ISSN (Print) - 0012-9976 | ISSN (Online) - 2349-8846

A+| A| A-

Key Drivers of Indian Greenhouse Gas Emissions

The underlying drivers of changes in the greenhouse gas emissions over time in India are investigated using several complementary approaches. Emission projections are developed based on India’s Intended Nationally Determined Contributions and compared with a range of emission scenarios. Projections show continued economic growth that leads to rising energy use, with per capita emissions possibly increasing by 40% by 2030, although new technologies may reduce energy consumption and emissions growth. To slow down emissions’ growth further will require strong decarbonisation of the energy sector.

India is the third largest emitter of carbon dioxide (CO2) in the world (not including the European Union [EU]), emitting 2.3 gigatonnes (gt) CO2 in 2015. While the two larger emitters, United States (US) and China, had a decrease in emissions in 2015, India increased its emissions by 5.2% (Le Quéré et al 2016). In fact, in 2014 and 2015, the largest increase in global emissions came from India. This large increase comes after more than a decade of rapid growth, which is likely to continue for many years. New plans to install many coal-fired power plants have raised serious concerns about India’s new trajectory, which is incompatible with the country’s climate goals and may jeopardise the global effort of limiting global warming to 1.5° Celsius (C) (Shearer et al 2017a; Timperley 2017).

Measured in absolute terms, India’s emissions have been the third-highest globally since about 2008 (Le Quéré et al 2016). Projections of economic growth, energy use and emissions per energy put India on a path of continuously increasing greenhouse gas (GHG) emissions (Murthy et al 1997; Raghuvanshi et al 2006; Sharma et al 2006). While current per capita emissions are very low, projections by the United Nations (UN) suggest that India’s population will continue to increase and surpass China’s around 2025. At the same time gross domestic product (GDP) is projected to increase faster than most countries from 2013 to 2040 at an average of 6.5%/year (IEA 2015b), indicating that energy consumption and emissions may see a large increase as more people use more energy. As standards of living improve, climate change impacts pose serious challenges to India’s economic growth, agricultural outputs, public health and development (IPCC 2014; Lobell et al 2012).

In the lead-up to the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of the Parties (COP21) meeting in Paris (December 2015), India published its Intended Nationally Determined Contributions (INDC) to help address global climate change. India’s INDC has several aims (MoEFCC 2015), including to reduce the emissions intensity of its GDP by 33% to 35% by 2030 compared with the 2005 level, and having 40% electric power installed capacity from non-fossil-fuel-based energy sources. Even if these aims are met, this could mean substantial emissions growth in the next decades (Climate Action Tracker 2016). The International Energy Agency (IEA) and other studies indicate that India’s INDC is not far from business as usual (Aldy et al 2016; IEA 2015b). In contrast to China, India has not announced when its emissions will peak, allowing India to retain some flexibility in its economic and technological development. However, it is also in India’s own interest to mitigate climate change as fast as possible, as the literature shows large negative impacts on GDP due to the consequences of global warming (Burke et al 2015). Thus, decision-makers will need to understand the underlying drivers of historic and current emissions to help shape future emissions pathways.

Previous articles have investigated specific historic causalities and drivers, that is, if coal consumption affects emissions (Chandran Govindaraju and Tang 2013), if increased household access to electricity affects India’s emissions (Pachauri 2014), what the sources of household emissions are (Das and Paul 2014) and how much trade openness affects energy consumption and emissions (Yang and Zhao 2014). However, there have not been any broad investigations into the recent drivers of Indian emissions now that India is the world’s fastest growing major economy and has at the same time promised to help combat climate change through the Paris agreement. India’s way forward may cause accelerated emissions and global warming, although signals of decarbonisation have also started to emerge, as several coal-based power projects have been put on hold and investments in renewables have increased (Bhagwat and Tiwari 2017; Shearer et al 2017b).

Given India’s importance as a global actor, and its broad possibilities, it is important to map and understand the drivers of the recent historic development in order to understand future development. This article, therefore, aims at identifying the historic and current drivers of GHG emissions and investigating emission projections based on scenarios. We first give an overview of India’s historic emissions and economic development in order to discuss the underlying drivers, before we list the data sets and methods used in our study. We use four complementary approaches to analyse the drivers of historic emissions in order to capture different effects: the Kaya identity, structural decomposition, consumption-based emissions accounting and structural path analysis. These methods highlight driving forces of Indian emissions in different parts of the economy (that is, domestic factors versus emissions embodied in trade), thus leading to different leverage points where policy can be focused. This gives an arguable broad overview, from which to highlight and contrast Indian development. Finally, we project emissions and forecast the most important drivers of future emissions, before concluding and discussing policy implications.

