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

Abstract

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.

Spatiotemporal Evolution of Carbon Emissions According to Major Function-Oriented Zones: A Case Study of Guangdong Province, China

“…From the perspective of MFOZs, this study focused on the spatiotemporal evolutionary characteristics. Carbon Emission Accounts and Datasets (CEAD; www.ceads.net.cn) previously published provincial-scale emissions from 1997 to 2017 across China, and its data have been used in much research [42][43][44]. For validating the results, we compared the carbon emissions in 2017 from CEAD (514.64 million tons) and this study (559.08 million tons), and found the error to be ~8%, which is acceptable.…”

The statistical projection of global GHG emissions from a consumption perspective

“…Our assessment is based on a decomposition of the Kaya identity, a top-down model, which allows us to gain insight into all sectors and assess emissions at a broader economic level. Earlier studies using a decomposition analysis of historical consumption-based emissions found that investment effects on emissions in developing countries (mostly in China, and also in Mexico, India and Indonesia) are larger than those in developed countries (Liu et al, 2019), but that this influence has weakened in China in recent years, due to the reduction in government subsidies (Mi et al, 2017;Meng et al, 2019;Wang et al, 2020). Based on these past trends, we show that, over the coming decades, although China and others (e.g., Australia and Russia) see declining infrastructure expenditure, the persistence of historical trends in the evolution of consumption pattern (a gradual increase in the shares of construction and investment) contributes to emission increases in major emerging nations (e.g., Brazil, Mexico and India) in a significant way (Fig.…”

Evaluation and Comparison of Research Methods on Driving Factors of Carbon Emission in Industrial Parks

“…Analysis of existing research shows that research literature on different countries and regions, departments, and driving factors is already quite comprehensive [4][5][6][7][8][9][10].Common driving factors include per capita GDP, digital economy, energy structure, carbon dioxide emission intensity, population size, technological level, urbanization level, industrial structureand energy intensity [11][12][13][14][15].Wang Zhi and others used SDA to study the driving factors of carbon emissions in India [5].Yang Jun and others used the extended LMDI method to construct regional contribution indices for different driving factors [7].Guo Jin et al conducted a study specifically targeting urbanization as a driving factor using Kaya's equation [10].However, the limitation of the above research is that it does not strictly define the concept of driving factors, and the selection of driving factors is conducted from a closed perspective.…”