Dynamic Driving Forces of India's Emissions From Production and Consumption Perspectives

While India becomes one of the largest carbon emitters in the world with a high emission growth rate, existing studies fail to capture the recent trends and the key driving factors behind it. Here, by using multiregional input‐output analysis and structural decomposition analysis, we measure the contribution of factors to the changes of India's domestic consumption and trade‐related emissions. This study finds that India's per capita consumption has a significant raising effect on India's consumption‐based emissions during 2000–2014; increasing coal proportion (especially in industry and electricity) and ineffective energy efficiency (especially in electricity) continuously push India's production‐based emissions upwards after 2003. Meanwhile, India's domestic industrial chain shows increasing and decreasing effects on domestic consumption and export‐related emissions after 2011, respectively. India's forward industrial chain always drives export‐related emissions upwards. In addition, the major contributor of final demand in domestic consumption emissions transfers from capital investment to household consumption after 2008, while the increasing power of services in export‐related emissions rapidly fades in the same period. India's climbing import‐related emissions embodied in final products shift to light industries, and the intermediate products shift to heavy industries and constructions over time.


Introduction
As a major factor in climate change, the annual carbon emissions aroused by the combustion of fossil fuels worldwide are stable after 2013 due to the global effort-through the United Nations Framework Convention for Climate Change (UNFCCC) and its Kyoto Protocol-to limit the global temperature increase to less than 2°. Emissions in China, the largest emitter, have a constant decrease during 2013-2016 (133.9 Mt) owing to its economy entering a new normal by industrial structure upgrade and green production Mi et al., 2018). In contrast, India becomes the new leader, with its emissions showing a rapid increase (225.0 Mt) in this period due to its economic success with an average annual growth rate of 7.0% in GDP.
India is at midstage of industrialization with low technology and added value (Mehta, 2012). Taking the manufacturing industry as an example, the output of that sector increases by an average annual rate of 6.3% from 2008 to 2014, which is lower than that of emissions emitted by manufacturing industry (9.4%). This illustrates that the manufacturing industry in India moves in a carbon-intensive direction. It can also be illustrated in Figure 1a, which shows that the proportion of emissions emitted by the manufacturing industry in India rapidly rises after 2008 and probably surpasses that of China in the following years. Meanwhile, the position index of global value chain, detailed algorithms refer to the existing paper (Koopman et al., 2010), of the manufacturing industry in India rapidly decreases after the international financial crisis (Figure 1b) due to the stimulation of low-end industries (e.g., labor-intensive or resource-intensive industries) by India's government to recover its economy (Akιn et al., 2017). Furthermore, India's emission intensity outweighs that of China after 2008 and shows a rapid upward trend (Figure 1c). In addition, India promises to cut its emissions unitary GDP by 33-35% by 2030, compared with 2005(UNFCCC, 2015, yet this will pose a dilemma of realizing the commitment to restrain the emissions, while maintaining rapid economic growth.
Given the purpose of designing efficient and viable policies to defuse this contradiction, it is critical to cultivate a thorough understanding of the main drivers of India's emission changes. Meanwhile, the lessons learned from India will have implications for other emerging economies in Southeast Asia and Africa to rapidly realize a low-carbon economy.