Historical Development

India’s recent development is characterised by increasing population and GDP (Figure 1). The average annual growth rate (AAGR) of GDP has increased from 3.6%/year (per year) in the 1970s to more than 5.3%/year in the 1980s and 1990s, to 7.3%/year in the 2000s due mostly to expanding service sectors (IEA 2015a). The most recent data shows the Indian GDP expanding 6.2%/year from 2010 to 2014 (World Bank 2015). Panagariya (2004) has argued that the liberalisation of foreign trade, the reduction in industrial licensing and opening to foreign direct investment, resulting from policies adopted since 1990, were responsible for accelerated economic growth in India. The population growth rate has declined from an average of 2.3%/year in the 1970s to an average of 1.6%/year in the 2000s. India is the second-most populous country after China, with the UN’s medium population projection peaking in about the year 2070 (United Nations 2015). In combination, GDP per capita has seen an increase in growth, where the largest was in the 2000s at an annual average of 5.8%/year. While India’s GDP per capita was only 26% of the global average GDP in 1970, this has now increased to 62% in 2014.

India’s emissions have also increased more than the global average, increasing its share of global CO2 emissions from 1.3% in 1970 to 6.3% in 2015. For comparison, China was responsible for 5.2% and the US for 29.1% in 1970, and 28.6% and 14.9% in 2015, respectively (Le Quéré et al 2016). The AAGR of Indian emissions has increased from 4.7%/year in the 1970s, to 5.8%/year in the 2000s. Energy consumption has also risen more sharply in the last decade than previously, with an AAGR of 4.4%/year in the 2000s, decreasing from 2010 to 2013 to 3.9%/year (IEA 2015a). Energy intensity of economic production, on the other hand, which illustrates advancements in the economy by technological and structural changes (for example, a shift from agriculture to energy-intensive manufacturing), declined by -2.9%/year in the 2000s, compared with -2% from 2010 to 2013.

Energy intensity has significantly decreased in magnitude since the 1970s, with 95% more GDP generated per unit of energy in 2013 than in 1971. The carbon intensity of energy has increased since the 1970s, although the growth rate has declined. In 1971, the carbon intensity was 29 tonnes (t) CO2/terajoules (TJ), while this increased to 58 t CO2/TJ in 2013 (IEA 2015a). This is mostly due to the increase in coal use, which in 2013 accounted for 44% of the primary energy mix, compared to 33% in 2000 (IEA 2015b). Additionally, the relative use of
bioenergy has been reduced significantly, although the absolute demand has gone up.

Furthermore, from a per capita perspective, with 1.28 billion people, India’s CO2 emissions are still very low, at 1.7 tCO2/capita in 2015, well below the world average of about 5 tCO2/capita. However, since its emissions have been growing faster than its population, India’s per capita emissions have doubled between 1996 and 2015. Following projections for emissions according to India’s INDC and population projections, per capita emissions will be around 2.4 tCO2/capita in 2030 (United Nations 2015). The equivalent numbers for China is 6.8, US is 10.2 and EU28 is 4.9 tCO2/capita, for 2030 (Peters et al 2015).

The dominant source of CO2 emissions in India is the combustion of coal, contributing 67% to total fossil CO2 emissions in 2015. Although there is more coal in the energy mix than other fuels, the proportion of coal in fossil fuel emissions in India has been generally declining since the 1940s, when coal accounted for more than 95% of all fossil CO2 emissions, until about 2000, when the proportion of coal was 61%. Since 2000, the share of coal has started to increase again (Le Quéré et al 2016). While India is the world’s third-largest producer of coal in physical terms (after China and the US), its coal is generally of poor quality, with significantly lower energy content than the coal of both China and Indonesia (BP 2014; IEA 2014). Both natural gas and oil have been gaining share in India’s total energy supply—oil particularly between 1940 and 1970, and gas since 1980. Emissions from cement production have also been growing steadily in the last 30 years, now contributing around 6% to India’s total CO2 emissions (Le Quéré et al 2016).

These historical developments are affected by underlying drivers putting upwards and downwards pressure on the economy, consumption, and energy use. To help explain  these developments and understand how they may affect future development, we explore these underlying drivers in the next section.

Data Sets and Methods

Our study uses different methods that rely on different data sets with different resolutions. In the first approach, we use the Kaya identity (Edenhofer et al 2014; Raupach et al 2007), which reveals major factors affecting historical CO2 emissions. The Kaya identity decomposes CO2 emissions into the product of population, GDP intensity (GDP/capita), energy intensity (energy/GDP), and carbon intensity (CO2/energy). We use energy, population, GDP and CO2 data from IEA (2015a) for consistency. We only consider emissions from fossil fuels and industry.

