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Two Low-Carbon Development Pathways in India
Michiko NAMAZU*1, Shinichiro FUJIMORI2,
Priyadarshi Ramprasad SHUKLA3 and Yuzuru MATSUOKA1
1

Department of Environmental Engineering, Graduate School of Engineering, Kyoto University
Kyoto daigaku-Katsura, Nishikyo-ku, Kyoto 606-8540, Japan
2
Center for Social and Environmental Systems Research, National Institute for Environmental Studies
16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
3
Public Systems Group, Indian Institute of Management, India*e-mail: namazu.michiko.22m@st.kyoto-u.ac.jp

Abstract
This study assumes two different scenarios for India and analyzes the energy system and macroeconomic
impacts of GHG emissions reductions which follow the global halving target. The two scenarios areconceptualized as: Advanced (ADV; a society which actively accepts innovations and changes) and Conventional (CNV; a society which is conservative and passive regarding innovations and changes).
Both scenarios are constrained by the GHG emissions target, which allows for a 51% increase in 2050
compared with 2005. This target was obtained on the basis of two ideas for 2050: a global 50% reduction
target and a globally equally distributed amount of emissions allowed per capita. A dynamic recursive CGE
model is applied to assess India’s GHG emissions reduction measures quantitatively. The contributions andfindings of this study are:1) Description of two development pathways to a low-carbon society for India.
2) That significant changes in energy systems were found as results from emissions reduction in bothscenarios; however, the reduction measures and the economic impacts differ fundamentally between the
scenarios because of differences in the macroeconomic, energy and technology dynamics.
3) That the ADV society, having a much higher potential GDP compared to the CNV, was found to have apotential to emit more GHG and hence would need enhanced emissions reduction measures and higher
emissions prices to meet the emissions target. Although the ADV society has the advantage of introducing
low-carbon technologies like CCS and renewables, the overall effect results in the ADV society having
higher macro-economic impacts from the emissions reduction target: the percentage GDP loss in ADV is
38% higher than that in CNV in 2050. However, since the ADV society’s GDP is more than twice that of
CNV in 2050, this does not simply mean that CNV is an economically more desirable society.
Key words: CGE model, economic effects, greenhouse gas (GHG), India, mitigation

1. Introduction
According to the Intergovernmental Panel on Climate
Change (IPCC), the signs of warming of the climate
system are unequivocal, and most of the observed increase in global average temperatures since the mid-20th
century is very likely due to the observed increase inanthropogenic greenhouse gas (GHG) concentrations(IPCC, 2007). Although India has the second largest
population in the world as of 2010, at 18% of the total
global population (United Nations, 2010), its GHG
emissions in 2005 were 2.1Gton of CO2eq, which is only
5% of global GHG emissions (European Commission,2011). Current emissions from India are relatively small;
however, United Nations (UN) predicts that the population in India will continue to grow, and in the year 2023,
it will surpass that of China to be the world’s largest
Global Environmental Research
17/2013: 119-128
printed in Japan(United Nations, 2010). This means that India has apotential to be a large emitter of GHG in the future. In
Copenhagen, India shifted away from its old negotiating
position, which was centered on allocation of the normative “equal per capita emissions entitlements” principle and committed to a voluntary and conditional targetof reducing the carbon intensity of its economy by
20%-25% from 2005 until 2020 (Shukla & Dhar, 2011).
The new position emphasized the responsibility of India
as one of the international community members whichtackle climate change and emissions reduction. India is
now facing a tipping point to commit to internationalemissions reduction, and towards a low-carbon society.
How India will develop in the future is a question that
attracts huge attention globally since India’s transformation pathway will have consequences for global sustainable and low-carbon growth. Shukla and Dhar (2011)©2013 AIRIES

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analyzed the effect of emissions reduction consistent
with a 450 ppm target under two different scenarios:
conventional and sustainable mitigation scenarios. The
study assumed two different attitudes to mitigation anddescribed two possible future visions of India by 2050.
The study provided impressive findings; however, it did
not take into account socioeconomic impacts fromemissions reductions which could be crucial to implementing mitigations. Moreover, the study used an exogenous carbon price which was a result from anothermodel of a 450 ppm scenario, though there was significant uncertainty regarding carbon prices in the future.
This study analyses the implications of India’s longterm low-carbon growth, especially focusing on economic effects from the mitigation measures. Because
India has been developing rapidly, its future development
includes huge uncertainties. Following Shukla and Dhar
(2011), this study assumes two different future scenarios
for India, advanced (ADV) and conventional (CNV). The
two scenarios represent two different societies: ADV
describes a society that actively accepts and welcomes
innovation and changes, while CNV describes a societythat is conservative and passive about achieving innovation and changes. Socioeconomic drivers, including
potential GDP, population and energy efficiency improvement, are set in accordance with the storylines of
ADV and CNV. Both societies attempt to achieve GHG
emissions reduction to reach the targets for 2050: i)halving global GHG emissions compared to those in1990, and ii) distribution of rights to equal amounts ofemissions per capita globally. This global halving target
is the one confirmed at the G8 summit in L’aquila in2009; however, the international community has notreached any agreement on achieving this target. There area variety of ideas about how to share the burden among
countries, for example, government-proposed burden
sharing arrangements submitted by country delegations
to the Ad Hoc Groups on the Berlin Mandate (Ringius etal., 2001). This study takes into account the idea of equalamounts of emissions per capita, which is one of thegovernment-proposed burden sharing arrangements,
mainly because of relatively small differences among
future population projections on which calculations ofeach country’s emissions reduction target are based.
The economic effects of the two scenarios underemissions constraints from the year 2013, when India is
supposed to start its emissions reduction, to the year 2050
are evaluated using a computable general equilibrium(CGE) model in this study. The model is a recursive
dynamic CGE model, and includes multi-gas (CO2, CH4
and N2O) emissions from both energy and non-energy
sectors.
In this paper, Section 2 provides a brief description of
the model. The concepts of the two scenarios, the socioeconomic drivers, emissions reduction targets and mitigation measures are introduced in Section 3. Section 4
presents the results from scenario modeling exercises;
and Section 5 presents the conclusions of this study.