Previous studies investigate the contributions of drivers to the changes of carbon emissions at country level by employing structural decomposition analysis (SDA) based on global multiregional input-output tables (Hoekstra et al., 2016;Jiang & Guan, 2016;Zhao, Wang, Yang, et al., 2016;, as well as national input-output tables, such as China (Minx et al., 2011;Su et al., 2013;Zhu et al., 2012), the United States (Weber, 2009), Australia (Wood, 2009), Singapore (Su, Ang, & Li, 2017), Italy (Cellura et al., 2012), and Norway (Yamakawa & Peters, 2011). Moreover, China is the main target for conducting investigations at a provincial level due to its perfect data set (Feng et al., 2012;Li, Zhou, et al., 2018;Meng, Zhang, et al., 2018). In addition, some studies deliver detailed analysis for emission changes embodied in domestic consumption and trade Soligno et al., 2019), as well as final and intermediate products . Furthermore, there are many literatures calculate the effects of factors on emission changes in industry level by using the index decomposition analysis (IDA) (Fan & Lei, 2016;Li et al., 2017;Tian et al., 2011). Meanwhile, some literatures quantify the impacts of drivers on changes of water (Soligno et al., 2019;Yang et al., 2016), energy  and air pollutants (Deng et al., 2016;Meng et al., 2016Meng et al., , 2019. The conclusions of the above studies generally accept that expanding per capita demand and population scale are the major factors in pushing emissions upwards in developed and developing countries; the former and latter obviously arouse the increase of emissions abroad and at home, respectively (Hoekstra et al., 2016;Lan et al., 2016;Lenzen, 2016). Meanwhile, emission intensity is a major factor influencing emission changes in developed countries and regions, e.g., the United States (Liang et al., 2016), the European Union (Deng et al., 2016), and Japan (Zhao, Wang, Yang, et al., 2016;, and developing countries, e.g., China (Ming et al., 2011;Raghuvanshi et al., 2006), with its effect in the latter being more conspicuous (Andreoni et al., 2016;Xu & Dietzenbacher, 2014). Meanwhile, improving energy efficiency and cleaning energy structure are propitious to reduce emissions worldwide (Kim & Kim, 2012), especially in China . Another important factor is production structure, the effect of which is more remarkable in emerging countries (e.g., China and India) Meng, Zhang, et al., 2018). In addition, with the globalization of the world economy, the international industrial chains (e.g., forward and backward industrial chains), as the main components of production structure, are an area of interest for researchers to investigate their impacts on emission changes and are confirmed as having inconsistent effects (Deng et al., 2019;Wood, 2009;Xia et al., 2015). Furthermore, consumption structure also has an increasing effect in countries with rapid industrialization and urbanization, e.g., China (Guan et al., 2009;Mi et al., 2017). There are limited studies that quantify the influence of factors on the emission changes in India. By employing the world input-output tables and SDA, some studies analyze the contributions of drivers (e.g., emission efficiency, production structure, demand structure, demand scale, and population) on the changes of India's national emissions (Arto & Dietzenbacher, 2014;, as well as trade-related emissions (Deng & Xu, 2017;Jiang & Guan, 2017;Xu & Dietzenbacher, 2014). Meanwhile, researchers investigate the relationship between India's economic development and carbon emissions by using IDA (Andreoni et al., 2016), and some scientists report the contributions of factors on India's carbon emissions based on its own input-output tables (Zhu et al., 2018) and official yearbook data (Paul & Bhattacharya, 2004). In addition, there are some researchers who investigate the key transmission channels of India's carbon emissions through structural path analysis  and the drivers of changes in India's energy consumption (Mukhopadhyay & Chakraborty, 1999), as well as water footprint (Roson & Sartori, 2015).
However, the current studies lack time series analysis about factors based on India's latest data and do not consider the influence of energy intensity and energy structure, both of which have outstanding contributions in the past . Meanwhile, they also pay less attention to in-depth decomposition of production structure to obtain detailed effects of industrial chains. To fill those gaps, this study presents a temporal picture about drivers (including energy intensity, energy structure, and industrial chains) on the changes of India's domestic consumption and trade-related emissions from 2000 to 2014.

