结构转型、技术进步选择对农业碳影子价格的影响

许标文; 王海平; 沈智扬

doi:10.12357/cjea.20220492

结构转型、技术进步选择对农业碳影子价格的影响基于BP技术与FGLS模型的实证分析

doi: 10.12357/cjea.20220492

1.
福建省农业科学院农业经济与科技信息研究所　福州　350003
2.
法国科学经济与管理学院巴黎校区　巴黎　92044, 法国

基金项目: 国家自然科学基金项目(72104028)、福建省自然科学基金项目(2020J011375)和福建省农业科学院科技创新项目(CXTD0098)资助

详细信息

作者简介:
许标文, 主要研究方向为农业可持续发展。E-mail: 13596447@qq.com

通讯作者:
沈智扬, 主要研究方向为效率与生产率估计。E-mail: z.shen@ieseg.fr

中图分类号: F323; X196
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出版历程
- 收稿日期: 2022-06-27
- 录用日期: 2022-08-16
- 网络出版日期: 2022-11-07
- 刊出日期: 2023-02-10

Impact of structural transformation, technological progress choice on agricultural carbon shadow price: An empirical analysis based on BP technology and a mediating effect model

1.
Institute of Agricultural Economy and Science Information, Fujian Academy of Agricultural Sciences, Fuzhou 350003, China
2.
IÉSEG School of Management, Paris 92044, France

Funds: This study was supported by the National Natural Science Foundation of China (72104028), the Natural Science Foundation of Fujian Province (2020J011375) and the Science and Technology Innovation Program of Fujian Academy of Agricultural Sciences (CXTD0098).

More Information

Corresponding author: E-mail: z.shen@ieseg.fr

摘要

摘要: 通过经济结构转型和技术进步有效实施减排策略, 已经成为实现中国经济社会低碳转型发展的必然选择。基于数据包络分析(DEA)框架建模、满足经济理论和物料守恒原则, 利用改进的By-production (BP)技术对1997—2020年我国31个省份农业碳影子价格进行了更加准确的测度, 并利用核密度分析了农业碳影子价格动态演变特征; 并采用可行广义最小二乘法(FGLS)模型考察结构转型、技术进步选择对农业碳排放影子价格的影响。结果表明: 1)我国农业碳影子价格呈上升态势, 东部、中部和西部地区农业碳排放影子价格分别为7759.69元∙t⁻¹、4192.35元∙t⁻¹和3997.51元∙t⁻¹, 且东、中、西部地区农业碳影子价格上升趋势依次降低。2)我国农业碳影子价格核密度值有增加趋势; 东部地区农业碳影子价格核密度曲线出现较为明显的右移趋势; 中部地区农业碳影子价格核密度曲线呈现左移-右移趋势, 且区域间差异在变大; 西部地区农业碳影子价格核密度曲线呈现明显的向下、变宽趋势。3)整体回归显示, 结构转型、劳动节约型技术进步显著提升了农业碳影子价格, 而资本深化抑制了农业碳影子价格提升, 经济发展水平、农业经营规模、城市化水平及对外开放水平等对农业碳影子价格也会产生重要影响。4)东、中、西部地区部分样本回归显示农业碳影子价格影响因素有所差异, 结构转型在东部地区显著提升农业碳影子价格, 在西部地区却显著抑制了农业碳影子价格; 劳动节约型技术进步在东部抑制了农业碳影子价格, 而在西部显著提升农业碳影子价格; 资本深化在东、西部显著抑制农业碳影子价格, 在中部显著提升农业碳影子价格。为此, 提出持续推进产业结构转型、制定差异化绿色协调发展政策、适时建立农业碳排放交易市场等政策建议, 以促进农业低碳绿色高质量发展。
- 结构转型 /
- 技术进步 /
- 农业碳排放 /
- 影子价格 /
- By-production技术
Abstract: The effective implementation of emission reduction strategies through economic structural transformation and technological progress has become an inevitable choice for achieving low-carbon economic and social transformation and development in China. Considering that the disposability assumption of the shadow price calculation method is inconsistent with the realistic theory in the existing literature, a new calculation method for the carbon shadow price based on the data envelopment analysis (DEA) framework modeling and satisfying the principles of economic theory and material conservation was applied, in which the expected output production sub-technology and the undesired output production sub-technology are linked according to the generation relationship of pollutants. Improved By-production technology was used to accurately measure the agricultural carbon shadow price in 31 provinces from 1997 to 2020, and kernel density was used to analyze the dynamic evolution characteristics of agricultural carbon shadow prices. A feasible generalized least squares (FGLS) model was used to examine the impact of structural transformation and technological progress choices on the shadow price of agricultural carbon emissions. The results showed that: 1) the shadow price of agricultural carbon price showed an increasing trend, which was 7759.69 ¥∙t⁻¹ in the east region, 4192.35 ¥∙t⁻¹ in the central region, and 3997.51 ¥∙t⁻¹ in the west region, and the rising rates decreased in that order. 2) Kernel density analysis revealed that the kernel density value of the agricultural carbon shadow price had an increasing trend. The kernel density curve in the east region was right-shifted, in the central region left-right shifted with increasing regional differences, and in the west region it was downward and widened. 3) The overall regression analysis showed that the shadow price of agricultural carbon was significantly increased by structural transformation and labor-saving technological progress but that this increase was inhibited by capital deepening. Meanwhile, the level of economic development, the scale of agricultural operations, urbanization, and the level of opening up all played important roles in the agricultural carbon shadow price. 4) The regional regression analysis results highlighted that the influencing factors of agricultural carbon shadow prices differed in different regions. Structural transformation significantly increased the carbon shadow price in the east region but significantly inhibited it in the west region. Labor-saving technological progress reduced the carbon shadow price in the east region while significantly increasing it in the west region. Therefore, it is necessary to promote the transformation of the industrial structure, formulate differentiated green and coordinated development policies, and establish an agricultural carbon emissions trading market to promote low-carbon, green, and high-quality agricultural development.
- Structural transformation /
- Technological progress /
- Agricultural carbon emissions /
- Shadow prices /
- By-production technology

