Keywords: India, energy intensity, second-best world, power sector, reference scenario

Reference emission scenarios in the literature have been the target of criticisms that suggest they convey too optimistic views on spontaneous energy-GDP decoupling of emerging countries economies. This article focuses on the case of India. It explores the role of current suboptimalities of the Indian power sector (structural under-investment in the sector leading to capacity shortage, power cuts and low efficiency) on future energy-GDP decoupling. To do so, it uses a hybrid general equilibrium framework, in which these suboptimalities are explicitly introduced. The results highlight that whether the constraints on investments in the power sector persist or not leads to contrasted trends in energy-GDP decoupling and GHG emissions. Over the short-term, capital scarcity in the power sector constrains the development of energy-intensive activities and therefore leads to higher energy-GDP decoupling. But on the longer-term, constrains on the power sector capacity limits substitution from fossil fuels to electricity, which entails both a low energy-GDP decoupling and a constraint on GDP growth when oil prices are high. The alleviation of suboptimalities appears thus as an insurance policy towards future oil price increase.

Energy-GDP decoupling in a second best world -A case study on India

Céline Guivarch a,* , Sandrine Mathy a a Centre International de Recherche sur l'Environnement et le Développement, Nogent-sur-Marne, France

Introduction

Reference scenarios play a critical role in the message conveyed to policy makers for the definition of GHG emission mitigation actions.

Recently, existing reference scenarios, either from the SRES (IPCC, 2000) or from other studies using global energy models, have been the target of repeated criticisms.

First, the relevance of reference scenarios is questioned by actual observations, as [START_REF] Raupach | Global and regional drivers of accelerating CO2 emissions[END_REF] and [START_REF] Sheehan | The new global growth path: implication for climate change analysis and policy[END_REF] showed that GHG emissions are increasing faster than predicted in the large panel of SRES modeling exercises (IPCC, 2000). One of the major reasons for the discrepancy between short term GHG emissions projection from models and recent trends is the persistent very high levels of economic growth in emerging countries.

Until recently economic growth rates considered for these countries in models were significantly lower than currently observed growth rates. This pushed modellers to recalibrate their modelling exercises on recent trends: Blandford et al. (2008) revised emission growth projections from MERGE for China with recent trends, the World Energy Outlook 2007 (IEA, 2007) and the International Energy Outlook 2008 (EIA, 2008) adopted much higher economic growth rates for China and India compared to preceding publications 1 .

A second side of criticisms (Urban et al. 2007[START_REF] Van Ruijven | Modeling energy and development: An evaluation of models and concepts[END_REF] discusses the suitability of methodologies used in existing global energy models to represent the specificities of the developing countries' energy systems. In particular, it appears that most models neglect some characteristics such as supply shortages, poor performance of the power sector, economic structural change, urban-rural divide, traditional bio-fuels etc, which may bias results.

A third trend of criticism, initiated by an article from [START_REF] Pielke | Dangerous assumptions[END_REF], questions the optimistic views on the automatic (without policies) decarbonisation of economies that are reflected by existing reference scenarios, in particular SRES scenarios. Moreover they warn these scenarios may under-estimate the challenge for climate change mitigation and convey biased views to policy makers Acknowledging these criticisms, this article proposes to build reference scenarios for India taking explicitly into account specificities of energy systems and particularly current suboptimalities of the power sector: structural under-investment leading to capacity shortage, power cuts and low efficiency. Our objective is to disentangle the mechanisms driving decarbonisation of the economy and the constraints that may stall this process. To do so we use IMACLIM-R a dynamic recursive energy-economy model that represents a second best world with market imperfections and short-run adjustments constraints along a long-term growth path. The first section is an overview of the energy context in India focusing on existing suboptimalities in the power sector, and of existing energy forecasts for India. The second section describes this modelling framework and details how the Indian power sector sub-optimalities are represented. The third section presents three alternative scenarios, based on contrasted assumptions on the evolution of these suboptimalities. Results show that future energy decoupling would increase in the future only if suboptimalities are alleviated. On the contrary persisting suboptimalities in the power sector would lock the Indian economy in a carbon and energy intensive technological pathway.

1 What energy decoupling in the future in India?

Energy intensity and carbon intensity of GDP in developing countries are determining indicators regarding climate change. In particular, climate mitigation targets developing

1 These revisions upwards of future growth rates for emerging countries date from before the financial crisis. More recent studies either keep assuming high growth rates for emerging countries in the coming years (see for instance IEO 2009 in Table 1), or take into account a relative slowdown of economic growth (see for instance WEO 2009 in Table 1).

countries adopt are often expressed in energy intensity or carbon intensity2 evolution because climate mitigation should not be a supplementary constraint on their development.

In this first part, we analyze what could be the evolution of energy intensity in India.

Indian energy intensity drivers

Figure 1 pictures Indian GDP energy intensity since 1975. After important increase until early 90s, energy intensity has decreased during the 20 following years.

During the phase of Indian take off, between the 70s and the 90s, important structural changes have driven economic growth and commercial primary energy consumption in India:  A fast industrialization for infrastructure building, relying on highly energy intensive industries, based on highly energy consuming technologies compared to international standards in industry and in the power sector [START_REF] Dasgupta | Energy efficiency and environmental improvements in small-scale industries: present initiatives in India are not working[END_REF][START_REF] Mongia | Policy reforms and productivity growth in India's energy intensive industries[END_REF].  The development of modern energy forms, allowing the substitution from traditional energy carriers towards electricity.  The evolution of consumption styles with a smooth diffusion of end-use equipments and motor vehicles.

Commercial primary energy consumption grew faster than economic growth (5.7% vs. 4.3%). This led to an important increase in energy intensity.

After this first period, energy intensity began to decrease due to the combined effect of:  A structural evolution of the economy: in 2008 the tertiary sector represents 54% of GDP;  Energy efficiency improvement in industry: energy intensities of industries such as iron and steel, fertilizer and cement have declined significantly: by 70% for iron and steel, and by 65% for cement industries, between 1990 and 2005 [START_REF] Kumar | Energy, industries and commerce, High-Level India-EU Seminar[END_REF].

Base 1=1975

Commercial primary energy intensity of GDP at US$ constant

Final electricity consumption intensity of GDP at US$ constant

This trend could continue in the future, as emerging countries could benefit from:  The "leapfrogging mechanism" [START_REF] Goldemberg | Leapfrogging energy technologies[END_REF] that argues developing countries should leapfrog to low energy GDP elasticity as they would benefit from transfers of modern and low GHG emitting technologies.

 The material content of consumption styles: whether emerging countries choose to reproduce industrialized countries consumption standards with high levels of saturations for equipments or dematerialized modes of consumptions will induce different structures of economies and contrasted energy needs.

Nevertheless, other drivers could deter future energy decoupling.

First, Indian energy intensive sectors still remain highly inefficient with energy consumption critically high compared to international standards [START_REF] Graus | International comparison of energy efficiency of fossil power generation[END_REF][START_REF] Kim | International comparison of CO2 emission trends in the iron and steel industry[END_REF], and one main objective of the government is to increase the (currently low) share of industry. This would tend to increase energy use per unit of GDP.

Second, energy services for both households and productive sectors are still significantly restrained:  Per capita commercial energy consumption is only 0.3 tep in India in 2007 compared to the world average of 1.7 tep, 1.4 tep for China and 7.7 tep for USA.  Electrification covers only 60% of Indian households. The energy needs of Indian households not connected to the grid rely mainly on biomass or on diesel generators to compensate for the deficiencies in the centralized power supply. Capacity shortage amounts to 10 GW (14.8% of peak power) and the gap between supply and demand rose to 66 TWh (9.6% of demand) in 2007 (Planning Commission), even though the installed power capacity has increased from 66 GW in 1990 to 146 GW in 2007. Capacity shortage leads to power cuts, affecting both households and productive sectors, thus leading to lower productivity and to the use of diesel generators.

One main challenge for the Government is thus to provide electricity and modern forms of energy to all. This would again mean an increase in final energy consumption and a proportional increase in primary energy consumption considering Indian power plant and grid performances are currently very low. A significant improve in energy efficiency of the power system only could moderate this increase. This will depend upon the investment capacity to respond jointly:

 to the enlargement of energy access by financing new plants and transmission and distribution (T&D) infrastructures,  to energy efficiency increase by improving the efficiency of existing and future plants and of currently deficient T&D of electricity.

It is thus very difficult to assert whether the recent decrease in energy intensity will continue or not. In the following, several Indian energy prospective exercises are presented focusing on the way energy decoupling drivers are taken into account and on the resulting energy decoupling.

Analysis of existing reference scenarios

Scenarios considered in this subsection are summarized in Erreur ! Source du renvoi introuvable.. Methodologies and assumptions are described below:

 Projections from the World Energy Outlook (WEO) realized by the International Energy Agency (IEA, 2006(IEA, , 2007(IEA, , 2008(IEA, and 2009) ) and from the International Energy Outlook (IEO) realized by the US Department of Energy (EIA, 2006(EIA, , 2007(EIA, , 2008[START_REF] Eia | International Energy Outlook 2008. Office of Integrated Analysis and Forecasting[END_REF] are based on bottom-up models: exogenous economic growth rates and assumptions related to structural changes in the economy and to the diffusion of energy efficient technologies and equipments determine the energy mix in (2015 and) 2030. Resulting decoupling of GDP is shown in Erreur ! Source du renvoi introuvable..  The Integrated Energy Policy (IEP) Report [START_REF] Parikh | Integrated Energy Policy, Report of the Expert Committee[END_REF] was commissioned by the government to undertake a comprehensive review of aspects like energy security, research, environmental concerns, and energy conservation in order to make policy recommendation. Economic growth rates are taken as political objectives and energy decoupling exogenous assumptions set future energy requirements. Two sets of assumptions related to economic growth (8% and 9%) and to primary energy decoupling (constant elasticity and falling elasticity) are used; they determine the level of commercial primary energy demand. In the second assumption, energy intensity remains constant. The reason for having kept these two sets of assumptions is that normally overall elasticity falls over time as is corroborated by the time series data for India's commercial energy consumption. However IEP states that for India the energy elasticity of GDP growth may not fall any further as rising income levels will foster more energy intensive lifestyle.