Structural Decomposition Analysis
The well-acknowledged tool employed to investigate the contribution of drivers on emission changes is SDA, which can remedy the deficiency of IDA for its failure to deliver an in-depth and detailed understanding of direct and indirect socioeconomic effects (Guan et al., 2008(Guan et al., , 2014. For more detailed information about the comparison of SDA and IDA, please see the previous study (Hoekstra & Van den Bergh, 2003;Su & Ang, 2012).
Based on the multiregional input-output table with m countries and n sectors, the emissions transferred from country q to country p (p, q = 1, 2 … m) can be calculated as follows: where D p represents the matrix of direct emission intensity of country p, L represents the Leontief inverse matrix, and Y q represents the matrix of final demand in country q. For more detailed information, please see our previous studies (Meng et al., 2016;Wang et al., 2018). Therefore, theoretically change of E in Equation 1 can be expressed as follows: Each of those terms in the right side of Equation 2 indicates the contribution to the overall change in emissions triggered by one factor while keeping the other two factors constant. For example, the first item represents the contribution of emission intensity. However, any emission changes may be aroused by a variety of reasons, such as the energy structure, industrial chains, consumption structure, and population (Ang & Zhang, 2000;Haan, 2001;Minx et al., 2011). Based on previous studies (Guan et al., 2014;Yanmei et al., 2013;Zhang et al., 2016), we make a further decomposition of matrix D through distinguishing the emissions and energy consumptions by different fuel types, including coal, oil, natural gas, and non-fossil fuels (e.g., solar, wind, and other renewables).
Supposing the elements e p c; j , e p o; j , and e p g; j in matrices E c , E o , and E g represent the emissions aroused by burning coal, oil and natural gas in industry j (j = 1, 2 … n) in country p, respectively. , respectively. r p c; j , r p o; j , and r p g; j represent the consumption scale of coal, oil, and natural gas in industry j in country p, respectively. Notably, we do not consider the emission coefficient of non-fossil fuels due to the fact that their emissions are very small or zero (e.g., solar). Meanwhile, the element h p j in matrix H represents the energy structure in industry j in country p and can be written as follows: where r p n; j represents the consumption scale of non-fossil energy of industry j in country p. h p c; j , h p o; j , and h p g; j represent the energy structure of coal, oil, and natural gas in industry j in country p, respectively. Thus, the element D p j in matrix D can be written as follows: where x p j represents the output of industry j in country p, and function 4 can be transferred to matrix format as follows: where G c , G o , and G g represent the matrix of emission coefficient of coal, oil, and natural gas, respectively. H c , H o , and H g represent the matrix of energy structure of coal, oil, and natural gas, respectively. K represents the matrix of energy intensity.
To distinguish the effects of industrial chains from both domestic and international perspectives, we further decompose the matrix L as follows: ΔL = L t1 ΔA L t0 , where the subscript t1 and t0 represent the terminal and base year, respectively. Reference to existing researches (Deng et al., 2019;Liu et al., 2018;Zhou et al., 2018), the matrix A can be decomposed into four departments (Assuming A 1 , A 2 , A 3 , and A 4 ) as follows: where the element a pp ij (i = 1, 2 … n) in matrix A 1 represents the intraregional industrial chain of country p, the element a pq ij q ≠ p ð Þ in matrix A 2 represents the interregional forward industrial chain of country p, the element a qp ij q ≠ p ð Þ in matrix A 3 represents the interregional backward industrial chain of country p, and the element a qq ij q ≠ p ð Þ in matrix A 4 represents the intraregional industrial chain of country q, which does not have direct trade relationship with country p. In detail, if the change in a pq ij has a positive effect on the increase of emissions, it needs to consume more intermediate products in period t1 than that in period t0 of industry i in country p when producing unitary output of industry j in country q, and this will arouse emission raise in industry i in country p due to produce more intermediate products to meet the demand of industry j in country q, thus stimulating the translation of emissions from country q to country p.
As illustrated in previous studies (Minx et al., 2011;Wang et al., 2013;Yamakawa & Peters, 2011), we consider the effect of social factors (e.g., population) on the emission changes through breaking down the final demand into the following forms: 10.1029/2020EF001485

