Can Policy-Based Agricultural Insurance Promote Agricultural Carbon Emission Reduction? Causal Inference Based on Double Machine Learning

Yuling Dong and Lili Gu ()
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Yuling Dong: School of Economics and Management, Jilin Agricultural University, Changchun 130118, China
Lili Gu: School of Economics and Management, Jilin Agricultural University, Changchun 130118, China

Sustainability, 2025, vol. 17, issue 9, 1-21

Abstract: Policy-based agricultural insurance plays a pivotal role in promoting agricultural carbon emissions reduction and driving the development of agricultural modernization. This study, based on panel data from 31 Chinese provinces spanning 2003 to 2021, employs the double machine learning method to conduct theoretical and empirical analyses on the carbon emission reduction effects, implementation mechanisms, and regional heterogeneity in policy-oriented agricultural insurance. The empirical findings indicate that the enforcement of policy-based agricultural insurance exerts a considerable influence on curbing agricultural carbon emissions. This conclusion remains robust across a rigorous suite of robustness checks. Under the “scale–structure–technology” logical framework, the carbon emission reduction effects of policy-oriented agricultural insurance operate through three key mechanisms: the scaling-up of agricultural production, the grain-oriented transformation of planting structures, and the advancement of agricultural technologies. Heterogeneity tests reveal that policy-based agricultural insurance exerts significantly stronger carbon mitigation impacts in major grain-producing areas, the Yangtze River Economic Belt, and regions with stringent environmental regulations.

Keywords: policy-based agricultural insurance; agricultural carbon emissions; double machine learning; policy evaluation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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