The objective of this study is to examine the impact of natural gas consumption, output, and urbanization on CO2 emission in China and India for the period, 1965-2013. A cointegraton test, which provides for endogenously determined structural breaks, has been applied to examine the long-run relationship and to investigate the presence of environmental Kuznets curve (EKC) in the two countries. The presence of causal relationship between the variables is also investigated. The findings show that there is a long-run relationship in the variables and natural gas, real GDP, and urbanization have long-run positive impact on emission in both countries. There is no evidence for EKC in China and India. The findings further suggest that there is a long-run feedback relationship between the variables. The policy inferences of these findings are discussed.
This paper examines the effects of energy consumption, economic growth, and financial development on carbon emissions in a panel of 122 countries. We employ both first-generation and second-generation cointegration and estimation procedures in order to address diverse economic and econometric issues such as heterogeneity, endogeneity, and cross-sectional dependence. We find a cointegration relationship between the variables. Energy consumption, economic growth, and financial development have detrimental effects on carbon emissions in the full sample. When the sample is split into different income groups, we reveal that economic growth and financial development mitigate carbon emissions in high-income group but have the opposite effects in low-income and middle-income groups. The implication of the findings is that energy consumption increases carbon emissions. While high levels of income and financial development decrease carbon emissions, low levels of income and financial development intensify it. Based on the findings, the paper makes some policy recommendations.
This paper investigates the non-linear impacts of the agricultural, industrial, financial, and service sectors on environmental pollution in Malaysia during the 1980-2018 period. It employs the extended STIRPAT model and two indicators of environmental pollution (carbon dioxide emissions and ecological footprints). It uses the autoregressive distributed lag (ARDL) technique to estimate the parameters. Evidence from the study indicate that the agricultural, industrial, and service sectors have inverted U-shaped non-linear impacts on carbon dioxide emissions and ecological footprints, while the financial sector has a U-shaped non-linear relationship with carbon dioxide emissions and ecological footprint. These empirical outcomes are robust to diagnostic tests, structural breaks, and alternative estimation technique and proxies. The economic implication of this paper is that, at the early stage of sectoral growth, the pollution intensity of sectoral output increases, but after a certain turning point, a further increase in sectoral output will reduce environmental pollution. Precisely, environmental pollution will reduce if the agricultural, industrial, and service sectors exceed threshold levels of 11%, 44%, and 49% of GDP, respectively, while environmental pollution will be aggravated if financial sector exceeds a threshold level of 94%. Therefore, efforts to mitigate environmental pollution in Malaysia should integrate sectoral growth to attain sustainable development.
This paper examines the causal relationship between industrialization, globalization, information communication technology (ICT) and environmental degradation in Malaysia during 1970-2019. It uses two indicators of environmental degradation (carbon emissions and ecological footprint), three dimensions of globalization (political, social, and economic) and three indicators of ICT (users of internet, mobile cellular, and fixed telephone subscriptions). It utilizes Granger causality technique in frequency domain which differentiates between permanent and temporary causality, Vector Error Correction approach as well as Variance Decompositions. The bound test shows that the variables have cointegration relationship. It reveals joint long-run and short-run causality from industrialization, globalization, and ICT to carbon emissions, albeit the causality to ecological footprint is tenuous. It indicates that industrialization, globalization, and ICT significantly predict carbon emissions at high frequency than at low frequency. A substantial percentage of the forecast error variance in environmental degradation are explained by industrialization, globalization, and ICT. The robustness of the empirical outcomes is confirmed by the alternative proxies of the variables. Our study implies that industrialization, globalization, and ICT are determinants of environmental degradation. Therefore, policies to mitigate environmental problem should prioritize these variables to attain green economy.