Energy system Optimization and co-benefit evaluation:
based on China-MAPLE model
Xi Yang1, Fei Teng2
1Assitant
Professor, academy of Chinese Energy Strategy, China University of Petroleum, Beijing 102249 China
2 Associate
Professor, Institute of Energy, Environment and Economy, Tsinghua University, Beijing 100084 China
 Study on Energy Supply Curves in China’s bottom-up model up to 2030
Xiaoqian Xi, Xi Yang*
Academy of Chinese Energy Strategy, China University of Petroleum, Beijing 102249 China
 Cost–benefit analysis of China’s INDC based on Carbon MACCs
Xi Yang*, Fei Teng, Xiaoqian Xi, Qi Zhang
Academy of Chinese Energy Strategy, China University of Petroleum, Beijing 102249 China
Institute of Energy, Environment and Economy, Tsinghua University, Beijing 100084 China
19th June 2017
Outline
1. Brief overview
2. Background & questions
3. China-MAPLE & MACCs
4. Results & Analysis
5. Conclusion
6. Next step
2
Brief Overview
• A cost–benefit analysis of China’s INDC in 2030 considering
auxiliary benefits that have been ignored for long time
• Marginal abatement cost can be offset by the marginal abatement
cost curves revised by environmental benefit
• The cost of carbon abatement in NEPC scenario can be fully
compensated by the benefit when mitigation rate is below 16.8%
• The cost-effectiveness of China’s INDC depends on the strictness of
its end-of-pipe technology
• China’s INDC target in the NEPC scenario is both achievable and
cost-effective. The cost of abatement in the EPC scenario can be
partially offset
3
Background & questions
4
Background—Pressure of Carbon mitigation
• Paris Agreement, enforced in November
4, 2016, aims to limit the rise of global
temperature to lower than 2 degrees.
• Deep de-carbonization efforts were
enacted to achieve these targets.
• China recently suffered from severe
haze problem because of rapid
development and urbanization.
• Heart diseases, such as stroke and
ischemic heart and lung problem (e.g.,
chronic obstructive pulmonary disease
and lung cancer) became the most
common causes of death in China.
Source: Ministry of Environment Protection, Anzhen hospital
5
Background—Co-control
• Local pollutant emissions are highly related to fossil fuel combustion.
• Actions of energy conservation to reduce carbon emissions often reduce coemitted air pollutants like SO2, NOx, and PM2.5 , bringing co-benefits for air
quality.
Contribution of coal combustion to the SO2, NOx, and PM2.5 emissions in 2012
Data source: MEIC model database (MEIC, 2013)
6
Questions?
China has stepped into a new normal economic development stage.
This development requires comprehensive analysis of main benefits to
examine China’s INDC targets.
For the INDC framework, several questions:
• What would be the net(real) carbon abatement cost if environmental
benefits are considered?
• Second, how much mitigation cost can be reduced and what is the best
carbon mitigation rate for cost–benefit analysis (CBA)?
• Third, is China’s INDC target, particularly its carbon reduction target, costeffective, or not? If so, what would be the real cost of fulfilling this target?
Otherwise, what is an alternative option for China’s mitigation schedule?
7
China-MAPLE & MACCs
8
Introduction of China-MAPLE
• China Multi-pollutant Abatement Planning and Long-term Benefit
Evaluation (China-MAPLE) model
• To evaluate the effects of the energy conservation policies and local
pollutant control measures on energy system
• Bottom-up model. Developed based on VEDA-TIMES. Minimizes the total
energy system cost when simultaneously meeting the final energy service
demands and external constraints.
• 5-year step, 2010-2050.
9
Structure of China-MAPLE
10
Characters of China-MAPLE
China-MAPLE differs from other China bottom-up model in three aspects:
• First, local pollutant control module has been integrated into the energy
system framework in China-MAPLE.
• Second, instead of based on fuel consumption or activity level, the link of
local pollutant to energy module is based on technological level in MAPLE.
This approach can help distinguish the local pollutant reduction due to energy
conservation and end-of-pipe control measures.
