【摘要】 将就业和碳排放纳入统一研究框架,重构"碳排放强度"指标,利用WIOD数据库的就业和二氧化碳排放量数据,测算并评估1995～2014年我国农业的就业和碳排放双重红利效应。在此基础上,借助SDA模型考察了我国农业双重红利的影响因素。研究表明,样本期内我国农业的就业碳排放强度呈快速上升趋势,农业的就业和碳排放双重红利不断下降。碳排放系数和单位能耗的下降有效抑制了农业双重红利的下降,且抑制作用呈倒"U"型特征。就业碳排放强度的快速上升主要由农业增加值率和劳动生产率变化所致,这意味着单纯依靠生产技术的进步,并不能有效释放农业双重红利,更应注重低碳技术的提升。应优化农业自身就业内部结构,打造中高端农业技术队伍,改善低碳农业技术的自主创新和国际合作环境,提升农业绿色生产率和农业附加值质量,推动我国农业高质量发展,以有效释放农业的就业和碳排放双重红利。更多还原

【Abstract】 This paper takes employment and carbon emission into the same framework, reconstructs the index of carbon emission intensity, and calculates and evaluates the double dividend of Chinese agriculture from 1995 to 2014 using employment and carbon dioxide data from WIOD database. On this basis, the influencing factors of China’s agricultural comprehensive income were investigated by SDA model decomposition. The results show that the carbon emission intensity of China’s agricultural employment shows a rapid upward trend during the sample period, and the double dividends of comprehensive employment and carbon dioxide emissions are declining. The decrease of carbon emission coefficient and energy consumption per unit effectively restrained the decline of agricultural double dividend, and the restraint showed an inverted U-shaped feature. The rapid increase of employment carbon emission intensity is mainly caused by the change of value-added rate and labor productivity, which means that relying solely on the progress of production technology can not improve the dual dividend effect of agriculture, but should pay more attention to the promotion of low-carbon technology. We should optimize the internal structure of agricultural employment, build up high-end agricultural technology team, improve the independent innovation and international cooperation environment of low-carbon agricultural technology, enhance the green productivity of agriculture and the quality of agricultural added value,promote the high-quality development of agriculture in China, and effectively release the double dividends of agricultural employment and carbon emissions.更多还原