In spite of these contrasted pictures in term of energy decoupling, primary energy demand drivers are quite the same in WEO and IEP 3 :  Slight increase in industry's share of total final energy demand. This trend is consistent with the political objective of reinforcement of Indian industry activities, but there is a shift away from energy-intensive heavy manufacturing towards lighter industries and services. In WEO 2007 4 , industry's share of energy demand edges up to 34% in 2030 compared to 28% in 2005;  Evolution of lifestyles. With the very high economic growth considered in all scenarios, and particularly until 2015, household energy service (household equipment, electric appliances, mobility) demand increase is very high. In WEO 2007, the total vehicle stock in India is projected to increase from 68 million in 2004 to 295 million 5 by 2030, overtaking that of the United States soon after 2025; refrigerator sales are expected to nearly triple by 2020;  Enlargement of electricity access: In 2005, the share of lighting met by electricity in urban households was about 90%, but only 50% in rural households. High electricity generation and consumption growth rates are consistent with the governmental priority for giving modern energy and electricity access to 100% of the Indian population. By 2030, in WEO 2007, all lighting is projected to be met by electricity in urban areas while in rural areas about 7% of households will still rely on kerosene for lighting. Electricity growth is even more important in IEP.  Important energy efficiency improvement. Indian power plants are among the least efficient in the world. In WEO 2007, efficiency is expected to increase from 27% now to 38% in 2030 6 . In all WEO scenarios electricity T&D losses decrease significantly.

In IEP also, the gross efficiency in power generation increases from the current average 30.5% to 34%: efficiency of new plants improves from the prevailing 36% to at least 38-40%.

The combined effect of these drivers leads, in all WEO and IEO scenarios, to a very significant GDP energy intensity decrease, while IEP scenarios are more conservative concerning the potential decrease in energy intensity. Actually, IEP report states that an aggressive pursuit of energy efficiency and conservation only7 could reduce India's energy intensity by up to 25% from current levels. Financing at the same time energy efficiency improvements of a very inefficient system and energy access enlargement in a more energy intensive economy (with the deployment of industry) and with higher energy consuming lifestyles appears the condition to reach important energy decoupling in the future.

Attracting investment in a timely manner will thus be essential and underlying assumption of the WEO 2007 scenario is that investment will be available and that this power infrastructure will be built in time, even if many challenges remain. Chronic underinvestment in power sector has been a major constraint to the country's development. The capacity addition targets set in the five-year plans have generally not been met and performance has deteriorated over the past three plans. Performance in the 10th plan period (2002)(2003)(2004)(2005)(2006)(2007) was the worst ever.

Less than half of the capacity envisaged was built. Insufficient investment resulted in electricity generation increasing at a rate well below the economic growth rate for five consecutive years (2001 to 2006), a situation never seen in the past. The pace of capacity additions stagnated in the 1990s. Economic reforms were introduced in 1991, in the expectation that part of the required investment would come from the private sector. But many of the projects proposed have not proceeded, mainly because of an inadequate legal and commercial framework, involving lack of law and contract enforcement and delays in obtaining regulatory approvals. The target in the 2007-2012 Plan foresees capacity additions of 69 GW, much higher than the unmet target of 41 GW set in the 10th Plan.

This under-investment is largely due to the high risks and low profitability of investments in the power sector. Administered prices do not cover production costs: in 2006, the average price of electricity sold covered only 77% of the average production cost. Official data estimate the total under-recovery of costs to 0.8% of GDP. The same report estimates that the residential tariff covers 56% of the generation costs and farmers tariff only 12%, while industries and the commercial sector partly compensate by paying respectively 108% and 122% of production costs. Official data reports that subsidies to household and agriculture electricity consumption tripled to represent respectively 0.4% and 1.1% of GDP. In 2000, agriculture electricity uses represented 1/3 of electricity sales in volume while the corresponding revenues constituted less than 5% of total revenues.

These subsidies are justified by positive externalities on development, particularly regarding access to cheap energy for irrigation to promote food production. Nonetheless, they have significant side effects. First, the very low tariffs for farmers induce overconsumption, which increases the magnitude of capacity shortage. Overconsumption of electricity in the agricultural sector amounts to 30% of its consumption due to the combination of critically low prices and of frequent but unpredictable power cuts that create a strong incentive to a continuous use of electric pumps for irrigation [START_REF] Dorin | Agricultural incentives in India. Past trends and prospective paths towards sustainable development[END_REF]. Second, low revenues from electricity sales induce maintenance under-financing and increasing inefficiencies in T&D. Technical and commercial T&D losses have increased from around 20% in 1993 to more than 30% in 2001 [START_REF] Thakur | Efficiency evaluation of the state owned electric utilities in India[END_REF] 8 . Moreover, power sector inefficiencies constrain economic activity and economic growth: electricity shortages hamper productivity and competitiveness.

It appears that the sub-optimalities of the Indian power sector described above 9 are structural characteristics of the Indian energy system. Therefore, they have to be taken into account in modelling framework to develop realistic prospective scenarios, as advocated fifteen years ago by [START_REF] Shukla | Greenhouse gas models and abatement costs for developing nations. A critical assessment[END_REF] and more recently by Urban et al. (2007) and [START_REF] Van Ruijven | Modeling energy and development: An evaluation of models and concepts[END_REF].

Next section describes a modelling framework able to embark sub-optimalities of the Indian power sector and to represent power and investment shortage and their impact on economic growth.

2 A modelling framework to represent a second best world

Model architecture and major features

The IMACLIM-R model 10 (Sassi and al. 2010) is a recursive, dynamic, multi-region and multisector Computable General Equilibrium (CGE) model of the world economy. It describes growth patterns in second best worlds through a hybrid and recursive dynamic architecture that captures market imperfections, partial uses of production factors and imperfect expectations.

A growth engine with gaps between natural and effective growth

IMACLIM-R relies on exogenous assumptions 11 of regional labour productivity and active population growths, the sum of which determines the 'natural' growth rates [START_REF] Solow | A contribution to the theory of economic growth[END_REF]. They represent real GDP growth that would be realized at constant unemployment and structure of the economy. For India, natural growth decreases from 7.2% to 3.9% in 2050 (Figure 2). 8 The level of T&D losses is in fact uncertain. For instance, the value stated by the Indian Planning Commission was 22% in 1998 while the World Bank (1998) pointed out that it could actually be twice this official level. Moreover, as there is no metering of the electricity consumption it is difficult to distinguish technical from commercial losses.

9 For further description of suboptimalities and of the institutional aspects of the Indian power sector, refer to [START_REF] Mathy | Climate policies in a second-best world -A case study on India[END_REF].

10 Details concerning calibration and disaggregation are given in SOM.

11 See SOM for details on these assumptions. The endogenous 'effective' growth rates can be different, higher or lower, from 'natural' rates, depending on the evolutions of unemployment and labour allocation across regions and sectors. These evolutions are constrained by financial resources for investments and shortterm rigidities like capital stock inertia, frictions in reallocating labour or wage rigidity.12

A recursive and modular architecture to account for inertia in technical systems and sub-optimalities

The IMACLIM-R model has a hybrid structure relying on a consistent representation of the economy in money values and physical quantities. Its recursive structure organizes a systematic exchange of information between a top-down annual static equilibrium providing a snapshot of the economy at each yearly time step, and bottom-up dynamic modules informing on the evolution of technical parameters between two equilibria.

Within the static equilibrium, domestic and international markets for all goods -except factors such as capital and labour -are fully cleared by a unique set of relative prices that depend on the behaviours of representative agents on the demand and supply sides. It determines the following variables: relative prices, wages, labour, quantities of goods and services, value flows. Within the static equilibrium, producers are assumed to operate under short-run constraints of (i) a fixed maximal production capacity defining the maximum level of physical output achievable, and (ii) fixed input-output coefficients representing current set of embodied techniques. The only margin of freedom of producers is to adjust the production capacities utilisation rate according to the relative market prices of inputs and output, taking into account increasing costs when the production capacities utilization rate approaches one13 . This represents a different paradigm from usual production specifications, since the 'capital' factor is not always fully utilized.

The dynamic modules make the short-term constraints of the static equilibriums evolve in time. They represent the accumulation of capital and its technical content. They include the modelling of (i) the evolution of capital and energy equipment stock described in both vintage and physical units, (ii) of technological choices of economic agent described as discrete choices in explicit technology portfolios for key sectors such as electricity, transportation and alternative liquid fuels, or captured through reduced form of technology rich bottom-up models, and (iii) of endogenous technical change for energy technologies. 14Aggregate capital accumulation is controlled by exogenous saving rates, but IMACLIM-R represents second-best investment decisions under imperfect foresight. Thus, installed capital resulting from past investment decisions may not be adapted to future economic settings. However, it cannot be renewed overnight due to inertias and acts as a constraint on growth pathways, like in the case of a sharp increase of fossil energy prices.

Representing market and institutional failures of the power sector in Imaclim-R modelling architecture

In the static equilibriums, the electricity price depends on the characteristics of the installed power generation capacities (the type of fuel used and the efficiency, including T&D losses) and on the utilization rate of these generation capacities: high utilization rates induce extracosts. Between each static equilibria, the evolution of power generation capacities depends on annual investment decisions, that determine new capacities installation (among 26 generation technologies in competition) to satisfy (imperfectly) anticipated future demand minimizing generation costs, given (imperfect) anticipations of future fuel prices. This minimization is run under the constraint of the actual amount of investment allocated to the electric sector Within this modelling framework, suboptimalities of the Indian power sector are embarked.