2. Model Description
The model used for this study is a dynamic recursive
country Computable General Equilibrium (CGE) model,
in which equilibrium is found every year. The details of
the model are explained elsewhere in this journal: see“Global low-carbon society scenario analysis based on
representative two socioeconomic scenarios” (Fujimori
et al., 2013), and the manual is available online (AIM/
CGE [basic] manual, Fujimori et al., 2012). The model
encompasses 30 industrial sectors, 21 commodities,
government, households and enterprises. A commodity
may be produced by several different industrial sectors,
for example, electricity, which is treated as a commodity
in the model, is generated by industrial sectors including
the wind and solar photovoltaic power sectors. The total
capital investment in a year is determined on the basis of
the target GDP growth for the year, which differs from
GDP in being a result which is endogenously calculated
in the model. The investment in one year becomes newcapital in the next year and is allocated to the production
sectors. The amount of the new capital for each sector
depends on the capital rate of return while old capital isfixed in its original sector, and each sector has capitalstock with 4% annual depletion. International trade isformulated as per the Armington assumption with constant elasticity of substitution (CES) (Arrow et al., 1961)
and constant elasticity of transformation (CET) (Powel &Gruen, 1968) functions for import and export, respectively. The international goods price is an exogenous
parameter for this model, and in this study, international
goods prices are calculated using the global CGE model(Fujimori et al., 2011).
The base year’s social accounting matrix (SAM) andthe energy balance table have been compiled from several sources, for example national accounts, industry
statistics, and energy statistics with adjustments to correct for inconsistencies among the data (Fujimori &Matsuoka, 2011).

3. The Two Scenarios
3.1 ADV and CNV
This study analyses two different scenarios, advanced(ADV) and conventional (CNV). ADV represents asociety that is proactive toward introducing brand-new,innovative social systems, institutions and technologies,
for example, technologies to utilize renewable energies
for realizing next-generation low-carbon societies. In
contrast, a CNV society is cautious about changing social
systems, institutions and technologies, and is unwilling toincur transaction costs for realizing next-generation lowcarbon societies.
3.2 Socioeconomic settings of the two scenarios
The drivers of the socioeconomic scenarios arequantified so as to be consistent and coherent with thestorylines of the two scenarios.

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3.2.1 Population
The population projections (Fig. 1) through to 2050
are as per the medium scenario from United Nations
Population Prospects 2010 (United Nations, 2010). The
population of India is thus assumed to grow through thewhole period at a 0.9% per year average growth rate andbecome the world’s largest in the year 2023, reaching
1.7 billion in 2050.

the energy supply and consumption results from themodel without the GHG emissions reduction targets areclose to the values in the estimations. With these AEEI
values (Table 1), in the absence of GHG emissions constraints, total primary energy supplies in ADV and CNV
are 2,800 and 2,000 Mtoe, respectively, and total final
energy consumption in the two scenarios is 1,600 and
1,000 Mtoe, respectively, in 2050.

3.2.2 GDP (MER)
There are wide variations in the literature about GDP
projections for India (Fig. 2). As target GDP, this study
uses GDP projections derived using an econometricmodel (Kawase & Matsuoka, under review), which
considers the governance index (D. Kaufman et al.,2010), average education period and population as input
data to project the future potential GDP. The two GDP
assumptions used for this study are almost the median ofthose of other references (Fig. 2). Note that the GDP
assumption for ADV is lower than the that of Shukla andChaturvedi (2012).

3.2.4 Intermediate input efficiency improvement
Although the demand for materials used in social
capital like steel and cement grows with economic
development, it gradually decreases following economic
development because less new capital is needed. According to Vuuren et al. (1999), historically metal consumption per GDP forms an inverted U shape mainly
because of three factors: the industrial structure shift
following economic development, the substitutability ofmetal, and technological improvement. This study
reflects this phenomenon by decreasing the input coefficient of iron-steel and mineral products including cement
at constant rates (Table 1). The reduction rates are determined based on the Directory of Iron and Steel Statistics(The Japan Iron and Steel Federation, 2010) and Energy
Technology Perspectives 2010 (International Energy
Agency, 2010).

