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

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  • Alin Lin

    (School of Architecture, Harbin Institute of Technology, Harbin 150006, China
    Heilongjiang Cold Region Urban-Rural Human Settlements Science Key Laboratory, No.66 Xidazhi St., Harbin 150006, China)

  • Ming Lu

    (School of Architecture, Harbin Institute of Technology, Harbin 150006, China
    Heilongjiang Cold Region Urban-Rural Human Settlements Science Key Laboratory, No.66 Xidazhi St., Harbin 150006, China)

  • Pingjun Sun

    (School of Architecture, Harbin Institute of Technology, Harbin 150006, China
    Heilongjiang Cold Region Urban-Rural Human Settlements Science Key Laboratory, No.66 Xidazhi St., Harbin 150006, China)

Abstract

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.

Suggested Citation

  • Alin Lin & Ming Lu & Pingjun Sun, 2018. "The Influence of Local Environmental, Economic and Social Variables on the Spatial Distribution of Photovoltaic Applications across China’s Urban Areas," Energies, MDPI, vol. 11(8), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:1986-:d:160961
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    1. J. Richard Snape, 2016. "Spatial and Temporal Characteristics of PV Adoption in the UK and Their Implications for the Smart Grid," Energies, MDPI, vol. 9(3), pages 1-18, March.
    2. Ming Lu & Alin Lin & Jiyi Sun, 2018. "The Impact of Photovoltaic Applications on Urban Landscapes Based on Visual Q Methodology," Sustainability, MDPI, vol. 10(4), pages 1-15, April.
    3. Federica Cucchiella & Idiano D’Adamo & Massimo Gastaldi, 2017. "Economic Analysis of a Photovoltaic System: A Resource for Residential Households," Energies, MDPI, vol. 10(6), pages 1-15, June.
    4. Labay, Duncan G & Kinnear, Thomas C, 1981. "Exploring the Consumer Decision Process in the Adoption of Solar Energy Systems," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 8(3), pages 271-278, December.
    5. Li, Guiqiang & Xuan, Qingdong & Pei, Gang & Su, Yuehong & Ji, Jie, 2018. "Effect of non-uniform illumination and temperature distribution on concentrating solar cell - A review," Energy, Elsevier, vol. 144(C), pages 1119-1136.
    6. Henrik Zsiborács & Nóra Hegedűsné Baranyai & András Vincze & István Háber & Gábor Pintér, 2018. "Economic and Technical Aspects of Flexible Storage Photovoltaic Systems in Europe," Energies, MDPI, vol. 11(6), pages 1-17, June.
    7. Henrik Zsiborács & Attila Bai & József Popp & Zoltán Gabnai & Béla Pályi & István Farkas & Nóra Hegedűsné Baranyai & Mihály Veszelka & László Zentkó & Gábor Pintér, 2018. "Change of Real and Simulated Energy Production of Certain Photovoltaic Technologies in Relation to Orientation, Tilt Angle and Dual-Axis Sun-Tracking. A Case Study in Hungary," Sustainability, MDPI, vol. 10(5), pages 1-19, May.
    8. Wang, Hongwei & Zheng, Shilin & Zhang, Yanhua & Zhang, Kai, 2016. "Analysis of the policy effects of downstream Feed-In Tariff on China’s solar photovoltaic industry," Energy Policy, Elsevier, vol. 95(C), pages 479-488.
    9. Chaianong, Aksornchan & Pharino, Chanathip, 2015. "Outlook and challenges for promoting solar photovoltaic rooftops in Thailand," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 356-372.
    10. Noll, Daniel & Dawes, Colleen & Rai, Varun, 2014. "Solar Community Organizations and active peer effects in the adoption of residential PV," Energy Policy, Elsevier, vol. 67(C), pages 330-343.
    11. Rai, Varun & Reeves, D. Cale & Margolis, Robert, 2016. "Overcoming barriers and uncertainties in the adoption of residential solar PV," Renewable Energy, Elsevier, vol. 89(C), pages 498-505.
    12. Gábor Pintér & Nóra Hegedűsné Baranyai & Alec Wiliams & Henrik Zsiborács, 2018. "Study of Photovoltaics and LED Energy Efficiency: Case Study in Hungary," Energies, MDPI, vol. 11(4), pages 1-13, March.
    13. Ma, Wei Wu & Rasul, M.G. & Liu, Gang & Li, Min & Tan, Xiao Hui, 2016. "Climate change impacts on techno-economic performance of roof PV solar system in Australia," Renewable Energy, Elsevier, vol. 88(C), pages 430-438.
    14. Lin He & Chang-Ling Li & Qing-Yun Nie & Yan Men & Hai Shao & Jiang Zhu, 2017. "Core Abilities Evaluation Index System Exploration and Empirical Study on Distributed PV-Generation Projects," Energies, MDPI, vol. 10(12), pages 1-18, December.
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