京津冀地区绿色金融发展水平与农业碳排放效率耦合度研究

doi:10.19788/j.issn.2096-6369.220319

摘要/Abstract

摘要：

京津冀地区是全国重要的高新技术产地与重工业基地，在前期发展中，由于过度追求经济效益而导致生态失衡，从而引发环境问题，影响农业生产，引起农业生产效率的下降。为了研究京津冀地区绿色金融的发展水平以及其对农业发展的作用，文章通过建立绿色金融指标体系，运用熵值法对京津冀绿色金融发展水平进行测算分析；基于DEA-Malmquist模型分析京津冀地区农业碳排放效率；将二者进行耦合协调度分析，展开对京津冀3地的区域差异性分析。在研究中发现，京津冀地区绿色金融发展尚且处在初级阶段，仍有较大发展空间；京津冀地区农业碳排放效率呈良性，京津两市农业碳排放技术效率有待提升；绿色金融发展水平与农业碳排放效率的耦合水平较低，需要不断提升二者的协调性。本文针对上述问题给出了以下建议：加快构建绿色金融市场体系；推动六大高耗能产业向绿色低碳发展型转变；重视金融人才与绿色金融发展相结合；北京市发挥带头作用，为天津市和河北省提供人才和技术支持，带动天津市和河北省不断发展；天津市应注重农业技术的更新，不断提高农业发展水平，实现高效率的农业生产；河北省应从多方面改进农业生产方式，提高农业技术发展水平，提升农业碳排放效率，以促进京津冀地区绿色金融的发展，保证农业低碳发展。

关键词: 绿色金融, 熵值法, DEA-Malmquist模型, 农业碳排放效率, 耦合协调度

Abstract:

The Beijing-Tianjin-Hebei region is an important high-tech producing area and heavy industrial base in China. In the early stage of development, the ecological imbalance caused by excessive pursuit of economic benefits has led to environmental problems, affecting agricultural production and causing a decline in agricultural production efficiency. In order to study the development level of green finance in Beijing-Tianjin-Hebei region and its role in agricultural development, this paper establishes a green financial index system and uses the entropy method to measure and analyze the development level of green finance in Beijing-Tianjin-Hebei region. Based on DEA-Malmquist model, the agricultural carbon emission efficiency in Beijing-Tianjin-Hebei region was analyzed. The coupling coordination degree analysis of the two is carried out to analyze the regional differences of Beijing, Tianjin and Hebei. In the study found that the Beijing-Tianjin-Hebei region green financial development is still in its infancy, there is still room for greater development; the efficiency of agricultural carbon emission in Beijing-Tianjin-Hebei region is benign, and the technical efficiency of agricultural carbon emission in Beijing and Tianjin needs to be improved. The coupling level of green financial development level and agricultural carbon emission efficiency is low, and the coordination between the two needs to be continuously improved. This paper gives the following suggestions for the above problems : accelerate the construction of green financial market system; to promote the six high energy-consuming industries to green low-carbon development; attaching importance to the combination of financial talents and green financial development; Beijing plays a leading role in providing talents and technical support for Tianjin and Hebei Province, driving Tianjin.

Key words: green finance, entropy method, DEA-Malmquist model, agricultural carbon emission efficiency, coupling coordination degree

中图分类号:

F062

王达, 宋超, 张馨幻, 徐雯, 杨念. 京津冀地区绿色金融发展水平与农业碳排放效率耦合度研究[J]. 农业大数据学报, 2022, 4(3): 116-125.

Da Wang, Chao Song, Xin Huan Zhang, Wen Xu, Nian Yang. Research on the Coupling Degree between Green Finance Development Level and Agricultural Carbon Emission Efficiency in Beijing-Tianjin-Hebei Region[J]. Journal of Agricultural Big Data, 2022, 4(3): 116-125.

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目标体系	一级指标	二级指标	说明
绿色金融	经济	存贷比	金融机构年末贷款/存款总额
		储蓄率	金融机构存款/GDP
		六大高耗能产业利息支出占比	六大高耗能产业利息支出/工业产业利息总支出
	环境	GDP能耗	能源消耗总量/GDP
		废水排放/GDP	工业废水总量/GDP
		废气排放/GDP	工业废气总量/GDP
		固体废弃物排放/GDP	工业固体废弃物总量//GDP
	社会	工业污染治理投资/GDP	工业污染治理投资/GDP
		环境污染治理投资/GDP	环境污染治理投资/GDP
		节能环保公共支出/政府总支出	节能环保公共支出/政府总支出

	北京市	天津市	河北省
存贷比	0.456	1.045	0.783
储蓄率	5.568	1.575	2.017
六大高耗能产业支出占比	0.393	0.274	0.094
GDP 能耗	0.293	0.323	0.757
废水排放/GDP	1.845	4.239	3.493
废气排放/GDP	5.076	0.398	4.138
固体废弃物排放/GDP	0.124	0.038	0.691
工业污染治理投资/GDP	3.633	3.377	15.350
环境污染治理投资/GDP	0.018	0.001	0.014
节能环保公共支出/政府总支出	4.980	0.899	1.993

维度	指标	指标属性
经济	存贷比	正向指标
	储蓄率	正向指标
	六大高耗能产业利息支出占比	逆向指标
环境	GDP 能耗	逆向指标
	废水排放/GDP	逆向指标
	废气排放/GDP	逆向指标
	固体废弃物排放/GDP	逆向指标
社会	工业污染治理投资/GDP	正向指标
	环境污染治理投资/GDP	正向指标
	节能环保公共支出/政府总支出	正向指标

维度	权重	指标	权重
经济	32.85%	存贷比	11.98%
		储蓄率	10.74%
		六大高耗能产业利息支出占比	10.12%
环境	34.31%	GDP 能耗	8.42%
		废水排放/GDP	8.77%
		废气排放/GDP	8.85%
		固体废弃物排放/GDP	8.27%
社会	32.84%	工业污染治理投资/GDP	12.05%
		环境污染治理投资/GDP	9.12%
		节能环保公共支出/政府总支出	11.67%

时间	京津冀地区	北京市	天津市	河北省
2008	0.333	0.372	0.506	0.122
2009	0.333	0.339	0.488	0.173
2010	0.333	0.333	0.443	0.224
2011	0.333	0.331	0.355	0.314
2012	0.333	0.369	0.442	0.189
2013	0.333	0.342	0.472	0.186
2014	0.333	0.461	0.377	0.162
2015	0.333	0.453	0.342	0.205
2016	0.333	0.336	0.381	0.282
2017	0.334	0.362	0.296	0.343
2018	0.333	0.379	0.313	0.308
2019	0.333	0.490	0.286	0.224
2020	0.333	0.410	0.283	0.307
2021	0.333	0.373	0.287	0.340