• Third, instead of setting resource cost as fixed-cost or increasing rate,
China-MAPLE introduces energy supply curve into the energy supply
module.
11
Data source
The data of the model mainly comes from:
• China Statistical Yearbook, China Energy Statistical Yearbook, China Electric
Power Yearbook, Yearbook of Industrial Statistics
• China 21st Century Energy Technology Development, 2010 electric power
production project cost briefing
• China Iron and Steel Statistics, China Chemical Industry Yearbook, China
Nonferrous Metals Industry Yearbook
• Technical data on electricity production and economic analysis of the
literature
• Technical parameter from production line of major industrial sectors
• As well as large amount of relevant reports and literature studies.
12
Original MACCs and revised MACCs
Shadow price of carbon
Marginal cost
MDCCC+AQ
MACCC
MACCC
MDCCC
MACCC+MDCAQ
p*
0
p*p’
q* q’
Carbon mitigation amount
(a)
q’’ q*
Carbon mitigation amount
(b)
Impact of co-benefit of air pollutant emission reduction on the marginal abatement cost
13
Benefit evaluation
Emission->Concentration change-> Health end point->Valuation
ExBenefiti  APi  Cill n ess
MOCi  ExBenefiti / ERi
c 
IF  Q
BR  P
RR  exp  c
Y 
RR  1
 Y0
RR
14
Supply curves in China’s Energy modelling
MC, P
MC, P
MC4
MC3
MC
MC2
MC1
Q
Horizontal supply curve
q1
q2
q3
q4
Q
Discrete energy supply curve
Some shortcomings of horizontal supply curve:
1.
can not analyse the changes of resources supply from different production regions;
2.
can not accurately reveal the resource substitution caused by technology
improvement;
3.
can not offer the accurate energy optimization solutions
Supply curves in China-MAPLE
The short-run cost curve moves upwards when the cumulative production
amount increases, and its slope also changes due to the new investment.
Marginal Cost
Marginal Cost
High average
cost
Mine 4
Mine 3
Low average
cost
Mine 3
Mine 2
Low average
cost
Mine 4
High average
cost
Mine 2
Mine 1
Mine 1
Maximum
production
capacity
Cumulative
Production
Base year MC curve for one supply region
Maximum
production
capacity
Cumulative
Production
The change of intercept based on base year curve
Supply curves in China-MAPLE
intercept= previous period's intercept + previous period's
slope*production in that year*intercept change factor
(1)
Marginal Cost
the increase rate of intercept is
Mine 4
High average
cost
Low average
cost
influenced by factors such as
Mine 3
the amount of recoverable resources
Mine 2
Mine 1
the annual production
Maximum
production
capacity
Cumulative
Production
The change of intercept based on base year curve
some other geological factors
Supply curves in China-MAPLE
The short-run cost curve moves upwards when the cumulative production
amount increases, and its slope also changes due to the new investment.
Marginal Cost
Marginal Cost
High average
cost
Mine 4
Mine 3
High average
cost
Mine 2
Low average
cost
Low average
cost
Mine 1
Maximum
production
capacity
Cumulative
Production
Base year MC curve for one supply region
Maximum
production
capacity
Cumulative
Production
The change of slope based on base year curve
Coal Supply curves in China-MAPLE
Figure China’s coal supply curve in 2010
Coal Supply curves in China-MAPLE
Figure China’s coal supply curve in 2030
Results & Analysis
21
Social-economic assumptions
Unit
2010
2020
2030
2040
2050
Population
Million
1360
1520
1890
1470
1420
GDP growth rate
%/per
year
7.5
6.2
4.1
3.2
2.5
GDP per capita
Thousan
d RMB/
person
29.5
57.4
98.8
150.8
198.1
Urbanization
%
51.1
58.2
67.1
72.4
75.2
•
GDP growth: Considering the recent economy “New-normal” in China. GDP growth rate will
decrease, 2020 around 6.2%, 2030 around 4.1%. (Cao et al. 2013)
•
The model assumes the population growth scenario that having a second child is allowed
publicly.
