic Bronchitis
1.4 Hospital Admissions
All respiratory
COPD
All cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
Pneumonia
Asthma
1.5 Emergency Room Visits (ERVs)
Respiratory Causes
Asthma
1.6 Restricted Activity Days
1.7 Minor Restricted Activity Days
1.8 School Absenteeism
128
95% CI
10
12
(4:19)
(3:25)
3
0
3
184
(1:5)
(0:0)
(1:6)
(75:336)
1
0
0
0
0
0
1
(-1:4)
(0:0)
(0:0)
(0:0)
(0:0)
(0:0)
(0:3)
19
15
5,575
48,591
18,814
(-4:49)
(-7:43)
(1,182:12,761)
(18,046:88,364)
(6,527:36,804)
Table V.23 Avoided mortality and morbidity cases for the measure of
Combined LPG Leakage Reduction 2003-2010
Mean
1.1 Acute Mortality
Total mortality
Infant mortality
12 Chronic Mortality
Total
Cardio-respiratory
Lung Cancer
1.3 Chronic Bronchitis
1.4 Hospital Admissions
All respiratory
COPD
All cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
Pneumonia
Asthma
1.5 Emergency Room Visits (ERVs)
Respiratory Causes
Asthma
1.6 Restricted Activity Days
1.7 Minor Restricted Activity Days
1.8 School Absenteeism
129
95% CI
11
6
(4:19)
(1:15)
1
0
1
89
(0:3)
(0:0)
(0:3)
(26:180)
39
7
0
0
0
9
4
(8:96)
(2:15)
(0:0)
(0:0)
(0:0)
(3:18)
(1:7)
190
176
2,663
90,682
39,723
(64:388)
(60:344)
(431:7,100)
(27,960:199,169)
(16,567:69,314)
Table V.24 Avoided mortality and morbidity cases for the measure of
Combined LPG Leakage Reduction 2003-2020
Mean
1.1 Acute Mortality
Total mortality
Infant mortality
12 Chronic Mortality
Total
Cardio-respiratory
Lung Cancer
1.3 Chronic Bronchitis
1.4 Hospital Admissions
All respiratory
COPD
All cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
Pneumonia
Asthma
1.5 Emergency Room Visits (ERVs)
Respiratory Causes
Asthma
1.6 Restricted Activity Days
1.7 Minor Restricted Activity Days
1.8 School Absenteeism
130
95% CI
9
5
(3:16)
(1:12)
1
0
1
76
(0:3)
(0:0)
(0:3)
(22:155)
33
5
0
0
0
7
3
(7:86)
(1:12)
(0:0)
(0:0)
(0:0)
(2:15)
(1:6)
154
144
2,320
73,350
32,756
(53:303)
(48:287)
(332:6,312)
(24,530:154,535)
(3,624:58,811)
Table V.25 Avoided mortality and morbidity cases for the measure of
Cogeneration 2003-2010
Mean
1.1 Acute Mortality
Total mortality
Infant mortality
12 Chronic Mortality
Total
Cardio-respiratory
Lung Cancer
1.3 Chronic Bronchitis
1.4 Hospital Admissions
All respiratory
COPD
All cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
Pneumonia
Asthma
1.5 Emergency Room Visits (ERVs)
Respiratory Causes
Asthma
1.6 Restricted Activity Days
1.7 Minor Restricted Activity Days
1.8 School Absenteeism
131
95% CI
1
0
(0:1)
(0:1)
0
0
0
4
(0:0)
(0:0)
(0:0)
(1:8)
2
0
0
0
0
1
0
(0:6)
(0:1)
(0:0)
(0:0)
(0:0)
(0:1)
(0:0)
12
11
123
5,207
2,336
(4:24)
(4:21)
(18:325)
(1,661:11,734)
(979:4,105)
Table V.26 Avoided mortality and morbidity cases for the measure of
Cogeneration 2003-2020
Mean
1.1 Acute Mortality
Total mortality
Infant mortality
12 Chronic Mortality
Total
Cardio-respiratory
Lung Cancer
1.3 Chronic Bronchitis
1.4 Hospital Admissions
All respiratory
COPD
All cardiovascular
Congestive Heart Failure
Ischemic Heart Disease
Pneumonia
Asthma
1.5 Emergency Room Visits (ERVs)
Respiratory Causes
Asthma
1.6 Restricted Activity Days
1.7 Minor Restricted Activity Days
1.8 School Absenteeism
95% CI
11
6
(4:19)
(1:15)
1
0
1
89
(0:3)
(0:0)
(0:3)
(26:180)
39
7
0
0
0
9
4
(8:96)
(2:15)
(0:0)
(0:0)
(0:0)
(3:18)
(1:7)
190
176
2,663
90,682
39,723
(64:388)
(60:344)
(431:7,100)
(27,960:199,169)
(16,567:69,314)
An interesting result from our analysis is that we are able to distinguish the health impacts
by the pollutant causing the effect. These results will change depending on the measure and
the health outcome of interest. For instance, if a particular measure is specifically targeted
at primary PM10 reductions, we can expect that the majority of the health impacts will be
from that pollutant. However, the results depend not only on the magnitude of the
concentration reduction, but also of the magnitude of the dose response values.
With the following graphs, we disaggregate the annualized impacts for 2003-2020 in order
to determine, for this set of measures, from where the greatest impact comes. We sum the
impacts across the 5 measures and calculate the percentage of cases caused by each
pollutant for acute and chronic mortality, cardiovascular and respiratory hospitalizations
and emergency room visits and restricted activity. Figure V.4 shows that approximately
half of the acute mortality effect for these 5 measures combined is due to reductions in
ozone, whereas primary PM accounts for 12% and secondary particulates for the remaining
39%. These results derive from the fact that most of the measures have most significant
impacts on ozone concentrations, and we gain the additional benefit from these measures in
secondary PM reductions. The effects of primary PM reductions come from the Hybrid
Bus measure, whereas the majority of the organic PM effects come from the Taxis and LPG
measures. Most of the secondary sulfate and nitrate impact is a result of the Taxi measure
as well.
132
Figure V.4 Percentage of mortality due to acute exposure by pollutant, 2003-2020
16%
43%
15%
PM10 Primary
PM10 Secondary Organic
PM10 Secondary Sulfate
PM10 Secondary Nitrate
Ozone
8%
18%
Figure V.5 shows the results when looking at the effect of mortality due to chronic
exposure to pollution. Given that there is only evidence for the effects of particulates, all of
the impacts are due to reductions in PM10 . We find that 69% of the effect is due to
secondary nitrate and organic particulate matter. This is driven primarily by the Ta xi and
Metro measures. The impact of primary particles is derived from the hybrid bus measure
alone. This pattern is repeated with the impacts of cardiovascular hospital admissions as
our evidence associates it only with exposure to particulate matter.
133
Figure V.5 Percentage of mortality due to chronic exposure by pollutant, 2003-2020
0%
28%
31%
PM10 Primary
PM10 Secondary Organic
PM10 Secondary Sulfate
PM10 Secondary Nitrate
Ozone
14%
27%
When conducting the same analysis with respiratory hospital admissions, we find that
nearly 90% of the effect results from ozone reductions. In the case for minor restricted
activity days, this is reduced to approximately 70%, with the rest distributed nearly equally
across secondary sulfates, nitrates and primary particulates. It should be stressed that these
results shift between measures. These results may therefo re be biased towards results from
the Taxi and Metro measures which primarily reduce ozone, but also have an additional
secondary particulate effect and the Hybrid Bus measure which mainly reduces particulates.
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138
Chapter VI. Valuation
VI.1. Introduction
The comparison of the costs to the benefits of air pollution control strategies requires that a
common metric be attached to the health outcomes. This can be accomplished by various
means, including economic valuation, medical-based metrics (such as Quality Adjusted
Life Years or Disability Adjusted Life Years) or indicator metrics as used in life-cycle
impact assessment (Levy and Spengler, 2002). For our analysis we have chosen to
evaluate both the economic benefits of the measures we are analyzing and the QALYs to
give us an alternative valuation metric. Here, we discuss key concepts for these two
methods, as well as the methods used and data necessary to complete the analysis. There
exists a considerable amount of uncertainty in the valuation component of this analysis.
We discuss some of the sources of uncertainty and how we characterize it.
VI.2. Economic Valuation
The economic burden of environmental damage can come in various forms. It can come in
the form of damage to human health, biodiversity, visibility etc. To evaluate these damages
in economic terms however, one must be able to put a dollar value on each of these goods.
There are a number of approaches to valuing environmental damage, which can range from
direct costs to society such as cost of illnesses for human health impacts to analyzing the
amount an individual would be willing to pay to improve the environment. When
evaluating the benefits in terms of risk reduction for morbidity and mortality, there are 3
common methodologies for calculating benefits. These include direct economic costs such
as the cost of illness and productivity loss as well as non- marketable costs as represented by
willingness-to-pay values.
