4.7 Article

A critical barrier analysis framework to the development of rural distributed PV in China

Journal

ENERGY
Volume 245, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2022.123277

Keywords

Rural distributed PV; Barrier analysis; Fuzzy environment; K-means; Improved DEMATEL-ISM

Funding

  1. National Social Science Fund of China [19AGL027]
  2. Fundamental Research Funds for the Central Universities [2020MS066]

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This paper comprehensively studies the obstacles to the development of rural distributed PV in China using objective methods and proposes suggestions for improvement.
Rural distributed photovoltaic (PV) is conducive to low-carbon development of China. Nevertheless, the development is hampered by multiple barriers and therefore lags behind expectations. Some studies have been devoted to this, but there are still problems including incomplete identification of obstacles and non-objective analysis methods. Thus, this paper is dedicated to establishing a complete and objective research framework to effectively identify obstacles and promote high level development of rural distributed PV. Firstly, a two-stage process is adopted to extract obstacles from the entire life cycle of rural distributed PV, and 10 obstacles are identified. Then the K-means clustering algorithm are introduced to improve the DEMATEL-ISM method for threshold determination and barrier analysis. Interactions between obstacles are divided into three levels and a network hierarchy that reflects these interactions is created. The results show that imperfect policies and regulations, diminished subsidy, high initial investment and aging equipment and poor quality are critical barriers, and corresponding suggestions on the collaboration between enterprises and the government are put forward. This paper comprehensively studies the obstacles to the development of rural distributed PV by objective methods and proposes suggestions for improvement, which is beneficial to the development of rural distributed PV in China. (c) 2022 Elsevier Ltd. All rights reserved.

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