摘要:准确测度农业碳排放效率并分析关键影响因素, 可为加快实现农业减排增效提供理论参考。已有研究未将碳排放与其他要素的共同作用进行分离, 研究实质为碳排放约束下的农业生产效率, 而非农业碳排放效率。为完善既有测算思路, 本文在农业全要素框架下搭建碳排放效率的理论模型, 基于GB-US-SBM模型测算2000—2019年间中国30省(市、自治区)的农业碳排放松弛量, 根据碳排放松弛量与实际值计算农业碳排放效率。在此基础上, 从产业、要素、环境3个方面出发, 采用空间杜宾模型探讨农业碳排放效率的影响因素与溢出效应。结果表明: 研究期内中国农业碳排放效率均值为0.778, 具有较大减排潜力。省级层面上, 仅内蒙古和青海两地的农业碳排放效率达1.000, 其余地区均存在不同规模的减排空间; 根据总量与效率的双重特征, 将30省(市、自治区)分为高排高效区、低排高效区、高排低效区和低排低效区。中国农业碳排放效率全局Moran’s I显著大于0 (P<0.01), 说明效率整体存在空间自相关性。空间杜宾模型结果显示, 农业碳排放效率具有显著的正向溢出效应, 表明邻近地区间的效率呈良性互动的演进特征。就直接效应而言, 本省的农业产业结构、农业投资强度、财政支农力度和受灾程度对本省农业碳排放效率存在负向影响, 有效灌溉率和城镇化率则表现为正向作用。从溢出效应来看, 邻近地区的受灾程度将负向影响本省农业碳排放效率, 而城镇化率则呈积极影响。研究结果可为我国分区域、分类别推进低碳农业发展提供理论依据。
关键词:农业碳排放效率/
全局参比/
GB-US-SBM模型/
空间杜宾模型/
空间溢出
Abstract:The efficiency of agricultural carbon emissions is a bridge between crop production and emission reduction, acting as a critical indicator of the potential for emission mitigation in agricultural production. In previous estimations, the outcomes yield the input-output efficiency of agriculture under the carbon emission constraint, rather than the efficiency of agricultural carbon emission, due to failing to separate the contribution of carbon emissions from other factors. To optimize the existing idea and understand the efficiency more precisely, a theoretical framework and a corresponding equation were developed for analysis in this study. In agricultural production, given the input factors, the efficiency of agricultural carbon emissions under the prerequisite of no desirable output was defined as the ratio of the minimum possible emissions to the actual emissons. On this basis, the GB-US-SBM model was employed to calculate the slack of emissions in 30 Chinese provinces from 2000 to 2019, reflecting the distance between the actual emission and production frontier. Then, the efficiency was estimated based on the slacks and actual emissions. Finally, the influencing factors and spillover effects of agriculural carbon emissions efficiency were explored using the spatial Durbin model. Results showed that: (1) From 2000 to 2019, the average agricultural carbon emissions efficiency was 0.778 in China, indicating considerable potential for emission reduction. At the provincial level, only Inner Mongolia and Qinghai had an efficiency of 1.000, while the rest of the provinces had different spaces for emission mitigation. (2) According to the emissions quantity and efficiency, the 30 provinces were divided into four groups. The five provinces, Henan, Hebei, Shandong, Heilongjiang, and Guangxi, belonged to a group of high emissions with high efficiency. The group of low emissions with high efficiency accounted for the majority, including 12 provinces, such as Inner Mongolia and Gansu. The group with high emissions and low efficiency covered seven provinces, such as Hunan and Hubei. Six provinces, including Zhejiang and Fujian, were classified as low emissions with low efficiency. (3) The global Moran’s index was significantly greater than 0, with a P-value under 0.01, verifying that there was a positive spatial autocorrelation in the provinces. The spatial econometric regression showed that efficiency had a significant positive spatial spillover effect, suggesting that an interactive evolution existed among close provinces. Specifically, four factors—industry structure, investment intensity, financial support for agriculture, and the degree of disaster, harmed the agricultural carbon emissions efficiency directly. By contrast, the irrigation effectiveness and urbanization indicated significant positive effects. In terms of spillover effects, the intensity of a disaster in a province negatively affected the efficiency of agricultural carbon emissions in neighboring provinces, while the urbanization rate exhibited a positive effect. Hence, it was essential to pay attention to the key factors that influence efficiency. Making full use of spillover effects could also help in achieving regional agricultural low-carbon transition. Additionally, local solutions should be addressed, owing to the regional characteristics of efficiency. This study results could provide a theoretical basis for the development of low-carbon agriculture in China.
Key words:Agricultural carbon emissions efficiency/
Global benchmark/
GB-US-SBM/
Spatial Durbin model/
Spatial spillover
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