China’s total population will peak around 2025–2030, and then reduce to 1.42 billion by 2050.
(Zeng et al. 2013).
22
Design of Scenarios
Abbreviatio Scenarios
n
Description
REF
Reference
Scenario
Taking the current energy policies, technologies and
regulations into simulation.
DEC
Deep DeTaking deep energy conservation measures and
carbonization technologies into account, especially strict coal control
Scenario
measures in power sector and industries.
EPC
End-of-Pipe
Control
Scenario
The maximum level of end-of-pipe measures promotion;
With the BATs (Best available Technologies) adopted and
with maximum application rate among sectors.
COC
Co-Control
Scenario
Combination of both DEC and EPC Scenarios.
23
Energy-related CO2 emission (billion ton)
REF Scenario—Carbon emission
14
12
In 2030, total energy related CO2 emission
11.9 billion tons.
10
8
6
4
2
0
2010
2015
2020
Agriculture
2025
Electricity
2030
Industry
2035
Tranportation
2040
2045
2050
Buildings
Million tons CO2
24
REF Scenario—Local pollutant emission
9000
12000
8000
7000
NOx emission（10^4 ton）
SO2 Emission（!0^4 ton）
10000
8000
6000
4000
6000
5000
4000
3000
2000
2000
1000
0
0
2010
2015
2020
2025
2030
2035
2040
2045
2050
2010
2015
2020
2025
2030
2035
2040
2045
Cement
Electricity
Industry Boilers
Non-Ferrous
Cement
Electricity
Industry Boilers
Non-Ferrous
Iron and Steel
Other Industry
Buildings
Transportation
Iron and Steel
Other Industry
Buildings
Transportation
1800
PM2.5 emission（10^4 ton）
1600
•
With the current end-of-pipe
control measures, SO2、NOX and
PM2.5 in 2030 will increase 163.2%，
81.9% and 60.2% to 2010 level.
•
Air quality will deteriorate in 2030.
•
Necessity of end-of-pipe control
measures
1400
1200
1000
800
600
400
200
0
2010
2015
2020
2025
2030
2035
2040
Cement
Electricity
Industry Boilers
Non-Ferrous
Iron and Steel
Other Industry
Buildings
Transportation
2045
2050
25
2050
EPC vs. REF Scenario—end-of-pipe control measures
Reference Scenario
End-of-Pipe Control
Technology
Current Level
Sector
Electricity
Application of End
Treatment
Best Promotion of
Application
100% installation of FGD
of coal power plant
FGD removal rate of
70%-80%;
FGD installation of 96% in
2030
Wet FGD removal rate of
92%–98%; Dry FGD
removal rate of 85%–92%
NOX
Low NOx combustion
technology with
removal rate of less
than 60%; SCR removal
rate of 85%.
Elec dust removal rate
of 93%; Bag removal
rate of 95%.
LNC installation of 75% by
2010; LNC installation of
84% by 2030; SCR+LNC
installation of 12%.
SCR removal rate of 80%– 100% installation of SCR of
95%
coal-based power plant by
2030
FGD removal rate of
65%–75%
Wet dust removal
efficiency of 80%
Sintering FGD
efficiency of 80%
Sintering, Elec, and
Bag efficiency of 90%
FGD installation around
50%
Wet dust installation of
95% by 2030
Sintering FGD installation
of 40%
Installation of 80% by 2030
SO2
PM2.5
Iron and Steel Sector
End-of-Pipe Control
Technology
Best Available Technology
SO2
PM2.5
Industry Boiler
Application of End
Treatment
Current Level
Strengthening End-of-Pipe Control Scenario
SO2
PM2.5
Elec installation of 80%;
Elec and bag dust removal Bag dust removal and elec
bag removal installation of rate of 99.7%
dust removal rate of 100%
20% by 2030.
by 2030
Building Sector
PM2.5
Coal stove and biomass Coal stove and biomass
stove efficiency of 40%. stove installation of 60%
Transportation
NOX
2030 EU IV and V
standard
PM2.5
FGD removal rate of 90%
FGD installation of 100%
by 2030
Bag and dust removal rate Bag dust removal
of 99%
installation 100% by 2030
Wet FGD efficiency of 98% WFGD installation of 100%
by 2030
Sintering, bag, and
Dust removal in Sintering
emission (0.155–0.255
process installation of
100% by 2030
kg/t product）
Low-pollution coal and
Coal stove and biomass
biomass stove efficiency
stove installation of 90%
of 70%.
