The Effect of Heterogeneous Environmental Regulations on Carbon Emission Efficiency of the Grain Production Industry: Evidence from China’s Inter-Provincial Panel Data

Abstract

Examining the impact of heterogeneous environmental regulations on the carbon emission efficiency of the grain production industry will help to provide a theoretical basis and decision-making reference for achieving the goal of carbon peaking and sustainable agricultural development. Based on the panel data of 30 provinces in China, the DEA-SBM method was used to measure the grain production industry’s carbon emission efficiency from 2011 to 2019. Separating environmental regulations into formal environmental regulations and informal environmental regulations in two parts, then the two-way fixed effect model, threshold effect model, and intermediary effect model are used to test the influence of heterogeneous environmental regulations on the grain production industry’s carbon emission efficiency. The results show that: (1) the grain production industry’s carbon emission efficiency continues to improve but still has space for improvement. (2) the relationship between formal environmental regulations and the grain production industry’s carbon emission efficiency exhibits a “U-shaped” curve; informal environmental regulations have a significantly positive effect on improving the grain production industry’s carbon emission efficiency. The conclusion is still valid after the robustness test. (3) A threshold mechanism test found that there is a single threshold effect between the formal environmental regulations and the grain production industry’s carbon emission efficiency, but it does not cross the “U” inflection point. (4) there is a “U” shaped non-linear mediating effect in the influence of formal environmental regulations on the grain production industry’s carbon emission efficiency; informal environmental regulations also have an intermediary transmission path of production agglomeration of the grain production industry. This study supplies a new perspective for promoting the grain production industry’s carbon emission efficiency.

Effect of River Chief System on Carbon Emission Efficiency: Evidence from China

“…This phenomenon has been explained in existing articles, with Porter arguing that firm competitiveness stems from the ability to innovate and improve rather than from static efficiency and that stringent environmental regulations can increase firm competitiveness by stimulating innovation to offset the costs of compliance [24]. However, with more detailed and in-depth research on environmental regulation, many scholars have found that environmental regulation and carbon emission efficiency are not a simple linear relationship [25] if the heterogeneities of industry and environmental regulations are taken into account [26]. Some scholars believe that there is a U-shaped relationship between the intensity of environmental regulations and carbon reduction technology.…”

“…Under strong environmental regulations, enterprises have already raised their carbon emission efficiency to a high level, and the RCS will not improve the carbon emission efficiency by a large margin. Carbon emission efficiency is strongly related to the intensity of environmental regulation already in place [26], and either too high or too low a regulatory intensity can affect the policy effectiveness of RCS.…”

Analysis of logistics capacity, influencing factors and spatial spillover effect in Yangtze River Economic Belt

“…The regional economy determines the scale of regional logistics demand, while logistics supports the development of the regional economy, promoting the flow of goods, capital, and technology. This paper selects three indicators to reflect the regional economic foundation: provincial GDP, total retail sales of consumer goods, and total import and export value [24,31].…”

“…Sun Ting analyzed the development level of regional logistics in Guangxi by constructing a first-level index system from four aspects: economic foundation, logistics infrastructure construction, regional logistics demand capacity, logistics information and talent competitiveness [23]. Hang Jiang used entropy weight TOPSIS method and FSQCA method to evaluate low-carbon logistics by using panel data of 30 provinces in China from 2008 to 2021 [24]. Zhang Guangsheng selected 10 indicators from three levels: logistics demand capacity, logistics facilities and economic capacity, and logistics development potential to build a comprehensive evaluation system for regional logistics capacity [25].…”

Measurement of CO2 Emissions Efficiency and Analysis of Influencing Factors of the Logistics Industry in Nine Coastal Provinces of China

“…Furthermore, Sangliang et al [29] and Chaoxia et al [30] have separately observed that environmental regulation promotes CO 2 emissions efficiency in urban and manufacturing industries. Within the realm of the grain production industry, Bin et al [31] reveal the relationship between formal environmental regulations and CO 2 emissions efficiency, forming a "U" curve. Furthermore, a U-shaped correlation between marine environmental regulations and carbon efficiency has been uncovered by Qiang et al [32].…”