Study On The Spatial Effect Of Green Finance On Agricultural Carbon Emissions In China

In recent decades,China has made significant achievements in the development of its agricultural economy,feeding nearly 20% of the world’s population with 7% of arable land.However,the accompanying ecological and environmental problems have become increasingly serious,with greenhouse gas emissions at a high level.China is facing serious environmental challenges in the international community.In order to fully realize carbon emission reduction,China has made a commitment to the world to achieve "carbon peak" by2030 and "carbon neutrality" by 2060.Now it is a key window period of the 14 th Five Year Plan and an important node to achieve "carbon peak".How to effectively achieve carbon emission reduction? This is a key issue facing our country.As the second largest source of carbon emissions in China,agricultural carbon emissions can be effectively controlled to curb the growth of total carbon emissions.There is a huge funding gap to achieve carbon reduction,so how to coordinate and achieve carbon reduction goals? China is the only economy in the world to build a green finance system from top to bottom.green finance is an important path to achieve carbon emission reduction at present.It is a practical issue of great significance to give full play to the role of green finance in agricultural carbon emission reduction and promote green finance to tilt to the agricultural field.This paper is mainly divided into six chapters.The first chapter introduces the research background and significance of this paper,combs the relevant research literature on green finance and carbon emissions at home and abroad,and describes the research methods,technical routes and innovations and shortcomings of this paper.The second chapter,based on various research theories,conducts theoretical analysis on the operation mechanism of green finance and the action path of green finance on agricultural carbon emissions,and puts forward research hypotheses.The third chapter analyzes the current situation of green finance,through building the evaluation index system of green finance,using the entropy method to empower it,and analyzes the temporal characteristics of the development level of green finance;Next,calculate agricultural carbon emissions using the carbon emission coefficient method,and analyze the temporal characteristics of the calculated agricultural carbon emissions.The fourth chapter uses the provincial panel data from 2009 to 2021 to establish the spatial correlation network of agricultural carbon emissions by building a Gravity model with green finance factor correction.The social network analysis method is used to analyze the overall network characteristics,network individual characteristics and block model of the spatial correlation network of agricultural carbon emissions,and QAP correlation and regression analysis methods are used to explore the factors affecting the spatial correlation network of agricultural carbon emissions.The fifth chapter builds an improved STIRPAT model based on the IPAT model according to the provincial panel data from 2009 to 2021,and combines it with three spatial econometric models: spatial lag model,spatial error model,and spatial Durbin model for selection.Through model identification testing,select the final empirical model and conduct empirical analysis.At the same time,use the selected model to conduct heterogeneity analysis in the eastern,central,and western regions.Finally,the mesomeric effect model is used to test the mesomeric effect of low-carbon technology in the impact of green finance on agricultural carbon emissions.Based on the theoretical analysis and empirical results,the sixth chapter tries to put forward policy recommendations for China’s current green finance carbon emission reduction.The results show that:(1)The development level index of green finance in various regions shows a steady growth trend,with the eastern region>the central region>the western region.The total carbon emissions from agriculture in China show a trend of first increasing and then decreasing...