The analysis focuses on under-investment in the sector, and its consequences, namely (i) power generation capacity shortage, and (ii) inefficiencies and T&D losses.

(i) Under-investment in the power sector is represented by a gap between the estimated need for investments (i.e. the investment to build the optimal mix of productive equipment to satisfy the anticipated electricity demand) and realized investments in the power sector.

Realized investments for all sectors are constrained by sectors profitability and total domestic investment plus net capital inflows. Additionally, to represent at the aggregate level the capital scarcity in the Indian power sector, investments in the sector are limited to a maximum share of GDP.

(ii) Power generation capacity shortage is represented by over utilization of generation capacities. In the model, a utilization rate superior to 0.8 means that the capacity is overused.

In 2008, the utilization rate of electricity production capacities is 0.86, which corresponds to 7.5% of capacity shortage (i.e. 7.5% more capacities would be needed to reach a utilization rate of 0.8) and is consistent with estimations given above. This over utilization of productive capacities entails extra generation costs15 and raises the electricity usage cost, which is a stylized representation of electricity shortage (power cuts) for the Indian economy16 .

(iii) Power plants characteristics, in particular their efficiencies, as well as T&D losses are calibrated on the sectoral model POLES (LEPII-EPE, 2006). In 2001, the calibration date, overall efficiency of power generation is equal to 32% and T&D losses are 35%.

The following section uses the Imaclim-R model to project over the period 2008-2050 baseline scenarios taking into account the evolution of the three elements described above.

3 Energy decoupling and power sector suboptimalities in baseline scenarios

Description of baseline scenario parameterization

Whether constraint on investments in the power sector will be alleviated or not in coming decades will depend upon several factors:  The evolution of regulatory and administrative barriers and of commercial framework in the power sector and the reduction of investment risks in order creating effective incentives for private investors.  Possible increased tensions on the international capital market as in the future Europe and China may experience a drop of their saving capacities due to an aging population [START_REF] Aglietta | World Growth and international capital flows in the XXIst century[END_REF].

These elements legitimate investigating three possible evolutions of the investment constraint in the power sector: the continuation of current trends (MEDIAN), the deterioration of the investment context (INV-) and an alleviation of investment constraint (INV+). These parameters are set respectively to 2.2%, 1.8% and 3% of annual GDP.

This financing is invested in new power capacities. Nevertheless to bridge the gap between demand and supply, financing T&D loss reduction should also become a priority. Historically, capital scarcity in the Indian power sector has prevented from investing in maintenance of the T&D network. This is why we assume that in India a stringent capital constraint prevents from improving T&D losses. On the contrary, the alleviation of the capital constraint would trigger T&D loss decrease. These assumptions are implemented in INV-, MEDIAN and INV+. Cost and technical potential parameterization (Table 2) is consistent with [START_REF] Ruet | Investment profitability in bridging the power gap in India[END_REF].

Results

Scenarios main characteristics are introduced in the following (Table 3), and economic mechanisms at play are then further detailed.

Energy decoupling in INV-, MEDIAN and INV+ are very different from scenarios presented previously.

Primary energy supply decoupling in Imaclim-R baseline scenarios is much lower than in WEO or in IEO reference scenarios, particularly during the period 2008-2015, but roughly at the same level as IEP assumptions. The second point concerns temporal trends:  In all WEO and IEO scenarios energy decoupling is very high during 2008-2015 (elasticity of primary energy consumption around 0.5) due to significant energy efficiency improvements compared to current trends (0.75) and followed by a relative decrease of the decoupling incurred by energy consuming end-use equipment penetration as Indian population get richer,  In IEP scenarios, trends show slightly decreasing, or constant, energy-GDP elasticity over the whole period. ,  In Imaclim-R scenarios, energy decoupling trajectory depends upon the level of investment constraint in the power sector as temporal profile is different for each scenario:  in INV-energy decoupling decreases on the whole period,  in MEDIAN it decreases and then increases again,  in INV+ it increases on the whole period.

BEFORE THE PEAK-OIL

In the first period, the less capital constrained scenarios (INV+ and MEDIAN) result in economic growth rates close respectively to WEO2008 (7.8%) and to WEO2009 (7%) forecasts. On the contrary, INV-economic growth rate is significantly lower than most recent WEO economic assumptions during this period (5.9%).

The effective investment constraint in the power sector can be evaluated, for a given value of T&D losses, by the gap between the estimated need for investments and realized investments.

The stringency of the constraint will be reinforced by higher T&D losses. In a context characterized by power shortage, producers have no choice but using more the existing capacities. Productive capacity over-utilization leads to generation extra-costs as described previously. They raise the electricity usage cost ( Figure 7), which limits the development of electricity use by productive sectors as well as by households.

In the most capital constrained scenario (INV-), the pessimistic assumptions related to the T&D losses explains the lower electricity consumption than in the MEDIAN scenario, the overall electricity generation is higher. The investment constraint stringency leads to important generation extra-costs ( Figure 7), which hinders an increase in electricity generation, in electricity consumption and in energy substitution towards electricity particularly in industry. The energy intensive sectors are more constrained than other sectors: for instance, in 2020 real GDP is reduced by 7.2% compared to the MEDIAN scenario, while industry output is reduced by 11.2% (Figure 8). This compensates the higher energy content of GDP due to high electricity T&D losses, which leads to almost the same energy-GDP decoupling (Figure 5). Since economic growth is lower, so are primary energy and total CO 2 emissions (Figure 4).

The release of the investment constraint in the power sector (INV+) makes possible, on the short term, the increase in electricity generation to meet demand. As a result, electricity consumption in the short term is higher: +7% in INV+ compared to the MEDIAN scenario (Figure 6, left panel). Even if T&D losses decrease power generation is higher than in the MEDIAN scenario, over the short-term (Figure 6, right panel). Extra-generation costs due to capacity shortage are rapidly absorbed and electricity price decreases ( Figure 7). This lowers industrial production costs, enhances industrial products competitiveness and also improves households' purchasing power. Industrial production is thus increased: for instance in 2020 real GDP is increased by 3.4% compared to the MEDIAN scenario, while industry output is increased by 8.5% (Figure 8). The improvement of households' purchasing power also leads to more electricity consumption and more transportation activities. Therefore, energy-intensive activities take a larger share in GDP and there is less energy-GDP decoupling over the short-term (Figure 5). Since GDP is higher, total CO 2 emissions are also higher than in MEDIAN scenario (Figure 4).

These results are consistent with short term WEO results forecasting high economic growth rates in an unconstrained investment context. Nevertheless, forecast differences between WEO reference scenarios and INV+ come from different views concerning the potential pace of energy efficiency diffusion, the rate of T&D losses reduction and the industrial content of households' consumption. It may be noted that in 2020, the three IMACLIM-R scenarios lead to a GHG-GDP intensity of respectively -21%, -23% and -20% of 2005 level, which is very close to the Indian pledge following Copenhagen Accord (-20% to -25% in 2020 compared to 2005 level). WEO and IEO scenarios give GHG-GDP improvement significantly higher than the Indian pledge (between -30% and -45% in 2020 compared to 2005).

AFTER THE PEAK OIL

These trends are reversed when peak-oil occurs: in a world of high oil prices, the less the investment in the power sector is constraint, the higher the energy decoupling is and the lower GHG emissions are.

After the peak oil, the electricity capacity shortage is absorbed in both MEDIAN and INV+ scenarios (Figure 7). The only significant difference between the two scenarios is then the T&D losses. Both scenarios have the same level of GDP (Figure 3), but INV+ is characterized by less electricity generation for the same level of consumption (Figure 6), hence more energy-GDP decoupling ( Figure 5) and lower emissions (Figure 4).

On the contrary, in INV-scenario, power generation capacity shortage remains a lasting constraint. This limits the substitution between oil and electricity in the industry when oil prices rise. In a first time (approximately 2018-2028), the higher fossil fuel content of industrial inputs in the INV-scenario is compensated by the carbon content of the substituted electricity input in the MEDIAN scenario (Figure 9). But, when power generation efficiency has improved, the limit to the substitution between oil and electricity leads to higher carbon content of industry production in INV-scenario. Moreover, this limit to substitution entails higher energy costs in industrial production (Figure 10), which hinders production (Figure 8). The composition of higher fossil fuel unitary input (Figure 9, lower-left panel) and lower output leads here to higher emissions from industry (Figure 11), which adds to the higher emissions due to important T&D electricity losses. It leads to less energy-GDP decoupling than in the MEDIAN scenario (Figure 5) and higher emissions (Figure 4) even if GDP is lower.

Conclusion

Main results can be summarized as follows. First, energy decoupling in Imaclim-R scenarios is much lower than in WEO and IEO scenarios, but comparable to Indian expert forecasts. Second, the three Imaclim-R scenarios lead to contrasted evolution of energy decoupling and of decarbonisation, showing capital scarcity. Over the short-term, capital scarcity in the power sector constrains the development of energy-intensive activities and therefore leads to higher energy-GDP decoupling. But on the longer-term, constrains on the power sector capacity limits substitution from fossil fuels to electricity and entail less energy-GDP decoupling.

Three kinds of conclusions can be drawn.

First, future decarbonisation and further energy decoupling of GDP in India should not be considered as granted as it may appear in WEO or IEO reference scenarios or in SRES scenarios. Persisting capital scarcity and deficiencies in the power sector could limit energy efficiency improvements, and prevent power supply capacity from responding to increasing energy demand. This can have negative impacts on economic growth on the short term, as well as on a longer term when oil prices increase.

Second, regarding climate policies, the realism of the reference scenario is critical for estimates of the costs of stabilization at a given GHG concentration and for policy recommendations. Persisting failures in the power sectors encloses India in a carbon and energy intensive technology lock-in. Climate policies such as a carbon tax in such a context would weigh significantly on economic growth [START_REF] Mathy | Climate policies in a second-best world -A case study on India[END_REF]. On the contrary, the alleviation of investment barriers in the power sector on the short term would both lift barriers to development and reduce GHG emissions on the long term. This potential for synergies between development and climate policies could help getting out of the deadlock in international negotiations.

