灌溉
环境科学
农业
固碳
耕作
温室气体
发射强度
播种
肥料
稻草
碳纤维
农业生产力
环境工程
农业工程
农学
数学
二氧化碳
工程类
化学
地理
有机化学
复合数
考古
电气工程
生物
激发
生态学
算法
作者
Zhangdong Guo,Xiaoning Zhang
标识
DOI:10.1016/j.scitotenv.2023.162483
摘要
Both contemporary policy makers and scholars focus on the role of agricultural sector in better achieving carbon reduction and green transition. This paper empirically analyzes the impact and mechanism of agricultural green production technology (AGPT) on agricultural carbon emission intensity at the national level using the panel data of China's 31 provinces from 2000 to 2019 as well as the panel OLS method and spatial Durbin model. The results show that China's AGPTs adoption level have improved greatly and the agricultural carbon emission intensity has declined drastically in the period of 2000 to 2019. No-till planting and straw returning significantly inhibited agricultural carbon emission intensity, while water-saving irrigation had the opposite effect. Chemical fertilizers are substituted effect through zero-tillage planting and straw-return, thus reducing the agricultural carbon emission intensity. Water-saving irrigation serves as a supplement effect to chemical fertilizers application, inhibiting fertilizer's carbon increase effects. The role of AGPTs reduce carbon emissions is particularly dominant in main grain-producing areas. Straw-return played a significant negative spatial spillover effect on the agricultural carbon emission intensity of neighboring regions through technology dissemination across regions, and the spillover effect of zero-tillage planting and water-saving irrigation was not significant. According to local conditions, no-tillage planting and straw returning should be promoted, water-saving irrigation efficiency should be improved, and the input characteristics of high carbon elements in traditional agriculture should be gradually changed. A regional collaborative emission reduction mechanism should be established to achieve a long-term mechanism for agricultural carbon emission reduction and green development.
科研通智能强力驱动
Strongly Powered by AbleSci AI