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Critical peak pricing and firm's asset efficiency: A double machine learning study with China's new energy manufacturing industry

Author

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  • Zhan, Yanhong
  • Sun, Chuanwang
  • Xu, Mengjie

Abstract

The effectiveness of electricity demand response incentives is a critical issue for developing countries striving to accelerate the large-scale deployment of new energy. By innovatively applying an enhanced double machine learning (DML) method, this study investigates the impact of critical peak pricing (CPP), serving as a demand response incentive, on the asset efficiency of new energy manufacturing enterprises in China. We find that CPP contributes to enhancing the asset efficiency of wind and solar manufacturing (WSM) enterprises. Further analysis indicates that optimizations of firms' asset utilization and asset structure are key driving forces for the improvement of asset efficiency. Moreover, non-state-owned and large-scale enterprises, as well as those located in regions with well-developed digital infrastructure, demonstrate a more pronounced improvement in asset efficiency under the CPP. This study presents new evidence on China's practice of CPP to support the development of new energy manufacturing industry, providing valuable insights for developing countries aiming to leverage electricity pricing mechanisms to facilitate energy transitions.

Suggested Citation

  • Zhan, Yanhong & Sun, Chuanwang & Xu, Mengjie, 2026. "Critical peak pricing and firm's asset efficiency: A double machine learning study with China's new energy manufacturing industry," Energy Economics, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:eneeco:v:153:y:2026:i:c:s0140988325008783
    DOI: 10.1016/j.eneco.2025.109048
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