Third, from a methodological point of view, it emphasizes the importance to improve the realism of modelling tools and to use methodologies that allow representing for each country specificities and sub-optimalities, and to consider the interactions and feedbacks between the energy system and economic mechanisms. Similar modelling methodologies, focusing on China in particular, would bring valuable insights to a comprehensive understanding of energy and economic interactions driving current and future GHG emissions trends.

Figure 1 :

 1 Figure 1: Evolution of key energy indicators, period 1975-2008. Source: Enerdata.

Figure 2 :

 2 Figure 2 : Exogenous potential growth of the Indian GDP over the 2050 time horizon, and its decomposition.

Figure 3

 3 Figure 3 shows three distinct periods: before 2020, between 2020 and 2030 and after 2030. The year 2020 corresponds to the "peak oil" with a steep increase in oil prices 17 until 2030 after which prices stabilize at 200$/bl. The decade between 2020 and 2030 corresponds to the delay for the large scale development of substitutes to oil (Coal-To-Liquid and biofuels) with default parameterization of the model. During this decade of increasing oil prices, Indian GDP growth rates drop down in the three IMACLIM-R scenarios. Moreover, around 2025, CO 2 emission and energy decoupling trajectories of INV-, MEDIAN and INV+ cross each other (Figure 4 and Figure 5): before this date, INV+ is the scenario with the highest CO 2 emissions and with the lowest energy decoupling; after, INV-becomes the scenario with highest CO 2 emissions and energy intensity of GDP.

Figure 3 :Figure 5 :

 35 Figure 3: GDP growth rate in the Imaclim-R scenarios and potential growth

Figure 6 :FigureFigure 8 :Figure 9 :Figure 11 :

 68911 Figure 6: Electricity generation and electricity final consumption compared to the MEDIAN scenario

Table 1 : Energy decoupling comparison between past tendencies, WEO and IEO energy prospective scenarios and IEP Report. Assumptions are in italics and results are in bold characters.

 1 Erreur ! Source du renvoi introuvable.compares energy intensity of WEOs and IEOs results to assumptions adopted in IEP: Primary energy decoupling decreases from 0.75 in 2005 till around 0.5 in 2015 in WEO and even less in IEOs and increases again after 2015. In 2030, energy intensity is halved compared to current levels.  In IEP, two exogenous assumptions are considered. In the first one, energy decoupling decreases during the whole period, from 0.75 in 2005 to 0.70 in 2015 and to 0.67 in 2030.

		Period	Elasticity of primary energy consumption to GDP
	Past tendencies	1975-95 1995-05	1.25 0.75
	WEO	Until 2015 2015-30	0.44 -0.51 0.57 -0.6
	IEO	Until 2015	0.36 -0.7

Table 2 : Typology of scenarios

 2

		INV+	MEDIAN	INV-
	Investment constraint (%GDP)	3%	2.2%	1.8%
		decrease linearly to	decrease linearly to	constant
	T&D losses	10% in 2050	20% in 2050

Table 3 : Economic growth and energy, electricity and GHG emission indicators comparison between MEDIAN, INV+ and INV- GDP growth rate Elasticity of primary energy consumption to GDP INV+

 3

		2008-15	7,6%	0,73
		2015-30	5,1%	0,66
		2030-50	4,6%	0,65
		2008-50	5,3%	0,67
		2008-15	6,8%	0,68
	MEDIAN	2015-30 2030-50	5,4% 4,6%	0,73 0,70
		2008-50	5,3%	0,71
		2008-15	5,9%	0,64
	INV-	2015-30 2030-50	5,3% 4,6%	0,75 0,78
		2008-50	5,1%	0,74

Following the Copenhagen Accord, India pledged to reduce the emission intensity of its GDP by 20-25% in 2020 compared to 2005 level, and China to lower its carbon dioxide emissions per unit of GDP by 40-45% in 2020 compared to 2005 level.

In IEO, energy demand growth drivers for India are not detailed.

In this subsection, we often refer to WEO 2007, as this edition was dedicated to India and China.

This number however corresponds to a relatively low equipment rate: around 200 vehicles for 1000 persons. It is around 800 vehicles for 1000 persons today in USA.

Over the projection period, the efficiency of coal fired power generation is projected to improve considerably, as the new plants will be larger and more efficient, and more supercritical units will be built.

These measures are not considered in the IEP reference scenario.

[START_REF] Guivarch | The costs of climate policies in a second best world with labour market imperfections[END_REF] analyze the impact of the real wages rigidities on the model results.

Following[START_REF] Corrado | Capacity Utilization[END_REF], we assume that this is generally caused by higher labour costs due to extra hours with lower productivity, costly night work and more maintenance works.

See SOM for further details on the representation of the evolution of energy supply and demand, energy efficiency and induced technical change.

Mean generation costs increase when capacity is overused due to the existence of static decreasing returns due to higher labour costs and because less efficient units are switched on last at the aggregate level. By default, following[START_REF] Corrado | Capacity Utilization[END_REF], in our model the increasing factor is attached to wages.

There are alternative representation of shortages, in particular explicit restriction of demand growth in some sectors, which could have different economic effects.


Sandrine Mathy
email: mathy@centre-cired.fr

Céline Guivarch

Climate policies in a second-best world-a case study on India

Keywords: India, second-best world, synergies between climate mitigation and development

The aim of this article is to analyze the potential for synergies between climate policies and development in a case study on India focusing on the power sector suboptimalities. To do so, we use IMACLIM-R, a dynamic recursive energy-economy model that represents a second best world with market imperfections and short-run adjustments constraints along a long-term growth path. The analysis suggests (i) global carbon pricing induces prohibitive macroeconomic costs for the Indian economy, even in the case of significant financial transfers associated with a global cap-and-trade system and a "Contraction and Convergence in 2100" allocation scheme; (ii) the most cost efficient climate policies are not uniform carbon pricing only. The implementation of domestic policies suited to the national context, for instance targeting sub-optimalities in the power sector for India, allows reducing significantly the macroeconomic costs induced by international mitigation policies.

Introduction

There is now a global scientific consensus that stringent GHG emissions reductions are necessary to limit the high risks associated with anthropogenic climate change, and this consensus is flagged as a long run collective target by an increasing number of policymakers from all countries (G8 declaration in l'Aquila in July 2009 1 , China 2 and Japan 3 Statements at the United Nations Climate Summit, 2009). But translating this long run target into an international agreement stumbles over the common argument that climate policies would harm economic growth, and in particular slow the development of emerging and developing countries.

The assessments of climate policies costs provide reassuring messages: the global macroeconomic mitigation cost is estimated below five percent of GDP in 2050, even for the most stringent emission constraints (IPCC 2007). In addition, a majority of economic analyses recommend a uniform carbon tax or a universal cap and trade system equalizing marginal abatement costs across countries as the most efficient policy (for instance [START_REF] Manne | On stabilizing CO 2 concentrations-cost effective emission reduction strategies[END_REF]Richels, 1997 or Weyant and[START_REF] Weyant | Introduction and Overview[END_REF].

1 "We recognise the broad scientific view that the increase in global average temperature above preindustrial levels ought not to exceed 2°C. Because this global challenge can only be met by a global response, we reiterate our willingness to share with all countries the goal of achieving at least a 50% reduction of global emissions by 2050, recognising that this implies that global emissions need to peak as soon as possible and decline thereafter. As part of this, we also support a goal of developed countries reducing emissions of greenhouse gases in aggregate by 80% or more by 2050 compared to 1990 or more recent years" 2 "We will endeavor to cut carbon dioxide emissions per unit of GDP by a notable margin by 2020 from the 2005 level [...] We will endeavor to increase the share of non fossil fuels in primary energy consulmption to around 15% by 2020". [START_REF] Jintao | Join Hands to Address Climate Challenge[END_REF] 3 "For its mid-term goal, Japan will aim to reduce its emissions by 25% by 2020, if compared to the 1900 level, consistent with what the science calls for in order to halt global warming". [START_REF] Hatoyama | Prime minister of Japan[END_REF] However these results are submitted to the critical caveat that most models used to analyze the climate change economics issues represent a first best world (optimizing agents with perfect foresight, market-clearing equilibrium in all markets, full utilisation of production factors) , as specified in Box SPM-3 of last IPCC assessment report: 'Most models use a global least cost approach to mitigation portfolios and with universal emissions trading, assuming transparent markets, no transaction cost, and thus perfect implementation of mitigation measures throughout the 21st century'. Such assumptions can be suspected of underestimating mitigation costs, making modelling results 'too good to be true' as [START_REF] Stavins | Too Good to Be True? An Examination of Three Economic Assessments of California Climate Change Policy[END_REF] argue in an examination of three economic assessments of California climate change policy. Moreover, any deviation from the assumed optimal equilibrium to accommodate environmental policies will by definition lead to costs in models representing a first best world [START_REF] Barker | The costs of greenhouse gas abatement: A meta-analysis of post-SRES mitigation scenarios[END_REF].

Therefore, these methodological choices exclude by construction the possibility of synergies between environmental policies and development policies, and thus prevent from analyzing this issue. Nevertheless, real economies are characterised by numerous imperfections contrasting with the first best representation in most environmentenergy-economy (E3) models. These imperfections can lead to higher mitigation costs than estimated as Guivarch et al. (2009) showed in the case of labour markets rigidities. But they can also provide opportunities for synergies between climate change mitigation and economic growth.

