Agricultural Ecological Efficiency under the Carbon Emissions Trading System in China: A Spatial Difference-in-Difference Approach

Abstract

The agriculture sector plays a significant role in the development of the national economy and providing raw materials to the industrial sector. Trying to get more agricultural productivity, most farmers ignored the adverse effects of agricultural chemicals or pesticides that have a negative impact on the environment. So, the importance of agricultural ecological efficiency needs to be understood. This study attempts to explore whether agriculture, as an important source of carbon dioxide production, can have an effective impact on the agricultural ecological efficiency of carbon trading pilot policies in the context of the global implementation of carbon trading. This study evaluated the agricultural ecological efficiency (AEE) and its spatial distribution characteristics of 31 provinces in China, the data period was from 2000 to 2018. By applying the spatial difference-in-difference (SDID) approach, the study investigates the effects of low-carbon policies on agricultural ecological efficiency in pilot areas. The results demonstrate that low-carbon trading pilot policies have a significant impact on agricultural ecological efficiency. At the same time, the effects of regional economic development, population growth, urbanization, and urban innovation on efficiency are also significant. The improvement of agricultural ecological efficiency requires not only the full implementation of low-carbon trading pilot policies but also the development of regional economy and high-quality agriculture. The findings provide further policy recommendations for high-quality agricultural development.

Effect of Zero Growth of Fertilizer Action on Ecological Efficiency of Grain Production in China under the Background of Carbon Emission Reduction

“…The micro level mainly included the agricultural acreage, the scale of farmers [28], the characteristics of farmers, agricultural technology training [6], farmers' education years [29] and so on. At the macro level, it mainly included financial development [30], technological progress [31], GDP [32], agricultural informatization [33], farmland transfer [34], level of agricultural mechanization [35], urbanization level [36,37] and agricultural policy included smart agriculture, environmental regulation and lowcarbon trade pilot [38][39][40]. In terms of the zero growth of fertilizer action, existing studies included the driving factors of fertilizer reduction [39][40][41][42][43][44] and the contribution of fertilizer to agricultural pollution reduction [45][46][47].…”

Can E-commerce development policies promote the high-quality development of agriculture?—A quasi-natural experiment based on a China’s E-commerce demonstration city

“…Most scholars regard agricultural total factor productivity (ATFP) as a test indicator for highquality agricultural development [22][23][24]. Scholars' research on agricultural total factor productivity mostly focuses on regional difference analysis [25,26], spatial distribution [27,28], and the impact of different factors on agricultural productivity.…”

Evaluating the Effect of China’s Carbon Emission Trading Policy on Energy Efficiency of the Construction Industry Based on a Difference-in-Differences Method

“…e empirical work of Chen and Lin identified the role of carbon emission trading policy in promoting energy conservation and emission reduction as an effective policy tool to promote carbon neutrality [21]. Wu et al confirmed that the carbon emission trading policy had a significant impact on agricultural ecological efficiency [22]. Chai et al called carbon emission trading policy an effective market-driven environmental regulation policy and demonstrated it from the perspective of carbon emission efficiency [11].…”

“…is article focuses on the impact of the carbon emission trading policy on EECI, which fills this research gap. Furthermore, recent articles on the evaluation of carbon emission trading policy make extensive use of DID approach [1,3,4,11,20,22].…”