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where the element s q j ¼ y q j / ∑ j y q j in matrix S represents the consumption structure in industry j in country q, the element v q ¼ ∑ j y q j / c q in matrix V represents the per capita consumption in country q, and the element c q in matrix C represents the population in country q.
The relationship among the decomposition types presented above is shown in Table 1; the overall formula applied to the SDA can be expressed as follows: There are 7! = 5,040 equally acceptable decomposition forms in our study, and different programs will exhibit different results for the same components (Dietzenbacher & Los, 1998;Hoekstra & Van Den Bergh, 2002;Rørmose & Olsen, 2005). In short, the total emission changes can be described by expression: ΔE = ΔE c + ΔE o + ΔE g , and one of the possible decomposition forms of changes in emissions aroused by coal combustion can be expressed as follows: The decompositions of ΔE o and ΔE g are similar to ΔE c . To gain the ideal results, we take the arithmetic average of all equivalent first-order decomposition forms as the relative contribution of each driver, as popularly applied in the existing studies (Guan et al., 2009;Liang et al., 2016). Meanwhile, we deliver the computation program of full decomposition for seven factors in Text S1 in the supporting information.

Data Sources
There are three data sets employed in our study: time series input-output tables, energy consumption, and carbon emission inventories. The input-output tables are derived from the World Input-Output Databases (WIOD, http://www.wiod.org/database/wiots16), which cover 44 countries and 56 industries. The energy consumption and carbon emission inventories come from the International Energy Agency (IEA, https:// www.iea.org/subscribe-to-data-services); those data sets include 143 countries, 36 industries, and five fuel categories (including coal, oil, gas, renewable, and solar/wind electricity). Meanwhile, we adjust them Matrix A with zeros in rows and cols of country p.
Final consumption (Y) Consumption structure (S) Proportion of each goods, which are consumed finally by household, government, fixed capital formation and inventory. Per capita consumption (V) Consumption of household, government, fixed capital formation and inventory in each person.

Population (C)
Quantity of population.
Note. The letters in parentheses represent the abbreviations for each determinant.

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Earth's Future into the classification of input-output tables, of which more details are described in our previous study . To avoid the influence of deflation and to facilitate the comparisons of results, we convert the data of input-output tables from current price into the constant price of year 2000 by dividing the GDP deflator, which can be obtained from the World Bank (https://data.worldbank.org/indicator/NY. GDP.DEFL.ZS).

The Increasing Consumption in India
In this study, we divide the emissions into three parts: part one is India's domestic consumption emissions, which are aroused by producing goods to meet the demands in India and directly emitted by itself. Part two is India's import-related emissions, which are aroused by producing goods to meet the demands in India and directly emitted by abroad. Part three is India's export-related emissions, which are aroused by producing

Increasing Coal Share and Energy Intensity Driving India's Emissions
Energy structure cumulatively decreases India's production-based emissions by 25.  Figure 3a) caused by its general decline in all selected sectors ( Figure 3b). This is due to India ratifying the Kyoto protocol in 2002, which helps India acquire some technologies and funds to improve clean production technology and energy utilization efficiency, especially in coal (Gupta, 2003). However, coal proportion subsequently transfers to an increasing effect during 2003-2014 due to its share increasing from 48.4% to 56.5%, which is associated with the substitution of coal for oil to meet the rapid economic development. This is due to the abundant reserves and low price of coal comparing with oil in India (IEA, 2008 -2011 (216.7 Mt), in which the energy intensity in electricity rapidly increases by 10.8 kg/$. This is due to the following two reasons: one is that India's electric enterprises do not have enough funds to improve the outdated power generation technology due to the low and irrational electricity price controlled by the government (Rai et al., 2013), especially after the Electricity Act comes into force in 2003 and emphasizes the developments of the transmission and distribution facilities to connect the national power grid to each village (Thakur et al., 2005), which requires a huge amount of money; the other is that India adopts excessive subsidies in electricity to reduce the economic burden of residents and improve the competitiveness of export-oriented enterprises by decreasing their production costs. However, this leads to a waste of electricity resources (Chattopadhyay, 2004;Mishra, 2013). Notably, the energy intensity in electricity has a small decrease (0.3 kg/$) during 2011-2014 due to the following two reasons: one is the falling subsidy in electricity due to the serious fiscal deficit in government (Akιn et al., 2017), and the other is the rapid decrease of transmission and distribution losses rate in electricity due to the upgrade of the electricity network as a result of the two severe power blackouts in 2012, both of which affect most of northern and eastern India and 400 million people (CERC, 2012). However, this decreasing effect on India's production-based emissions is completely offset by basic metals and non-metallic minerals, but the increasing emissions are small (24.0 Mt) in this period.