The second way of analysing drivers of change in emissions is using structural decomposition analysis (SDA) between years, which expands on the Kaya identity (formally, Index Decomposition Analysis) to provide sector detail (Hoekstra and Van den Bergh 2003; Su and Ang 2012). By extending the Kaya identity, SDA uses input–output data to additionally decompose the emissions into technology and structure effects, reflecting the changing relationship between industries over time. We decompose changes into three effects. The scale effect represents the impact of increases in the size of the economy measured in terms of GDP growth. The technology effect captures the changing technology reflected through technical coefficients of an input–output table. The structure effect describes structural shifts of demand in the economy, measured as the contribution by each sector to total GDP.

Finally, since the three terms interact leading to non-unique decompositions (Hoekstra and Van den Bergh 2003), we show the interaction between the three terms separated instead of arbitrarily allocating the interactions to each effect. In this section, we cover all GHG emissions (CO2, methane [CH4], and nitrous oxide [N2O]), allowing the explanation of changes in sectors such as agriculture as well as other non-energy intensive industries. We consider trends from 1996 to 2000, from 2000 to 2004, and from 2004 to 2009. We use input–output data from the World Inpu–Output Database (WIOD) because of its annual time-series, and GDP data from Reserve Bank of India (RBI 2013). The mathematical details of the decomposition methods are described in Pal et al (2014).

The third way of investigating drivers of changes in emissions is the allocation of consumption-based emissions, which takes the global supply chain into account using a multi-regional input–output (MRIO) table, allocating emissions to where purchases of final goods and services occur. Countries with more embodied emissions in imported products than in exports, including most developed countries, are allocated more emissions from a consumption perspective. The opposite is true for many developing countries, and in particular for China. We follow previous studies (Karstensen et al 2015; Peteret al 2011), using economic data from GTAP (global trade analysis project) (Narayanan et al 2015) and GHG emissions (CO2CH4N2O and fluorinated gases) from GTAP and EDGAR (electronic data gathering, analysis, and retrieval system). This MRIO database has a high level of detail for particular years (the latest being 2011). We additionally use an annual time series of consumption-based CO2 emissions from 1990 to 2013 at the national level, which is built on the GTAP data, in order to  explain historical developments (Figure 1, p 47; Peters et al 2011).

The fourth way of investigating the underlying drivers of emissions in the Indian economy uses the techniques of structural path analysis (SPA) in order to highlight specific high-emissions paths and “hotspots” (Lenzen 2007; Peters and Hertwich 2006). This individually enumerates all sector-level supply chains, as described in the MRIO table, connecting emissions happening in every Indian sector with domestic and international trade. This analysis shows more specific details on where emissions enter the supply chain (hotspots, that is, large direct emissions) and where the largest accumulated supply-chain emissions are (indirect emissions, hereafter “paths”). This method uses the same data set at the previous approach but provides results with higher detail.

We furthermore estimate future emissions that are compatible with the INDCs, which stated a decline in the emissions intensity of GDP by 33% to 35% by 2030. We use the Organisation for Economic Co-operation and Development’s (OECD) GDP forecast, which estimates that GDP will increase at 5.9%/year in 2015, declining to 5.6%/year in 2030 (OECD 2014). Combining the pledged decline in CO2/GDP with the growth in GDP, we estimate emissions will grow at about 3.2%–3.5%/year in 2020 and 3.1%–3.3%/year in 2030, with absolute CO2 emissions of 3.6 Gt CO2 to 3.7 Gt CO2 in 2030 (method based on Peters et al 2015). This is compared to a revised version of the GCAM (global change assessment model) computable general equilibrium (CGE) model (Capellán-Pérez et al 2014), which is additionally used to project Indian CO2 emissions based on its INDC and GDP projections (Dasgupta 2014). The Indian version of the model has improvements to the structure, in particular with a more detailed Indian sectoral representation, a producer-behaviour sub-model, and revised parameter estimates (Dasgupta S 2014). Data from the GCE model is also used to extend the Kaya decomposition of the emissions up to 2030.

Drivers of Historical Emissions

We use four different approaches to investigate the drivers of India’s emissions: (i) the Kaya identity, (ii) structural decomposition analysis (SDA), (iii) analysis of emissions embodied in trade, and (iv) structural path analysis (SPA). These approaches all have different strengths and limitations, helping to identify different drivers at different levels of the economy, which in turn can point to different policy levers.