The aim of this article is to analyze the potential for synergies between climate policies and development in a case study on India focusing on the power sector suboptimalities. To do so, we use IMACLIM-R a dynamic recursive energy-economy model that represents a second best world with market imperfections and short-run adjustments constraints along a long-term growth path. The first section describes this modelling framework, and details how the Indian power sector sub-optimalities are represented in this framework. The second section analyses the macroeconomic impacts for India of international climate policies, in the form of a worldwide coordinated carbon tax or through a global cap-and-trade system. We disentangle the mechanisms explaining why global carbon pricing induces prohibitive macroeconomic costs for the Indian economy, even in the case of significant financial transfers associated with a "Contraction and Convergence in 2100" allocation scheme. The third section analyses a 'package of domestic policies and measures' implemented in India jointly with the participation to the global cap-and-trade system, and focused on combining (i) electricity tariff reforms in the agricultural sector accompanied by demand side management measures, and (ii) efficiency of the power sector. We show that this package of domestic policies and measures allows reducing significantly both the duration and the magnitude of India GDP losses following the introduction of the global cap-and-trade system. While, the second and third sections focus on disentangling the mechanisms leading to our results, the fourth section examines the robustness of results through sensitivity tests. Last section concludes. Conclusions are twofold (i) from a methodological point of view, considering second best mechanisms and sub-optimalities arising in the real world in a modelling framework opens the possibility to study potentials for synergies between mitigation policies and development; (ii) from a policy point of view, the most cost efficient climate policies are not uniform carbon pricing only. The implementation of domestic policies suited to the national context, for instance for India targeting sub-optimalities in the power sector, allows reducing significantly the macroeconomic costs induced by international mitigation policies. Second, it is hybrid in the sense of [START_REF] Solow | Toward a macroeconomics of the medium run[END_REF] 4 , i.e. it tries and bridges the gap between long-run and short-run macroeconomics, as efforts were devoted not only to model long-term mechanisms but also focus on transition and suboptimal pathways through possible underutilization of production factors. We seek, indeed, to capture the transition costs with a modeling architecture that allows for endogenous disequilibrium generated by the inertia in adapting to new economic conditions due to both imperfect foresight and non flexible characteristics of equipment vintages 4 Solow (2000): 'I can easily imagine that there is a « true » macrodynamics, valid at every time scale. But it is fearfully complicated [...] At the five-to-ten-year time scale, we have to piece things together as best we can, and look for a hybrid model that will do the job.' available at each period (putty-clay technologies). In the short run the main available flexibility lies in the rate of utilization of capacities, which may induce excess or shortage of production factors, unemployment and unequal profitability of capital across sectors. Therefore, the model architecture departs from the picture of a first best world.

Technically, the model can be labelled as 'recursive dynamic', since it generates an energy-economy trajectory by solving successive yearly static equilibria of the economy, interlinked by dynamic modules.

Within the static equilibrium, domestic and international markets for all goodsexcept factors such as capital and labour -are fully cleared by a unique set of relative prices that depend on the behaviours of representative agents on the demand and supply sides. The calculation of this equilibrium determines the following variables:

relative prices, wages, employment, quantities of goods and services, value flows.

Within each yearly static equilibrium, the behaviour of producers is not represented by a flexible production function allowing for substitution between factors. These substitutions are treated between two equilibria in sector-specific dynamic modules.

Producers are therefore assumed to operate under short-run constraints of (i) a fixed maximal production capacity Cap k,i , defined as the maximum level of physical output (ii) technical progress that increases labour productivity, as in Solow's neoclassical model of economic growth [START_REF] Solow | A Contribution to the Theory of Economic Growth[END_REF]. We use exogenous trends of productivity growth, as it is a common practice in the energy-environment modelling community (e.g. [START_REF] Edmonds | Second General Model 2004: An Overview[END_REF][START_REF] Paltsev | The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Version 4[END_REF]. To build these trends we draw on 5 Mean generation costs increase when capacity utilisation approaches one due to the existence of static decreasing returns due to higher labour costs and because less efficient units are switched on last at the aggregate level. By default, following [START_REF] Corrado | Capacity Utilization[END_REF], in our model the increasing factor is attached to wages.

stylized facts from the literature, in particular the convergence assumption [START_REF] Barro | Convergence[END_REF] and two empirical analyses on economic convergence, one investigating the past trends by [START_REF] Maddison | Monitoring the world economy: 1820 -1992[END_REF], and the other one looking at future trends, by [START_REF] Martins | The Impact of Ageing on Demand, Factor Markets and Growth[END_REF]. We retained a "leader", the US, whose labor productivity growth trend lies between 2% today and 1.65% in the long run. The other regions labor productivity trends catch up with the leader's, i.e. their labor productivity growth is higher all the more as their absolute labor productivity is far from the leader's level.

These two sets of assumptions on demography and technical change, although exogenous, only prescribe potential growth. For India, potential growth decreases from 8% to 2.7% over the 2050 time horizon. Effective growth results endogenously from the interaction of these driving forces with short-term constraints: (i) available capital flows for investments and (ii) rigidities, such as fixed technologies, immobility of the installed capital across sectors or rigidities in real wages, which may lead to partial utilization of production factors (labor and capital).

Calibration

Our model is calibrated on 2001 data from GTAP 6 database [START_REF] Dimaranan | Global trade, assistance, and production: The GTAP 5 data base[END_REF]) that provides, for the year 2001, a set of balanced input-output tables of the world economy, detailed in 87 regions and 57 sectors. We modified the original GTAP-6 dataset (i) to aggregate regions and sectors according to the IMACLIM-R mapping ( In the two following subsections we describe how specific sub-optimalities of the Indian power sector can be embarked within the Imaclim-R model.

Sub-optimalities in the power sector in India

India is characterized by a restrained access to electricity services for both households and productive sectors. In 2007, capacity shortage was estimated to 10 GW (14.8% of peak power) and the gap between supply and demand reached 66 TWh (9.6% of total demand). Electrification covers only 60% of Indian households. The remaining 40% mainly on traditional biomass or on diesel generators for their energy needs.

Productive sectors are also affected by power cuts, which hinder productivity and development, in particular for industry, and force the use of diesel generators as well.

Power cuts and capacity shortage are caused by structural under-investment in the power sector, rooted between market and institutional failures. The opening of the sector to independent power producers began in 1991 in order to absorb the shortage and to compensate for the constraints on public funding. But it failed to improve significantly the situation and the private sector contributes only to 11%, 0.4% and 12% of total generation, transport and distribution respectively. In 2008, realized investments in the power sector equal to 14 billion US$ and 21.5 billion US$ would be needed to respond to total demand (i.e. the investment gap is equal to 7.5 billion US$ or 35%of estimated needs for investments). During the 10 th Plan implementation period (2002 -2007), less than half of the additional power capacity that had been programmed, has actually been built. This under-investment is largely due to the high risks and low profitability of investments in the power sector. Administered prices do not cover production costs: in 2006, the average price of electricity sold covered only 77% of the average production cost. Official data (Government of India, 2008) estimate the total under-recovery of costs to 431 billion rupees in 2008 (i.e. 8.8 US$ billion). The same report estimates that the residential tariff covers 56% of the generation costs and farmers tariff only 12%, while industries and the commercial sector partly compensate by paying respectively 108% and 122% of production costs.

Official data reports that subsidies to household electricity consumption tripled to 80.8 billion rupees (US$ 1.7 billion) between 1992-1993 and 1999-2000. Subsidies to agriculture electricity consumption more than tripled to 227 billion rupees (4.7 US$ billion, 1.1% of Indian GDP) over the same period. In 2000, agriculture electricity uses represented one third of electricity sales in volume while the corresponding revenues constituted only 4 or 5% of total revenues.

These subsidies are justified by positive externalities on development, particularly regarding access to cheap energy for irrigation in an effort to promote food production [START_REF] Tongia | Price of Power in India[END_REF]. Nonetheless, they have significant side effects. First, the very low tariffs for farmers induce overconsumption, which increases the magnitude of capacity shortage. [START_REF] Dorin | Agricultural incentives in India. Past trends and prospective paths towards sustainable development[END_REF] estimate that overconsumption of electricity in the agricultural sector amounts to 30% of its consumption due to the combination of critically low prices and of frequent but unpredictable power cuts that create a strong incentive to a continuous use of electric pumps for irrigation. Second, low revenues from electricity sales induce maintenance under-financing and increasing inefficiencies in transmission and distribution (T&D). Technical and commercial T&D losses have increased from around 20% in 1993 to more than 30% in 2001 [START_REF] Thakur | Efficiency evaluation of the state owned electric utilities in India[END_REF] 7 .

Moreover, power sector inefficiencies constrain economic activity and economic growth: electricity shortages hamper productivity and competitiveness.

It appears that the sub-optimalities of the Indian power sector described above are structural characteristics of the Indian energy system. Therefore, they have to be taken into account in modelling framework to develop realistic prospective scenarios, as advocated almost fifteen years ago by [START_REF] Shukla | Greenhouse gas models and abatement costs for developing nations. A critical assessment[END_REF] and more recently by Urban et al (2007) and [START_REF] Van Ruijven | Modeling Energy and Development: An Evaluation of Models and Concepts[END_REF]. Next section describes how stylised representations of these sub-optimalities are introduced in our modelling framework.

Representing market and institutional failures of the power sector in our modeling architecture

In the static equilibriums, the electricity price depends on the characteristics of the installed power generation capacities (the type of fuel used and the efficiency, including T&D losses) and on the utilization rate of these generation capacities: high utilization rates induce extra-costs. Between each static equilibrium, the evolution of power generation capacities is represented as follows: when describing annual investment decisions within the electric sector, the model anticipates, ten years forward, the potential future demand for electricity, taking into account past trends of demand. The module then computes an optimal mix of electric productive capacities to face the future demand at the lowest cost given (imperfect) anticipations of future fuel prices, including carbon pricing if applicable. The share of each technology in the optimal capacity mix results from a classical competition among available technologies depending on their mean total life-cycle costs (fixed costs, variable costs, investment costs). In practice, 26 producing technologies are in competition according to a non flat load curve.