Two Distinct Industrial Chain Effects on India's Emissions
The

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Notably, India's domestic industrial chain has contrary effects on domestic consumption and export-related emissions during 2011-2014 due to the implementation of new economic strategies, named Made in India, which is launched by the government in 2014 with the aim of improving the share of manufacturing in GDP from 16% to 25% in the next 10 years (Modi, 2014). Therefore, India increases the production capacity in manufacturing (e.g., basic metals and furniture) and can provide more intermediate products used to import from abroad for domestic production processes. This increasing effect aroused by the expansive production scale outweighs the decreasing effect of domestic technological improvement in domestic consumption emissions and sees weak export-related emissions. This is attributed to bulk domestic consumption emissions concentrates on heavy industries and constructions, and a lot of export-related emissions focus on services, which are less affected by the expansive production scale in manufacturing.
Meanwhile, the forward industrial chain increases India's export-related emissions embodied in intermediate products during 2000-2014, especially in 2003-2008. This is due to India deeply integrating into the world economy and there being more intermediate products imported from India when producing unitary good abroad. However, the increasing effects of the forward industrial chain are weak during 2008-2011 and 2011-2014 because of economic adjustment, though the reasons for them are different. The former is due to the rise of trade protectionism worldwide aroused by the international financial crisis, especially during 2008-2009 (Kumar, 2009a(Kumar, , 2009bSubbarao, 2009), and the latter is due to the expansion of domestic demands aroused by India's new economic growth model. Each of those two reasons will lead to the decreasing effect of India's forward industrial chain due to the declining export scale in intermediate products.
Furthermore, the domestic industrial chain of other countries invariably has an increasing effect on India's export-related emissions embodied in intermediate products during 2000-2014 due to frequent commercial intercourse among them, but the volume is small (12.5 Mt). Notably, India's domestic consumption emissions embodied in intermediate products are very small , and as a result, we do not consider them here.

Changing Structure of Final Demand in India
The

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Furthermore, another important ascender of domestic consumption emissions is household consumption during 2003-2008, which becomes the primary force during 2008-2011. This is due to India's economy is driven by household consumption after the financial crisis as the result of the vulnerable industrial strength in India failing to support massive infrastructure construction and undertake the responsibility of restoring the economy (Bajpai, 2011;Kumar & Soumya, 2010). Notably, the proportion of transportation and heavy industry in household consumption rises rapidly during 2008-2011 due to the decreasing commodity transaction taxes and expanding subsidies on durables (Murthy, 2009). For example, the scale of new cars brought by residents during 2008-2011 is 1.5 times than that during 2003-2008. In addition, the leadership of household consumption is more obvious during 2011-2014, and the proportion of electricity has a rapid increase, due to the improvement of the national grid in India and with more rural residents having access to electricity.  Table S1.

India's Change as a Trade Hub
The export structure has a decreasing effect ( Figure 6). This is due to India encouraging the development in manufacture of transport equipment by giving more preferential loans and tax incentives in export-oriented enterprises after the financial crisis (Bajpai, 2011;Joseph, 2009). Meanwhile, light industry in final products has a rapid increase (24.0 Mt) during 2008-2014, and 61.8% of which are focus on food and textiles. In contrast to that, the increasing emissions of services in final products have a rapid atrophy during 2008-2011 and even transfer to negative during 2011-2014, with 70.9% of them focus on information activities. This is due to the services not being able to support adequate employment for vast numbers of low-quality workers and diminish the higher unemployment rate aroused by the financial crisis. It also reflects that the service-oriented economic growth pattern in India is unsustainable and its ability to resist economic fluctuations is limited. In addition, the construction and heavy industry in intermediate products sharply rise during 2003-2008 and then see a rapid decrease in 2008-2011, which is contrary with that in final products. This is due to the rapid development of industry in India with a large number of intermediate products being used in the domestic production stage. Meanwhile, construction and heavy industry return to the expressway during 2011-2014 due to the enhancement of industrial production capacity in India. The