EU VI standard reduction
of 80%
EU VI standard reduction
of 66%
Shift from V to VI standard
by 2030
26
EPC vs. REF Scenario—Local pollutant emission
NOx emission
SO2 emission
3500
3500
3000
Nox emission (10^4 ton)
SO2 emission (10^4 ton)
3000
2500
2000
1500
1000
500
2500
2000
1500
1000
500
0
2010
Electricity
Industry Boiler
Residential
PM2.5 emission (10^4 ton)
1200
2015
2020
Cement
Nonmetallic Industry
Iron and steel
2025
0
2030
2010
Coking
Other Industry
Transportation
2015
Electricity
Industry Boiler
Residential
2020
Cement
Nonmetallic Industry
Iron and steel
2025
2030
Coking
Other Industry
Transportation
PM2.5 emission
1000
•
Obvious reduction
800
•
Reduction PM>NOx>SO2;
•
•
•
SO2: 2020(51.5%),2030(68%);
NOx: 2020(43%),2030(61%);
PM2.5: 2020(54%),2030(73.4%);
600
400
200
0
2010
2015
Cement
Nonmetallic Industry
Iron and steel
2020
Electricity
Other Industry
Transportation
2025
2030
Industry Boiler
Residential
27
Previous study—Reduction Effect
Reduction in 2030, compared to 2010 level（%）
Electricity
generation
Cement
industry
Industry
boilers
Non-mental
industry
Other industry
SO2
NOx
PM2.5
91.4%
92.3%
98.7%
90.0%
82.8%
99.3%
75.2%
81.5%
96.6%
84.2%
81.5%
90.2%
84.2%
81.5%
90.2%
30.0%
Residential
buildings
Iron and steel 92.3%
Industry
Transportation 10.0%
10.0%
89.1%
92.5%
93.3%
70.0%
70.0%
68.1%
61.3%
73.4%
80.0%
80.0%
80.0%
National
average level
Target level
•
National average: SO2 reduced
by 68.1%, NOx reduced by
61.3%, PM2.5 reduced by
73.4%.
•
By sectors: iron and
steel/electricity/cement >
Industry boilers/industry
process > residential/transport
•
Not enough to fulfill the air
quality target.
28
INDC DDP Scenario—Primary Energy Consumption &Carbon emission
8000
Primary energy Consumption (million ton)
7000
•
In 2030, 6.12 billion
tce (REF) to 5.86
billion tce (DDP);
•
In 2050, 7.29 billion
tce (REF); 6.17 billion
tce (DDP)
6000
5000
4000
3000
2000
1000
0
REF/DDP
REF
2010
Coal
DDP
REF
2020
Gas
Oil
DDP
2030
Nuclear
Hydro
REF
DDP
2040
Biomass
Wind
REF
DDP
2050
Solar
Other
•
billion ton
REF Scenario
DDP Scenario
2010
7.84
7.84
2020
10.88
10.44
2030
11.88
10.58
2040
12.87
9.96
2050
13.91
7.70
•
Carbon emission peaking in
2030 reduced from 11.9 to
10.6 billion ton, reduced by 1.3
billion ton.
Carbon intensity (per GDP) 60%
reduction 2030/2010.
