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An Integrated Performance Evaluation Model for the Photovoltaics Industry

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  • Amy H. I. Lee

    () (Department of Technology Management, Chung Hua University, Hsinchu 300, Taiwan
    Ph.D. Program of Technology Management-Industrial Management, Chung Hua University, Hsinchu 300, Taiwan
    Department of Industrial Management, Chung Hua University, Hsinchu 300, Taiwan)

  • Chun Yu Lin

    () (Ph.D. Program of Technology Management-Industrial Management, Chung Hua University, Hsinchu 300, Taiwan)

  • He-Yau Kang

    () (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411, Taiwan)

  • Wen Hsin Lee

    () (Department of Industrial Management, Chung Hua University, Hsinchu 300, Taiwan)

Abstract

Global warming is causing damaging changes to climate around the World. For environmental protection and natural resource scarcity, alternative forms of energy, such as wind energy, fire energy, hydropower energy, geothermal energy, solar energy, biomass energy, ocean power and natural gas, are gaining attention as means of meeting global energy demands. Due to Japan’s nuclear plant disaster in March 2011, people are demanding a good alternative energy resource, which not only produces zero or little air pollutants and greenhouse gases, but also with a high safety level to protect the World. Solar energy, which depends on an infinite resource, the sun, is one of the most promising renewable energy sources from the perspective of environmental sustainability. Currently, the manufacturing cost of solar cells is still very high, and the power conversion efficiency is low. Therefore, photovoltaics (PV) firms must continue to invest in research and development, commit to product differentiation, achieve economies of scale, and consider the possibility of vertical integration, in order to strengthen their competitiveness and to acquire the maximum benefit from the PV market. This research proposes a performance evaluation model by integrating analytic hierarchy process (AHP) and data envelopment analysis (DEA) to assess the current business performance of PV firms. AHP is applied to obtain experts’ opinions on the importance of the factors, and DEA is used to determine which firms are efficient. A case study is performed on the crystalline silicon PV firms in Taiwan. The findings shall help the firms determine their strengths and weaknesses and provide directions for future improvements in business operations.

Suggested Citation

  • Amy H. I. Lee & Chun Yu Lin & He-Yau Kang & Wen Hsin Lee, 2012. "An Integrated Performance Evaluation Model for the Photovoltaics Industry," Energies, MDPI, Open Access Journal, vol. 5(4), pages 1-21, April.
  • Handle: RePEc:gam:jeners:v:5:y:2012:i:4:p:1271-1291:d:17374
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Wenyi Liu & Linzhi Liu & Gang Xu & Feifei Liang & Yongping Yang & Weide Zhang & Ying Wu, 2014. "A Novel Hybrid-Fuel Storage System of Compressed Air Energy for China," Energies, MDPI, Open Access Journal, vol. 7(8), pages 1-23, August.
    2. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    3. Ma, Zhixiao & Xue, Bing & Geng, Yong & Ren, Wanxia & Fujita, Tsuyoshi & Zhang, Zilong & Puppim de Oliveira, Jose A. & Jacques, David A. & Xi, Fengming, 2013. "Co-benefits analysis on climate change and environmental effects of wind-power: A case study from Xinjiang, China," Renewable Energy, Elsevier, vol. 57(C), pages 35-42.
    4. Jangid, Jayant & Bera, Apurba Kumar & Joseph, Manoj & Singh, Vishal & Singh, T.P. & Pradhan, B.K. & Das, Sandipan, 2016. "Potential zones identification for harvesting wind energy resources in desert region of India – A multi criteria evaluation approach using remote sensing and GIS," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1-10.
    5. Li, Nan & Liu, Cengceng & Zha, Donglan, 2016. "Performance evaluation of Chinese photovoltaic companies with the input-oriented dynamic SBM model," Renewable Energy, Elsevier, vol. 89(C), pages 489-497.
    6. repec:spr:jecstr:v:6:y:2017:i:1:d:10.1186_s40008-017-0073-z is not listed on IDEAS
    7. Huiru Zhao & Nana Li, 2016. "Performance Evaluation for Sustainability of Strong Smart Grid by Using Stochastic AHP and Fuzzy TOPSIS Methods," Sustainability, MDPI, Open Access Journal, vol. 8(2), pages 1-22, January.
    8. Tsung-Yung Chiu & Shang-Lien Lo & Yung-Yin Tsai, 2012. "Establishing an Integration-Energy-Practice Model for Improving Energy Performance Indicators in ISO 50001 Energy Management Systems," Energies, MDPI, Open Access Journal, vol. 5(12), pages 1-16, December.
    9. Uyan, Mevlut, 2013. "GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 11-17.
    10. Gigović, Ljubomir & Pamučar, Dragan & Božanić, Darko & Ljubojević, Srđan, 2017. "Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: A case study of Vojvodina, Serbia," Renewable Energy, Elsevier, vol. 103(C), pages 501-521.
    11. repec:gam:jsusta:v:8:y:2016:i:2:p:129:d:63208 is not listed on IDEAS
    12. Min-Ren Yan & Kuo-Ming Chien, 2013. "Evaluating the Economic Performance of High-Technology Industry and Energy Efficiency: A Case Study of Science Parks in Taiwan," Energies, MDPI, Open Access Journal, vol. 6(2), pages 1-15, February.
    13. Mohammad Pakkar, 2015. "An integrated approach based on DEA and AHP," Computational Management Science, Springer, vol. 12(1), pages 153-169, January.

    More about this item

    Keywords

    photovoltaics (PV); performance evaluation; analytic hierarchy process (AHP); data envelopment analysis (DEA);

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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