3.2.3 AEEI
The scenario analyses explicitly consider autonomous
energy efficiency improvement (AEEI), i.e., AEEI is aheuristic representation of non-price driven improvements in technology that create a progressively energysaving bias in technical change. Various studies(Appendix 3) show that in the year 2050, India’s total
primary energy supply will be 1,200-2,900 Mtoe, while
its total final energy consumption is estimated to be
900-1,400 Mtoe. This study takes into account theseestimations and sets AEEI parameter values with which

2050

United Nations Population
Prospects 2010 Medium

2

3.3 Emissions reduction target
The emissions constraint in 2050 is drawn from theglobal halving target, which aims to halve GHG emissions compared with the 1990 levels by the year 2050,
with allocating an emissions allowance to each country in
2050 based on the equal distribution of emissions
per capita globally. The emissions constraint in 2020
was determined from India’s national reduction target(Ministry of Environment and Forests, India, 2010), a
20%-25% reduction of CO2 intensity in 2020 comparedwith the 2005 level. To summarize, in this analysis,
India’s emissions reduction target for 2020 is set at a 23%
reduction of GHG intensity of GDP compared to 2005
levels, and the target for 2050 is set at a 51% increase in
GHG emissions compared with 2005 levels. India isassumed to begin emissions reduction from 2013, andduring the periods from 2013 to 2019 and from 2021 to
2049, constant reduction rates of GHG emissions pertarget GDP in each scenario are assumed.

2050

Population (bil. people)

2.5

3.4 Mitigation measures
Several mitigation measures are considered in thisstudy, and the detailed settings of the mitigation measures
for the two scenarios differ in accordance with differences in the scenario concepts.

1.5
1
0.5

2045

2040

2035

2030

2025

2020

2015

2010

2005

0

Fig. 1 Population prospects in India.

35
ADV

25

CNV

20
15
10
5

Fig. 2 GDP prospects in India.

2045

2040

2035

2030

2025

2020

2015

2010

0
2005

GDP (tril. 2005US$/year)

40
30

3.2.5 Power generation (fossil fuel)According to Energy Technology Perspectives 2010
(International Energy Agency, 2010), the investment
costs of coal and natural gas burning plants are expected
to decrease at an annual rate of 0.5%-0.6% during 2010
to 2050. The costs of fossil fuel thermal power are
assumed to decrease by 0.6% a year in this study.

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3.4.1 GHG emission taxation
A GHG emission tax is that which is imposed per unitof GHG emissions on the end-use emitter, including emissions from not only energy combustion but also industrial
processes. Hereinafter, this tax per ton of GHG emissions
is termed the “emissions price.” The tax restrains activities entailing GHG emissions, and as a result, the switch
to high energy efficiency equipment and low-carbon
energy is accelerated. In this study, the revenue from the
GHG emission tax is recycled to the household sector. In
the model, the GHG emissions price is described as acomplementary problem, so that the price is determined
endogenously to satisfy emissions constraints.
3.4.2 Household energy consumption
The study assumes that households have positive
elasticity of energy consumption vis-à-vis income, andnegative elasticity of energy consumption vis-à-vis energy prices. The household energy consumption mix isdefined by the logit function (Clarke & Edmonds, 1993),
which determines energy types to be consumed based on
the prices and subject to total household energy demand.
3.4.3 Industrial energy consumption
Except in the case of energy transformation sectors
such as the power generation sectors, industrial sectors
have production functions to describe their production
activities in the model. In the production functions, value
added and energy composite are aggregated by the CES
function and the energy composite is determined using
the logit function; the shares of energy commodities
consumed by industrial sectors depend on their prices,
just as for the household sector.

3.4.4 Transport
Both passenger and freight transport by road, bymeans of cars or trucks, is assumed to increase rapidly
compared to the other transport modes in the absence of
GHG emissions constraints in India (Shukla, 2010). In
this study, the two societies, ADV and CNV, are assumed
to have different attitudes to transport. In the ADV society, people prefer public transportation while in the CNV
society people prefer private transportation like privatecars. An increase in the total transport volume of passengers is determined by an increase in household income orGDP, while the total transport volume of freight is determined by service demands from industries and services.
The energy types consumed by each type of transport are
determined using the logit function and based on energy
prices.
3.4.5 Power generation (low-carbon energy)
Although still now the costs of introducing solar
photovoltaic and wind powers are relatively expensive,
they are expected to decrease significantly in the future.
According to International Energy Agency (IEA) (2010),
the cost of investing in solar photovoltaic power will
decrease from $3,500-5,600/kW in 2010 to $1,0001,600/kW in 2050 and that for wind power will decrease
from $1,450-2,200/kW in 2010 to $1,200-1,600/kW in
2050. In this study, introduction cost reductions are assumed for solar photovoltaic and wind power generations
and the rates of these reductions are set based on theabove information from IEA. The specific numbers areshown in Table 1.
Governments often base their decisions to introducehydroelectric power on its joint outputs like electricity,