29
COC vs. EPC Scenario—local pollutant reduction
100%
0%
8%
5%
90%
21%
13%
80%
70%
3%
6%
15%
60%
50%
47%
19%
14%
10%
4%
40%
15%
18%
30%
0%
1%
5%
20%
10%
27%
10%
0%
1%
3%
3%
3%
9%
10%
2%
2%
5%
EPC2030
COC2030
0%
2010
1%
2%
5%
2%
1%
36%
10%
2010
EPC2030
SO2
Electricity
•
•
Cement
4%
18%
Sintering
5%
1%
2%
5%
2%
1%
6%
COC2030
7%
0%
NonMentallic Industry
1%
4%
12%
7%
1%
1%
0%
1%
7%
1%
1%
0%
1%
2010
EPC2030
COC2030
NOX
Industry Boilers and process
1%
4%
PM25
Buildings
Steel making
Transportation
In 2030, SO2, NOx, PM2.5 reduced to 21.15%、22.44% and 16.68% of 2010 level
Contribution of end-of-pipe measures 69%-76%; Contribution from source control 24%-31%.
30
Emission and Concentration change
Health end point
31
Original MACCs
•
When the carbon tax in 2030 is below
100 RMB/ton CO2, carbon mitigation
rate is 14.5%;
•
when the carbon tax reaches 200
RMB/ton CO2, the carbon mitigation
rate increases to 24.2%.
•
Carbon mitigation rate will sharply
increase to 43.8% as the carbon tax
keeps on increasing from 200 RMB/ton
CO2 to 800 RMB/ton CO2.
•
When the carbon tax is above 800
RMB/ton CO2, the effect on carbon
mitigation will fail, and MACCs will
become perpendicular
32
Original MACCs vs. Revised MACCs
•
When the carbon tax level is below 120 RMB/ton
CO2, the carbon emissions mitigation cost can be
balanced by environmental benefit.
•
Positive cut-off from the original cost when
carbon tax in the range of 120–800 RMB/ton CO2.
•
The average environmental co-benefit is in the
range of 100.8–175.2 RMB/ton CO2.
•
When the carbon mitigation rate
increases by 10%, environmental
benefit will rise by 0.1% of the
GDP.
•
In this case, environmental cobenefit can fully compensate
carbon abatement cost when the
carbon mitigation rate is below
16.8%.
33
Revised MACCs under strict end-of-pipe control measures
•
a trade-off exists between the improvement of
carbon mitigation rate and the increase in
environmental benefit
•
co-benefit can still be observed even if the
strictest end-of-pipe control measures are
implemented.
•
increase along with the carbon
mitigation. However, lower.
•
With mitigation rate below 3%, the
carbon mitigation cost can still be
compensated by the environmental
benefit.
•
The environmental benefit
becomes increasingly obvious if the
end-of-pipe control measure is
weak.
•
This phenomenon also explains
why the benefit evaluation is much
higher in several developing
countries
34
Conclusion & Discussion
35
Conclusion & Discussion: the INDC case
INDC case: the energy-related carbon emission in 2030 will reach 10.56 billion tons of CO2. China’s INDC
shows a 11.8% deviation from the REF scenario. (based on our previous study)
•
Marginal abatement cost will reach 82.5 RMB/tCO2 at 11.8% mitigation rate without considering
environmental benefit.
•
When transferred to the GDP loss, the abatement cost of INDC target becomes 0.08% of the GDP
loss.
•
When frozen end-of-pipe measures are considered, CBA shows a break-even point of 16.8% of
reduction rate, which corresponds to 9.9 billion tons of CO2 in 2030.
•
net benefit can be observed. Optimal reduction rate is the cross point where the marginal benefit is
equal to the marginal cost. A reduction of 500 million tons can be achieved in the INDC scenario with
a positive net benefit.
•
The net cost of INDC with the frozen end-of-pipe measures is negative, which shows a no-regret
mitigation target.
•
However, if the most stringent end-of-pipe measures are achieved, the benefit of the INDC will not
compensate the mitigation cost. This result will lead to a net cost of 48.6 RMB/tCO2. Overall GDP loss
will be 0.06%.
36
Next step
37
Current work
• Data transparency
• Detailed supply curve for China-MAPLE;
• Health damage of co-benefit evaluation;
Next step
• Natural gas and energy security co-benefit evaluation;
• Energy-water-climate change nexus
38
Thank you for your attention!
Any comments?
[email protected]
39