许标文, 王海平, 沈智扬. 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[J]. 中国生态农业学报 (中英文), 2023, 31(2): 241−252. DOI: 10.12357/cjea.20220492
引用本文: 许标文, 王海平, 沈智扬. 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[J]. 中国生态农业学报 (中英文), 2023, 31(2): 241−252. DOI: 10.12357/cjea.20220492
XU B W, WANG H P, SHEN Z Y. Impact of structural transformation, technological progress choice on agricultural carbon shadow price: An empirical analysis based on BP technology and a mediating effect model[J]. Chinese Journal of Eco-Agriculture, 2023, 31(2): 241−252. DOI: 10.12357/cjea.20220492
Citation: XU B W, WANG H P, SHEN Z Y. Impact of structural transformation, technological progress choice on agricultural carbon shadow price: An empirical analysis based on BP technology and a mediating effect model[J]. Chinese Journal of Eco-Agriculture, 2023, 31(2): 241−252. 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

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

  • 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。

     

    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.

     

/

返回文章
返回