Study on the impact of digital finance on agricultural carbon emissions from a spatial perspective: an analysis based on provincial panel data

Bingfei Bao, Baoxiang Fei, Gaigai Ren and Shengtian Jin

International Journal of Low-Carbon Technologies, 2024, vol. 19, 497-507

Abstract: China is committed to achieving the goal of ‘carbon peaking and carbon neutrality’, which requires reducing carbon emissions from various sectors, especially agriculture. Agriculture is a major source of greenhouse gas emissions, and its carbon intensity is influenced by many factors, including the development of digital finance. Digital finance, as an important driver of economic growth and technological innovation, can affect the carbon emissions of agricultural production and consumption through various channels. However, the existing literature has not paid enough attention to the spatial effects of digital finance on agricultural carbon emissions, which may lead to biased estimates and policy implications. Therefore, this paper aims to fill this gap by using the Spatial Durbin Model to analyze the impact of digital finance on agricultural carbon emissions from a spatial perspective, based on the provincial panel data of 28 provinces (municipalities and autonomous regions) in China from 2011 to 2020. The main findings are as follows: (1) The development of digital finance has a positive and significant effect on China’s agricultural carbon emissions, and this effect is robust to different spatial weight matrices. (2) The impact mechanism of digital finance on agricultural carbon emissions is mainly through affecting the spatial spillover of carbon emissions and the cross-regional financial coordination. Based on these results, this paper provides some policy suggestions for achieving the goal of ‘carbon peaking and carbon neutrality’ in China’s agricultural sector.

Keywords: digital finance; agricultural carbon emissions; Spatial Durbin Model (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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