Investigating the Presence of Environmental Kuznets Curve Hypothesis in India and China: An Autoregressive Distributive Lag Approach

Abstract

The Environmental Kuznets Curve (EKC) hypothesis advocates a reversed U-shaped association between different pollutants and per capita income. EKC postulates that speedy growth certainly results in environmental degradation due to glut use of natural resources and emission of pollutants. The study used carbon dioxide (CO2) emissions, economic growth, energy consumption, and the annual growth rate of population to investigate the EKC hypothesis in India and China for the period of 1971–2013. Furthermore, to explore the long-run and short-run relationship among competing variables, the autoregressive distributed lag model (ARDL) is used. Granger causality test is used to investigate the long-run and short-run causality between variables under study. The results support the EKC hypothesis in India and China, in both long-run and short-run, and inverse U-shaped association is found between CO2 emission and economic growth. Unidirectional causality seen in both countries in terms of economic growth and CO2 emissions. In addition, the coefficient of economic growth in a short-run model provides the evidence that there has been a gradual decline in environmental degradation (downward sloping of EKC) and the quality of the environment is gradually improving in China. Based on the findings, the study suggests that environmental policymakers, especially in India, should seriously address the issue of CO2 emissions as it has a tendency to move faster in the coming years.

Environmental Effects of China’s Overseas Direct Investment in South Asia

“…The financial sector development increased carbon emissions in emerging countries so the research studies conducted by Jiang and Ma (2019) support our finding in case of India. When considering individual countries Table 5, reports that EKC hypothesis the inverted U-shape curve exist in India and these finding are consistent with Sinha and Bhatt (2016) and Rani and Kumar (2019) and Ritu and Naresh (2019)…”

“…The financial sector development increased carbon emissions in emerging countries so the research studies conducted by Jiang and Ma (2019) support our finding in case of India. When considering individual countries Table 5, reports that EKC hypothesis the inverted U-shape curve exist in India and these finding are consistent with Sinha and Bhatt (2016) and Rani and Kumar (2019) and Ritu and Naresh (2019) whereas, Pakistan and Nepal shows U-shape curve while Bangladesh manifest N-shape curve and the research finding of Choi et al (2010), Sirag et al (2018), Lee and Chen (2021), Uzair Ali et al (2020), and Aljadani et al (2021) proved the various temporal pattern of EKC hypothesis in their research studies.…”

Regional differences in the factors that affect vegetation cover in China

“…The economy in western China is much less developed than those of northeastern and southern China, so its impact on the environment was smaller (contributions <5% for all economic factors). Many previous studies (Fang, Hao, Wang, & Hao, 2019;Li, Wang, & Zhao, 2016;Rani & Kumar, 2019) showed that the relationship between China's environment and economic development reveals an environmental Kuznets curve, suggesting that environmental degradation initially becomes more serious with increasing economic development in western China. How to successfully move past the inflection point of the environmental Kuznets curve, so that additional development is accompanied by decreasing environmental impacts, will be a tough problem to solve, but it is essential that governments begin trying to solve that problem.…”

Investigating the moderating role of financial development in environmental degradation in India

“…Using the PMG technique, Sharma et al (2020) in their study found that economic and demographic indicators such as per capita income, urbanization, population density, fertility, and consumption of non‐renewable energy increase the level of pollutants in the atmosphere. Rani and Kumar (2019) present a positive and significant impact of GDP per capita, population, and consumption of energy on the CO 2 emissions in India and China. Yang et al (2017) estimate that energy consumption explains around 65% of the total economy‐related greenhouse gas emissions during the period 1998 to 2013 in Russia.…”

“…observe a positive effect of energy consumption and population density on CO 2 emissions. Using the PMG technique,Sharma et al (2020) in their study found that economic and demographic indicators such as per capita income, urbanization, population density, fertility, and consumption of non-renewable energy increase the level of pollutants in the atmosphere Rani and Kumar (2019). present a positive and significant impact of GDP per capita, population, and consumption of energy on the CO 2 emissions in India and China Yang et al (2017).…”