Once the optimal mix of productive equipment for year t+10 has been computed, the model accounts for the time constraints in the deployment of capacities: the new capacity built at year t results from a minimization of the gap between the mix of capacity currently installed and the mix of capacity that is expected to be optimal to face the demand at year t+10. This minimization is run under the constraint of the actual amount of investment allocated to the electric sector. This process of planning with imperfect foresight is repeated at every period and anticipations are adapted to changes in prices and demand.

Within this modelling framework, specificities of the Indian power sector namely (i) power generation capacity shortage, (ii) under-investment, (iii) tariffs not reflecting costs and subsidies to electricity consumption for farmers and households and (iv) inefficiencies and T&D losses, are embarked.

(i) Power generation capacity shortage is represented by over utilization of generation capacities. In the model, a utilization rate superior to 0.8 means that the capacity is overused. In 2008, the utilization rate of electricity production capacities is 0.86, which corresponds to 7.5% of capacity shortage (i.e. 7.5% more capacities would be needed to reach a utilization rate of 0.8) and is consistent with estimations given in the first section. This over utilization of productive capacities entails extra generation costs 8 and raises the electricity usage cost, which is a stylized representation of electricity shortage (power cuts) for the Indian economy.

(ii) Under-investment in the power sector is represented by a gap between the estimated need of investments and realized investments in the power sector. Realized investments for all sectors are constrained by sectors profitability and total domestic investment plus net capital inflows.

(iii) Tariffs and subsidies for all commodities, including electricity, are encompassed in the sectors cost structure of the Social Accounting Matrix for India from GTAP-6 database [START_REF] Dimaranan | Global trade, assistance, and production: The GTAP 5 data base[END_REF] that is used to calibrate our model on the year 2001.

(iv) Power plants characteristics, in particular their efficiencies, as well as transmission and distribution losses are calibrated on the sectoral model POLES [START_REF] Lepii-Epe | The POLES model, POLES State of the Art[END_REF]. In 2001, the calibration date, overall efficiency of power generation is equal to 32% and Transmission and Distribution losses are 35%.

In sections 2 and 3, we investigate different Indian climate policies and their impact on economy. Within the following scenarios, the tariffs and subsidies structure for all commodities, including electricity, is by default kept unchanged through the entire time horizon. Moreover, without specific policies, the electricity transmission and 8 Mean generation costs increase when capacity is overused due to the existence of static decreasing returns due to higher labour costs and because less efficient units are switched on last at the aggregate level. By default, following [START_REF] Corrado | Capacity Utilization[END_REF], in our model the increasing factor is attached to wages.

distribution losses are assumed to evolve at the same pace as generation capacities efficiency improvement.

2. Climate policies and Indian development: a deadlock?

Experimental protocol

We use the IMACLIM-R model described in the previous section to develop scenarios with climate policies starting in 2013 so as to fit a given global emissions profile corresponding to stabilisation target at a concentration of 450ppm CO2. This places us in a "cost-efficiency" context.

For the setting of climate policies, we test, in this section, the prescriptions straightforward in a first best world: to equalize marginal abatement costs across countries with uniform carbon pricing so as to minimize the costs of a given environmental target. Two polar variants of climate regimes assure uniform carbon pricing: either a worldwide coordinated carbon tax or a global cap-and-trade system.

The allocation of emission quotas in this second setting induces transfers between countries, therefore giving a lever on equity issues. In the following, we first consider a uniform carbon tax for all regions (Simulation 1); the level of this carbon tax is endogenously determined so that global emissions respect the emissions profile target.

The carbon tax revenues are rebated to households in a lump-sum manner 9 . In a second step, we consider a global cap and trade system (Simulation 2). The quota system is represented by the assumption that countries auction emission permits and 9 It has previously been show that the alternative uses of tax revenues, for instance to reduce other preexisting taxes, can change results significantly (Goulder, 1995;[START_REF] Bovenberg | Green Tax Reforms and the Double Dividend: an Updated Reader's Guide[END_REF]. It is also the case in our model (Guivarch et al, 2009), as we will see in the section 4 "Sensitivity analysis and robustness".

return the revenues to households in lump-sum payments. Comparing these two simulations will allow us to separate the respective roles of the carbon pricing and of the international transfers due to quota allocation. The allocation rule chosen for Simulation 2 corresponds to a "Contraction and Convergence" (Meyer, 2000) of per capita emission at the 2100 horizon (CC2100), under a contracting global emission profile. This rule has been proposed as a bridge between the two polar rules: the grandfathering approach and the immediate equalitarian per capita criteria.

Implications of sub-optimalities in the reference scenario and in mitigation scenarios

Before describing and analyzing mitigation scenarios, we give a short description of major features of the baseline. An analyze of this reference scenario is detailed in [START_REF] Guivarch | What if energy decoupling of emerging economies were not so spontaneous ? An illustrative example on India[END_REF].

The first major characteristic of the baseline is the fact that sub-optimalities in the power sector are long lasting. The power shortage persists due to the constraint on investment and the induced over-utilization of installed capacity drives to significant extra usage costs of electricity (the extra costs due to over-utilization of power generation capacities represent between 1.5% and 15% of production costs in the baseline scenario). As a consequence, industries are forced to resort to diesel generators to compensate for deficiencies of the centralized power supply. This limits the substitution from other energy sources, in particular oil, to electricity and constraint industrial production.

The second characteristic is therefore India's economy dependency on oil, the majority of which is imported. The power sector deficiencies and the induced obstacles to substitution to electricity reinforce India's vulnerability to the rise of oil price. As a result India is deeply affected by its oil dependency: oil imports reach 10% of GDP in 2027 when the oil prices rise steeply due to the beginning of the depletion phase of reserves.

The interplay between the structural capacity shortage of the power sector and the rising profile of oil prices drives to slower economic growth than in other existing prospective scenarios (IEA, 2007;[START_REF] Shukla | India's GHG emission scenarios: Aligning development and stabilization paths[END_REF]EIA, 2007). In particular, economic growth declines significantly during the steep rise in oil price from 5.7% GDP growth rate in 2019 to 3.8% in 2027. GDP growth constrains energy efficiency improvement through the (limited) capacity to finance carbon sober technologies for productive capital or final equipment, and through the pace of capital vintages replacement: the lower the GDP growth, the lower the investment in new capital, the older is the average capital age, the worse its average energy efficiency. Therefore with important constraints on investments in the power sector, energy efficiency improvements are mechanically also constrained. As a result, energy decoupling of GDP and economic growth are much slower than in other existing scenarios (IEA 2006(IEA , 2007)).

In mitigation scenarios, the electricity price increase and the overall activity decrease due to the macroeconomic impact of carbon pricing induces lower electricity demand significant enough to absorb the capacity shortage as soon as 2017 in Simulation 1 and 2020 in Simulation 2. But the pace of energy efficiency improvement in the power sector remains constrained by an even slower pace of capital vintage replacement.

Moreover, subsidies mask incentives for end-use efficiency and maintain overconsumption habits. Given these implications of the power sector sub-optimalities in mitigation scenarios, we further investigate the macroeconomic impacts of mitigation policies in Simulations 1 and 2.

A universal carbon tax: prohibitive costs for India

The stabilisation objective leads to a world carbon price quasi-linear from 2013 to This carbon tax induces prohibitive macroeconomic costs for India (Figure 1): Indian GDP losses reach almost 20% of baseline GDP in 2030. These GDP losses are composed of (i) final consumption losses, (ii) trade losses (fewer exports and more imports) and (iii) less investment. Next paragraphs explore the mechanisms at stake in the evolution of the three components. First, final consumption losses come from the combination of (i) an increased energy budget for households, which crowds-out other goods consumption; and (ii) an income effect: the carbon tax rebated to households does not compensate the revenue losses due to less activity and lower real wages level10 . Figure 2 Second, trade losses are due to competitiveness losses of Indian products following the introduction of carbon pricing. The competitiveness loss can be measured by the evolution of the ratio of the selling price on international markets to the local production cost (Table 2). Throughout the simulation period, the competitiveness index remains about 5% inferior in simulation 1 than in the reference scenario. This competitiveness loss is due to higher energy share in industrial production costs in India than in other regions of the world. These effects are reinforced by persisting suboptimalities in the power sector and thus the high electricity usage costs for industry.

Therefore production costs are relatively more affected by carbon pricing than in other regions.

And third, the investment term showed in Figure 1 follows the general economic activity, and is therefore reduced compared to the reference scenario.

2030 2050

Competitiveness index 0.96 0.95 0.96 Electricity price 1.29 1.41 1.17

If GDP losses reach dramatic levels around 2030, we may nonetheless note that Indian GDP losses are 'transition losses' over the period 2013-2050: they increase in the first part of the period and decrease in the second part. This latter decrease is due to (i) carbon tax induced technical change that lowers abatement costs and energy shares in production costs; but also to (ii) less vulnerability of the Indian economy to oil price rise at the end of the modelling period.

The first element is illustrated in Table 22, which shows the evolution of the energy share in Indian industrial production costs over the 2012-2050 period in Simulation 1 compared to the reference scenario. It reveals that, at the end of the period, technical change induced by the global carbon tax leads to a lower energy share than in the reference case.

As for the second element, Guivarch et al. (2009) and [START_REF] Guivarch | What if energy decoupling of emerging economies were not so spontaneous ? An illustrative example on India[END_REF] showed that, in IMACLIM-R simulations, in the absence of specific policies such as subsidises to oil consumption or capital inflows, India is very vulnerable to a rise of international oil prices. This vulnerability is explained by mechanisms similar to those described above in the case of a carbon tax (loss of competitiveness due to high energy share in industrial production costs, crowding-out of other goods consumption by the energy burden, wage effect), aggravated by the transfer of income to oil exporting countries implied by higher oil prices. This vulnerability is reduced by climate policies for two reasons (Table 3). This uniform carbon tax scenario reveals very significant economic impacts for India.

