ABSTRACT
Based on the open data of 11 provinces in the Yangtze River economic belt from 2006 to 2018, this paper uses GML index and spatial Durbin model to describe the temporal and spatial evolution trend and interfering factors of agricultural green total factor productivity. The results show that, since 2006, the green total factor productivity of agriculture in the Yangtze River economic belt has maintained an upward trend, mainly driven by technological progress, while the technical efficiency has a certain inhibitory effect. In general, the upstream shows a trend of "low and low agglomeration", while the middle and lower reaches show a trend of "high and high agglomeration", but this state is not stable. Economic development, human capital, mechanization, financial self-sufficiency rate and the rate of disaster all have a certain influence on agricultural green total factor productivity, but the influence degree of each factor is significantly different. Finally, in order to ensure the stability and sustainability of regional agricultural green development, it is necessary to accelerate the construction of regional agricultural green development cooperative governance mechanism, establish and improve the ecological benefit compensation mechanism, and innovate the form of ecological products.
- Stijn Reinhard, C. A. Knox Lovell and Geert Thijssen. 1999. Econometric Estimation of Technical and Environmental Efficiency: An Application to Dutch Dairy Farms. American Journal of Agricultural Economics 81, 1 (February 1999), 44-60. https://doi.org/10.2307/1244449Google ScholarCross Ref
- Yeimin Chung, Almas Heshmati. 2015. Measurement of environmentally sensitive productivity growth in Korean industries. Journal of Cleaner Production 104, 1 (October 2013), 380-391. https://doi.org/10.1016/j.jclepro.2014.06.030Google ScholarCross Ref
- Dong-hyun Oh. 2010. A metafrontier approach for measuring an environmentally sensitive productivity growth index. Energy Economics 32, 1 (January 2010), 146-157. https://doi.org/10.1016/j.eneco.2009.07.006Google Scholar
- Marthin Nanere, Iain Fraser, Ali Quazi and Clare D'Souza. 2007. Environmentally adjusted productivity measurement: An Australian case study. Journal of environmental management 85, 2 (October 2007), 350-362. https://doi.org/10.1016/j.jenvman.2006.10.004.Google ScholarCross Ref
- Yufeng Chen, Jiafeng Miao. 2021. Measuring green total factor productivity of China's agricultural sector: A three-stage SBM-DEA model with non-point source pollution and CO2 emissions. Journal of Cleaner Production 318, 10 (October 2021), 128543. https://doi.org/10.1016/j.jclepro.2021.128543Google ScholarCross Ref
- Haoran Wang, Herui Cui and Qiaozhi Zhao. 2021. Effect of green technology innovation on green total factor productivity in China: Evidence from spatial durbin model analysis. Journal of Cleaner Production 288, 15 (March 2021), 125624. https://doi.org/10.1016/j.jclepro.2020.125624Google ScholarCross Ref
- Kaoru Tone. 2001. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research 130, 3 (May 2013), 498-509. https://doi.org/10.1016/S0377-2217(99)00407-5Google ScholarCross Ref
- E Loizou, C Karelakis and K Galanopoulos. 2019, The role of agriculture as a development tool for a regional economy. Agriculture Systems 173, (July 2019), 482-490. https://doi.org/10.1016/j.agsy.2019.04.002Google Scholar
- Taniya Ghosh, Prashant Mehul Parab. 2021. Assessing India's productivity trends and endogenous growth: New evidence from technology, human capital and foreign direct investment. Economic Modelling 09, (April 2021), 182-195. https://doi.org/10.1016/j.econmod.2021.02.003Google ScholarCross Ref
- Sharmistha Banerjee, Ravi Mokashi Punekar. 2020. A sustainability-oriented design approach for agricultural machinery and its associated service ecosystem development. Journal of Cleaner Production 264,10(August 2020), 121642. https://doi.org/10.1016/j.jclepro.2020.121642Google ScholarCross Ref
- Muhammad Akbar, Faisal Jamil. 2012. Monetary and fiscal policies' effect on agricultural growth: GMM estimation and simulation analysis. Economic Modelling 29, 5 (September 2012), 1909-1920. https://doi.org/10.1016/j.econmod.2012.06.001Google ScholarCross Ref
- Lan Fang, Rong Hu and Hui Mao. 2021. How crop insurance influences agricultural green total factor productivity: Evidence from Chinese farmers. Journal of Cleaner Production 321, 25 (October 2021), 128977. https://doi.org/10.1016/j.jclepro.2021.128977Google ScholarCross Ref
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