Published November 2020 | Version v1
Journal article

New insights into an old issue: exploring the nexus between economic growth and CO2 emissions in China

  • 1. European University of Lefke. Department of Banking and Finance, Faculty of Economic and Administrative Sciences (Turkey)

Description

It is globally acceptable that carbon dioxide (CO2) emissions are one of the greenhouse gases are considered the main factor influencing global warming and environmental degradation. The present study focuses on China, the world's largest carbon emitter. The study aims to capture the time-frequency dependency of economic growth and CO2 emissions in China for the time period 1950-2016 using a wavelet coherence approach, which allows us to investigate both the long-run and short-run causal links of the estimated variables. In order to capture the long-run and causal linkage between economic growth and CO2 emissions, the study employs Maki cointegration, wavelet coherence, Toda-Yamamoto causality, Fourier Toda-Yamamoto causality, and nonparametric Granger causality tests. The findings of this study reveal that (i) there is a significant vulnerability between economic growth and CO2 emissions throughout the 2000s both the short-term and medium-term; (ii) there is long-run cointegration linkage between economic growth and CO2 emissions in China; (iii) economic growth in China has an important power for predicting CO2 emissions over the selected study period, especially in the short-term and medium-term; and (iv) it was observed that there is positive correlation between economic growth during the 1980s and 1990s in the short-term only. The outcome of the Toda-Yamamoto causality, Fourier Toda-Yamamoto causality, and nonparametric Granger causality tests underlines that economic growth is a robust policy variable for predicting CO2 emissions in China.

Additional details

Identifiers

Publishing Information

Journal Title
Environmental Science and Pollution Research International
Journal Volume
27
Journal Page Range
40777-40786
ISSN
0944-1344
CODEN
ESPLEC

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(c) 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020