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<p>The efficiency of low-carbon energy transition (LET) critically influences the socioeconomic costs of achieving carbon neutrality. Assessing and improving the LET efficiency thus become relevant. Process complexity and multiple uncertainties significantly impact the effective advancement of LET. Therefore, this paper devises a novel efficiency assessment framework for LET, where the entire complex process of LET from energy production to energy consumption is modeled, while accounting for inherent uncertainties in energy systems. Then, to implement the efficiency calculation, we further develop a robust network data envelopment analysis (DEA) model, which extends conventional network DEA models by introducing data uncertainties of intermediate indicators and the general nested-network structure. The proposed approach thus can robustly measure the overall efficiency of the entire LET process while further examining the coordination status of its subprocesses. Applied to study the LET efficiency across coastal and inland provinces in China from 2015 to 2023, the results show that southeastern coastal provinces lead the efficient LET in China, with the optimal internal coordination. Uncertainties hinder the upward trend of China's LET efficiency and exacerbate inland regional disparities. These results offer valuable insights for designing measures to enhance and balance LET practices across&#xa0;regions.</p>

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Efficiency assessment of low-carbon energy transition in China: A robust network DEA approach

  • 期刊论文

  • 发表日期：

    2025年10月9日

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  • Xiaoyou Wang,
  • Shasha Yu,
  • Peng Zhou

摘要

The efficiency of low-carbon energy transition (LET) critically influences the socioeconomic costs of achieving carbon neutrality. Assessing and improving the LET efficiency thus become relevant. Process complexity and multiple uncertainties significantly impact the effective advancement of LET. Therefore, this paper devises a novel efficiency assessment framework for LET, where the entire complex process of LET from energy production to energy consumption is modeled, while accounting for inherent uncertainties in energy systems. Then, to implement the efficiency calculation, we further develop a robust network data envelopment analysis (DEA) model, which extends conventional network DEA models by introducing data uncertainties of intermediate indicators and the general nested-network structure. The proposed approach thus can robustly measure the overall efficiency of the entire LET process while further examining the coordination status of its subprocesses. Applied to study the LET efficiency across coastal and inland provinces in China from 2015 to 2023, the results show that southeastern coastal provinces lead the efficient LET in China, with the optimal internal coordination. Uncertainties hinder the upward trend of China's LET efficiency and exacerbate inland regional disparities. These results offer valuable insights for designing measures to enhance and balance LET practices across regions.