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Furthermore, the consumption structure has an increasing effect (9.8%) on India's import-related emissions during 2003-2008 ( Figure 2b); however, its effect is weak (6.4%) during 2008-2011 and even transfers to negative during 2011-2014. This illustrates that India's imports transfer to low-carbon products after the financial crisis. In detail, light industry and heavy industry are the major contributors of India's import-related emissions embodied in final products during 2000-2008. However, the share of heavy industry decreases to 14.8% during 2008-2011 due to India's trade protectionism after the financial crisis to encourage the development of domestic-related industries. For example, the import tariffs of steel and zinc increase by 10% and 5% in this period, respectively (IPCC, 2007;Kumar & Alex, 2009). Meanwhile, imports in light industry show a sharp increase (23.1 Mt) in this period due to most of them focus on daily necessities (e.g., textiles and foods). Their demands are further expanded by India's government lowering prices and declining the Central Value Added Taxes (CENVAT) (Kumar & Soumya, 2010), and the volume of India's residents spends on imports increasing by 4.7 billion dollars in this period, 38.1% of which focus on light industry. Meanwhile, heavy industry, construction, and light industry are responsible for India's increasing import-related emissions embodied in intermediate products during 2000-2011 due to the rapid development of infrastructures in India . Notably, the import-related emissions embodied in final and intermediate products have a rapid decline during 2011-2014, mainly due to the decreasing import scale, in which household consumption and capital investment decrease by 4.4 and 16.5 billion dollars, respectively. Meanwhile, the implementation of India's manufacturing revitalization plan reflects that India can support more intermediate and final products for domestic demands, which are previously imported from abroad.

National Contribution of the Changes in India's Trade-Related Emissions
From the export perspective, the consumption in developing countries is the major contributor (77.7%) of India's increasing export-related emissions during 2000-2014 (Figure 7a). Their share has a rising trend during 2000-2011, in which the contribution of China rapidly decreases to 6.4% during 2003-2008 due to China's imports focus on construction and low-tech heavy industry, both of which are replaced by domestic goods at a time of its improving industrial level (Minx et al., 2011). Meanwhile, the contribution of China has a small increase (10.7%) during 2008-2011 due to its enormous demands aroused by the 4 trillion yuan investment to stimulate economic growth (Zheng & Chen, 2009). Notably, the contribution of other developing countries (excluding China and Russia) shows a significant decrease during 2011-2014 due to their improving capacity of self-sufficiency (IMF, 2015). In contrast, the share of developed countries continuously decreases from 19.7% to 15.0% during 2000-2011, in which the contribution of the European Union has a major increase (17.9%) during 2003-2008 due to the expanding demands (especially service activities) aroused by a booming economy. However, its share is very small (0.3%) during 2008-2011 due to the serious influence of the European debt crisis, which leads to its GDP decreasing by 4.1% in this period. On the contrary, the economy in the United States achieves a swift recovery, and its GDP increases by 5.4% at the same time. Therefore, the contribution of the United States increases by 10.3% during 2008-2011, and notably, the share of developed countries increases to 35.0% during 2011-2014, 36.9% and 36.0% of which focus on heavy industry (e.g., motor vehicles) and light industry (e.g., textiles and leathers), respectively. This is due to the developed countries riding themselves of the adverse influence of the financial crisis and realizing high-speed economic growth.
From the import perspective, most of the changes in India's import-related emissions are contributed by developing countries during 2000-2014 (Figure 7b), and the share increases from 80.8% to 93.2% continuously during 2000-2011. This is due to India's rapid development of construction with a significant amount of the increasing imports focused on light industry (e.g., textiles and furniture) and low-tech heavy industry (e.g., steel and cement), both of which are the leading products exported by developing countries (especially China; Mi et al., 2018). Notably, China has a smaller contribution on decreasing India's import-related emissions during 2011-2014 than that of an increasing one during 2000-2011 due to most of the products imported from China with lower price and strong market competitiveness (IMF, 2009). In contrast, the contribution of developed countries in increasing India's import-related emissions has a continuous decrease during 2000-2011 (from 19.2% in 2000-2003 to 6.8% in 2008-2011) due to their cleaner production technology and continuously upgrading of the industrial chain. Meanwhile, most of the products exported by developed countries focus on high-tech ones (e.g., electronic device and mechanical equipment), which will see the emergence of a mismatch with the real demands in India to some degree, and their imports are limited. Therefore, the proportion of the contribution of developed countries decreases much faster during 2011-2014 compared with their increasing contribution during 2008-2011, and the dominant role of developing countries is further clear.