Kaya analysis: The individual components together with changes in CO2 emissions shows that population and GDP/population have put continuous upward pressure on emissions, while energy/GDP and CO2/energy have driven emissions down. Since the 1977 peak in India’s population growth at 2.3%/year, annual growth has started to slow down, from 2%/year in 1990 to 1.2%/year in 2013 (World Bank 2015). Our analysis demonstrates that population increase has put upward pressure on emissions between 1971 and 2013; although this has been reduced in the later years making it one of the smallest effects (Figure 2). India’s GDP per capita growth has seen large fluctuations in the last two decades, from a reduction in 1991 of -1.1% to a peak in 2010 of 10.3% (World Bank 2015). Although the financial crisis reduced the GDP growth from nearly 10% in 2007 to 4% in 2008, India has not experienced an economic recession in the last three decades, and GDP has nearly quadrupled since 1990, indicating a shift to a higher standard of living (World Bank 2015). Increasing income usually leads to more energy use, which is closely connected to emissions. Our analysis reveals that India is no different in this regard. Economic growth has pushed emissions upward, with continuous improvements in energy efficiency ensuring that energy consumption and CO2 emissions grow slower than the economy (Mukhopadhyay 2008).

Energy use in India has been closely connected with emissions over time, as most of India’s energy needs are met by using fossil fuels. However, decreasing energy intensities, due to technological advancements and changing production processes, have driven emissions down since the 1970s. Although this has been overshadowed by the increase in GDP/capita, major structural improvements have been made in the Indian economy, such as reducing energy consumption per unit of GDP and expanding the service sectors (Roy et al 2013), but there
remains significant scope for further improvement (Khanna and Zilberman 2001).

The carbon intensity of energy is closely linked to the technology being used to produce energy and has a large potential for emission reductions. In India, CO2/energy has increased over the last three decades, due to the increasing use of coal in the production of electricity. The increase in energy use combined with increasing carbon intensity has thus resulted in CO2 emissions growing faster than energy consumption. While most economies, over time, experience improvements in energy efficiency (Edenhofer et al 2014), emissions scenarios that lead to temperature stabilisation require large reductions in CO2/energy (Edenhofer et al 2014).

Structural decomposition analysis: The structural decomposition analysis expands on the Kaya identity, and decomposes changes into three effects: scale effect (economic growth), technology effect (changing technologies in the economies) and structure effect (changes in the structure of economic demand). The SDA shows that the scale effect exerted significant upward pressure on GHG emissions from 1996–2000, while the structure and technology effects simultaneously pulled emissions down (Table 1). However, the scale effect dominates leading to a positive growth rate of GHG emissions of 2.6% during this period. In the other time periods, the different effects have the same sign, but different magnitudes. During 2000–04 the scale effect was larger, but was compensated by a larger technology effect leading to moderate emissions growth. During 2004–09, the scale effect remained large leading to higher growth in emissions.

The structure effects indirectly measure the change in the contribution of each sector to total GDP, and these have changed in the three-time periods. The share of the service sector’s GDP was 51% in 1996, but this increased to 65% in 2009. By contrast, the share of the agriculture sector has gradually declined from 28% to 17% in 2009. The secondary industry’s share has been stable at around 20%. Since the service sector is less emission-intensive compared to agriculture and industry (Pal et al 2014), the structural shift towards the service sector has helped to reduce the growth in Indian GHG emissions via structural effects.

Consumption-based emissions: The annual time series of CO2 consumption results reveal that emissions generated in India in the production of exported goods have exceeded those generated in other countries in the production of goods imported into India. Therefore, under the consumption perspective, India’s emissions are lower than they are under the standard territorial perspective. From 1990 to 2013, the gap between emissions embodied in imports and exports has increased in recent years, due to large increases in exported goods and services. The latest data shows that India emitted 2393 Mt (metric tonne) CO2 in 2013 (production-based emissions), while the consumption-based emissions were 2125 Mt (Le Quéré et al 2016). India is also shown to be a net exporter of GHGs over time, which is a robust result across data sets or both GHGs and CO2 (Lenzen et al 2012; Timmer et al 2015).

While total trade (export + import) was only 15% of GDP in India in 1990, it has seen large growth and increased to 49% in 2014 (World Bank 2015), resulting from the removal of trade restrictions and reduced tariff barriers (Jayanthakumaran et al 2012). Time series from 1990 to 2013 reveals an increase in CO2 emissions from the production of 5.6%/year and from consumption of 5.1%/year. This means that the difference between production and consumption emissions (emission transfers) is increasing, which is because emissions embodied in exports are increasing faster than imports and that India is increasingly producing goods and services destined for other nations. In the 1990s, Indian emissions embodied in exports had an AAGR in emissions of 10%/year while imports increased by 1.1%/year. In the 2000s, exports grew 9%/year and imports 17%/year. From 2010 to 2014, export grew 7%/year while imports changed by -1.9%/year. While exports and imports were relatively similar in 1990 (exports were 7% higher than imports), this changed mostly during the 90s, leading to 95% higher exports than imports in 2014.