Here, we discuss the main principles behind these three
valuation methods and how we quantified them for this study.
Monetary benefits are calculated by placing a monetary value on each case and summing
the benefits across outcomes in the following form:
i
j
HBT ($ / a ) = ∑ (Vi ($ / case) ×∑ ( H ij( cases/ a ) ) )
Equation VI.1
Where Hij is the number of cases of the ith health effect (deaths, hospitalizations, incidence
of chronic bronchitis etc.) per year due to the j th pollutant and the Vi is the unit social cost of
the ith effect. Thus we are summing across pollutants, placing a common dollar metric on
the outcome, and then summing across outcomes. The results of this calculation will be the
total dollar benefits from reductions in air pollution for a given measure.
139
VI.2.1. Direct Costs
The cost of illness (COI) for morbidity outcomes can be understood as direct costs of an
illness and can include expenditures on medications, doctors visits, hospitalizations,
laboratory tests and human resource costs. This metric aims to include all incurred costs
for an illness or medical attention. These costs can be paid directly by the sick individual,
through public or private insurance and/or general taxation. In this analysis, we include
costs to the health care providers as well as individual costs, as estimated by health experts.
Prior to estimating the direct costs per unit of medical attention for each disease, an
information collection stage was required in order to know: a) the actions taken by the
patients in the three levels of attention for diagnosis and treatment, b) the different services
available to people who go to medical attention units, c) human resources (in hours per type
of profession), and materials and medications (in physical units) that are utilized at each
event. These costs were summed for two events: 1) Emergency Room Attention, and 2)
Hospital Attention.
The estimation of the total cost of medical attention at the institutional level was
determined on the basis of the costs per unit, the integration of costs per event, and the
integration of the annual cost per patient for each of the diseases under examination. A cost
analysis of disease based on health provider (IMSS) perspective was also conducted. A
nominal group technique was utilized to define Therapeutic Diagnostic Guidelines (TDG)
and two “Typical Cases” for each disease. The unit costs were estimated taking into
account the components of fixed costs and variables that confronted the provider for the
year 2001.
The costs estimated by event and by disease are shown in Table VI.1. Given that level III
medical attention (the highest possible level) was considered, for some diseases, as in the
case of AMI and CHF, total costs include the hospital costs, costs for intensive care unit
attention, plus the attention in the room.
Table VI.1 COI per case for IMSS health care in 2001
Costs of medical attention by cause, IMSS, year 2001
Type of medical attention
Emergency Visit
ICU
Hospital Admission
$3,538.00
$65,640.00
$19,172.00
$3,538.00
$91,896.00
$41,083.00
$2,502.83
$15,075.00
$2,526.78
$21,105.00
$3,173.45
$6,030.00
$2,560.00
$132,230.00
$45,238.00
$2,978.00
$18,488.00
Disease
AMI*
CHF*
Acute Bronquitis
Pneumonia
Asthma
COPD*
Lung Cancer
* Population over 15 years
Medical costs were estimated on the level III
140
VI.2.2. Productivity Loss
Productivity loss (PL) uses the difference in output (production) due to illnesses as the
basis of valuing costs. This value can also be interpreted as the cost of time, or the value of
the time an individual loses from being in the hospital, in bed or from premature mortality.
While the cost of time would ideally include the opportunity cost of leisure (Cesar et al.,
2002), this type of information was not available to us at the time of analysis.
Loss of productivity can also be calculated for environmental contingency episodes, when
industries must temporarily close their operations due to air pollution levels. This has not
been taken into consideration in our calculation of productivity loss, as we are not
estimating environmental contingencies in the air quality modeling. It should be noted,
therefore, that the productivity loss due to air pollution could be significantly larger than
what we are estimating.
To calculate the productivity loss per mortality case or medical attention, we multiply the
days lost per case by the average daily wage. For medical attention, we obtained average
hospital stays from the IMSS database as described in Table VI.2. It should be noted that
these are only for days spent in the hospital. It is reasonable to assume that individuals
would miss more days of work beyond their stay in the hospital, thereby increasing the
productivity loss. This, however, was not estimated here, and therefore we have probably
under-estimated work loss days for hospital attention.
Table VI.2 Average length of stay for medical care in IMSS
Average Hospital Stays
Mean ICU Days
AMI
CHF
COPD
Mean Hospital Days
AMI CHF
CHF
Acute Bronchitis
Pneumonia
Asthma
COPD
Lung Cancer
5
7
10
7
15
5
7
2
15
5
To calculate productivity loss for premature mortality, we use the results from the life years
loss analysis for chronic and acute mortality and assume that for each year lost, 260 work
days are lost. Data on average daily salaries was obtained from INEGI’s Survey of
Household Salaries and Spending (ENIGH). From this information we established 3
income scenarios, as described by Table VI.3.
141
Table VI.3 Average mexican salaries in 2000
Scenario
Low
Medium
High
Daily Salary
(Pesos)
467
2,491
5,178
% of the
population
6
30
64
These salaries are for the year 2000. We do not take into account the growth in salary to
our 2003 base-year for valuation, which could increase our estimates of productivity loss.
VI.2.3. Willingness-to-Pay
One of the most common methods for valuing environmental and health impacts is to
determine an individual’s willingness to pay (WTP) for risk reduction. WTP can be
determined from contingent valuation and compensating wage studies, which in theory
should account for the full cost of disease to an individual including pain and suffering.
Contingent valuation studies rely on an individual’s stated preferences through the use of
surveys, in which individuals are asked how much they would be willing to pay to reduce
their risk of mortality or morbidity. Hedonic wage studies, on the other hand, rely on
revealed preferences through the analysis of data on the labor market. Here information on
wages and risk levels in certain jobs are obtained and econometric models are used to
determine the amount individua ls are compensated for additional risk in the workplace.
These methods for estimating individual preferences rely on the concept of consumer
sovereignty and people’s ability to make rational tradeoffs. In theory, values determined
from either of these studies would incorporate all the costs to the individual, including
medical costs, income loss as well as pain and suffering. We should not, however, expect
an individual to incorporate costs he won’t have to pay in his valuation. In theory
“external” costs (e.g. publicly provided health care) should be added to WTP. The results
from these studies describe the value individuals place on a unit change in risk. The
individual WTP divided by the unit risk yields the value of a statistical life (VSL) or
morbidity case for the population.
There have been numerous studies on willingness to pay to avoid risk of adverse health
outcomes including premature mortality, chronic bronchitis, and respiratory symptoms in
the U.S. and a few other countries. Results from these studies could be extrapolated to
Mexican conditions by adjusting for income differences. There is, however, concern over
the relationship between the value of health and economic and cultural factors between the
different countries. These concerns prompted a study to be conducted in Mexico City by
Ibarrarán et al. (2002), which involved both contingent valuation and hedonic wage
components to estimate WTP for reductions in risks of mortality, chronic bronchitis and a
cold.
In this study we use results from contingent valuation studies in the U.S. and Mexico. We
place greater weight on the evidence from Mexico. Since we cannot base our results on a
142
single study, we have included a range of WTP estimates, using results from the metaanalysis of the EPA’s Benefit and Cost of the Clean Air Act (1999). We have adjusted the
central estimates from this report to Mexican income using the following equation:
VMexico
 GNPMexico 

= VUSA × 
 GNPUSA 
ε
Equation VI.2
Where ε is the income elasticity of demand for health. This can be translated as the
proportional change in a persons demand for health, as described by their willingness to
pay for risk reduction, associated with a proportional change in per capita income. An
elasticity between zero and one indicates that demand is relatively insensitive to income,
whereas a value greater then one would indicate that health is considered a luxury item.
Income elasticities can be derived from hedonic wage or contingent valuation studies, or by
comparing values between studies, where populations differ in income, risk and other
factors. Additionally, elasticities can be found by analyzing how WTPs change through
time as income changes.
Findings from WTP studies estimate income elasticities from 0.2 to greater than two
(Bowland and Beghin, 2001, Alberini et al., 1997). Comparing results from the Mexican
WTP study to the central estimates from the U.S. yields an income elasticity of about 1,
indicating that the differences in VSL between Mexico and the U.S. is nearly proportional
to income differences. From the hedonic wage study, however, the individual willingness
to pay was less then proportional to individual income, with an elasticity of 0.68. In this
study we use elasticities of 2 and 0.3 to adjust the U.S. estimates. The results for three
outcomes are summarized in Table VI.4.