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图 1 1997—2020年东部、中部和西部地区及全国农业碳影子价格时序变化

Figure 1. Time series of agricultural carbon shadow price in the east, central, west regions and whole nation from 1997 to 2020

下载: 全尺寸图片幻灯片

图 2 1997—2020年农业碳影子价格Kernel核密度图

Figure 2. Kernel density of agricultural carbon shadow price from 1997 to 2020

下载: 全尺寸图片幻灯片

表 1 结构转型、技术进步选择对农业碳影子价格影响效应的估计结果

Table 1. Estimation results of impact of structural transformation and technological progress choices on agricultural carbon shadow price

变量 Variable	符号 Symbol	模型1 Model 1	模型2 Model2	模型3 Model3
结构转型 Structural transformation	st	0.299^* (0.171)	0.794^*** (0.168)	0.454^*** (0.021)
劳动力节约型技术进步 Labor-saving technological progress	ls	−0.366 (0.250)	−1.275^*** (0.190)	0.455^*** (0.067)
资本体现式技术进步选择 Capital-deepening technological progress	cd	0.778^*** (0.144)	1.127^*** (0.198)	−0.171^*** (0.018)
农业经营规模化 Scale of agricultural management	sam	−0.139 (0.092)	−0.150 (0.100)	−0.084^*** (0.016)
经济发展水平 Economic development	ed	0.038 (0.037)	−0.010 (0.019)	−1.096^*** (0.086)
城镇化 Urbanization	ur	−0.543^** (0.262)	−0.539^** (0.234)	0.595^*** (0.072)
对外开放 Opening-up	op	0.619 (0.476)	0.921^* (0.512)	−0.597^*** (0.041)
常数项 Constant	cons	0.120 (1.333)	−2.863 (1.941)	−289.916^*** (20.564)
Wald test				54329.05^***
F test				3.932^**
Frees test				1.969^***
模型(1)~(3)分别用随机效应模型、固定效应模型和全面用可行的广义最小二乘法(FGLS)。、和*分别表示回归系数在P<10%、P<5%和P<1%水平显著, 括号内为标准差。Model 1, model 2 and model 3 are random effects model, fixed effect model and full feasible generalized least squares (FGLS). , and * denote the significance levels of the regression coefficients of P<10%, P<5%, and P<1%, respectively. Data in parentheses are the corresponding standard deviations.

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表 2 东、中、西部地区农业碳影子价格影响因素分析

Table 2. Analysis of influencing factors of agricultural carbon shadow price in eastern, central and western regions

变量 Variable	符号 Symbol	模型4 Model 4	模型5 Model 5	模型6 Model 6
结构转型 Structural transformation	st	1.496^*** (0.197)	0.368^*** (0.164)	−0.293^*** (0.064)
劳动力节约型技术进步 Labor-saving technological progress	ls	−0.483^*** (0.336)	0.116^*** (0.016)	1.986^*** (0.407)
资本体现式技术进步选择 Capital-deepening technological progress	cd	−0.142^*** (0.039)	0.027^*** (0.133)	−0.502^*** (0.085)
农业经营规模化 Scale of agricultural management	sam	−0.124^* (0.074)	−0.010 (0.028)	−0.336^*** (0.083)
经济发展水平 Economic development	ed	0.457^** (0.182)	0.303 (0.319)	−1.549^*** (0.387)
城镇化 Urbanization	ur	0.324^*** (0.098)	0.462 (1.000)	1.566^*** (0.299)
对外开放 Opening-up	op	−0.119 (0.131)	−4.932^*** (1.160)	−1.704^*** (0.640)
常数项 Constant	cons	−113.352^*** (34.211)	−60.546 (91.962)	−479.476^*** (65.956)
Wald test		2640.73^***	27 216.47^***	13 967.00^***
F test		2.540	2.739	0.672
Frees test		0.338^***	0.301^***	0.930^***
模型(4)~(6)分别是东部、中部、西部地区回归结果。、和*分别表示回归系数在P<10%、P<5%和P<1%水平显著, 括号内为标准差。Model 4, model 5 and model 6 are regression results of the east, central and west regions. , and * denote the significance levels of the regression coefficients of P<10%, P<5%, and P<1%, respectively. Data in parentheses are the corresponding standard deviations.

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