| 摘要: |
| 【目的】中国水资源短缺问题严重，不同地区农业生产条件和效率存在较大差异。在这一背景下，对中国各省、市和自治区的农业生产效率进行计算和分析，有助于识别出各省市区农业发展的制约因素，对提高农业生产效率和水资源利用效率均有重要意义。【方法】本文使用数据包络分析（DEA）方法对中国31个省市区2004—2022年的农业生产综合效率、纯技术效率和规模效率进行测算，利用DEA-Malmquist生产率指数进一步评价了生产效率的构成及时间变化特征。【结果】北京、辽宁、吉林、黑龙江、上海、山东、海南和重庆的农业生产综合效率在2004—2022年均达到DEA有效，天津、江苏、浙江、福建、河南、广东、四川、贵州、西藏、陕西、青海和新疆在部分年份中达到DEA有效，河北、山西、内蒙古、安徽、江西、湖北、湖南、广西、云南、甘肃和宁夏在2004—2022年均为非DEA有效；全国及各省市区2004—2022年农业投入全要素生产率变化指数均大于1.000，说明从2004—2022年各省市区的农业生产效率均有所提高，提高的主要原因是技术进步，但技术效率、纯技术效率和规模效率基本保持稳定。部分非DEA有效省区（如河北、山西）的农业水资源或有效灌溉面积有冗余，这些省区需要减少有冗余变量的投入或适当补充松弛为0变量的投入，以提高农业生产效率和农田水利投入要素效率。所有省市区在2004—2022年纯技术效率和规模效率变化指数都在1.000附近波动，均需提高现有技术生产效率，并调整生产规模，推动纯技术效率和规模效率的提升。对于技术进步变化指数较低的省区（如新疆、云南）应优化技术，推广高效节水技术，提高农田水利设施建设水平。【结论】中国各省市区的农业生产效率和全要素生产率存在明显的地区差异。本研究针对不同限制性因素的情况分别提出了建议，为在水资源匮乏的地区有针对性地提高农业生产力和用水效率提供了依据。 |
| 关键词: 农业生产效率；数据包络分析；综合效率；纯技术效率；规模效率；全要素生产率 |
| DOI：10.13522/j.cnki.ggps.2024367 |
| 分类号: |
| 基金项目: |
|
| Evaluating spatiotemporal variations in agricultural production efficiency and total factor productivity change index across China |
|
CHANG Siyuan, SHANG Songhao
|
|
1. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China;
2. Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
|
| Abstract: |
| 【Objective】Water shortage is a pressing issue in many regions of China, where significant differences exist in agricultural production conditions and efficiency among provinces, municipalities, and autonomous regions. Assessing the agricultural production efficiency of these regions is essential for identifying developmental bottlenecks and devising strategies to enhance both productivity and water use efficiency.【Method】A Data Envelopment Analysis (DEA) model was used to evaluate the comprehensive efficiency, pure technical efficiency, and scale efficiency of agricultural production in 31 provinces, municipalities, and autonomous regions across China from 2004 to 2022. Additionally, the DEA-Malmquist productivity index was applied to analyze the components of efficiency change and the temporal variation characteristics.【Result】The results indicate that, from 2004 to 2022, eight provinces and municipalities achieved DEA efficiency in agricultural production in all years, 12 regions were DEA efficient in some years, and 11 provinces and autonomous regions were consistently non-DEA efficient. Despite total factor productivity indices exceeding 1.000 both nationally and regionally, indicating an overall increase in agricultural production efficiency, the technical efficiency, pure technical efficiency, and scale efficiency remained largely unchanged. Technological progress was identified as the primary driver of the observed increase in efficiency. In regions that are non-DEA efficient, such as Hebei and Shanxi, redundant inputs in agricultural water resources and effective irrigated areas suggest that reducing excessive input or reallocating underutilized inputs could enhance production efficiency and the effectiveness of farmland water conservancy investments. Regions with fluctuating pure technical and scale efficiency indices around 1.000 should focus on optimizing production scale and improving the efficiency of existing technologies. Conversely, regions with low technological progress indices, such as Xinjiang and Yunnan, should prioritize technology optimization, adopt water-saving innovations, and enhance farmland water infrastructure.【Conclusion】Our findings reveal significant regional variations in agricultural production efficiency and total factor productivity across China. Strategies for improving these efficiencies are outlined, providing a basis for targeted interventions to enhance agricultural productivity and water use efficiency in water-scarce regions. |
| Key words: agricultural production efficiency; data envelopment analysis; comprehensive efficiency; pure technical efficiency; scale efficiency; total factor productivity change index |