Table VI.4 WTP estimates for Mexico
Health Effect
Mortality
Chronic bronchitis
MRAD
1. å is the elasticity of VSL
2. For a minor illness (cold)
Value per Statistical Case (US$)
Lower Estimate
Central Estimate
(å1 = 2)
(Ibarrarán, 2002)
$81,120
$506,000
$4,394
$28,000
0
$202
Upper Estimate
(å =0 .3)
$2,600,000
$140,980
$30
For the medical attention outcomes we use the WTP from Cesar et al. (2002) in which they
assumed medical attention would be valued as ‘casualty’ in the definition by CSERGE et
al. (1999). Using this approach we assumed all hospitalizations to have a WTP of $432 and
ERV a value of $223. Table VI.5 summarizes the values we use per each case of mortality
or morbidity for the three methods.
143
Table VI.5 Health values for each outcome (US$/case)
COI
Productivity
Loss1
WTP
(Central
estimate)
WTP
IC 95%
1.1 Acute Mortality
Total mortality
9,005
506,000
81,120 – 2,600,000
Infant mortality
212,400
1.2 Chronic Mort ality
Total
45,420
Cardio-respiratory
45,420
Lung Cancer
45,420
2
1.3 Chronic Bronchitis
17,750
80.9
30,000
4394 – 141,000
1.4 Hospital admissions
All Respiratory
2,186
115.6
330
154 – 550
Asthma
603
23.1
330
154 – 550
COPD
17,7503
173.4
330
154 – 550
Pneumonia
2,111
92.5
330
154 – 550
All Cardiovascular
10,890
127.1
330
154 – 550
Congestive Heart Failure
13,3003
173.4
330
154 – 550
Ischemic Heart Disease
8,4813
80.9
330
154 – 550
1.5. Emergency room visits (ERVs)
Respiratory Causes
269
57.8
170
79 – 284
Asthma
317
23.1
170
79 – 284
1.6. Restricted Activity Days
11.64
20
0 – 28
1.7 Minor Restricted Activity Days
5.84
20
0 – 28
4
1.8 School Absenteeism
11.6
20
0 – 28
1
For Average daily salary
2
COPD hospitalization
3
Summation of ICU cost and Hospital Admissions Cost
4
Assume that each case of Restricted Activity Days and School Absenteeim is 1 day, whereas MRAD is ½
day.
VI.3. Economic Scenarios
Since we use 3 different methods for calculating the unit social cost (Vi) for a given
outcome, we combine these methods in 2 different scenarios. It is somewhat unclear how
much of an overlap there is between the three methods. In theory, WTP should cover all
three, however when medical costs are paid by insurance, or in some studies cost of time
lost are specifically not included, WTP may just be considered personal ‘pain and suffering
costs’. We are basing our assumptions on the analysis done by Cesar et al. (2002) in which
they assume that WTP does not include COI or productivity loss for morbidity. For
mortality it can obviously be assumed that there is no cost of illness to the individual (as it
is being counted as the event of dying and not the illness preceding a death). However we
are assuming that productivity loss is included in WTP estimates for mortality.
Using these assumptions we calculate a ‘low scenario’ in which we sum direct costs and
productivity loss to calculate the total social benefits. This captures costs that can be
observed in the market, but not personal costs such as pain and suffering.
144
Low Scenario = Morbidity (COI + PL) + Mortality (PL)
Equation VI.3
For the ‘high scenario’ we include WTP which in theory should include the personal
burden of risk. The reason for summing these methods in 2 ways is that there is
considerable controversy over WTP values, and which is the appropriate value to use for
Mexico. Therefore we have decided to give the users of our model the option on whether
or not to take into account WTP in the results. To avoid double counting, we exclude some
of the health outcomes in the scenarios.
High Scenario = Morbidity (COI+PL+WTP) + Mortality (WTP)
Equation VI.4
Finally, we leave the option for the user to evaluate cases of mortality or years of life saved
when evaluating monetary benefits. When years of life lost is analyzed instead of cases, we
use an annualized value for the value of a statistical life and multiply it by the years of life
saved by the measure. The value of a statistical life is annualized for 35 years, since this is
the average life expectancy of an individual responding to contingent valuation studies.
Conducting the analysis using years of life saved reduces significantly the value of WTP
for mortality risk reduction. Therefore the High scenario when running the model in this
manner will reduce significantly, whereas the low scenario will remain the same.
VI.4. QALY Analysis
It is somewhat unclear whether or not policy analysts should be valuing each life and
morbidity case, or if some consideration should be made on the length of lives saved and
duration of diseases avoided. If in fact these considerations should be taken into account
when designing policy (i.e. if we should be protecting young rather than old, healthy rather
than sick individuals) then the economic valuation paradigm may be inappropriate. To
account for such discrepancies in health status, the quality adjusted life year (QALY)
approach could be applied, which accounts for both duration and quality of life in each
health state when calculating health benefits.
This analysis is used routinely in the medical and public health fields (Hammitt, 2001).
QALY analysis allows us to aggregate mortality and morbidity outcomes in a single unit,
and gives us a metric of evaluating health benefits, without necessarily needing to place a
monetary value on the outcomes. The QALYs gained by an intervention are simply the sum
of quality-adjusted life years gained by avoiding premature mortality and disease. QALYs
are calculated by the following equation:
QALYs = u ( H i ) × Ti × H
Equation VI.5
Where u(Hi ) is a utility weight assigned to a given health outcome which is a number
between zero and one, one corresponds to perfect health and zero to death, H is the number
of cases and Ti is the duration of that outcome. QALY weights can be estimated by direct
elicitation and generic utility scales, such as the Quality of Well Being (QWB) and Health
Utilities indices. As none of these surveys have been administered in Mexico, we choose to
145
use QALY weights from studies conducted elsewhere, such as the U.S. (Fryback et al.,
1993), Taiwan (Liu et al., 2000) and the Netherlands (Stouthard et al., 2000). Ti is found
by two separate analyses: length of hospital stay (from IMSS analysis) and Potential Years
of Life Lost analysis, both described earlier in this chapter. Table VI.6 describes the
QALY weights and durations used in our analysis for 4 outcomes.
Table VI.6 QALY Weights and Durations of Select Health Outcomes
Outcome
QALY Weight
Duration (years)
Mortality
0.0
see PYLL analysis
Chronic Bronchitis
0.81
10
2
Restricted Activity Day
0.5
1/365
Minor Restricted Activity Day
0.73
1/365
1.
Approximate midpoint between results from Viscusi’s (1991) riskrisk tradeoff of chronic bronchitis and mortality finding of 0.68 and Beaver
Dam (Fryback et al, 1993) study results of 0.86. Results from the
Ibarrarán (2002) study found a risk-risk tradeoff between chronic
bronchitis and mortality of approximately 4%, indicating a QALY weight
of .96. The weighting depends closely on the severity of the symptoms
described in the study.
2.
Estimated as ½ ‘dead for a day’
3.
Approximated from Quality of Well Being (QWB) Health State
Index results from Liu et al. (2000) study of WTP for minor illness (cold)
in Taiwan. The study found the mean QALY weight for women in the
study of 0.656 and 0.769 for their children.
For hospital admissions and emergency room visits, we use QALY weights of 0.5, similar
to the estimate for a restricted activity day. The durations for these outcomes can be found
in Table VI.2.
VI.5. Results and Conclusions
Results for the high and low economic scenarios for the two time horizons are presented in
Tables VI.7 and VI.8. Consistent with the previous results, we find the greatest benefits for
the Taxi Fleet Renovation measure. In the shorter time horizon, the Metro Expansion does
not have large monetary benefits because less than 10% of the total implementation will
have been completed by 2010. However, in the longer time frame the Metro Expansion
measure provides relatively large benefits, on the same scale of the Hybrid bus measure.
Benefits using the low scenario are significantly lower than the high, by roughly 1 order of
magnitude lower, reflecting the fact that we do not use WTP in those results.
146
Table VI.7
Taxi Fleet Renovation
Metro Expansion
Combined LPG
Hybrid Buses
Cogeneration
Table VI.8
Taxi Fleet Renovation
Metro Expansion
Combined LPG
Hybrid Buses
Cogeneration
Monetary Benefits for 2003-2010 (2003 US$ / yr)
High scenario
Mean
CI 95%
152.00M (57.30M:293.00M)
4.97M
(2.08M:9.07M)
28.7M
(9.11M:59.90M)
38.4M
(12.3M:80.2M)
1.46M
(0.48M:2.95M)
Low scenario
Mean
17.80M
0.59M
3.42M
4.61M
0.17M
CI 95%
(9.30M:29.8M)
(0.33M:0.95M)
(1.35M:6.36M)
(1.81M:8.62M)
(0.07M:0.32M)
Monetary Benefits for 2003-2020 (2003 US$ / yr)
High scenario
Mean
CI 95%
96.00M (37.70M:182.00M)
44.70M (19.20M:83.30M)
24.40M
(8.05M:52.10M)
41.60M (13.50M:88.10M)
2.03M
(0.68M:4.09M)
Low scenario
Mean
11.40M
5.33M
2.90M
4.94M
0.25M
CI 95%
(6.08M:19.20M)
(2.99M:8.44M)
(1.17M:5.42M)
(2.09M:9.15M)
(0.10M:0.45M)
When valuing life years instead of lives in the WTP part of the economic analysis, our
results shift a bit. We show the monetary benefits for the high scenario using life years
saved instead of lives saved to calculate the benefit in terms of WTP in Table VI.9.