India’s trading partners have changed over time: while India was exporting mainly to US (6.7 Mt CO2), Japan (3.7 Mt CO2) and Germany (3 Mt CO2) in 1990, emissions in exports have increased from being 7% of production emissions to 19% in 2014. In 2014, US (66 Mt), United Arab Emirates (34 Mt) and China (27 Mt) were the largest importers of emissions embodied in Indian goods. On the Indian import side, Russia (9 Mt CO2) was the leading region in 1990, while Ukraine (8.6 Mt) and US (3.3 Mt) were the second and third largest, respectively. In 2014, emissions embodied in imports into India had grown from 7% of production emissions to 10%, with export mainly from China (54 Mt), the United Arab Emirates (15 Mt) and South Africa (11 Mt). After the dip in emissions during the financial crisis in 2009–10, the net export of emissions from India has more than doubled to 2013.

A detailed assessment of a single year allows the inclusion of other GHGs (CH4, N2O and fluorinated gases), thus capturing large emissions in other sectors such as agriculture. In 2011, the latest year for which detailed MRIO data are available in our model, India’s production and consumption-based GHG emissions are similar, being 2650 Mt and 2574 Mt CO2-equivalent (eq), respectively (Figure 3). Domestic production that is exported is responsible for 13% of India’s production emissions. The emissions embodied in imported products that are originally produced elsewhere are equivalent to 10% of Indian production emissions. In a sectoral consumption perspective, emissions are allocated from the source sectors (where emissions occur, plus imports-less exports) to the final sectors via the global supply chains, where purchases are made by final demand (following Karstensen et al 2015).

The construction sector itself emits only around 4 Mt CO2–eqbut as it has significant inputs in its supply chain from electricity, manufacturing and transport sectors, its total contribution from the consumption-by-destination perspective was 318 Mt CO2–eq. The agriculture sector is a significant contributor from all perspectives, in contrast to many other countries, where most emissions from agriculture are re-allocated to food sectors under this perspective shift. One of the explanations may be that India’s markets, and in particular its food markets, are much less developed and formal than other economies, so that many purchases by households of food products are made directly from firms engaged in agriculture, without going through processing and retailing stages in India. However, we cannot rule out that this is due to different definitions between countries of agriculture and food sectors.

Overall, the re-allocation of emissions shows that the agriculture (17% of consumption-based emissions), non-energy intensive manufacturing (14%), services (14%) and constructio(12%) sectors are the largest drivers of emissions, as they either emit significantly themselves and/or firms in their supply chains are significant emitters.

Structural path analysis: In this perspective, the five largest hotspots in 2011 were direct purchase of electricity (203 Mt CO2–eq), raw milk (196 Mt), services from the government (public administration, defence, education and health sectors: 143 Mt), road and rail transport (77 Mt) and non-metallic minerals (64 Mt; Figure 4, p 51). Direct emissions from households (including personal transportation and heating) are an additional hotspot, with 188 Mt or 7% of India’s total emissions.

These hotspots have large direct emissions, but other paths have even larger indirect emissions, such as the Indian construction sector, which emits only small amounts directly but is heavily reliant on large emitting sectors such as electricity, mineral and metal production and transportation. The construction sector can be thought of as an emission “funnel,” where the activity in the construction sector induces emissions to occur elsewhere. Further upstream, the largest paths often lead to the electricity sector, as is clear from the right-most tier in Figure 4. The electricity sector is a significant emitter and its contribution to the supply chain is widely dispersed throughout the economy. The four next largest paths are also incidentally the top four hotspots (that is, their own direct emissions are large and they sell directly to consumers).

Outlook

India is expected to have one of the highest rates of economic growth towards 2040 (IEA 2015b; Johansson et al 2013). At the same time, India’s population is expected to increase to become the world’s largest around the year 2025 (United Nations 2015). India’s share of global emissions may rise from today’s 7.5% to 14% in 2040 (IEA 2015b). This puts India in a vital position in climate negotiations, and a pivotal position in terms of stabilising global CO2 emissions (Jackson et al 2015).

Analysis of historical data highlights trends that are highly likely to continue: increasing per capita consumption of energy, increasing total trade and net exports, decreasing energy intensity of GDP and near-term increasing emissions intensity of energy (as coal will still be important in the near future). Energy use per capita will very likely increase, as about 240 million people in India, or 19% of the population, were without access to electricity in 2013 (IEA 2015b). The IEA (2015b) project energy demand per capita to increase by 3.4%/year from 2013 until 2040 (Figure 5a, p 52). India passed the US and the world average in terms of the carbon intensity of energy in 2012 (Figure 5b, p 52), after a growth rate of 1.8%/year from 1980 to 2013 (IEA 2015a), which is projected to increase by 8% by 2030 (IEA 2015b). Together, this will have significant impacts on national emissions as more people use more energy that has higher emissions in the near term.