Table 1 Scenario setting overview.
Advanced (ADV)
Conventional (CNV)
A society that is proactive and willing to take risks to innovate A society that hesitates to change social systems, institutions andsocial systems, institutions and technologies for realizing technologies, putting more focus on transition costs for realizing
next-generation low-carbon societies
next-generation low-carbon societies
GHG emissions
In 2020: 23% reduction of GHG emissions per GDP from 2005 levels (this target is not active because emissions without constraint in
reduction targets
2020 are lower than the target in this study)
In 2050: 51% increase in GHG emissions from 2005 levels
Population
Continuously increases and reaches around 1.7 billion in 2050
GDP growth rate (% per year) 2006-2015: 8.1, 2016-2030: 7.0, 2031-2050: 5.3
2006-2015: 8.1, 2016-2030: 4.9, 2031-2050: 3.4
AEEI(% per year)
Coal/coal products: 2.3, Oil/petroleum products: 1.7, Gas: 1.0 Coal/coal products: 2.0, Oil/petroleum products: 1.0, Gas: 1.0
Electricity: 1.0
Electricity: 0.2
Intermediate input efficiency Construction sector, Mineral products (cement) input: 2.0
Construction sector, Mineral products (cement): 2.0
improvement (% per year)
Other manufacturing sector, Iron and steel input: 1.0
Other manufacturing sector, Iron and steel: 1.0
Power generation (fossil fuel) Cost reduction speed: 0.6% per year
GHG emission mitigation measures
Household energy
Demand decreases with an increase in energy price while it increases with an increase in household income. The energy commodity
consumption
allocation is determined using the logit function.
Industrial energy consumpEnergy demand is determined by the CES function and the energy commodity share is determined using the logit function.
tion (non-energy
transformation sector)
Transport
The ADV society prefers public transportation while the CNV prefers private transportation like private cars. The increase in totaltransport volume of passenger transport is determined by the increase in household income or GDP while the total transport volume of
freight transport is determined by service demands from industries. The energy types consumed by each type of transport are
determined depending on energy prices, using the logit function.
Power generation
The cost reduction rate for solar photovoltaic energy is 5.2% per year in ADV and 1.6% per year in CNV while the cost reduction rateof wind power is 1.8% per year in ADV and 0.8% per year in CNV.
The maximum amount of hydro energy introduction (potential) is 51.6 Mtoe/year.
CCS technology is available after 2020 in ADV with a maximum introduction rate of 3% per year. The assumed additional costs
toward the introduction of CCS technology are shown below.
CCS technology introduction costs:
Thermal power plant
Coal
Oil
Gas
Biomass$/tCO250
70
70
70
Industry
Mineral products
Paper and pulp
Coal refineries$/tCO2200
150
100
Non-energy-related GHG
The reduction of non-energy-related GHG is determined based on GHG emissions prices or scenarios.
Concept

Two Low-Carbon Development Pathways in India

water distribution and flood control. The hydro-power
capacity is therefore introduced exogenously, referring to
the Department of Atomic Energy (2010) in this study.
The hydro potential, i.e., maximum annual generation isassumed at 52 Mtoe per year.
3.4.6 CCS technology
Carbon dioxide capture and storage (CCS) technology is assumed to be available only in ADV societies andonly after 2020. CCS technology is assumed to be introduced to thermal power plants, coal refineries, and mineral products and paper-pulp industries at a maximum
introduction rate of 3% per year. The additional cost of
introducing CCS technology is set based on IEA (2004)
estimates.
3.4.7 Non-energy related GHG reduction
This study assumes that GHG constraints also lead to
reduction in GHG emissions from non-energy sources.
The emissions reduction in the agricultural sector will
depend on the emissions price, and the coefficient which
determines the reduction ratio depending on energy priceis set based on Hasegawa & Matsuoka (2010) in thisstudy. On the other hand, without GHG constraints, GHG
emissions from land use and land use change follow
Riahi et al.(2007) and with GHG constraints, the emissions are assumed to become zero in 2050.
3.5 Overview of the scenarios
The scenario characteristics and quantifications mentioned above are summarized in Table 1.

4. Results
In this section, the two scenarios which assume no
explicit GHG emissions mitigation are denoted as REF(i.e., ADV_REF and CNV_REF) and the scenarios that
include GHG emissions reduction are denoted with theextension MIT (i.e., ADV_MIT and CNV_MIT). All
prices below are to be taken at the 2005 value of the US
dollar, unless stated otherwise.
4.1 GHG emissions and the emissions price
Figure 3a shows GHG emissions pathways for different scenarios and Fig. 3b shows GHG emissions pricepaths for the mitigation scenarios.
GHG emissions are 9.1 and 11.9 Gt of CO2 eq in 2050
in the CNV_MIT and ADV_MIT scenarios, respectively.
a)123