Conclusions
In this paper, we investigate the contributions of seven major factors (including emission coefficient, energy structure, energy intensity, industrial chains, consumption structure, per capita consumption, and population) on the changes in India's production-and consumption-based emissions during 2000-2014 by using the multiregional input-output analysis and SDA. Meanwhile, we further decompose the global industrial chain into four parts (including the domestic industrial chain of India, the forward industrial chain of India, the backward industrial chain of India, and the domestic industrial chain of other countries). The new findings can be summarized as follows: 1. The major contributor of increasing consumption-based emissions in India is per capita final consumption; however, its contribution declines after the financial crisis. Meanwhile, population is another driver in pushing India's consumption-based emissions upwards.

Policy Implications
1. Upgrading the industrial chain in India is a significant way to reduce its production-based emissions (Figures 2a and 2c) by adjusting industrial structure. Services (e.g., information and software) are the protagonist of the modern economy with high added values and low emissions. However, it is unrealistic for India to quickly realize a complete service-oriented economy (e.g., the United States) due to its population characteristics with large-scale and low-skilled labors, which endogenously determines that it is the golden opportunity for India to vigorously develop the labor-intensive manufacturing industry (e.g., leathers and textiles) to eradicate mass poverty and then reduce government subsidies for paupers to reserve sufficient funds for infrastructure construction (e.g., electricity and road) to satisfy the basic conditions for large-scale industrial development. However, the prosperity of low-tech manufacturing industry is always accompanied by huge emissions (Sadavarte & Venkataraman, 2014). Therefore, the feasible pathway is employing the carbon trading market to tighten India's domestic emission quota step by step and encourage manufacturing enterprises to apply energy-saving equipment in their production progress through self-researching or importing low-carbon technology. Services are the irresistible trend of industrial restructuring, and it is meaningful for India to encourage enterprises to acquire their core technology through independent innovation to improve energy efficiency of services and avoid the lock-in status aroused by excessively undertaking the service outsourcing business at present. Meanwhile, many India's export-related emissions flow to developed countries in recent years (Figure 7a), and the implementation of India's pledge in the Paris Agreement heavily depends on climate finance and technology transfer from developed countries (Atteridge et al., 2009). Therefore, India can strive for advanced low-carbon technology and financial assistance from developed economies (e.g., the European Union and the United States) through the Clean Development Mechanism (CDM) to narrow the technology gap of clean production as quickly as possible and avert unnecessary emissions. 2. Decreasing the proportion of coal in the energy mix can effectively curb the excessive increase of emissions emitted by India (Figure 3a). However, it is difficult for India to thoroughly change the coal-dominated energy structure in the short-term due to the rich reserves and low price of coal in India, but the quota of non-fossil energy (e.g., solar and nuclear) can be carried out in the major energy consumption sectors (e.g., electricity). Therefore, the priority for India is in encouraging the exploitation of renewable energy through providing subsidies and concessionary loans for enterprises, as well as 10.1029/2020EF001485 continuously reducing the cost of utilization in renewable energy by promoting Indian self-innovation of related technology. In addition, increasing energy efficiency is another outlet for the decline of India's production-based emissions, especially in electricity (Figure 3c). About 16.3% or 210 million of India's population do not access to electricity in 2014 (Timperley, 2019), and establishing the national smart grid is a better choice for India to improve the overall energy efficiency of the power system, which can be divided into three parts, namely, generation, transmission, and distribution. For the generation part, the major assignment is realizing the marketization of electrovalence to motivate electric companies to gradually