支农财政对农业碳排放强度的空间效应:基于空间杜宾模型的实证分析

doi:10.19741/j.issn.1673-4831.2020.0783

生态与农村环境学报 ›› 2021, Vol. 37 ›› Issue (11): 1404-1412.doi: 10.19741/j.issn.1673-4831.2020.0783

支农财政对农业碳排放强度的空间效应:基于空间杜宾模型的实证分析

韩金雨¹, 曲建升^1,2, 徐丽¹, 李恒吉^1,2, 刘莉娜²

1. 兰州大学西部环境教育部重点实验室/资源环境学院, 甘肃兰州 730000;
2. 中国科学院西北生态环境资源研究院, 甘肃兰州 730000

收稿日期:2020-07-20 出版日期:2021-11-25 发布日期:2021-11-18
通讯作者: 曲建升,E-mail:jsqu@lzb.ac.cn E-mail:jsqu@lzb.ac.cn
作者简介:韩金雨(1988-),男,山东潍坊人,博士生,主要研究方向为农业碳排放和气候变化政策。E-mail:hanjinyu00000@163.com
基金资助:
国家重点研发计划（2016YFA0602803）

The Spatial Effect of Agricultural Finance on Agricultural Greenhouse Gas Emission Intensity: An Empirical Analysis Based on the Spatial Durbin Model

HAN Jin-yu¹, QU Jian-sheng^1,2, XU Li¹, LI Heng-ji^1,2, LIU Li-na²

1. Key Laboratory of Western China's Environmental Systems, Ministry of Education/College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China;
2. Northwest Institute of Eco-environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, Chinas

Received:2020-07-20 Online:2021-11-25 Published:2021-11-18

摘要/Abstract

摘要： 作为政府支持和调控农业发展的主要手段，支农财政极大地促进了农业的发展，而农业碳排放已成为我国温室气体排放的重要组成部分。为探究支农财政与农业碳排放之间的关系及其空间机制，基于31个省级单元1997-2017年面板数据，采用空间杜宾模型进行实证研究。结果表明，各省份支农财政比例大幅提升，导致农业碳排放强度普遍下降；支农财政具有抑制农业碳排放强度的作用，且存在明显的空间溢出特征，邻近省份支农财政对本省份农业碳排放的减排效果较本省份支农财政更强；除支农财政外，经济结构、城镇化水平、城乡差距、农业发展水平、农业机械化水平和耕地占用率等因素都不同程度影响着农业排放强度，且存在着不同程度的空间溢出效应。鉴于支农财政对碳的减排效果，其可以作为一项降低农业碳排放强度的政策手段，进一步优化支农财政资金结构和统筹空间布局应成为未来支农财政工作的方向。

关键词: 支农财政, 农业碳排放强度, 低碳农业, 空间依赖, 空间杜宾模型

Abstract: To support and regulate agricultural development, Chinese government is providing financial subsidies to farmers. This has greatly improved the agricultural production, and it has also an implication on greenhouse gas (GHG) emission intensities. The aim of this research is to assess the relationship between agriculture finance and GHG emission intensities. Based on the panel data of 31 provinces from 1997-2017, the research was carried out through the Spatial Durbin Model. The results show that the proportion of provincial agricultural finance increased significantly, leading to a decline in agricultural GHG emission intensities. The agriculture-supporting finance had a reducing effect on the intensity of agricultural GHG and had obvious spatial spillover characteristics; that is to say, the effect of a neighboring province's agriculture-supporting finance on the province's agricultural GHG reduction is stronger than that of the province's agriculture-supporting finances itself. In addition to agricultural finance, factors such as economic structure, urbanization level, urban-rural gap, agricultural development level, agricultural mechanization level, and cultivated land occupancy have affected the agricultural GHG intensities to varying levels, and there are spatial spillover effects to varying degrees. Because the agricultural finance has decreased the GHG intensities, it is necessary to choose this instrument to develop and promote lowcarbon agriculture. In addition, optimizing the agriculture structure and coordinating the spatial distribution should also be the direction of future agricultural finance.

Key words: agriculture-supporting finance, agricultural greenhouse gas intensity, low-carbon agriculture, spatial dependence, Spatial Durbin Model

中图分类号:

[S-9]
F81

韩金雨, 曲建升, 徐丽, 李恒吉, 刘莉娜. 支农财政对农业碳排放强度的空间效应:基于空间杜宾模型的实证分析[J]. 生态与农村环境学报, 2021, 37(11): 1404-1412.

HAN Jin-yu, QU Jian-sheng, XU Li, LI Heng-ji, LIU Li-na. The Spatial Effect of Agricultural Finance on Agricultural Greenhouse Gas Emission Intensity: An Empirical Analysis Based on the Spatial Durbin Model[J]. Journal of Ecology and Rural Environment, 2021, 37(11): 1404-1412.