Because this study does not consider international emissions trading, it assumes an identical 3.2 Gt of CO2eq
emissions allowance in 2050 for the two scenarios-ADV_MIT and CNV_MIT.
The emissions price in ADV_MIT reaches$180/tCO2eq in 2050 while the price in CNV_MIT islower than that in ADV_MIT throughout the entire
period; the maximum emissions price in CNV_MIT is$144/tCO2eq in 2050. The reason why the price in
ADV_MIT is higher than that in CNV_MIT is because
the ADV_MIT scenario has higher potential to emit GHG
because of its rapid economic growth compared with the
CNV scenario. Figure 3a shows that in order to achieve
the reduction targets, the ADV_MIT society needs to cut
its emissions by 73% from ADV_REF, while on the other
hand, the CNV_MIT society has to cut its emissions by
64% from CNV_REF, which means that the ADV_MIT
society would need to cut about 1.5 times more emissions
than the CNV_MIT by 2050. ADV_MIT has CCS technology as one of its mitigation measures and a relatively
rapid decrease in renewable energy introduction costs;
therefore it can be said that ADV_MIT will take advantage of it in reducing GHG emissions. The results,
however, show that even with this advantage, the emissions price in ADV_MIT will become higher than that in
CNV_MIT.
4.2 GDP and GDP loss
The GDP in ADV_REF is almost twice as large asthat in CNV_REF in 2050 (Fig. 4a). The GDP loss, which
results from the emissions constraints, is greater in
ADV_MIT than in CNV_MIT throughout the period, andin 2050 the GDP loss in ADV_MIT is 6.9% while that in
CNV_MIT is 5.0% (Fig. 4b). The main reason the GDP
loss in ADV_MIT is higher than that in CNV_MIT is
because the ADV_MIT society has to reduce emissions
more, as mentioned in the previous section. In order toreduce emissions further, the emissions price becomes
higher and activities entailing emissions are strictly constrained. However, a higher GDP loss does not mean alower GDP, and the absolute GDP of ADV_MIT is almost twice as large as that of CNV_MIT (Fig. 4a).
4.3 Total Primary Energy Supply
Figure 5 shows that the REF scenarios have a higher
primary energy supply than the MIT scenarios. Thismeans that the emissions constraints have impacts on
the primary energy supply systems in both ADV and
b)

14
200

CNV_MIT

Fig. 3 a: GHG emissions, b: GHG emissions price.

2050

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0

2030

50

2025

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2040

2035

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2025

2020

2015

2010

0

ADV_MIT

2020

2

100

2015

4

2010

6

150

2005

ADV_REF
CNV_REF
ADV_MIT
CNV_MIT

8

Emission price(2005US$/t CO2 eq)

10

2005

GHG emissions(GtCO2 eq/ year)

12

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a)

b)

2050

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2030

2050

2045

2040

2035

2030

2025

2020

2015

0

2010

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CNV_MIT

2025

4

ADV_MIT

2020

6

2015

ADV_REF
ADV_MIT
CNV_REF
CNV_MIT

8

2010

10

8
7
6
5
4
3
2
1
0

2005

12

GDP loss (%/year)

14

2005

GDP (tril. 2005US$/year)

16

Fig. 4 a: GDP, b: GDP loss.

CNV societies. A significant increase in primary energy
supply, especially biomass and nuclear, is seen for the
ADV_MIT scenario in 2050. In ADV_MIT, the increasein nuclear power supply is accelerated by the higher
emissions price and higher demand for electricity; but on
the other hand, the increase in biomass supply is accelerated mainly by the introduction of CCS technology tobiomass-burning plants. In addition, Fig. 5 shows thatboth ADV_MIT and CNV_MIT depend on oil supply
even in 2050. Supplied oil is mainly consumed ultimatelyby the transport sector, which is expected to show rapid
growth following India’s economic development.
Although electric vehicles will expand their share in both
AVD_MIT and CNV_MIT, most vehicles will use oil as
their energy source because of relatively high electricity
prices. The coal supply also accounts for some share evenin 2050. India’s emissions target allows India to increase
its emissions by 51% by 2050; therefore, there is someroom for India to consume fossil fuels.

demand change are the decrease in traditional biomass
use and the balance between household income increaseand energy price increase. The household energy demand
increases following an increase in household income
while the demand decreases following an increase inenergy prices. In CNV_MIT, an increase in energy prices
has stronger impact on household energy demand than anincrease in household income; therefore, the energy consumption excluding biomass does not increase so much.
On the other hand, the total household energy consumption in ADV_MIT shows a constant increase, and in 2050,
the energy consumption excluding traditional biomass ismore than six times larger than that in 2005. This meansthat a household income increase has a stronger impact,
and the demand for energy excluding biomass increases
constantly in ADV_MIT. As proof of this, the householdincome in ADV_MIT in 2050 is almost twice as large asthat in CNV_MIT, and energy prices increase in
ADV_MIT almost as much as in CNV_MIT.

