Performance evaluation of China's photovoltaic poverty alleviation project using machine learning and satellite images

Hui Yin and Kaile Zhou

Utilities Policy, 2022, vol. 76, issue C

Abstract: Photovoltaic poverty alleviation project (PPAP) is one of China's essential targeted poverty alleviation projects. This study proposes a machine learning model and uses satellite images to evaluate the performance of PPAP in China. The trained deep convolutional neural network (DCNN) with transfer learning was first used to identify the scale of photovoltaic (PV) power stations. Then the PV power capacity for poverty alleviation and carbon emission mitigation were estimated. The results identified 38 large-scale centralized and approximately 5,063,293 m2PV power stations built in Jinzhai County, Anhui province, China, by November 2020. The main findings are as follows. (1) The power generation and carbon mitigation of PPAP in Jinzhai County is about 1.8×103 MWh and 1.389 Mt per year. (2) The PPAP in Jinzhai County can recover the total costs and get benefits within three years, at least after completion. (3) Dynamic subsidy policy is needed to prevent over-scale or excessive government investment in PPAP. (4) The utilization of PPAP needs to be strengthened to transform the current “blood transfusion type” of poverty alleviation that relies more on government subsidies into more sustainable “hematopoietic style” poverty alleviation. This study is of significance to more accurately and comprehensively evaluate the performance of PPAP and give better utilize the role of renewable energy in promoting energy conservation, carbon emission reduction, and economic development.

Keywords: Photovoltaic poverty alleviation project (PPAP); Deep convolutional neural network (DCNN); Transfer learning; Satellite images (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:juipol:v:76:y:2022:i:c:s0957178722000431

DOI: 10.1016/j.jup.2022.101378

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