The Influence of Local Environmental, Economic and Social Variables on the Spatial Distribution of Photovoltaic Applications across China’s Urban Areas

• Main Authors: Alin Lin, Ming Lu, Pingjun Sun
• Format: Article
• Language: English
• Published: MDPI AG 2018-07-01
• Series: Energies
• Subjects: solar photovoltaic applications; spatial distribution; influencing variables; decision-making; urban areas
• Online Access: http://www.mdpi.com/1996-1073/11/8/1986

The capacity of new installed photovoltaic (PV) in China in 2017 was increased to 53.06 GW, reaching a total of 402.5 GW around the world. Photovoltaic applications have a significant role in the reduction of greenhouse gas emissions and alleviating electricity shortages in the sustainable development process of cities. Research on the factors that influenced the spatial distribution of photovoltaic applications mostly focus on a certain project or a region. However, it is a complicated process for decision-making of photovoltaic installations in urban areas. This study uses zip code level data from 83 cities to investigate the influence of local environmental, economic and social variables on the spatial distribution of photovoltaic applications across China’s urban areas. By analyzing the current situation, the locations of urban photovoltaic applications are collected and presented. Statistical analysis software is used to evaluate the influence of selected variables. In this paper, correlation analysis, principle component analysis (PCA) and cluster analysis are generated to predict urban photovoltaic installations. The results of this research show that Gross Domestic Product (GDP), electricity consumption, policy incentives, the number of photovoltaic companies, population, age, education and rate of urbanization were important factors that influenced the adoption of urban photovoltaic systems. The results also indicate that Southeast China and Hangzhou Province are currently the most promising areas as they have a higher rate of solar photovoltaic installation. These conclusions have significancefor energy policy and planning strategies by predicting the future development of urban photovoltaic applications.