参考文献

[1] 张广胜, 王珊珊.中国农业碳排放的结构、效率及其决定机制[J]. 农业经济问题, 2014, 35(7):18-26, 110.[ZHANG Guangsheng, WANG Shan-shan.China's Agricultural Carbon Emissions:Structure, Efficiency and Its Determinants[J]. Issues in Agricultural Economy, 2014, 35(7):18-26, 110]
[2] 夏四友, 赵媛, 许昕, 等.近20年来中国农业碳排放强度区域差异、时空格局及动态演化[J]. 长江流域资源与环境, 2020, 29(3):596-608.[XIA Si-you, ZHAO Yuan, XU Xin, et al.Regional Inequality, Spatial-temporal Pattern and Dynamic Evolution of Carbon Emission Intensity from Agriculture in China in the Period of 1997-2016[J]. Resources and Environment in the Yangtze Basin, 2020, 29(3):596-608.]
[3] 高玉强, 宋群.中国省域财政支农支出的减贫效率研究[J]. 中南林业科技大学学报(社会科学版), 2020, 14(1):59-65, 100.[GAO Yu-qiang, SONG Qun.A Study on the Efficiency of Poverty Alleviation of China's Provincial Financial Expenditure on Agriculture[J]. Journal of Central South University of Forestry & Technology (Social Sciences), 2020, 14(1):59-65, 100.]
[4] 赵璐, 吕杰.我国财政支农效率的区域差异研究[J]. 经济问题, 2011(4):102-105.[ZHAO Lu, LÜ Jie.Research on Efficient Regional Differences for China's Financial Agricultural Support[J]. On Economic Problems, 2011(4):102-105.]
[5] 张东玲, 陈景帅, 王保贵.区域异质性:财政支农与农村居民消费:基于泰尔指数和区域面板的经验数据分析[J]. 农林经济管理学报, 2018, 17(1):36-44.[ZHANG Dong-ling, CHEN Jingshuai, WANG Bao-gui. Regional Heterogeneity:Financial Support for Agriculture and Consumption of Rural Residents:An Empirical Analysis of Data Based on Theil Index and Regional Panel[J]. Journal of Agro-forestry Economics and Management, 2018, 17(1):36-44.]
[6] 李晓嘉.财政支农支出与农业经济增长方式的关系研究:基于省际面板数据的实证分析[J]. 经济问题, 2012(1):68-72.[LI Xiao-jia.A Study on Relation between Fiscal Agriculture Expenditure and Economic Growth Mode Transformation:Dynamic Effect Analysis on Provincial Panel Data[J]. On Economic Problems, 2012(1):68-72.]
[7] 金芳, 金荣学.财政支农影响农业产业结构变迁的空间效应分析[J]. 财经问题研究, 2020(5):82-91.[JIN Fang, JIN Rongxue.Analysis on the Spatial Effect of Fiscal Support for Agriculture on the Change of Agricultural Industrial Structure[J]. Research on Financial and Economic Issues, 2020(5):82-91.]
[8] 马远, 龚新蜀.城镇化、财政支农与农民收入增加的关系[J]. 城市问题, 2010(5):60-66.[MA Yuan, GONG Xin-shu.On the Relations between Urbanization, Finance Expenditure for Agriculture and Peasant's Income Growth[J]. Urban Problems, 2010(5):60-66.]
[9] 王丙毅, 徐鹏杰.农业产业结构趋同的负效应与政策建议:基于山东省农业产值数据的分析[J]. 农村经济, 2008(2):35-38.
[10] 宋燕平, 王艳荣.面向农业产业集聚发展的技术进步效应分析[J]. 科学学研究, 2009, 27(7):1005-1010.[SONG Yan-ping, WANG Yan-rong.Research on the Effects of Technology Improvement of Agricultural Agglomeration Development[J]. Studies in Science of Science, 2009, 27(7):1005-1010.]
[11] 王兴, 马守田, 濮超, 等.西南地区农业碳排放趋势及影响因素研究[J]. 中国人口·资源与环境, 2017, 27(增刊2):231-234.[WANG Xing, MA Shou-tian, PU Chao, et al.Trends and Influencing Factors of Agricultural Carbon Emissions in the Southwest China[J]. China Population, Resources and Environment, 2017, 27(Suppl. 2):231-234.]
[12] 仇伟, 卢东宁.基于VAR模型的农业碳排放影响因素及其动态响应机制分析[J]. 湖北农业科学, 2019, 58(24):271-276.[QIU Wei, LU Dong-ning. Analysis of Factors Affecting Agricultural Carbon Emission Based on VAR Model and Its Dynamic Response Mechanism[J]. Hubei Agricultural Sciences, 2019, 58(24):271-276.]
[13] 黄燕, 周买春, 陈瑛.中国与巴西农业碳排放动态变化及影响因素分析[J]. 世界农业, 2018(6):116-121.