4.4 Total final energy consumption
Figure 6 shows that the emissions constraint causes
significant decreases in energy consumption in both
ADV and CNV societies. The final energy consumption
in ADV_MIT is higher than that in CNV_MIT throughout the period. The most significant difference between
ADV_MIT and CNV_MIT can be found in residential(household) energy consumption. In ADV_MIT, consumption continuously increases while in CNV_MIT it
decreases after 2020. The reason is explained in the next
section.

4.6 Industries
The trend in total industrial energy consumption issimilar to that of households mentioned in the previous
section. Because CCS technology is not available, theconsumption of fossil fuels, especially coal, decreases in
the CNV_MIT scenario. In the ADV_MIT scenario, CCS
technology is introduced to the coal refinery industry
from 2038 and to the paper and pulp industry from 2043.
In 2050, 33 Mt CO2 is captured and stored by this technology, accounting for around 8% of the emissions fromall industrial sectors. Figure 8 shows that both of thesescenarios are reliant on oil even in 2050. The main consumer of the oil is the chemical, plastic and rubber products industry. After GHG emissions constraints are posed,
this industry will start to reduce its coal and biomass
consumption and increase electricity, oil and natural gas
consumption. The results show that the emissions price is
not high enough for the industry to shift from oil to otherlower-carbon energies, especially in ADV_MIT.

4.5 Households
As mentioned in the previous section, the householdenergy consumptions of the two scenarios are totallydifferent. Figure 7 shows the detailed energy mixes ofhousehold energy consumption. Note that in Fig. 7, “Bio”
means so-called “traditional biomass,” and this study
assumes that traditional biomass use decreases with norelation to GHG emissions constraints, following thegeneral historical trend. After 2020, the year in which
GHG emissions reduction starts, the total householdenergy consumption in CNV_MIT decreases significantly, although the consumption excluding traditional
biomass increases slightly with increasing share of electricity consumption. The reasons for this total energy

4.7 Power generation
Figure 9 shows the power generation mix in all four
scenarios. The power generation mixes of the REF cases
seem similar: almost all electricity is generated fromcoal-burning plants. The total power generation in
ADV_MIT is almost twice that in CNV_MIT. In

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renewable energies like solar, wind and biomass energies,
and in the end, the power generation in ADV_MIT in
2050 becomes almost the same as that in ADV_REF. On
the other hand, in CNV_MIT, even in 2050, the greatest
power supplier is coal and the second greatest is biomass,and the combined share of these two sources reaches 46%.
This is because in CNV_MIT, the cost of introducing
renewable energy is not cheap enough, so the emissions
targets are achieved by reducing electricity demand andcoal consumption.
Fig. 5 Total primary energy supply.

Fig. 6 Total final energy consumption.

Fig. 7 Household energy consumption.

Fig. 8 Industry energy consumption.

ADV_MIT, because of the emissions constraint, the increase in speed of power generation slows down after
2020; however, gradually power generation starts to besupplied by various types of power sources including

4.8 GHG reduction measures
Because ADV_REF and CNV_REF have different
emissions paths, ADV_MIT and CNV_MIT require different amounts of emissions reduction to satisfy the GHG
emissions constraints (Fig. 10). The most importantmitigation measure is shared by the two scenarios –renewable energy, especially biomass energy. In the
ADV_MIT scenario, the second largest measure is CCS
technology, and various other measures are also introduced. On the other hand, the second most importantmeasure for CNV_MIT is a decrease in demand forelectricity, and total decrease in renewable and electricity
demand accounts for 60% of the total reduction.

5. Discussion
This study uses a CGE model to analyze the economic
effects in India of GHG emissions reduction during theperiod from 2005 to 2050. Two scenarios are assumed:
the first one is ADV, which describes a society that isopen to innovation and changes so that the introductionof new technologies is accelerated and energy efficiency
is rapidly improved, and the other is CNV, which describes a conservative society that is passive to innovation and changes so that the speeds of new technology
introduction and energy efficiency improvement arecomparatively slow. The emissions reduction target in
2050 for India used in this study was a 51% increase fromthe 2005 level, and this target was drawn from the global
halving target to reduce global GHG emissions by 50%
compared with 1990 levels by the year 2050 and aiming
for global equality in emissions per capita by the year
2050. Socioeconomic scenarios, GHG emissions pathways to 2050, and GHG mitigation measures were set inaccordance with the concepts of the two scenarios. A
dynamic recursive CGE model was applied and theeconomic effects of GHG emissions reductions wereanalyzed quantitatively.
This study’s results describe the two societies quantitatively, as follows:
The ADV scenario- The ADV society achieves a GDP of $13.0 trillion in
2050, which is 15.9 times greater than that in 2005.
- In order to achieve the GHG emissions target, themaximum emissions price becomes $180/tCO2eq and
the GDP loss becomes 6.9% in the year 2050.
- Energy consumption increases drastically even under
GHG emissions constraints.

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Fig. 9 a: Power generation - ADV_MIT, b: Power generation - ADV_REF,
c: Power generation - CNV_MIT, d: Power generation - CNV_REF.

Fig. 10 a: Mitigation measures in ADV_MIT, b: Mitigation measures in CNV_MIT.

