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Energy efficiency gap and target setting: A study of information asymmetry between governments and industries in China

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  • Liu, Yang
  • Yao, Xilong
  • Wei, Taoyuan

Abstract

Information asymmetry between governments and industries is a major impediment to set an optimal energy efficiency target for reduction in energy demand. Typically, the regulators face challenges of incomplete and imperfect information on the minimum improvement in energy intensity that industries would achieve in baseline cases and the maximum technical potential of energy efficiency that industries are able to achieve. By applying a two-tier stochastic frontier model based on provincial panel data 2006–2015 in China, we disentangle the impacts of information asymmetry between the governments and firms on industrial energy intensity from other major regulatory, technological and economic factors. Results show that, on average, nearly 75% of the unexplained variation in industrial energy intensity is attributed to the information gap between firms and governments. On average, the realized energy intensity is upper biased by 7.25% due to the information gap compared to the expected level without the information gap although the net impacts vary considerably across provinces and over time. One observation is that firms have much stronger impact on the energy intensity in the period when the government prioritized economic growth rather than energy savings. In addition, the impact of firms and governments varies markedly across the quartiles of economic development level, technological progress, policy stringency, and energy resource endowment.

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  • Liu, Yang & Yao, Xilong & Wei, Taoyuan, 2019. "Energy efficiency gap and target setting: A study of information asymmetry between governments and industries in China," China Economic Review, Elsevier, vol. 57(C).
    • Handle: RePEc:eee:chieco:v:57:y:2019:i:c:s1043951x19301026
    DOI: 10.1016/j.chieco.2019.101341
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    Cited by:

    1. Azhgaliyeva, Dina & Liu, Yang & Liddle, Brantley, 2020. "An empirical analysis of energy intensity and the role of policy instruments," Energy Policy, Elsevier, vol. 145(C).
    2. Nyangon, Joseph & Darekar, Ayesha, 2024. "Advancements in hydrogen energy systems: A review of levelized costs, financial incentives and technological innovations," Innovation and Green Development, Elsevier, vol. 3(3).
    3. Yunpeng Yang & Zhiqiang Liu & Hongmin Chen & Yaqiong Wang & Guanghui Yuan, 2020. "Evaluating Regional Eco-Green Cooperative Development Based on a Heterogeneous Multi-Criteria Decision-Making Model: Example of the Yangtze River Delta Region," Sustainability, MDPI, vol. 12(7), pages 1-20, April.
    4. Shirong Zhao & Jeremy Losak, 2024. "Two-tiered stochastic frontier models: a Bayesian perspective," Journal of Productivity Analysis, Springer, vol. 61(2), pages 85-106, April.
    5. Wang, Mengjiao & Liu, Jianxu & Rahman, Sanzidur & Sun, Xiaoqi & Sriboonchitta, Songsak, 2023. "The effect of China’s outward foreign direct investment on carbon intensity of Belt and Road Initiative countries: A double-edged sword," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 792-808.
    6. Mertzanis, Charilaos & Garas, Samy & Abdel-Maksoud, Ahmed, 2020. "Integrity of financial information and firms' access to energy in developing countries," Energy Economics, Elsevier, vol. 92(C).
    7. Weixin Yang & Yunpeng Yang, 2020. "Research on Air Pollution Control in China: From the Perspective of Quadrilateral Evolutionary Games," Sustainability, MDPI, vol. 12(5), pages 1-23, February.
    8. Lin, Boqiang & Jia, Huanyu, 2024. "Present-biased individuals and their underinvestment in household energy efficiency: Evidence from first-tier Chinese cities," Energy Policy, Elsevier, vol. 185(C).
    9. Cheng, Zhonghua & Yu, Xuejin & Zhang, Yi, 2023. "Is the construction of new energy demonstration cities conducive to improvements in energy efficiency?," Energy, Elsevier, vol. 263(PA).
    10. Wang, Pengyu & Fang, Debin & Wang, Shuyi, 2022. "Optimal dynamic regulation in retail electricity market with consumer feedback and social learning," Energy Policy, Elsevier, vol. 168(C).

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