Determinants of the global and regional CO₂ emissions: What causes what and where?

ABSTRACT

Using the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and an unbalanced panel dataset of 128 countries covering 1990–2014, this study aims to examine the key impact factors (KIFs) of the global and regional carbon dioxide (CO₂) emissions and analyse the effectiveness of non-renewable and renewable energies. Given the potential cross-sectional dependence and slope heterogeneity, a series of econometric techniques allowing for cross-sectional dependence and slope heterogeneity is applied. The overall estimations imply that the KIFs at the global level are economic growth, followed by population size, non-renewable energy, and energy intensity in order of their impacts on CO₂ emissions; conversely, the KIFs at the regional level vary across different regions and estimators. The results also suggest that renewable energy can lead to a decline in CO₂ emissions at the global level. At the regional level, only for two regions (i.e., S. & Cent. America and Europe & Eurasia) renewable energy has a significant and negative effect on CO₂ emissions, which may be affected by the share of renewable energy consumption in the primary energy mix. Finally, the results indicate varied causality relationships among the variables across regions.

Abbreviations: AMG: Augmented mean group; BP: British Petroleum; BRICS: Brazil, Russia, India, China, and South Africa; CCEMG: Common correlated effects mean group; CD: Cross-section dependence; CIPS: Cross-sectionally augmented Im, Pesaran, and Shin; CO₂: Carbon dioxide; PS: Population size; D-H: Dumitrescu-Hurlin; EI: Energy intensity; EU: European Union; EU-5: Germany, France, Italy, Spain, and the United Kingdom; Europe & Eurasia, Europe and Eurasia; GDP: Gross domestic product; IEA: International Energy Agency; KIF: Key impact factor; LM: Lagrange multiplier; Mtoe, Million tonnes oil equivalent; NRE: Non-renewable energy; RE: Renewable energy; S. & Cent. America, South and Central America; STIRPAT: Stochastic Impacts by Regression on Population, Affluence, and Technology; VECM: Vector error correction model; WDI: World Development Indicators

KEYWORDS:

• CO₂ emissions
• Non-renewable and renewable energies
• KIFs
• STIRPAT model
• Global and regional analysis

JEL CLASSIFICATION:

• C23
• Q20
• Q30
• Q56

Acknowledgments

This work is supported by the National Social Science Foundation of China (Grant No. 18VDL017), the Postdoctoral Science Foundation of China (Grant No. 2018M641289), and the Fundamental Research Funds for the Central Universities in UIBE (Grant No. CXTD10-02). The authors wish to gratefully acknowledge the helpful reviews and comments from the editors and anonymous reviewers, which greatly improved this manuscript. All remaining errors are our own.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Social Science Foundation of China [18VDL017]; Postdoctoral Science Foundation of China [2018M641289]; Fundamental Research Funds for the Central Universities in UIBE [CXTD10-02].