- Since the price of fossil fuels becomes relatively
expensive under the GHG emissions reduction target,
electrification is accelerated in household and industrial sectors.
- The electricity supply mix consists of multiple sources
and the share of new and renewable energy sources
rises to more than 70% in 2050.
The CNV scenario- The total GDP becomes $6.7 trillion in 2050, which is
7.5 times as large as that in 2005.
- The maximum emissions price is $144/tCO2eq in 2050,
with a 5.0% GDP loss.
- The emissions reduction target is achieved mainly by adecrease in demand for electricity and an increase inrenewable power, especially biomass power.
- Electricity generation is half of that in ADV in 2050;
and more than 40% and 55% of total power is suppliedby conventional fossil fuels and new and renewableenergy sources, respectively.
The results of this study underscore the fact that energy demand and the shift to renewable energy could
possibly partly offset the impact of GDP losses due toreduced pollution loads from fossil fuel use and improved energy security, since a significant fraction ofIndia’s fossil energy demand is expected to be met byimports (Shukla & Dhar, 2011). Appropriate governanceand policy implementation will be required to achieve
any significant change of energy system in India, however, an excessive shift to biomass energy may add towater stress and food security risks, too (Shukla et al.,2010). These barriers should be considered carefully inadvance.
In addition, compared with other developing countries like China, India has less strict emissions reduction
targets under the equal emissions per capita scheme because of its continuously growing population. The emissions target allows India to use fossil fuels even in 2050;
therefore, compared with China (Namazu et al., 2013),
India has a comparatively lower emissions price. Thismeans that India has the possibility to become an exporter in the international emissions trading market if it isrealized, and the trading system may give a significant
financial advantage to India.
In conclusion, this study analyzes two development
pathways to a low-carbon society for India. Emissions
reduction leads to significant changes in energy systems
in both scenarios, and the reduction measures and theireconomic impacts differ greatly between the two. Especially with rapid economic growth (ADV scenario), India
is still able to achieve its emissions target with significant
energy systems change; however, it leads to a higher
emissions price and macroeconomic impacts. This does
not simply mean that higher economic development is
economically less desirable for India, however. India
needs to consider a balance between development and theimpacts from emissions reduction if it wants to contributeto global emissions reductions as an international community member. Although there is huge uncertainty
about India’s low-carbon development, this study describes the country’s future development visions quantitatively which can provide hints to help realize lowcarbon development pathways for India.

Two Low-Carbon Development Pathways in India

Acknowledgments
This research was supported by the “Environment
Research and Technology Development Fund by the
Ministry of the Environment, Japan” S-6-1.
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Michiko NAMAZU
Michiko NAMAZU is a master’s degree student at
the Department of Urban and Environmental
Engineering, Kyoto University. Ms. Namazu got
her Bachelor’s of Engineering from the Department of Global Engineering, Kyoto University.
She has been conducting research for the Asianpacific Integrated Modeling (AIM) team since
her senior year. Her field of study is climate change modeling, especiallycomputable general equilibrium (CGE) modeling. Currently she is working
on quantitative evaluation of the impact of greenhouse gas emission
reduction in Asian regions, using the AIM/CGE [basic] model. She hasapplied the CGE model to Japan, India and China so far, and will apply it toother Asian regions as well. Through application of the model, she isdeveloping scenarios for Asian regions toward a reduced GHG emissions
society, i.e., the so-called low-carbon society.

Shinichiro FUJIMORI
Shinichiro FUJIMORI is a researcher at the National
Institute for Environmental Study in Japan. Dr.
Fujimori got his Bachelor’s, Master’s and PhD
degrees from the Department of Environmental
Engineering, Kyoto University. Now he is amember of the AIM (Asia-Pacific integrated
Assessment Model) team, where he is working on
development and application of a CGE model. In the early period of his
career, he tackled development of a method to create a consistent social
accounting matrix and energy balance table. He later applied that method
to the world, classifying it into more than 100 regions spanning the last
three decades, and using more than 30 international data sets, e.g.: national
accounts, trade statistics, energy statistics, etc. The reconciled data are usedin the CGE model or a bottom-up-type energy model. During the last
couple of years, he has been working on development and application of a
CGE model. The model will find wide use, for instance, in global climate
mitigation assessment, development of new IPCC socioeconomic
scenarios, and national level climate mitigation policy.

Priyadarshi Ramprasad SHUKLAPriyadarshi R. SHUKLA has been lead author ofseveral prestigious international reports, including nine reports of the Intergovernmental Panelon Climate Change (IPCC). He has also been amember of the official Government of India delegation to the Conference of Parties to the UN
Framework Convention on Climate Change.
Prof. Shukla provides consultation and advisory
services to governments and international organizations. He has led
numerous international research and consulting projects and is a member ofseveral international teams working on energy and environment modelingand policy assessments. His key contributions include concepts andmethods of representing developing country dynamics in integrated
assessment modeling and policy research. Prof. Shukla is an invitedmember of the editorial advisory board of several international journals. He
has co- authored thirteen books and numerous publications in international
journals in the areas of sustainable development, energy and environment
modeling and policies.