India’s submitted INDC has a goal to reduce CO2/GDP by -33% to -35% in 2030 compared to 2005 levels. From 2000 to 2014, the CO2/GDP has decreased -1.3%/year, and under the INDC, GDP is forecasted to decline at -2.3% to -2.5%/year from 2015 to 2030 (thick yellow line in Figure 5c). Combining the pledged decline in CO2/GDP with the growth in GDP, we estimate emissions will growth at about 3.2%–3.5%/year in 2020 and 3.1%–3.3%/year in 2030, with absolute CO2 emissions of 3.6 to 3.7 Gt CO2 in 2030 (thick blue line in Figure 5c; method based on Peters et al 2015). This is consistent with the IEA’s new policies scenario (red line in Figure 5c, p 52), which estimates 3.7 Gt CO2 in 2030. The Indian INDC also aims to
increases solar energy from the estimated installed capacity of 4 GW in 2015. This goal does not supersede the CO2 intensity target, and so we do not analyse the solar target here.

The GCAM CGE model projects Indian CO2 emissions to be 4.0 Gt in 2030 (Figure 5c), higher than our estimate of 3.6–3.7 Gt CO2. Using this model, we also show the Kaya components, including the transition from historical to future (Figure 5d, p 52). The model has a strong growth in GDP/capita into the future. Assuming the INDC targets are gradually implemented, energy per GDP is likely to continue to put downward pressure on emissions but at a sustained rate. The carbon intensity is likely to continue to push emissions up, reflecting a continual carbonisation of the Indian economy. On balance, the stronger improvements in energy per GDP ensure that the annualzgrowth rate of CO2 emissions will decrease over time. The modelling, thus, indicates that the extent and scope of current energy efficiency policies will be insufficient for India to achieve the emissions reductions required under a 2°C target, with India’s emissions still growing at more than 3%/year in 2030 (Figure 5d).

Conclusions and Policy Implications

Our study uses four different approaches to find the underlying drivers of changes in Indian emissions. They reveal drivers on different levels of the economy, pointing to different policy measures to reduce emissions. While the Kaya and structural decomposition analysis capture the temporal trends of drivers on emissions, the structural path analysis, and consumption-based emissions gives higher sectoral details for single years. The temporal trends show (i) that increasing GDP/capita is a major driver of increasing emissions, and will very likely continue in the near future; (ii) the population increase has put upward pressure on emissions, although this has been decreasing and is expected to continue to decrease; (iii) the emissions intensity of energy has put upward pressure on emissions due to increasing use of coal, particularly in power plants; and (iv) energy intensity of economic production has offset the other effects by putting downward pressure on emissions via decreasing use of energy per economic output primarily related to structural changes in the economy towards more services.

The temporal trends point to different policy measures with different feasibilities: (i) reducing per capita consumption is not desirable due to conflicting goals of economic growth; (ii) reducing population growth has been a controversial issue in India for decades, however, the population growth is projected to slow down and will have a progressively smaller effect on emissions in the next decades; (iii) reductions in the emission intensity of energy consumption are urgently needed, and this requires stronger commitments such as expanding renewable energy capacity and
improving the efficiency of coal power plants; (iv) continuous structural changes to the Indian economy, including changes from a focus on manufacturing to service sectors, is already taking place and should be encouraged in the coming years. Interestingly, a recent micro behavioural-based study showed that as Indian industries develop technologically, carbon taxes do not adversely affect industrial productivity, thus becoming an increasingly better policy option (Dasgupta and Roy 2015).

Our detailed analysis of consumption-based emissions at the sector level provides additional granularity to the analysis. From a consumption perspective, emissions are mainly allocated to food and agriculture, non-energy intensive manufacturing, services, and construction. Understanding the supply-chains stemming from these consuming sectors, such as through structural path analysis, may identify efficiency improvements on the production and consumption side. From the production side, sectors such as electricity generation, energy-intensive manufacturing, and agriculture become important. Since India urgently needs to develop its economy and increase the living standard of its population, measures that reduce consumption in the lower and middle classes are not desired. However, as with other countries, India has a responsibility to reduce emissions, and identifying points for policy intervention in the supply chains linking consumption and production of particular product groups or lifestyles may help India meet its climate objectives.

References

Aguiar, Angel, Badri Narayanan and Robert McDougall (2016): “An Overview of the GTAP 9 Data Base,” Journal of Global Economic Analysis, Vol 1, No 1, pp 181–208.

Aldy, J et al (2016): “Economic Tools to Promote Transparency and Comparability in the Paris Agreement,” Nature Climate Change, Vol 6,
pp 1000–04.