Benefits reduce significantly when life years are used, as is expected: by approximately
40%. However, results are still nearly 6 times higher than in the low scenarios.
Table VI.9 Monetary Benefits, High Scenario using Life Years Saved (2003 US$ / yr)
Taxi Fleet Renovation
Metro Expansion
Combined LPG
Hybrid Buses
Cogeneration
2003-2010
Mean
CI 95%
98.90M (43.80M:186.00M)
3.42M
(1.55M:6.30M)
19.20M
(6.38M:40.90M)
31.10M
(9.64M:64.9M)
0.94M
(0.33M:1.97M)
2003-2020
Mean
CI 95%
64.20M (19.20M:118.00M)
31.40M
(14.6M:56.90M)
16.40M
(5.45M:33.60M)
33.60M (10.70M:71.10M)
1.33M
(0.46M:2.79M)
When summing across measures and across health outcomes we find the follo wing
distribution of effects by contaminant, shown in Figure VI.1.
147
Figure VI.1 Percentage of Monetary Benefits by Pollutant, 2003-2020
21%
22%
PM10 Primary
PM10 Secondary Organic
PM10 Secondary Sulfate
PM10 Secondary Nitrate
Ozone
21%
25%
11%
According to this distribution, around 20% of the benefits from all the measures combined
is derived from ozone, whereas the remaining 80% comes from particulate matter. Of the
particulate matter effect, nearly 60% comes from secondary nitrate and organic, mostly due
to the Taxi and Metro measures. These measures also have the largest impact on ozone.
Primary and secondary sulfate contribute the remaining 40% of the particulate effect.
These distributions shift for the individual measures, reflecting the relative values of the
different outcomes.
It is also interesting to analyze from which health outcome do the majority of the total
benefits come. These results depend on the magnitude of the health outcomes as well as
the unit value of each outcome. When analyzing the results for the high scenario, in which
we consider willingness to pay values for mortality and morbidity outcomes as well as cost
of illness and productivity loss for morbidity, we find that approximately 65% of the
benefits come from reduction in premature mortality, whereas the remaining 35% comes
from morbidity outcomes. When the low scenario is considered, in which we only use
productivity loss and cost of illness to value both mortality and morbidity outcomes, we
find that the distribution shifts quite drastically, with 80% of the benefits now coming from
morbidity outcomes, and 20% from mortality. This result is driven primarily by the
magnitude of the restricted activity impact.
When analyzing the results in terms of QALYs, the patterns remain very similar, with the
Taxi Fleet Renovation measure delivering much greater benefits than the other measures.
148
In the short time horizon, the Taxi Fleet Renovation has approximately 3 times greater
benefits than Hybrid buses, 6 times greater than LPG, 30 times that of Metro Expansion
and nearly a hundred times greater than Cogeneration. With the longer time horizon the
pattern shifts, with the Taxi measure ranking only 2 times better than Metro Expansion and
Hybrid Buses. This comparison contrasts the temporary nature of a vehicle fleet renovation
program with the lasting impact of an infrastructure program such as the Metro Expansion.
Table VI.10 Annual QALYs Saved for 2003-2010
Mean
Taxi Fleet Renovation
Metro Expansion
Combined LPG
Hybrid Buses
Cogeneration
2,935
102
574
972
28
CI 95%
(1,543: 4,694)
(57: 159)
(209: 1,110)
(415: 1,718)
(10: 52)
Table VI.11 Annual QALYs Saved for 2003-2020
Mean
Taxi Fleet Renovation
Metro Expansion
Combined LPG
Hybrid Buses
Cogeneration
1,914
946
493
1,050
39
CI 95%
(1,009: 3,003)
(554: 1,425)
(175: 944)
(440: 1,902)
(15: 74)
In future work, population, salaries, and WTP values will be adjusted for changes with
time. Projections of these parameters are needed, in a similar manner to that done in
Chapter III with costs and emissions.
VI.6. References
Alberini, A., M. Cropper, T. Fu, A. Krupnick, J. Lui, D. Shaw, W. Harrington (1997)
“Valuing health effects of air pollution in developing countries: The case of Taiwan,”
Journal of Environmental Economics and Management, 34: 107-126.
Bowland, B. and J. Beghin (2001) “Robust estimates of value of a statistical life for
developing economies,” Journal of Policy Modeling, 23: 385-396.
Cesar, H., G. Schadler, M. Hojer, P. Cicero-Fernandez, L. Brander, T. Buhl, A. C.
Villagomez, K. Dorland, A. C. G. Martinez, H. Hasselknippe, P. M. Oritz, A. V. Montero,
A. Salcido, J. Sarmiento, and P. V. Beukering (2002) “Air pollution abatement in Mexico
City: an economic valuation,” World Bank Report.
CSERGE (1999), Benefit Transfer and the Economic Valuation of Environmental Damage
in the European Union: With Special Reference to Health, Report to the European
Commission under the European Union’s Environmental and Climate Change Research
Programme (1994-1998).
149
Fryback, D., E. Dasbach, R. Klein, B. Klein, N. Dorn, K. Peterson, and P. Martin (1993)
"The beaver dam health outcomes study: initial catalog of health-state quality factors,"
Medical Decision Making, 13: 89-102.
Hammitt, J. (2002) “QALYs v. WTP”. Risk Analysis 22(5): 985-1001.
Ibarrarán, M., E. Guillomen, Y. Zepeda, and J. Hammit (2002) “Estimate the economic
value of reducing health risks by improving air quality in Mexico City,” preliminary
results.
Levy, J and J. Spengler (2002) “Modeling the Benefits of Power Plant Emission Controls
in Massachusetts”. J. Air & Waste Manage. Assoc., 52:5-18.
Liu, J., J. Hammitt, J. Wang, and J. Liu (2000) “Mother’s willingness to pay for her own
and her child’s health: a contingent valuation study in Taiwan,” Health Economics, 9: 319326.
Reynales-Shigematsu L.M. y Cols. “Costos de atención médica de tres enfermedades
atribuibles al consumo de tabaco en la Delegación Morelos del Instituto Mexicano del
Seguro Social. IMSS.” (In Publication).
Stouthard, M., M. Essink-Bot and G. Bonsel (2000) “Disability weights for disease: a
modified protocol and results for a western European region,” European Journal of Public
Health, 10: 24-30.
U.S. Environmental Protection Agency (1999) "The Benefits and Costs of the Clean Air
Act 1990-2010," Washington, D.C., Office of Air and Radiation, EPA report no. 410/R99/001.
150
Chapter VII. Integration: The Co-Benefits Model
VII.1. Introduction
In the Co-Benefits Model, developed using the Analytica software package, we integrate
the calculations described in Chapters III – VI. All air quality (Chapter IV), health impact
(Chapter V) and valuation (Chapter VI) calculations are performed within the Model itself.
However, emissions and cost calculations (Chapter III) are performed exterior to the
Model. Summarized results from the emission and cost calculations (such as Tables III.3.4
and III.3.6 for emission reductions and costs, respectively) are input to the Model.
This Model is a user- friendly application that facilitates instruction about and dissemination
of our work, and that enhances our analytical work. It allows easy integration of
calculations, the inclusion of uncertainty, and rapid propagation of changes in one
calculation to all subsequent calculations.
VII.2. Analytica
We chose to use Analytica as the software for the Co-Benefits Model because it provides a
intuitive, graphically-based modeling platform for seamless integration. It allows multipledimension matrices to be preserved through the calculations. Additionally, the software has
inherent functions that facilitate the inclusion and propagation of explicit uncertainty.
Another benefit of Analytica is that a free “Browser” version of the software is available to
any interested user. This allows the Co-Benefits Model to be viewed and used without the
need to purchase the software. The software should be purchased if users desire to modify
the model. Please see Appendix C for information on acquiring Analytica software.
VII.3. Co-Benefits Model
The User’s Module of the Co-Benefits Model is shown in Figure VII.1. Note that the model
is in Spanish, in order to facilitate dissemination to Mexico City decision-makers. From
this page, input options for the control measures to be analyzed, the time horizon, the
discount rate, and the methodology for valuation of mortality are selected by the user (gray
buttons on the left), and essential results can be quickly accessed (pink buttons on the
right). Details regarding the use of the model from this page are described in a brief User’s
Guide provided in Appendix C.