The following sub-section analyses whether the financial transfers induced by a capand-trade system are able to reduce the costs of carbon pricing to an acceptable level for India.

Quota allocations to solve equity issues?

The second simulation considers a global cap-and-trade scheme with a 'Contraction and Convergence' (at the 2100 horizon) quotas allocation rule. With this allocation rule, India receives quotas equal to its emissions when the global cap-and-trade system is implemented; its per capita allocation increases to 0.4 tC/cap in 2030 and decreases afterwards. Table 4 shows that the allocation rule becomes stringent before 2020. The carbon price resulting from the implementation of the global cap-and-trade system induces effective emissions significantly lower than the allocation India receives with the contraction and convergence rule. The carbon price emerging from this climate policy design is very close to that of the scenario with a global carbon tax. Financial transfers linked to quotas exchanges take place: India is a quota seller throughout the whole time horizon and receives up to 2.6% of its GDP from these sells (Table 5). In spite of the very significant transfers received12 , India economy is still heavily affected the first fifteen years of the climate policies, with GDP losses reaching 6% of the reference scenario GDP in 2019 (Figure 3). Two main elements due to capital inflows linked to quotas selling explain the differences from the scenario with a global carbon tax: (i) the income effect of quotas revenues, and (ii) the trade balance and real exchange rates effect due to quotas exchanges. On the one hand the revenues from quotas partially offset the revenue losses due to less activity and lower real wages level in the years following the introduction of carbon pricing, and contribute to the increase of final consumption. On the other hand, the capital inflow from quotas selling causes an appreciation of real exchange rates, which penalizes Indian products for exports and favours imported goods on domestic markets. This appreciation of real exchange rates corresponds to one of the symptoms of the so-called "natural resource curse" or 'Dutch Disease', which have been observed in most oil-producing countries [START_REF] Sachs | The curse of natural resources[END_REF], and has previously been highlighted in the case of capital flows due GHG quotas exchanges by [START_REF] Mckibbin | Emissions trading, capital flows and the Kyoto Protocol[END_REF].

The resulting time profile of GDP variation (Figure 3) shows that financial transfers due to quotas selling do not offset the negative macroeconomic effect of competitiveness losses and crowding out of non energy goods consumption, during a transition period. After 2030, however, the positive macroeconomic effect of financial inflows dominates and Indian GDP becomes higher than in the reference scenario.

The transition macroeconomic losses happen when the carbon price is still low and when allocations are still close from the grandfathering allocation rule. One solution could be to define another allocation rule generating more financial transfers towards India in the early years of implementation of the cap-and-trade scheme. Nevertheless, high transfers face a credibility gap 13 and that have little chance to be acceptable for Annex I countries [START_REF] Jacoby | Sharing the burden of GHG reductions[END_REF]. Moreover, this solution would not solve the issue of real exchange rate appreciation due to large amount of financial inflows in India.

In the following we propose an alternative scenario based on Indian domestic policies targeting sub-optimalities in the power sector.

Looking for synergies between climate policies and development

Further simulations

This part explores a 'package of domestic policies and measures' implemented in India jointly with the participation to the global cap and trade system. The domestic policies we consider are meant to reduce transition economic losses due to the international mitigation policy and to create synergies between mitigation policies and development policies in India. As it was highlighted that sub-optimalities in the power sector hinder both growth and energy efficiency [START_REF] Guivarch | What if energy decoupling of emerging economies were not so spontaneous ? An illustrative example on India[END_REF], the 13 One should not forget that donor countries committed themselves to dedicate 0.7% of their GDP to Official Development Assistance (ODA), but do not respect this commitment, only dedicating 0.4% of their GDP dedicated to ODA.

policies considered are specifically targeting these sub-optimalities. In the following simulation (Simulation 3), the focus is on combining (i) electricity tariff reforms in the agricultural sector accompanied by demand side management measures, and (ii) efficiency of the power sector. Indeed, literature on energy issues in India shows the need for a tariff reform so that prices better reflect costs [START_REF] Filippini | Elasticities of electricity demand in urban households[END_REF]. In addition to giving energy savings incentive, it would increase revenues for the power sector. However, to overcome strong social and political obstacles to a tariff reform, it appears, as pointed out by Bose et al. (2006) 14 , that it has to be combined with an ambitious program improving the power supply quality.

In practice, the alternative scenario we consider still follows global emissions profile corresponding to stabilisation target at a concentration of 450ppm CO2, and assumes a global cap and trade system starting in 2013 with quota allocations based on the CC2100 rule, as in the preceding section. The 'package of domestic policies and measures' is assumed to start also in 2013 and encompasses:

The progressive reduction of subsidies to electricity consumption for the agricultural sector, accompanied by demand side management (improvement of irrigation pumps, evolution of consumption habits 15 ). Following the reform suggested by the World Bank (2001), it is assumed that on the one hand, the subsidies decrease and the unit price increases, but on the other hand, the consumption level decreases. This joint evolution is supposed to occur at a constant budget for electricity for farmers and to reach a cut of 50% of the subsidies to electricity consumption for the agricultural sector, over a 10 year period.

14 [START_REF] Bose | Cost of unserved power in Karnataka, India[END_REF] evaluate for different consumer categories their willingness to pay more for a better supply quality (with less shortage) in the Karnataka region. 15 In particular, if the grid quality and the service are improved, the number of unpredictable power cuts will be significantly lower, and farmers will not have to leave their pumps switched on all day long.

-The implementation of a program to reduce electricity T&D technical and commercial losses. This program would partly be composed of refurbishment of obsolete installations of power distribution all around the country but also of new managerial methods to reduce commercial losses (details of such a program can be found in IEA, 2002). The cost and the potential for improvement are calibrated on [START_REF] Ruet | Investment profitability in bridging the power gap in India[END_REF]. The level of T&D losses decreases to 15% over a 20 year period.

-The revenues following the partial removal of electricity consumption subsidies in the agricultural sectors are used to finance the demand side management program in the agricultural sector, the reduction of technical and commercial losses in electricity T&D, and the additional power generation capacities if needed. Remaining additional revenues, if any, are rebated to households through lump-sum transfers. In this simulation, Indian GDP losses are confined to the first three years after the start of mitigation policies and peak GDP losses are limited to 2% of the reference scenario GDP. These lower macroeconomic costs of the international mitigation policy than in Simulation 2 are brought by the package of domestic policies and measures via three main channels.

Results

First, the reduction of electricity T&D losses decreases the electricity usage cost by 4% in 2015, 5% in 2020 and 8% in 2030 in Simulation 3 compared to Simulation 2.

This cost decrease induces substitution from fossil fuels to electricity in the industry section, which leads to a lower share of energy budget in industrial production costs.

Therefore Indian products lose relatively less competitiveness in this simulation than in Simulation 2, and salaries are less squeezed to restore commercial balance: wage levels are 3% higher than in Simulation 2 in 2015, and 9% in 2020 and 2030.

Second, the same decrease of electricity usage cost reduces households energy budget share, which leads to less crowding-out of other goods consumption. However, this effect is non monotonous as it derives from complex interactions between energy prices, revenues dynamics and equipment dynamics (personal vehicles ownership for instance): between Simulations 2 and 3, households energy budget shares are reduced by 0.7 percentage point in 2015, 0.4 in 2020 and 0.5 in 2030.

Third, there is a positive income effect stemming from higher wage levels, more wages volume due to more overall activity and transfers to households coming from the remaining revenue generated by subsidies decrease not used to finance the domestic policies and measures (which represent up to 0.4% of GDP in the Simulation).

These results show that the package of domestic policies and measures considered in this scenario reduces significantly the magnitude and duration of transition macroeconomic losses due to international mitigation policies. While last sections aimed at disentangling the cost formation mechanisms under alternative policy settings, next section will test the robustness of the results through a sensitivity analysis to the model parameterization.

Sensitivity analysis and robustness

In this antepenultimate section we reproduce the same policies setting as in Simulation 1, 2 and 3 with alternative parameterization of the model to take into account the uncertainties on a large set of exogenous parameters. The uncertain parameters include inter alia: the amount of fossil fuel reserves, the coal market responsiveness to demand increase, the rhythm of technical change (date of availability of low carbon technologies such as carbon capture and sequestration, rate of learning by doing, etc.), the synfuels supply curve characteristics and its evolution, the future development styles (rate of car ownership increase when revenue increase for instance). A detailed description of these parameters and the method used to conduct a sensitivity analysis is given in [START_REF] Vogt-Schilb | Methodology for scenarios development and sensitivity analysis with Imaclim-R[END_REF]. Climate policies international architecture is either a uniform carbon tax (as in Simulation 1) or a global cap-and-trade scheme with emission quotas allocated following the CC2100 rule (as in Simulation 2). Domestic policies are either uniform carbon pricing alone (as in Simulation 1 and 2) or in parallel with the package of domestic policies and measures focussing on electricity tariff reform for the agriculture sector and on efficiency in the power sector (as in Simulation 3). Moreover, we test alternative uses of carbon pricing revenues: they are either rebated to households in a lump-sum manner or used to reduce other taxes.

Figure 5 confirms that a global carbon tax implies prohibitive costs for India: the mean peak GDP loss is 10.8% of reference GDP, with results ranging between 4.7% and 24.3%. We may note this result varies significantly with the use of carbon pricing revenues: the mean peak GDP loss is 15.7% of reference GDP when carbon revenues are rebated to households and 8.1% when they are used to reduce other taxes. Figure 6 (left panel) shows that a global cap and trade system with CC2100 quota allocation rule does not solve the transition cost issue. Indeed, the mean duration of GDP losses is 9 years, with results ranging from 5 to 21 years, and the magnitude of peak GDP loss ranges from 2.3% of reference GDP to 10.9%, with a mean value equal to 4.8%. In this policy setting again the use of carbon pricing revenues influences significantly the results: the duration of GDP loss is reduced on average by 4 years when revenues are used to reduce other taxes compared to the cases when they are rebated to households in a lump-sum manner.