Barros, V R et al (eds) (2014): Climate Change 2014: Impacts, Adaptation, and Vulnerability: Part B: Regional Aspects: Contribution of Working Group II to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge, United Kingdom and New York: Cambridge University Press.

Bhagwat, A P and R Tiwari (2017): “Renewable Energy in India: Policies, Trends and Foreign Direct Investments in Research and Development,” Lead Market India: Key Elements and Corporate Perspectives for Frugal Innovations, C Herstatt and R Tiwari (eds), Cham: Springer International Publishing.

BP (2014): “Statistical Review of World Energy June 2014,” http://large.stanford.edu/courses/2014/ph240/milic1/docs/bpreview.pdf.

Burke, M, S M Hsiang and E Miguel (2015): “Global Non-linear Effect of Temperature on Economic Production,” Nature, Vol 527, pp 235–39.

Capellán-Pérez, I, M González-Eguino, I Arto, A Ansuategi, K Dhavala, P Patel and A Markandya (2014): “New Climate Scenario Framework
Implementation in the GCAM Integrated Assessment Model,” BC3 Working Papers, Basque Centre for Climate Change.

Chandran Govindaraju, V G R and C F Tang (2013): “The Dynamic Links between CO2 Emissions, Economic Growth and Coal Consumption in China and India,” Applied Energy, Vol 104,
pp 310–18.

Climate Action Tracker (2016): “India,” http://climateactiontracker.org/countries/india.html.

Das, A and S K Paul (2014): “CO2 Emissions from Household Consumption in India between 1993–94 and 2006–07: A Decomposition Analysis,” Energy Economics, Vol 41, pp 90–105.

Dasgupta, S (2014): Response of Indian Industries to Sustainability Goal : An Economic Analysis with Special Reference to NAPCC, Diss, Jadavpur University.

Dasgupta, S and J Roy (2015): “Understanding Technological Progress and Input Price as Drivers of Energy Demand in Manufacturing Industries in India,” Energy Policy, Vol 83, pp 1–13.

Edenhofer, O et al (2014): Climate Change 2014: Mitigation of Climate Change: Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, United Kingdom and New York: Cambridge University Press.

European Commission (2011): “EDGAR-Emission Database for Global Atmospheric Research,” Joint Research Centre, http://edgar.jrc.ec.europa.eu.

Hoekstra, R and J C Van den Bergh (2003): “Comparing Structural Decomposition Analysis
and Index,” Energy Economics, Vol 25, No 1,
pp 39–64.

IEA (2014): Energy Balances of Non-OECD Countries, Paris: International Energy Agency.

— (2015a): CO2 Emissions from Fuel Combustion Highlights, Paris: International Energy Agency.

— (2015b): World Energy Outlook 2015, Paris: International Energy Agency.

IPCC (2014): “Fifth Assessment Report,” Intergovernmental Panel on Climate Change, Geneva.

Jackson, R B et al (2015): “Reaching Peak Emissions,” Nature Climate Change, Vol 6 , pp 7–10.

Jayanthakumaran, K, R Verma and Y Liu (2012): “CO2 emissions, Energy Consumption, Trade and Income: A Comparative Analysis of China and India,” Energy Policy, Vol 42, pp 450–460.

Johansson, Å et al (2013): Long-term Growth Scenarios, OECD Economics Department Working Papers, No 1000, https://doi.org/10.1787/
5k4ddxpr2fmr-en
.

Karstensen, J, G Peters and R Andrew (2015): “Allocation of Global Temperature Change to Consumers,” Climatic Change, Vol 129, pp 43–55.

Khanna, M and D Zilberman (2001): “Adoption of Energy Efficient Technologies and Carbon Abatement: The Electricity Generating Sector in India,” Energy Economics, Vol 23, No 6,
pp 637–58.

Le Quéré, C et al (2016): “Global Carbon Budget 2016,” Earth System Science Data, Vol 8, No 2, pp 605–49.

Lenzen, M (2007): “Structural Path Analysis of Ecosystem Networks,” Ecological Modelling, Vol 200, No 3–4, pp 334–42.

Lenzen, M, K Kanemoto, D Moran and A Geschke (2012): “Mapping the Structure of the World Economy,” Environmental Science & Techno­logy, Vol 46, No 15, pp 8374–81.

Lobell, D B, A Sibley and J Ivan Ortiz-Monasterio (2012): “Extreme Heat Effects on Wheat Senescence in India,” Nature Climate Change, Vol 2, pp 186–89.

MoEFCC (2015): “India’s Intended Nationally Determined Contribution: Working Towards Climate Justice,” Ministry of Environment, Forest and Climate Change, Government of India.

Mukhopadhyay, K (2008): “Air Pollution and Income Distribution in India,” Asia-Pacific Development Journal, Vol 15, No 1, pp 35–64.