replace outdated thermal power plants with more advanced ones (e.g., ultra-supercritical units), which can be imported from China under the cooperative framework of the Belt and Road Initiative. Meanwhile, prohibiting the off-grid power devices such as diesel engines in the marginal and populous areas by guaranteeing the electricity supply covers the whole territory of India. In addition, improving the compatibility of the national grid to balance the continuous changes in renewable power plants (e.g., wind and solar) helps realize the diversification of electricity generation. For the transmission and distribution parts, gradually upgrading obsolete electric wires and utilizing the superior transfer technology (e.g., ultrahigh voltage direct current) to decrease the loss rate have a positive impact. 3. Household consumption is the major ascender of Indian consumption-based emissions after the international financial crisis ( Figure 5). However, it is unrealistic for India to decline the emissions through limiting consumption, about 21% or 270 million of India's population live below the international poverty line (1.9 dollars per day) in 2014, and increasing per capita consumption scale is the precondition for residents to improve material and cultural living standards. Meanwhile, India's per capita emissions are 1.7 Mt in 2014, which are much lower than that of the United States (16.5 Mt) and the European Union (6.4 Mt) (WB, 2019). Therefore, it is logical and reasonable for developed economies to moderately decrease their excessive consumption to compensate for the affluence increase in India (Muradian & Martinez-Alier, 2001;Parikh & Painuly, 1994) and maintain the fairness of emission mitigation responsibility (Hyder, 1992;Parikh, 1992). In addition, the emission intensity of household consumption in India is 1.9 Mt per thousand dollars in 2014, which is about four and six times than that in the United States (0.5 Mt per thousand dollars) and the European Union (0.3 Mt per thousand dollars), respectively. This means that India's household consumption generally focuses on low-quality and carbon-intensive products when coming out of poverty, though India's emission intensity of household consumption is still much higher than those in western countries. Therefore, other than the measures from the production side, it is a feasible plan for India to decrease emission intensity of household consumption through supporting the moderate government subsidy and preferential taxation for low-income households on low-carbon appliances (e.g., energy-efficient television and LED light), which are accredited by the Indian Bureau of Energy Efficiency. Furthermore, there is a big gap in the income of Indian residents, and the Gini coefficient of India is about 0.36 in 2014 (WB, 2019); however, most of the consumption-based emissions are contributed by high-income households (Hubacek et al., 2017;Wiedenhofer et al., 2017). Therefore, a wise economic strategy for India is to moderately curb the excessive consumption in wealthy households through increasing the personal income tax and commodity tax on luxury goods. Meanwhile, about 22.7% of the increasing emissions aroused by household consumption are contributed by transportation during 2011-2014 with rising incomes in residents ( Figure 5). However, the share in cumulative registered vehicles of electric cars is about 1% in this period (Shukla et al., 2014). Therefore, culturing the low-carbon consumption patterns and encouraging residents to use the buses and subways to replace gasoline cars in the personal commutes can be potential pathways to mitigate India's consumption-based emissions. Furthermore, transportation will be a major source in pushing India's production-based emissions upwards by consuming a lot of oil. Therefore, increasing the government subsidies for new energy automobiles will be a better choice for India to reduce its territorial emissions. In addition, the urbanization rate (proportion of urban population) of India is still low (32.4% in 2014), and capital investment will inevitably play an important role in propelling India's consumption-based emissions upwards in the next decades due to the rapid and irresistible process of urbanization in India, despite its contribution on increasing emissions being weak in the last few years ( Figure 5). Therefore, a critical step to decline emissions is realizing the green supply chain in the lifecycle of investment in construction by issuing the strict low-carbon standardization of building material and equipment.