151
Figure VII.1. The User’s Module of the Co-Benefits Model
Double-clicking on the icon marked “Modelo” in the upper right corner of this page opens
the main page of the Co-Benefits Model (Figure VII.2) where calculations occur. This
figure illustrates the graphical nature of the inter-relations of variables and modules the
Model. The four central modules are those that correspond to Chapters III, IV, V, and VI,
respectively. The bottom two modules are where comparisons between GHG emission
reductions and benefits are made, and where costs and benefits are rela ted. In the top right,
a module for inputs is found.
152
Figure VII.2. The Model (“Modelo”)
VII.4. Uncertainty and Sample Size
Analytica facilitates the inclusion and propagation of uncertainty. Uncertain variables are
defined as probability distribution functions of various forms or as a tables of discrete
probabilities. When calculations are performed that include these variables, Analytica
performs multiple runs of the model, sampling randomly through the distributions in order
to cover the full range of possibility associated with the combination of many uncertain
variables. There are various methods available for this sampling; we use median latin
hypercube.
When multiple runs are performed in this manner, the sample size is very important. A
larger sample means that more combinations of the values across each uncertain variable
153
will be made, and thus a more precise result will be achieved. However, a large sample size
means that the model will take longer to run. For the Co-Benefits Model, we use a default
sample size of 1000.
VII.5. Next Steps
Our next steps with the Co-Benefits Model are to include the cost and emissions
calculations, with uncertainty, directly in the Model.
Additionally, we hope to continually improve the documentation of the Model. The refers
to both the documentation within the model of each variable and operation, as well as the
external documentation in terms of a more complete User’s Guide. All documentation
should be available both in Spanish and in English.
The Co-Benefits Model must be maintained and continually improved as improved
information becomes available. This job will be undertaken by INE staff.
154
VIII. Results
VIII.1. Introduction
In this chapter, we compare the results presented in the previous chapters and discuss the
opportunities for joint control of local and global pollution that they illustrate. We present
our benchmark results which use a 5% discount rate.
VIII.2. Local Impacts
The total cost per QALY saved indicates the net input of funds required for public health
improvement in terms of quality adjusted life-years. In Tables VIII.1 and VIII.2, we
present these results for the time horizons 2003-2010 and 2003-2020, respectively. For
both time horizons, the Taxi Fleet Renovation measure provides the least-cost method per
QALYs ($3,000 for 2003-2010). For the longer time horizon, the result is negative because
there is a net cost savings.
On the short time horizon, Cogeneration is the second-best measure, with a mean of
$23,000. For the other measures, the cost per QALY is approximately $60,000. On the
longer time horizon, the long-term benefits of the Hybrid Bus measure also become more
evident, with the cost dropping to about $23,000.
Table VIII.1. Cost per QALY (2003-2010, 5%)
Taxis Fleet Renovation
Metro Expansion
Hybrid Buses
LPG Leaks
Cogeneration
Mean
$3,287
$58,090
$56,600
$58,382
$23,220
95% CI
$1,774 : $5,551
$34,360 : $91,280
$25,360 : $114,800
$20,299 : $129,542
$9,791 : $48,910
Table VIII.2. Cost per QALY (2003-2020, 5%)
Taxis Fleet Renovation
Metro Expansion
Hybrid Buses
LPG Leaks
Cogeneration
Mean
-$10,930
$50,770
$23,040
$40,083
$30,470
95% CI
-$18,590 : -$6,246
$30,890 : $79,480
$10,430 : $45,080
$6,622 : $97,332
$12,600 : $62,570
These cost per QALY values are low in comparison to the results of Cohen et al. (2003)
who find that emission-controlled diesel buses in the US provide health benefits at
$270,000 per QALY, while compressed natural gas buses are less cost-effective, at $1.7 $2.4 million per QALY. This indicates that, in comparison to the technologies study by
Cohen et al. (2003), all of the measures studied her are quite cost-effective.
155
We now compare the costs of the measures to monetized health benefits using our high
scenario that includes willingness-to-pay. In Figures VIII.1 (2003-2010) and VIII.2 (20032020), it is evident that the benefits of these measures are generally as large, if not larger or
even significantly larger, than the costs.
Figure VIII.1. Costs and health benefits, 2003-2010 (million US$ / yr)
Negative indicates a net cost, positive indicates a net savings
Figure VIII.2. Costs and health benefits, 2003-2020 (million US$ / yr)
Negative indicates a net cost, positive indicates a net savings
For the short time horizon, the benefits of the Taxi Fleet Renovation are far greater than the
costs. Costs are small for this measure because of significant fuel efficiency gains realized
156
with newer vehicles. Benefits are high because of large ozone reductions, and also because
of significant reductions in secondary particulate concentrations reductions. On the longer
time horizon, net costs turn into net savings as the fuel cost savings continue to accumulate
without additional investment costs. Annualized benefits are still la rge, though less so, for
the long time horizon because there is deterioration of the fleet of aging vehicles that
gradually increases local emissions, and thus decreases local benefits with time.
Consistent with existing government proposals, this analysis assumes that only 5 km of
Metro would be built from 2003-2010, and an additional 71 km from 2011-2020. For this
reason, it appears as to be a relatively inexpensive measure for the short time horizon
(Figure VIII.1) and a larger measure for the long horizon (Figure VIII.2). Because Metro
Expansion involves significant capital investment, the inclusion of the recuperation value
for the Metro (30 year useful life) offsets a significant portion of these initial costs. We find
that the local emission reduction benefits can also be large and compensate for a majority,
if not all, of the net costs for both time horizons. This analysis assumes that the extension of
the Metro causes a significant reduction in the use of on-road public bus transportation,
which means local emissions are significantly reduced. However, increase in Metro length
requires more electricity and increases emissions from power plants that are primarily
located outside the valley. The Metro Expansion causes a net transfer of local emissions
from inside to outside the valley. We assume that population density is substantially lower
where the electricity is generated than in Mexico City, and for this reason, public health
impacts will be negligible from increased power generation. This transfe r of local emission
helps to make local benefits large enough to offset much, if not all, of the costs for this
measure.
The Hybrid Buses measure has large upfront investment costs due to the expensive nature
of the technology, but also generates significant cost savings on the long term due to greatly
enhanced fuel efficiency. Benefits are large for both time horizons because of reductions in
primary particulate emissions. This measure is implemented between 2003 and 2006, and
this is why annualized costs are lower and benefits slightly higher for the long time horizon
(Figure VIII.2) than for the short time horizon (Figure VIII.1).
The LPG leak reduction measure, on the other hand, has low costs because of the low unit
costs for each stove repair. Benefits are much larger than the costs because of the
significant reduction in hydrocarbon emissions that reduces both ozone and secondary
organic particulate exposure.
For Cogeneration, net costs are low because of the significant gains in fuel efficiency and
the inclusion of the recuperation value of the equipment at the end of each time horizon (20
year useful life). Benefits are not very large for this measure because the gains in efficiency
derive from simultaneous on-site production of thermal and electrical energy that replaces
off-site electricity generation and on-site thermal energy production. As explained for the
Metro measure, only 3.1% of the electricity consumed in Mexico City is generated in the
valley. Though Cogeneration significantly reduces the total emissions by substantially
increasing efficiency, the measure moves emissions of local pollutants into the valley, and
thus local benefits are small.
157
VIII.3. Global impacts
Reduction in GHG emissions are presented in Figures VIII.3 (2003-2010) and VIII.4
(2003-2020) in terms of thousand metric tons (=109 g) of C equivalent per year. Due to the
large positive impact of the Taxi Fleet Renovation and Cogeneration measures on fuel
efficiency, these measures create the largest GHG emission reductions. For the longer time
horizon, when much more of the Metro has been built, this measure also begins to cause
significant GHG reductions. The LPG Leaks and Hybrid Bus measures create smaller
GHG emission reductions for both time horizons.
Figure VIII.3. GHG emission reductions, 2003-2010
158
Figure VIII.4. GHG emission reductions, 2003-2020
VIII.4. Comparing Local and Global Results
In Figure VIII.5, we present the local and global net benefits. The local net benefits are
defined as the Health Benefits minus Costs, while the global net benefit is the reduction in
GHG emission. Results for both time horizons are presented.
Figure VIII.5 illustrates that the Taxi Fleet Renovation measure is clearly the best measure
from the joint local – global perspective. The Hybrid Bus measure for 2003-2020 and the
LPG Leak measure on both time horizons are the next- most promising for joint local /
global control. The Metro Expansion, in large part because of its very high costs, is less
promising from the joint perspective.
Cogeneration also does not have sufficient local benefits to make it interesting for joint
local – global control. However, if only the global perspective is considered, the
Cogeneration measure could be of great interest. We also note that were a similar study
conducted at the national level, Cogeneration may turn out to be a promising joint local –
global option because health benefits derived in populations living near to power plants
would be considered. This will depend, of course, on population exposure to emissions
generated by electricity production across the country.