Yuzuru MATSUOKA
Yuzuru MATSUOKA is a Professor at the Graduate
School of Engineering, Kyoto University. He
specializes in environmental engineering. He
started researching global environmental issues in
the late 1980s. He has developed various kinds ofintegrated models, including bottom-up and topdown models. Recently he has been leading
projects for development of Asian low-carbon societies.

(Received 1 June 2012, Accepted 5 October 2012)

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Appendices
Appendix 1 Reference list (population scenario).
Title
A Strategy for Growth of Electrical Energy in India.
Energy Revolution Sustainable Energy Global Energy Outlook
2nd Edition
The GGI Scenario Database (version 2.0)
Low-Carbon Society Vision 2050 INDIA
Downscaling Drivers of Global Environmental
Change-Enabling Use of Global SRES Scenarios at the
National and Grid Levels
National Energy Map for India-2030 Technology Vision
India Vision 2020
The Future POPULATION of India A Long-range
Demographic View PFI/PRB
Development of Long-term Socioeconomic Scenarios–Population, GDP–
World Population to 2300
World Population Prospects 2008
World Population Prospects 2010
US Census International Database

Year Author
2010 Department of Atomic Energy
2010 Greenpeace
2006 International Institute for Applied Systems Analysis
2009 Indian Institute of Management Ahmedabad, National Institute for Environmental
Studies, Kyoto University, Mizuho Information & Research Institute2006 Netherlands Environmental Assessment Agency
2006 Office of the Principal Scientific Adviser (GOI) &The Energy Resource Institute2002 Planning Commission
2007 Population Foundation of India & Population Reference Bureau
2011

Research Institute of Innovative Technology for the Earth

2004 United Nations Population Division2008 United Nations Population Division2011 United Nations Population Division2010 United Sates Census Bureau

Appendix 2 Reference list (GDP scenario).
Title
Energy Outlook for Asia and the Pacific 2009
Country-level GDP and Downscaled Projections
The World Order in 2050
CEPII BASELINE V1.0 GDP
The Long Term Growth prospects of the World
Economy-Horizon 2050
International Energy Outlook 2010
Global Economic Paper The N-11-More Than an Acronym
The world in 2050
The GGI Scenario Database (version 2.0)
Low-Carbon Society Vision 2050 INDIA
Energy Technology Perspectives 2010
Long-term Forecast of Global Economy and Population
2006-2050 Demographic Change and the Asian Economy
Statistics
Downscaling drivers of global environmental change-Enablinguse of global SRES scenarios at the national and grid levels
National Energy Map for India-2030 Technology Vision
Economic Survey
The World in 2050 Beyond the Brics-a Broader Look at
Emerging Marked Growth Prospects
The World in 2050-the Accelerating Shift of Global Economic
Power; Challenges and Opportunities
Development of Long-term Socioeconomic Scenarios–Population, GDP–
Asia/World Energy Outlook 2009 -the Role of Technology
towards the Resolution of Energy & Environmental Issues in
AsiaAsia/World Energy Outlook 2010 -the Role of Technology
Towards the Resolution of Energy & Environmental Issues in
Asia and the WorldAsia/World Energy Outlook 2011 -Growing Uncertainty over
International Energy Trends and the Future of AsiaMid-term Appraisal Eleventh Five Year Plan

Year
2009
2002
2010
2009
2006

AuthorAsia-Pacific Economic Cooperation, Asian Development Bank
Center for International Earth Science Information Network
Carnegie Endowment for international peace Uri Dadush, Bennett Stancil
Centre D’etudes Prospectives et D'infomarions Internationales (CEP)
Centre D’etudes Prospectives et D'infomarions Internationales (CEP)

2010
2007
2011
2006
2009

Energy Information Administration
Goldman Sachs
HSBC Global ResearchInternational Institute for Applied Systems Analysis
Indian Institute of Management Ahmedabad, National Institute for Environmental
Studies, Kyoto University, Mizuho Information & Research Institute2010 International Energy Agency
2007 Japan Center for Economic Research2011 Ministry of Statistics and Program Implementation
2006 Netherlands Environmental Assessment Agency
2006 Office of the Principal Scientific Adviser (GOI) &The Energy Resource Institute2010 Planning Commission
2008 Price Water House Coopers
2011

Price Water House Coopers

2011

Research Institute of Innovative Technology for the Earth2009 The Institute of Energy Economics, Japan
2010 The Institute of Energy Economics, Japan2011

The Institute of Energy Economics, Japan2011

Planning Commission

Appendix 3 Reference list (energy projection).
Title
A Strategy for Growth of Electrical Energy in India
Energy Revolution Sustainable Energy Global Energy outlook
2nd edition
Low-Carbon Society Vision 2050 INDIA
Energy Technology Perspectives 2010

Year Author
2010 Department of Atomic Energy, India
2007 Green Peace2009 Indian Institute of Management Ahmedabad, NIES, Kyoto university and Mizuho
Information & Research Institute2010 International Energy Agency