[14] 胡婉玲, 张金鑫, 王红玲.中国农业碳排放特征及影响因素研究[J]. 统计与决策, 2020, 36(5):56-62.[HU Wan-ling, ZHANG Jin-xin, WANG Hong-ling.Characteristics and Influencing Factors of Agricultural Carbon Emission in China[J]. Statistics & Decision, 2020, 36(5):56-62.]
[15] ANSELIN L, SYABRI I, KHO Y.GeoDa:An Introduction to Spatial Data Analysis[J]. Geographical Analysis, 2006, 38(1):5-22.
[16] 杨丽霞, 杨桂山, 姚士谋, 等.基于ESDA-GWR的粮食单产及其驱动因子的空间异质性研究[J]. 经济地理, 2012, 32(6):120-126.[YANG Li-xia, YANG Gui-shan, YAO Shi-mou, et al.A Study on the Spatial Heterogeneity of Grain Yield Per Hectare and Driving Factors Based on ESDA-GWR[J]. Economic Geography, 2012, 32(6):120-126.]
[17] SRIDHARAN S, TUNSTALL H, LAWDER R, et al.An Exploratory Spatial Data Analysis Approach to Understanding the Relationship between Deprivation and Mortality in Scotland[J]. Social Science & Medicine, 2007, 65(9):1942-1952.
[18] LESAGE J, PACE R. Introduction to Spatial Econometrics[M]. Boca Raton:CRC Press/Taylor & Francis, 2009:46-50.
[19] ANSELIN L. Spatial Effects in Econometric Practice in Environmental and Resource Economics[J]. American Journal of Agricultural Economics, 2001, 83(3):705-710.
[20] 韦沁.我国农牧业碳排放的时空差异和影响因素分析及预测研究[D]. 兰州:兰州大学, 2018.[WEI Qin.Research on Spatialtemporal Differences, Influence Factors and Prediction of Carbon Emission in Agriculture and Animal Husbandry in China[D]. Lanzhou:Lanzhou University, 2018.]
[21] 杜江, 罗珺, 王锐, 等.粮食主产区种植业碳功能测算与时空变化规律研究[J]. 生态与农村环境学报, 2019, 35(10):1242-1251.[DU Jiang, LUO Jun, WANG Rui, et al.The Estimation and Analysis of Spatio-temporal Change Patterns of Carbon Functions in Major Grain Producing Area[J]. Journal of Ecology and Rural Environment, 2019, 35(10):1242-1251.]
[22] 徐丽, 曲建升, 吴金甲, 等.中国农牧业碳排放时空变化及预测[J]. 生态与农村环境学报, 2019, 35(10):1232-1241.[XU Li, QU Jian-sheng, WU Jin-jia, et al. Spatial-temporal Dynamics and Prediction of Carbon Emission from Agriculture and Animal Husbandry in China[J]. Journal of Ecology and Rural Environment, 2019, 35(10):1232-1241.]
[23] 韦沁, 曲建升, 白静, 等.我国农业碳排放的影响因素和南北区域差异分析[J]. 生态与农村环境学报, 2018, 34(4):318-325.[WEI Qin, QU Jian-sheng, BAI Jing, et al.Influencing Factors of Agricultural Carbon Emission and Regional Differences between South and North in China[J]. Journal of Ecology and Rural Environment, 2018, 34(4):318-325.]
[24] 张俊飚, 程琳琳, 何可.中国农业低碳经济效率的时空差异及影响因素研究:基于"碳投入"视角[J]. 环境经济研究, 2017, 2(2):36-51.[ZHANG Jun-biao, CHENG Lin-lin, HE Ke. The Difference of China's Agricultural Low-carbon Economic Efficiency in Spatial and Temporal and Its Influencing Factors:A Perspective of Carbon Input[J]. Journal of Environmental Economics, 2017, 2(2):36-51.]
[25] 李晓嘉.财政支农支出与农村居民消费的动态效应分析[J]. 经济学动态, 2010(9):31-34.
[26] 吴贤荣, 张俊飚, 田云, 等.中国省域农业碳排放:测算、效率变动及影响因素研究:基于DEA-Malmquist指数分解方法与Tobit模型运用[J]. 资源科学, 2014, 36(1):129-138.[WU Xian-rong, ZHANG Jun-biao, TIAN Yun, et al.Provincial Agricultural Carbon Emissions in China:Calculation, Performance Change and Influencing Factors[J]. Resources Science, 2014, 36(1):129-138.]

支农财政对农业碳排放强度的空间效应:基于空间杜宾模型的实证分析

The Spatial Effect of Agricultural Finance on Agricultural Greenhouse Gas Emission Intensity: An Empirical Analysis Based on the Spatial Durbin Model

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