159
Figure VIII.5. Local and Global Net Benefits
In Tables VIII.3 and VIII.4, the local health benefits per tons of C equivalent are reported
for each measure. Mean values range from $8 / ton C to approximately $2,500 / ton C, with
all measures except for the Cogeneration coming in at greater than $800 / ton C. In the IES
study in Chile, these values ranged from $60-480 / ton; in Korea the value was $22 / ton.
This indicates that, for these measures and with the exception of Cogeneration, local
benefits are quite high with respect to the GHG emission reductions. This finding is due in
part to the fact that all of these measures, with the exception of Cogeneration, have been
designed for the purpose of air quality improvement, as opposed to specifically focused on
GHG emission reductions. This analysis indicates the potential for joint control of both
local and global pollutant that has large benefits for both, independent of the original intent
of the measures.
Table VIII.3. Health benefit per ton of GHG reduced, 2003-2010 (US$ / ton C eq.)
Taxis Fleet Renovation
Metro Expansion
Hybrid Buses
LPG Leaks
Cogeneration
Mean
$1,312
$912
$2,602
$2,468
$9
160
95 % CI
$496 : $2,532
$382 : $1,665
$837 : $5,436
$910 : $5,918
$3 : $18
Table VIII.4. Health Benefit per ton of GHG Reduced, 2003-2020
Taxis Fleet Renovation
Metro Expansion
Hybrid Buses
LPG Leaks
Cogeneration
Mean
$824
$906
$2,187
$2,447
$8
95 % CI
$349 : $1,677
$430 : $1,864
$815 : $5,323
$924 : $5,950
$3 : $18
VIII.5. References
Cohen, J.T., J.K. Hammitt, and J.I. Levy (2003) Fuels for urban transit buses: A costeffectiveness analysis. Environ. Sci. Technol 37. 1477-1484.
161
IX. Conclusions and Future Work
IX.1. Conclusions
Taxi fleet renovation offers the most promising opportunity for the joint control of local
and global pollution of the measures studied here. Further, benefits might be found to be
significantly larger than estimated here if changes in primary particulate matter emissions
could be estimated. The large potential benefits of this measure have already been
recognized by decision- makers in Mexico City, and the implementation of this measure has
begun as of 2002-2003 with public funding for the replacement of 3,000 taxis.
The LPG leak measure also provides benefits than are much larger than the total costs.
Emissions reductions and local benefits from this measure are small compared to the taxi
fleet renovation, but investment costs are quite small, making implementation of the LPG
leak measure relatively feasible from a decision- making standpoint.
Cogeneration provides more than 50% of the GHG benefits from this set of measures, but
essentially no local benefit because it moves emissions of local pollutants into the valley,
and health benefits from the reduced emissions at power plants located outside the valley
are assumed negligibly small. Were a similar study conducted at the national level,
Cogeneration may turn out to be a promising joint local / global option because health
benefits derived in populations living near to power plants could be considered. This will
depend, of course, on population exposure to emissions generated by electricity production
across the country.
Metro Expansion has large local benefits, particularly for the long time horizon when the
measure has been fully implemented. However, investment costs for building more Metro
are extremely high making its implementation unlikely.
Finally, the Hybrid Bus measure may have positive net benefits if the long time horizon is
considered. However, the analysis of this measure has large uncertainty because the
emission factors used were derived for the altitude, driving conditio ns, and fuel mix of New
York City, not for Mexico City. Altitude has been shown (Yanowitz et al. 2000) to
significantly impact emissions behavior from heavy-duty vehicle technology, but these
impacts have not been specifically calculated for the technologies under consideration here.
We recommend that a better understanding of emissions factors be obtained and also that
the cost-effectiveness of other types of advanced technologies (e.g. Cohen et al., 2003) also
be considered in order to determine what would be the best advanced bus technology to
introduce in Mexico City.
This work indicates that measures to improve the efficiency of transportation are key to
joint local / global air pollution control in Mexico City. The three measures in this category
that are analyzed here all have monetized public health benefits that are larger than their
costs when the appropriate time horizon is considered. Global benefits, due to improved
fuel efficiency, are also large. In contrast, we find that traditional “no-regrets” electricity
efficiency do provide large GHG emission reductions, but do not provide local benefits to
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Mexico City because the majority of electricity is produced away from the valley in which
Mexico City is located.
IX.2. Future Work
Further work is needed to analyze more measures that cover a wider range of opportunities
for joint local / global air pollution control. Also very important is to quantify the air
pollution improvements and cost savings that could be acquired were congestion reduced in
MCMA. Such an analysis may indicate that the benefits from transportation efficiency
improvement are, in fact, much larger than estimates here. Improved understanding of
emission factors from new and old vehicles under Mexico City driving conditions is also
greatly needed, and could significantly impact results.
Sensitivity analysis is also needed on the control options studied in this project. The results
presented here are, of course, dependent upon assumptions made about baselines, emission
factors, implementation plans, etc. Since we are attempting to predict the future, there is
much uncertainty. In order to address this uncertainty, sensitivity of results to these basic
assumptions needed to be tested. Results that are robust to the gamut of possible futures is
the ultimate goal of this kind of analysis, making a complete sensitivity analysis a key next
step.
Technical working groups among various agencies and institutions in Mexico City are
needed in order to more precisely define control measures, and to improve the emissions
factors used. Working groups would be mutually beneficial to all parties involved,
particularly given the limited resources available for this work in Mexico City, by
facilitating interchange of the best-available information.
This project has evidenced in many ways the pressing need that decision-makers have for
reliable rapid-assessment tools. Reduced form air quality modeling techniques is one
example; and the Co-Benefits Model that integrates this analysis is, of course, another. The
methods used here should be further studied and improved so that they can give ever- more
reliable answers. On the long term, maintenance and technical support must be continued
so that the Model and the methodology upon which it is based can remain pertinent to the
decision- making process. In the near future, improved documentation and a more complete
User’s Guide (ref. Appendix C) is also needed.
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Appendix A: Air Quality Modeling
A.1. Box Model
Box models are the simplest of numerical models. The region to be modeled is treated as a
simple cell, or box, bounded by the ground on the bottom, the inversion base (or some other
upper limit mixing) on the top, and the east-west and north-south boundaries on the sites.
The box may enclose an area on the order of several hundred square kilometers. Primary
pollutants are emitted into the box by the various sources located within the modeled
region, undergoing uniform and instantaneous mixing. The ventilation characteristics of the
modeled region are represented by specification of characteristic wind speed and rate of
rise of the upper boundary.
Fundamental to the box model concept is the assumption that pollutant concentration in a
volume of air, a “box”, are spatially homogeneous and instantaneously mixed. Under this
assumption, pollutant concentrations can be described by a simple balance among the rates
at which they are transported in and out of the air volume, their rates of emission from
sources within the volume, the rate at which the volume expands or contracts, the rates at
which pollutants flow out the top of the volume, and the rates at which pollutants flow out
the top of the volume, and rates at which pollutants react chemically or decay.
Because of their formulation, box models can predict, at best, only the temporal variation of
the average regional concentration for each pollutant species. Consequently, they are
capable of addressing only broad-scale regional questions. The combined effects of local
emission patterns and meteorological conditions generally give rise to significant spatial
variations in pollutant concentrations. So, clearly box models cannot be used to asses the
effectiveness of emission control strategies that lead to spatially inhomogeneous emissions.
We have developed a box model for the MCMA that represents emissions, advection and
dry deposition of primary PM10 . The governing equation is:
dPM
 PM o − PM
 PM o − PM 
= u ⋅
 + v ⋅ 
dt
∆x
∆y




E
PM  − vd h⋅ ∆ t 
 +
+
⋅ e
− 1
∆t 
 ∆x∆y∆z

Equation A.1
Where u and v are mean zonal and meridional winds, respectively; Äx, Äy and Äz are the
horizontal and vertical dimensions of the box; PMo is the concentration of PM on the
boundaries of the box; PM is the concentration inside the box; E is the emission of primary
particles; Ät is the residence time of a parcel of air in the box (=Äy/v); vd is the dry
deposition velocity for particles of 0.1-10ìm ; and h is the height of the deposition layer.
Thus, the first two terms represent advection, the third represents emissions into the volume
of the box, and the fourth represents dry deposition (Scire et al., 2000).
