International Dialogue Forum on “Low
Carbon Development and Poverty
Reduction”
Indian Statistical Institute, New Delhi, February 2122, 2013
Results from a research project on
“Climate protection, development and
equity: Decarbonisation in developing
countries and countries in transition”
Funded by the German Federal
Ministry of Education and Research
Stephan Klasen (University of Göttingen)
Michael Jakob (PIK)
Jann Lay (GIGA and University of Göttingen)
Poverty reduction and climate change
• As income increases
Steckel et al. (forthcoming)
A more direct link: Energy and human development
Threshold at around 40 GJ per capita
10 GJ per capita can be explained by subsistence needs
Source: Steckel et al. (2011)
Historical contributors to global emissions
Since 2000,
China has
been the
main factor
behind the
increase of
global
emission…
But emission levels still differ
• In rich countries: Emissions have leveled off and are falling
– US move to gas, economic crises, high energy prices
– Europe: Growth of renewables, EUETS, economic crisis
• Per capita emissions (energy-related CO2)
– in China approx. 30%
– in India approx. 80%
… below German levels
Drivers of CO2 emissions in developing countries
China
Newly Industrializing Countries
… increase has mainly been driven by economic
growth!
Driving forces of changes in emissions
GDP per capita
Energy intensity
CO2 emissions per capita
Carbon Intensity
• Before 2000,
growth partially
off-set by lower
energy intensity
• After 2000: slowdown or even
reversal, and
increasing
carbon intensity
Driving forces of changes in emissions
Understanding changes in carbon intensity
World
OECD
China
Other NICs
• China’s increasing
carbon intensity
almost exclusively
due to rising share
of coal in the
energy system
(“renaissance of
coal”)
Source: Jakob et al. (2012)
Energy use patterns
Pronounced
differences between
OECD and non-OECD
countries w.r.t.
energy use patterns
on the level of
primary energy
carriers and economic
sectors…
Economic and energy use convergence
• … and economic
convergence is
closely related to
conver-gence of
energy use
patterns.
Inequality, growth, and emission dynamics
• Energy use and consumption patterns (lifestyles) differ
considerably within countries between income groups
• Last 20 years have seen high growth and rising inequality in
many developing countries (including India), alongside rising
emissions
• Are the rich responsible for rising carbon emissions?
• Micro level analysis using carbon footprint analyses for India,
Indonesia, Philippines
– Linking household expenditure patterns with input-output
tables and emission intensity of production
• Income largest driver of
carbon footprint
• Rising middle class will
strongly increase
emissions (move to
carbon-intensive
lifestyles)
• Higher emission due to
urbanization and
education (over and
above income effect)
Source: Grunewald et al. 2012
Micro-level analysis: India’s Carbon Footprint 2004
Carbon Footprint in Philippines
2006
3
2
1
0
Source: Serino (2012)
4
5
2000
1
2
3
4
income quintiles
5
1
2
3
4
income quintiles
5
cereals & rootcrops
fruits & vegetables
meat, dairy & egg
fish & marine
other foods
beverages & tobacco
hh operation
personal care
water
fuel & light
transportation
communication
clothing & footwear
education
recreation
medical care
nondurables
durables & equipment
repair & maintenance
other expenditure
Carbon footprint in Indonesia
5,000
Carbon Footprint by Income Group
4,000
2005
3,000
kg CO2
2,000
1,000
2,000
0
0
kg CO2
3,000
Carbon Footprint by Income Group
1,000
Source: Irfany (2013)
2009
1
2
3
4
5
1
2
3
4
5
Remarkably similar patterns in 3 countries
• Income growth by far the biggest driver of carbon footprint at
the household level (elasticity close to 1)
• Urbanization and education are additional drivers of carbon
footprint (but effect weakening over time);
• Reducing inequality will speed up poverty reduction but will
(moderately) increase emissions
• Does this also hold in a cross-section of countries?
– Macro level analysis using cross-country data
• Non-linear effects of income, inequality, and the interaction
of the two;
Macro-level analysis
Source: Grunewald et
al 2012
• Non-linear effect of
income growth
(Environmental Kuznets
Curve, but turning point
very high)
• The higher inequality, the
lower aggregate emissions
• In poor and middleincome countries, this
trade-off particularly
severe
• In rich countries less so
(and win-win possible)
How to grow without increasing emissions?
• Breaking the convergence between economic development and
energy use patterns: Renewables as one pathway
• Trade-offs
– Often more expensive than fossil fuels
– Higher energy prices
• With adverse distributional implications
• With negative externalities for economic development
(industrialization!)
– Technological challenges (e.g. grid integration)
• Synergies/win-win situations
– “Green” energy systems with positive ancillary (environmental )
benefits
– Pro-poorness of decentralized energy systems
– Green jobs?
19
Renewable Energy in Developing Countries (DCs)
– Wide-spread adoption: generated by 83% of all DCs
– Average share of total electricity: 38 percent (11 percent
weighted with total country electricity consumption)
– Top 3 DC producers (billion kWh in 2009): China (549), Brazil
(387), Russia (162), also have top technically exploitable
capability
• Non-hydropower (biomass, geothermal, solar, and wind)
–
–
–
–
–
Generated by about 45% of DCs
Average share of total electricity: 1.4 percent
Most important: biomass, geothermal
Very uncommon: solar and wind
But: High growth rates from low basis
Source: Pohl et al. (2012)
• Hydropower
• Study of diffusion of non-hydro renewable energy technologies for
electricity generation (NHRE) across 108 developing countries
(between 1980 and 2010)
• Main findings: NHRE diffusion accelerates with
– Implementation of economic and regulatory instruments
– Higher per capita income and schooling levels
– Stable, democratic regimes
• NHRE diffusion is slower with
–
–
–
–
Greater openness and aid
Institutional and strategic policy support programs
Growth of electricity consumption
High fossil fuel production
21
Source: Pohl and Mulder (2013)
Macro analysis of RET adoption
• Kenya's SHS market one of the biggest worldwide
• Data on households from the Kenyan Integrated Household
Budget Survey (KIHBS) 2005/06, 13 430 households
• With information on SHS use and potential drivers
– Income, education, residence (rural, urban), housing situation
– Kerosene price
– Potential grid access, prevalence of SHS
• Lighting fuel choice model
22
Source: Lay et al. (2012)
Micro analysis: Solar home systems in Kenya
Main findings
• Evidence for a crosssectional energy ladder
with very high income
threshold for modern
fuel use – including
solar energy use – to
move beyond
traditional and
transitional fuel
• Income, education and SHS clustering are key determinants of SHS
adoption
23
Conclusions
• In the past: Close relationship between economic growth and
emissions growth
• No sign of decoupling in fast-growing developing countries
– “Renaissance” of coal
– Convergence of energy use patterns
• Inequality of per capita emissions within countries is
important: Middle-class effect
• Very low adoption rates of non-hydro RET, consistent with
very late ‘takeoff’ of SHS at the micro level
• ‘Decarbonization’ of economic development – in particular
the transition – is no trivial task
Policy implications
• Short to medium-term: Reducing emission intensity of
production patterns (energy source, use, and systems)
• Longer-term: Lifestyle and urbanization patterns (but here
bigger task is first in industrialized countries)
• General policy guidelines: (1) Avoid lock-ins, (2) Consider
possible synergies and trade-offs
• If the policy objective are GHG emissions reductions: Think
global, seek cooperative solutions
– Compensation mechanisms, NAMAs
– Linking of emissions trading schemes
• Tax/subsidy policies as feasible and effective domestic
instrument: Green and progressive tax reforms possible