4.7 Article

Impacts of agricultural industrial agglomeration on China's agricultural energy efficiency: A spatial econometrics analysis

期刊

JOURNAL OF CLEANER PRODUCTION
卷 260, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2020.121011

关键词

Industrial agglomeration; Energy efficiency; Spatial econometrics; Agriculture in China

资金

  1. National Natural Science Foundation of China [71703159]
  2. Fundamental Research Funds for Central Non-profit Scientific Institution [JBYW-AII-2019-08]

向作者/读者索取更多资源

The rapid development of traditional agriculture in China was achieved at the expense of high energy consumption and investments. However, the global green development trend made it necessary for the country to transform its agricultural energy utilization. Energy efficiency changes are affected by many factors, particularly industrial agglomeration. In recent years, the Chinese government has introduced a series of policies, including setting major producing regions for grains and advantageous regions for characteristic agricultural product. These have caused significant changes to the spatial layout of the agriculture industry. However, there is still a lack of research on the impact of these changes on agricultural energy efficiency (AEE). In this study, panel data of 30 Chinese provinces from 2000 to 2016 were entered into stochastic frontier models to measure the country's AEE at the provincial level. A series of spatial econometric models were also used to analyze the impact of agricultural industrial agglomeration on China's AEE. The results indicated that the country's AEE exhibited obvious spatial gradients and correlations. After controlling the impacts of spatial correlation and other factors in the models, agricultural industrial agglomeration was found to have an overall positive impact on China's AEE. In the future, policies should be formulated to increase AEE by establishing agricultural functional areas, strengthening the innovation and sharing of green development technologies at the farm level, and promoting the optimization of energy structures in agricultural and rural areas. (C) 2020 Elsevier Ltd. All rights reserved.

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