164
The steady-state (i.e. time invariant) concentration in the box is found by setting
 u
v 
E
PM o ⋅ 
+
 +
 ∆x ∆y  ∆x∆y∆z
PM =
v ⋅∆ t

 u
v  1  − dh

+
 −
⋅ e
− 1

 ∆x ∆y  ∆t 
dPM
=0:
dt
Equation A.2
We can solve this equation for the baseline emissions (E1) and for emissions under a given
control scenario (E2), and then difference the results to arrive at the change in PM (ÄPM)
concentration due to the emission change. If ÄE = E2-E1:
∆E
∆x∆y∆z
∆PM =
v ⋅ ∆t

 u
v  1  − dh
 +
 − ⋅  e
− 1

 ∆x ∆y  ∆t 
Equation A.3
The result is an estimate of the change of concentration of primary particulates in the
MCMA that results from the changes in emissions. To estimate the reduction fraction of
primary particulates using the box model, we simply divid e equation B3 by equation B2, to
find:
∆E
∆PM
∆x∆y∆z
RF =
=
PM
 u
v 
∆E
PM o ⋅ 
+
 +
 ∆x ∆y  ∆x∆y∆z
Equation A.4
It is important to say that following commentary from technical staff of the government
agencies attending our regular meetings, we determined that the box model previously used
in the study is particularly uncertain, and a less useful tool than Source Apportionment.
Thus, we have eliminated the box model as an explicit component of the analysis.
A.2. Marginal PM Method for Primary and Secondary PM
By using a 3-dimensional pho tochemical model for Mexico City (MIT-CIT) to determine
the sensitivity of 2o particulate precursors to changes in emissions of SO2 and NOx, and
then an chemical equilibrium model to determine the sensitivity of 2o particulate formation
to change in precur sor concentrations, West and San Martini (2001) estimate changes in
secondary sulfate and nitrate particle formation with changes in SO2 and NOx emissions.
Using data from the La Merced monitoring station, during the IMADA campaign in March
1997, they find the following relationships:
(dPM10 /dNOx) = 2.25e-5 (ug/m3 ) / (ton/y)
(dPM10 /dSO2 ) = 3.36e-5 (ug/m3 ) / (ton/y)
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We apply these relationships to the estimated emissions reductions from the control
measures to estimate changes in secondary particle concentrations.
A.3 References for Appendix A: Air Quality Modeling
Scrire, J.S., D.G .Strimaitis, and R.J. Yamartino (2000), “A User’s Guide for the
CALPUFF Dispersion Model (version 5),” Earth Tech, Inc. 521 pp.
West, J. and I. San Martini (2001) Report of the Fourth Workshop on Mexico City Air
Quality, March 8-10, 2001, El Colegio de Mexico, Mexico. MIT-Integrated Program on
Urban, Regional and Global Air Pollution Report No. 25, November 2001.
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Appendix B. Capacity Building
B.1. Introduction
A key component of this co-benefits project has been in the building of capacity in the INE
team and Mexican policy makers. The main goals of the capacity building have been to:
1) Facilitate the continuation of the project by INE staff once this phase of project is
completed
2) Introduce the analysis and the Co-Benefits model to Mexican policy makers, and to
encourage their use of the model
3) Train individuals on Analytica software so that they are able to use this program to
conduct integrated analyses for other projects.
Some of the main activities of the capacity building component have been regular meetings
with the CAM, a final workshop, several short-courses and close collaboration with INE
staff. In this Appendix, we discuss some of our key accomplishments and lessons learned
during the project.
B.2. Key Accomplishments
Throughout the project we have held regular meetings with members of the Metropolitan
Environmental Commission (CAM) and other environmental agencies in the MCMA,
including the Secretariat of the Environment of the Government of the Federal District
(SMA-GDF), the Secretariat of the Environment of the government of the State of Mexico
(SEGEM), and the Directorate of Air Quality of the Federal Secretariat of the Environment
and Natural Resources (SEMARNAT). These meetings have encouraged active
participation in this project and to aid the integration of this work with other efforts in the
MCMA, particularly the first revision of PROAIRE.
Our close collaboration with INE personnel has also been a fruitful one. From the
beginning of the project, INE researchers were encouraged to attend our regular
presentations to the CAM and other government agencies. From the beginning of 2003, we
have also made efforts to bring multiple INE researchers into active participation on the
Co-Benefits team.
Following is a list of the major capacity building activities undertaken:
•
On November 26, 2002 , we held a meeting with the CAM and other government
representatives in which they explained their plans for the first 2-year revision of
PROAIRE and we presented the goals for this project. The discussion that followed
considered how we can make this work useful to their PROAIRE revision. There
was much interest in this analysis from the CAM staff and several members from
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the State of Mexico asked to be involved closely in the development of the CoBenefits model.
•
On December 16, 2002, we met with Soledad Victoria from the State of Mexico to
introduce her to the Analytica software and an initial version of the model.
•
On February 12, 2003, we held a meeting with the CAM and other government
representatives to illustrate the Analytica software and to introduce the developing
Co-Benefits model. Much valuable feedback was received about the level of detail
appropria te for the model if is to be useful to decision- makers.
•
On March 6, 2003, we had a productive meeting in which the estimation of costs
and emissions were presented. We focused on the accelerated retirement of taxis as
an example.
•
Our capacity building with a specific focus on INE researchers began with a shortcourse was led by Miriam Zuk on the estimation of health impact and valuation of
these impacts (April 1, 2003), and on the Analytica model (April 3, 2003). INE
investigators then began to study and work on specific exercises related to each of
the modules.
•
On April 22 and 24, 2003 intensive working meetings with the entire INE team
were led by Dr. Fernandez and Dr. McKinley to discuss results, uncertainties,
priorities for future work, and to begin the planning for the final workshop.
•
On April 24, 2003, the air quality models were discussed with CAM and other
governmental representatives. We received valuable feedback during this meeting
that led us to eliminate the box model from our final results.
•
On April 30, 2003, the health and valuation modules were presented to CAM and
other governmental representatives, and valuable feedback was received.
•
On May 20, 2003, the final workshop was held. Policy makers and technical staff
were invited and many key figures attended. Consistent with their ever- increasing
involvement with the project, INE investigators presented the bulk of the technical
details of the project. Interest in the project and the integrated analysis was high,
and valuable comments regarding the work were provided by the audience.
•
The depth and breadth of the comments during the final workshop led to an
additional meeting for technical comments and discussion that occurred at INE on
May 26, 2003. Representatives from the National University of Mexico Center for
Atmospheric Science, the Mexican Institute of Petroleum (IMP), the SEGEM, and
SEMARNAT attended to share their thoughts and for further discussion of the
details of the analysis. It is promising possibility that technical working groups,
particularly between INE, IMP and SEGEM, will develop out of this meeting.
168
•
On June 4, 2003, we met with Dr. Adrián Berrera of IMP to begin discussions about
such collaboration. These kinds of cross- institutional working groups would be
highly beneficial to moving forward this type of integrated analysis in Mexico City.
We believe that INE is in a very good position to become a focal point for such
effort.
•
On June 16, 2003, we held a day- long course on the use of Analytica software and
on the use of the Co-Benefits model. The majority of time was spent doing
modeling exercises using the software and the model. Eight attendees were from the
CAM, SEGEM, and GDF. Another 9 attendees from INE also took the course. The
feedback on the course was extremely positive, and there were multiple requests for
an advanced course in the near future. This course was a key step in the
dissemination of results to the multiple government offices responsible for air
quality in Mexico City, and therefore to improving decision-making on local and
global pollution control.
B.3. Lessons Learned
While capacity building has been a key component of the project since its initiation, we
have learned many things over the course of 10 months and our understanding of the best
ways to truly achieve capacity building has significantly improved. At the start of the
project, our focus was on designing the analysis and determining its scope. Most time was
spent in this development phase on detailed technical issues; few INE personnel
participated in the planning phase. Later, it was determined that if the analysis was to
eventually be transferred completely to INE staff, more people needed to be involved. As
such we developed a large work group and had many fruitful meetings on the project as a
whole and collaborations in the execution of specific parts of the analysis.
Through this process, we have learned that it is essential to involve key INE staff in the
project planning and implementation phase from the beginning. It is difficult to encourage
participation and re-train staff every time new work groups are determined. We
recommend that in the future, a maximum of 4 to 5 staff be selected to work on the project
and be asked by their superiors to dedicate a substantial portion of their time to the project.
Of this group, one leader would be assigned who understands the broad vision of the
project, and is able to integrate the pieces. Depending on the time available of the leader,
either they could be responsible for maintaining and updating the model, or perhaps another
staff member could be responsible for model maintenance. Additionally, 3 or 4 members
should be able to conduct and constantly improve the technical analysis in each of the
modules: emissions and costs, air quality modeling, and health benefits assessment. If
tasks are not assigned and time is not dedicated, the gains achieved through this project
may be lost once this phase is over.
We have also learned that in order for people to be truly involved with the project, they not
only need to be present at meetings and understand the basics of the analysis, but they must
be responsible for a part the project. After key decisions are made, it is difficult to help
people understand how the project evolved into its existing form. Though we have made
169
much progress with increasing technical knowledge in the fields touched by this project,
capacity building about developing and growing a project is still very much needed at INE.
