Impact assessment of agriculture, energy and water on CO₂ emissions in China: untangling the differences between major and non-major grain-producing areas

ABSTRACT

Due to the use of abundant fossil and non-fossil fuels, the agricultural sector has contributed to large amounts of carbon dioxide (CO₂) emissions in China. Meanwhile, the agricultural sector is the largest consumer of water in China. To investigate the effects of agricultural output, energy consumption and water use on CO₂ emissions in China, a panel of 30 provinces for 2004–2017 is utilized. Also, to control the potential endogeneity, the orthogonal-difference Generalized Method of Moments (GMM) is used as the benchmark estimation method. Moreover, the Dumitrescu and Hurlin (D-H) panel causality test is employed to discover the causal relations among all the variables. Considering the differences in various agricultural areas in China, the 30 provinces are further divided into major and non-major grain-producing areas. The empirical results indicate that CO₂ emission is responsive to economic growth, energy consumption, agricultural output and water use with positive elasticities in China. However, at the regional level, agricultural output only has significant impact on CO₂ emissions in major grain-producing areas. Also, major and non-major grain-producing areas provide mixed directionality of causality among the variables. Finally, several policy implications are highlighted for the Chinese government with respect to mitigating CO₂ emissions and promoting growth in the agricultural sector.

KEYWORDS:

• CO₂ emissions
• agricultural output
• energy and water uses
• major and non-major grain-producing areas
• China

JEL CLASSIFICATION:

• C33
• Q10
• Q43
• Q54
• R11

Acknowledgments

The authors acknowledge the useful comments from the Editor and anonymous reviewers. Certainly, all remaining errors are our own.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is financially supported by the Fundamental Research Funds for the Central Universities in UIBE [Grant No. 19QD03]; the National Social Science Foundation of China [Grant No. 18VDL017]; and the Innovation Methods Special Foundation of the Chinese Ministry of Science and Technology [Grant No. 2018IMO40100].