Modelling Economic Growth, Carbon Emissions, and Fossil Fuel Consumption in China: Cointegration and Multivariate Causality.

Lv Lv,Chu Chu,Mcaleer Mcaleer,Wong Wong

doi:10.3390/ijerph16214176

Abstract

Most authors apply the Granger causality-VECM (vector error correction model), and Toda–Yamamoto procedures to investigate the relationships among fossil fuel consumption, emissions, and economic growth, though they ignore the group joint effects and nonlinear behaviour among the variables. In order to circumvent the limitations and bridge the gap in the literature, this paper combines cointegration and linear and nonlinear Granger causality in multivariate settings to investigate the long-run equilibrium, short-run impact, and dynamic causality relationships among economic growth, emissions, and fossil fuel consumption in China from 1965–2016. Using the combination of the newly developed econometric techniques, we obtain many novel empirical findings that are useful for policy makers. For example, cointegration and causality analysis imply that increasing emissions not only leads to immediate economic growth, but also future economic growth, both linearly and nonlinearly. In addition, the findings from cointegration and causality analysis in multivariate settings do not support the argument that reducing emissions and/or fossil fuel consumption does not lead to a slowdown in economic growth in China. The novel empirical findings are useful for policy makers in relation to fossil fuel consumption, emissions, and economic growth. Using the novel findings, governments can make better decisions regarding energy conservation and emission reductions policies without undermining the pace of economic growth in the long run.

Highlights

Fossil fuel consumption is an important topic because it is a symbol of modern civilization.fossil fuel consumption could increase pollution and make a significant impact on the global natural ecosystem
We find that the means of all the variables are significantly positive at a 1% level, CO2t is more volatile than all the other variables, according to the value of coefficient of variation (CV), whereas GDPt is more dispersed than all the other variables, according to the value of range and interquartile range (IQR) as GDPt has the highest range and the highest value of IQR among the variables
Dickey–Fuller (ADF), Phillips–Perron (PP), DF-GLS, Kwiatkowski–Phillips–Schmidt–Shin (KPSS), Elliott, Rothenberg and Stock (ERS), Kapetanios–Shin (KS), and Kapetanios–Shin–Snell (KSS) tests, and the Leybourne–Newbold–Vougus (LNV) stationarity test, due to limitations in the sample size and the linear (UECMs, vector error correction model (VECM), and Vector Autoregression (VAR)) models used in the paper, all the tests generally lead to similar conclusions

Summary

Introduction

Fossil fuel consumption is an important topic because it is a symbol of modern civilization. Fossil fuel consumption could increase pollution and make a significant impact on the global natural ecosystem. To reduce fossil fuel consumption, control carbon dioxide emissions, and retain economic growth is a common task for countries worldwide. Academics have demonstrated the relationships between environmental pollutants and economic growth nexus. The emissions of CO2 have been used as a proxy for environmental pollution because CO2 emissions have been increasing sharply every year, thereby resulting in greenhouse gas effects and global warming, which affects the environment (see, for example, References [2,3,4,5]). It is interesting to study the relationships among fossil fuel consumption, environmental pollutants, and economic growth

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Discussion

Conclusion