Impact of Digital Village Construction on Agricultural Carbon Emissions: Evidence from Mainland China

Int J Environ Res Public Health. 2023 Feb 26;20(5):4189. doi: 10.3390/ijerph20054189.

Abstract

Reducing agricultural carbon emissions is required to reach the goal of carbon neutrality and mitigate the effects of climate change. With the advent of the digital economy, we aimed to determine if digital village construction can achieve carbon reduction in agriculture. As such, in this study, we used balanced panel data for 30 provinces in China from 2011 to 2020 to conduct an empirical analysis based on measuring the digital village construction level in each province. We found the following: Firstly, digital village construction is conducive to reducing the carbon emitted from agriculture, and the results of further tests showed that the carbon reduction effect of digital villages is mainly based on the reduction in carbon emissions from chemical fertilisers and pesticides. Secondly, the digital village construction has a stronger inhibiting effect on agricultural carbon emissions in major grain-producing areas than in non-major grain-producing areas. The level of rural human capital is the limiting condition for digital village construction to enable green agricultural development; in areas with higher levels of human capital, digital village construction has a significant inhibiting effect on agricultural carbon emissions. The above conclusions are valuable for the future promotion of digital village construction and the design of a green development model for agriculture.

Keywords: agricultural carbon emissions; agricultural green development; digital village; mainland China; nonagricultural employment.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Agriculture
  • Carbon Dioxide / analysis
  • Carbon* / analysis
  • China
  • Economic Development
  • Humans
  • Pesticides* / analysis
  • Rural Population

Substances

  • Carbon
  • Pesticides
  • Carbon Dioxide

Grants and funding

This research was funded by the National Social Science Foundation of China, grant number 19AZD011, and the National Natural Science Fund Project, grant number 71673137.