Gathering an interdisciplinary team is difficult, only to be made more difficult when people
have little time to devote or have minimal intellectual investment in a project. We therefore
recommend that for the continuation of this phase of the project and for future phases (or
for other projects), it is very important to:
1) Designate an in-country leader who is responsible for both learning the technical
details of the analysis and for further development and dissemination of the project.
2) Give in-country participants tasks and responsibilities for the work to encourage their
participation and learning.
3) Keep in mind that the capacity building process must be started during the planning
stage of the project. If staff participate from the start of the project, their ownership and
intellectua l interest will drive the capacity building process forward. Capacity building
in a top-down format is inherently ineffective.
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Appendix C. Basic User’s Guide for the Co-Benefits Model
C.1. Introduction
This appendix is meant to provide the reader with basic instructions on how to access and
use the Co-Benefits Model. It indicates where to find the Model and the Analytica software
required to run it on the worldwide web. It assumes that the user has access only to the
“Browser” version of Analytica software, which is free of charge. This documentation is
written based on version 5.7 of the Model.
This guide is written on the assumption that the user has read and fully understands the
entirety of this Final Report in which the methodology implemented in the Model is
discussed at length. We strongly recommend all users to carefully read this Report before
beginning to use the Model.
It is essential that the user remember that this Co-Benefits Model is designed only for use
in Mexico City. It is not applicable to other locations in its current form. It is also key to
remember that the emission reductions and costs required as inputs to the Model are:
• Annualized emissions changes at discount rates of 0%, 3%, 5%, 7% for 2003-2010
and 2003-2020.
• Annualized costs 0%, 3%, 5% and 7% for 2003-2010 and 2003-2020.
The use of emission changes and / or costs calculated on other bases will cause erroneous
results to be derived from the Model.
We also note that because our goal is to attract decision- makers in Mexico City to using the
Model, the Model’s text is in Spanish.
C.2. Accessing the Co-Benefits Model
The most recent version of the Model, as well as other information about this project, can
be downloaded from:
http://www.ine.gob.mx/dgicurg/cclimatico/benlg.html
C.3. Accessing Analytica
It is free to download the “Browser” version of Analytica from the website of Lumina
Systems, Inc.
http://www.lumina.com
With this version, the Model can be run, but it cannot be modified and input choices cannot
be saved. Input choices in the first window can be changed for each run, and new measures
can be evaluated under the “Medida Nueva” module. However, such changes cannot be
saved and must be re-entered after the Model has been closed and re-opened.
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We suggest tha t before trying to use the Co-Benefits Model, the prospective user first
familiarize herself with Analytica by following the Analytica tutorial guide, which can also
be downloaded from Lumina, or by taking an Analytica training course.
C.4. The User’s Module of the Co-Benefits Model
Upon opening the Model, the user will find the screen shown in Figure C.1.
Figure C.1. The User’s Module of the Co-Benefits Model
All options found in this first window are further defined and implemented under the
“MODELO” module that appears in the upper right corner of the window. All technical
details required to understand these calculations are described in Chapters IV, V, VI, and
VII of this report.
C.4.1. Input Options
This section describes options that the user can change in order to customize their Model
run. All options are found on the left side of the first window of the Co-Benefits Model
(Figure C.1). Input buttons are gray in color.
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Part 1: All Measures or Individual Ones (“Parte I: Todo o Individua l”)
With this drop-down button, the user chooses if she will analyze all measures included in
the Model (“Todo”) or only a single measure (“Individual”). If “Todo” is selected, results
for all measures will be calculated simultaneously, and the Model will take longer to run.
Also, if “Todo” is selected, Parte II is deactivated by the Model and can be ignored by the
user.
Part II: Control Measures (”Parte II: Medidas de Control”)
If “Individual” is chosen in Part I, then this button is used to choose which measure to
evaluate, or the option “Combination” can be selected. If “Combination” is selected, the
user also needs to use the “Tabla para Combinacion” to determine which measures will be
summed together before the Model is run.
Combination (“Tabla para Combinacion”)
Clicking on this button will bring the user to a table with all the control measures listed to
the left and a series of 0’s and 1’s in the right column. If the user wants to include a
measure in her combination, she should alter the right column in order to have a “1” next to
that measure. Excluded control measures should have a “0” beside them.
Add a new measure (“Añadir su ‘Medida Nueva’”)
If the user wants to define her own set of emission reductions and costs to be evaluated by
the Co-Benefits Model, she should use this module. Before doing so, there is likely
significant work to be done (see Chapter III) in order to calculate emissions reductions and
costs as:
• Annualized emissions changes at discount rates of 0%, 3%, 5%, 7% for 2003-2010
and 2003-2020.
• Annualized costs 0%, 3%, 5% and 7% for 2003-2010 and 2003-2020.
Once emissions reductions and costs are calculated in this way, the user should double click
on the “Añadir su ‘Medida Nueva’” module.
In part 1a, emission changes are entered into a table. Pay careful attention that the measure
identified is “Medida Nueva” and that the data are entered under the appropriate time
horizon and discount rate. Here, total changes in primary PM10 (PM2.5 is not currently
active) should be entered (combustion + geological).
In part 1b, changes in geological emissions of primary PM10 (PM2.5 is not currently active)
should be entered. The Model accounts for the fact that the total (combustion + geological)
was entered in part 1a.
In part 2, costs are entered.
In each part, it is also possible to alter the values for the 8 control measures inherent to the
Model by entering the altered values into the “Medida Nueva” table.
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Time Horizon and Discount Rate (“Horizonte de tiempo” and “Tasa de Descuento”)
With the first button, the user can choose to evaluate either the 2003-2010 time horizon or
the 2003-2020 horizon. With the second, the user opts for either not using discounting (0%)
or to run the Model using a 3%, 5% or 7% discount rate.
Mortality Valuation (“Valoración de Mortalidad”)
Here, the user chooses if mortality will be valued based on each case of mortality (”Casos”)
or if each year of life lost will be valued (“Años”). Morbidity is always valued based on
cases.
C.4.2. Output Options
Result buttons are pink in color, and are located on the right side of the Model’s first
window.
“Costos Acutal” returns annualized costs (US$ / yr) for the measure(s), time horizon, and
discount rate chosen.
“Reducciones actual” gives the emission reductions (ton / yr) for the measure(s), time
horizon, and discount rate chosen.
“Reduccion C equivalente” gives the change in emissions of greenhouse gases (tons Cequivalent / yr) for the measure(s), time horizon, and discount rate chosen.
“Cambio de concentración por medida” returns, for each measure and each contaminant,
the change in population-weighted exposure (ug/m3) for the representative annualized year
as estimated by the Model.
“Impactos totales” gives the number of avoided cases per year (if “Casos” is selected) or
QALYs per year (if “Años” is selected) due to the selected control measures.
“QALYs totales” returns the total number of QALYs saved each year by the measure,
independent of whether “Casos” or “Años” is selected in the input options section.
“Beneficios Monetarios” returns the monetary benefits, calculated with each of the three
valuation metrics, for each avoided health impact (US$ / yr).
“Escenarios de Beneficios” provides the total benefits for the high “Alta” and low “Baja”
valuation scenarios (US$ / yr).
“Costos / beneficios totales” provides Cost over Benefit ratios for the high “Alta” and low
“Baja” scenarios. If costs are negative, a negative ratio will be returned. If benefits are
negative, the result will be zero.
174
“Costo / QALY” compares the cost of the control measure(s) to the QALYs it saves (US$ /
QALY).
“Beneficios Netos” returns the net benefits of the control measure(s), which is the Benefits
minus Costs (US$ / year).
“Beneficio / ton C eq.” compares the local health benefit and the GHG reduction (US$ / ton
C eq.)
“Precio de C equivalente” calculates the income per ton of GHG reduction (US$ / ton) that
would be needed in order to make the sum of benefits and such “GHG Income” equivalent
to the costs of the control measure. If the result is zero, it indicates that either the benefits
are already larger than costs without consideration of “GHG Income”, or that there is a net
increase in GHG emissions due to the measure.
C.5. Citing Results
If the results from the Model are used in a report or publication, it should be cited (with the
appropriate year and version number) as:
Co-Benefits Model for Mexico City, Version x.x,
Instituto Nacional de Ecología, Mexico, 200y.
This report should also be cited:
McKinley et al. (2003) Final Report of the Mexico City Co-Benefits Project,
Instituto Nacional de Ecología and the US Environmental Protection
Agency Integrated Strategies Program, August 2003.
If a modified version of the Model is used, or new control measures are entered into the
existing Model, please cite this report as indicated above. For the Model, please indicate the
changes that have been made and by whom:
Co-Benefits Model for Mexico City, Version x.x,
Instituto Nacional de Ecología, Mexico, 200y. Modified by (who) of (institution)
in inputs for (costs and emissions of measure X, de dose-response Z, etc.)
and in modules (A,B,C) in manner (D,E), etc.
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