Abstract
This paper quantifies the impact of rural industrial integration(RII) on rural carbon emissions(RCE) in China's provinces. Firstly, collected literature to discover the intrinsic relationship between RII and RCE and selected five indicators based on the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) method to measure the level of RII.Secondly, based on the panel data of 30 provinces (autonomous regions and municipalities) in China from 2009 to 2022, uses the spatial Durbin model(SDM)to quantify the spatial benefits of RII and RCE.Thirdly, through empirical analysis, the main findings of this paper include: (1) The level of RII increases with the year, and the TOPSIS score is higher.(2) Use the spatial Durbin model to better explain the spatial relationship between RII and RCE. Among them, RII has a significant positive effect on local RCE, while the spillover benefit on rural carbon emissions in neighboring areas is not significant, showing a positive contribution to the total effect.（3）From the spatial level, explain the control variables of regional fiscal tax revenues(RTL), urban-rural income gap(RUP), rural population development scale(RP), education level(EL), and forest cover rate(FCR) on the significant role of RCE, reflecting the impact of regional differentiation, and introduce the lag term of spatial variables to improve the fit and explanatory ability of the model.（4）This paper provides robustness and Endogenous tests to improve the explainability of the model.Finally, based on the above findings, policy makers can propose optimized policies and safeguard measures from the aspects of industrial integration mechanism, green and low-carbon development path, agricultural science and technology support, and carbon trading of agricultural enterprises.