Murthy, N, M Panda and J Parikh (1997): “Economic Growth, Energy Demand and Carbon Dioxide Emissions in India: 1990-2020,” Environment and Development Economics, Vol 2, No 2,
pp 173–193.

Narayanan, G, A Badri and R McDougall (2015): “Global Trade, Assistance, and Production: The GTAP 9 Data Base,” Purdue University, Centre for Global Trade Analysis.

OECD (2014): “Economic Outlook No 95—May 2014- Long-term Baseline Projections,” Organisation for Economic Co-operation and Development, https://stats.oecd.org/Index.aspx?DataSetCode
=EO95_LTB.

Pachauri, S (2014): “Household Electricity Access: A Trivial Contributor to CO2 Emissions Growth in India,” Nature Climate Change, Vol 4, No 12, pp 1073–76.

Pal, B D, V P Ojha, S Pohit and J Roy (2014): GHG Emissions and Economic Growth: A Computable General Equilibrium Model Based Analysis for India, New Delhi: Springer.

Panagariya, A (2004): “lndia’s Trade Reform,” India Policy Forum, pp 1–68.

Peters, G P, R M Andrew, S Solomon and P Friedlingstein (2015): “Measuring a Fair and Ambitious Climate Agreement Using Cumulative Emissions,” Environmental Research Letters, Vol 10, No 10.

Peters, G P and E G Hertwich (2006): “Structural Analysis of International Trade: Environmental Impacts of Norway,” Economic Systems Research, Vol 18, No 2, pp 155–81.

Peters, G P, J C Minx, C L Weber, and O Edenhofer (2011): “Growth in Emission Transfers via International Trade from 1990 to 2008,” Proceedings of the National Academy of Sciences, Vol 108, No 21, pp 8903–8908.

Raghuvanshi, S P, A Chandra and A K Raghav (2006): “Carbon Dioxide Emissions from Coal Based Power Generation in India,” Energy Conversion and Management, Vol 47, No 4,
pp 427–41.

Raupach, M R, G Marland, P Ciais, C L Quere, J G Canadell, G Klepper and C B Field (2007): “Global and Regional Drivers of Accelerating CO2 Emissions,” Proceedings of the National Academy of Sciences, Vol 104, No 24, pp 10288–93.

RBI (2013): Handbook of Statistics on Indian Economy, 2012–13, Mumbai: Reserve Bank of India.

Roy, J, S Dasgupta and D Chakravarty (2013): “Energy Efficiency: Technology, Behavior, and Development,” The Handbook of Global Energy Policy, https://doi.org/10.1002/9781118326275.ch17.

Sharma, S, A Bhattacharya and A Garg (2006): “Greenhouse Gas Emissions from India: A Perspective,” Current Science, Vol 90, No 3, pp 326–33.

Shearer, C, R Fofrich and S J Davis (2017a): “Future CO2 Emissions and Electricity Generation from Proposed Coal-fired Power Plants in India,” Earth’s Future, Vol 5, No 4, pp 408–16.

Shearer, C, N Ghio, L Myllyvirta, A Yu and T Nace (2017b): Boom and Bust 2017: Tracking the Global Coal Plant Pipeline, CoalSwarm, Greenpeace, and Sierra Club.

Su, B and B W Ang (2012): “Structural Decomposition Analysis Applied to Energy and Emissions: Some Methodological Developments,” Energy Economics, Vol 34, No 1, pp 177–88.

Timmer, M P, E Dietzenbacher, B Los, R Stehrer and G J de Vries (2015): “An Illustrated User Guide to the World Input–Output Database: The Case of Global Automotive Production,” Review of International Economics, Vol 23, No 3, pp 575–605.

Timperley, J (2017): “India’s Planned Coal Plants Could ‘Single-Handedly Jeopardise’ 1.5C Target,” https://www.carbonbrief.org/india-planned-coal-plants-could-single-handedly-jeopordise-one-point-five-target.

United Nations (2015): World Population Prospects: The 2015 Revision, https://www.un.org/en/development/desa/publications/world-population-prospects-2015-revision.html.

World Bank (2015): World Development Indicators, http://data.worldbank.org/indicator.

Yang, Z and Y Zhao (2014): “Energy Consumption, Carbon Emissions, and Economic Growth in India: Evidence from Directed Acyclic Graphs,” Economic Modelling, Vol 38, pp 533–40.

To read the full text Login

Get instant access

New 3 Month Subscription
to Digital Archives at

₹826for India

$50for overseas users

Updated On : 13th Apr, 2020

Comments

(-) Hide

EPW looks forward to your comments. Please note that comments are moderated as per our comments policy. They may take some time to appear. A comment, if suitable, may be selected for publication in the Letters pages of EPW.

Back to Top