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Conclusion

To conclude, we may first summarize the findings of the modelling exercises explored in this article. First, it appears that global carbon pricing induces prohibitive macroeconomic costs for the Indian economy, even in the case of significant financial transfers associated with a "Contraction and Convergence in 2100" allocation scheme.

Second, we identified a 'package of domestic policies and measures' focussing on combining (i) electricity tariff reforms in the agricultural sector accompanied by demand side management measures, and (ii) efficiency of the power sector. We showed that this package of domestic policies and measures allows reducing significantly both the duration and the magnitude of India GDP losses following the introduction of the global cap-and-trade system. This analyse confirms, in the case of India that policies focusing on the current sub-optimalities of the power sector appear as a centre piece of domestic development policies in the context of global mitigation policies.

We acknowledge that this study is limited because of the focus on the power sector only. Conclusions regarding the potential for implementing synergies between development and mitigation could be reinforced by considering a larger panel of sectors. Further research is needed to broaden the scope of sectors and types of domestic policies and measures considered. Moreover, the time sequence of policies and entry into a global agreement should be analysed.

Nethertheless, two strong messages can be drawn from our results. First, from a methodological point of view, our approach calls for the representation of second-best mechanisms and of national specificities in modelling frameworks used to assess climate policies. Second, from a policy point of view, our results open room for synergies between mitigation policies and development. At the core of these synergies, appears the crucial role of domestic policies and measures focusing on removing energy consumption subsidies and enhancing energy efficiency, and adapted to national specificities. This result suggest rephrasing the issue for international climate policies from the question of the fairness of burden sharing to the design of an architecture in which climate policies could contribute to development over the long run but also here and now.

  is a hybrid recursive general equilibrium model of the world economy that is split into 12 regions and 12 sectors[START_REF] Sassi | IMACLIM-R: a modeling framework to simulate sustainable development pathways[END_REF].It is hybrid in two senses. First, it is a hybrid model in the classical sense: its structure is designed to combine Bottom-Up information in a Top-Down consistent macroeconomic framework. Energy is explicitly represented in both money metric values and physical quantities so as to capture the specific role of energy sectors and their interaction with the rest of the economy. The existence of explicit physical variables allows indeed a rigorous incorporation of sector based information about how final demand and technical systems are transformed by economic incentives.

Figure 1 :

 1 Figure 1: Decomposition of GDP variation between final consumption losses, decrease in investments and trade losses between Simulation 1 (uniform carbon tax) and the reference scenario.

Figure 2 :

 2 Figure 2: Evolution of household budget share for non energy goods consumption in Simulation 1 (uniform carbon tax) compared to the reference scenario.

  First, international oil prices are lowered by less global demand: compared to the baseline, oil prices are almost 30% lower in 2030 and 12% in 2050. Secondly, technical change and consumption structure change induced by climate policies reduce Indian dependence on imported.

Figure 3 :

 3 Figure 3 : Decomposition of GDP variation between final consumption losses, decrease in investments and trade losses between Simulation 2 (global cap-and-trade CC2100) and the reference scenario.

Figure 4 &Figure 4 :

 44 Figure 4 focuses on the transition and shows the Indian GDP variation between

Figure 5 :

 5 Figure 5 : Range of Indian GDP variation when a global uniform carbon tax is introduced (% of corresponding reference GDP). The shaded area gives the full range of variation for the alternative parameterization of the model tested, the plain line gives the mean across scenarios where the carbon tax revenues are rebated to households and the dashed line gives the mean across scenarios when they are used to reduce other taxes.

Figure 6 (Figure 6 :

 66 Figure 6 (right panel) shows the robustness, in alternative future worlds, of the

Figure 7

 7 Figure7shows histograms and smoothed densities of peak GDP losses (in % of

Figure 7 :

 7 Figure 7 : Histogram and smoothed densities of Indian peak GDP losses (in %) due to mitigation policies. Grey bars and dashed line corresponds to scenarios where domestic policies are uniform carbon pricing alone, while black bars and plain line corresponds to scenarios including the package of domestic policies and measures focussing on electricity tariff reform for the agriculture sector and on efficiency in the power sector.

.2. A growth engine allowing gaps between potential growth and effective growth

	capacities utilization rate approaches one 5 . This represents a different paradigm from
	usual production specifications, since the 'capital' factor is not always fully operated.
	The dynamic modules shape the accumulation of capital and its technical content, they
	are driven by economic signals, such as prices or sectoral profitability, that emerge
	from former static equilibriums. They include the modelling of (i) the evolution of
	capital and energy equipment stock described in both vintage and physical units (such
	as number of cars, housing square meter, transportation infrastructure), (ii) of
	technological choices of economic agent described as discrete choices in explicit
	technology portfolios for key sectors such as electricity, transportation and alternative
	liquid fuels, or captured through reduced form of technology rich bottom up models,
	and (iii) of endogenous technical change for energy technologies (with learning
	curves).
	1.1Our model growth engine is composed of:
	(i) exogenous demographic trends. The demographic assumptions are drawn from the
	United Nations World Population Prospects "median variant" scenario (United
	Nations 2005).
	achievable with the equipment built and accumulated previously, and (ii) fixed input-
	output coefficients representing that, with the current set of embodied techniques,
	producing one unit of a good i in region k requires fixed physical amounts IC j,i,k of
	intermediate goods j and l k,i of labour. In this context, the only margin of freedom of
	producers is to adjust the utilisation rate Q k,i /Cap k,i according to the relative market
	prices of inputs and output, taking into account increasing costs when the production

Table 1 )

 1 (ii) to make it fully compatible with the 2001 IEA energy balances 6 .

	Regions	Sectors
	USA	Coal
	Canada	Oil
	Europe	Gas
	OECD Pacific (JP, AU, NZ, KR)	Liquid Fuels
	Former Soviet Union	Electricity
	China	Air
	India	Water
	Brazil	Other transports
	Middle-East Countries	Construction
	Africa	Agriculture
	Rest of Asia	Energy-intensive industry
	Rest of Latin America	Composite (services and light
		industry)

Table 1 : Regional and sectoral disaggregation of the IMACLIM-R model

 1

Table 2 : Evolution of the competitiveness index and of the electricity price in Simulation 1 (uniform carbon tax) compared to the reference scenario (reference scenario index=1).

 2

  Table3shows indeed that Indian oil imports are 40% lower in value in 2030 and 20% lower in 2050 with climate policies compared to the reference scenario. This reduction of vulnerability to oil price rise can be explained by a better anticipation of oil tensions, thanks to earlier and more regular increases in final energy price. In a second-best world where anticipations are imperfect, indeed, brutal and unexpected increases in energy prices cause larger macroeconomic losses than regular and planned increases. Here, the more regular increase in energy price with climate policies prevents economic lock-in in an oil-dependent scheme and promotes the development of oil-free technologies before the beginning of the depletion phase in oil production.

Table 3 : Variations, between Simulation 1 and the reference scenario, of the value of oil imports and decomposition of this variation between a price effect and a volume effect (second and third line)

 3

			2020	2030	2050
	Oil imports (value)	-31%	-39%	-17%
	World oil price	-20%	-28%	-11%
	Oil	imports	-13%	-15%	-7%
	(volume)

Table 4 : Emission quotas allocated to India compared to the reference scenario emissions and effective emissions in Simulation 2 (in tons of carbon per capita).

 4

	Reference scenario	0.31 0.40 0.52 0.62 0.76
	Contraction and Convergence	0.31 0.36 0.40 0.38 0.32
	Effective emissions	0.31 0.27 0.26 0.24 0.24

Table 5 : Financial transfers received by India as a percentage of its GDP, compared to transfers received by other quotas sellers and paid by quota buyers as a percentage of their respective GDP.

 5 The group of quota selling countries evolves with time. India, China, Brazil, Africa are quota sellers during the whole period; USA, Canada, Europe, OECD Pacific, FSU and Middle East are quota buyers during the whole period; Rest of Asia and Rest of Latin America are alternatively quotas sellers and quota buyers. 2. Figures related to quota buyers are inferior to figures related to quota sellers because of the difference in GDP levels between the two groups of countries.

		2020	2030	2040	2050
	India	1.5%	2.6%	2.3%	0.6%
	Other quota sellers 0.4%	1.0%	1.4%	0.4%
	Quota buyers	0,1%	0,3%	0,3%	0,2%

Note: 1.

This process of building hybrid input-output matrices is very precisely discussed in[START_REF] Sands | The Second Generation Model: Comparison of SGM and GTAP Approaches to Data Development[END_REF].

The level of T&D losses is in fact uncertain. For instance, the value stated by the Indian Planning Commission was 22% in 1998 while the World Bank (1998) pointed out that it could actually be twice this official level. Moreover, as there is no metering of the electricity consumption it is difficult to distinguish technical from commercial losses.

This latter effect comes from the representation of the labour markets through aggregate regional wage curves that link real wages to unemployment rates[START_REF] Blanchflower | Estimating a wage curve for Britain 1973-1990[END_REF]. SeeGuivarch et al (2009) for a detailed analysis of the implications of such a representation of labour markets imperfections on stabilisation costs.

To put these financial flows into perspective, Official Development Assistance (ODA) currently received by India represents about 0.3% of its GDP, and donor countries devote between 0.3 and 0.4% of their GDP to ODA. &

Acknowledgements

The authors wish to thank Jean-Charles Hourcade, Stéphane Hallegatte and Olivier Sassi for their useful comments and suggestions on a previous version of this article, as well as all the team who contributed to IMACLIM-R development. We are also grateful to two anonymous referees whose comments helped us improve this text significantly. Part of this article is based on studies that have benefited from financial support from the GICC (Gestion et Impacts du Changement Climatique) research program of the French environmental department.