Development pathways for low carbon cities in China: A dual perspective of effectiveness and efficiency

Abstract

• Proposed an innovative LCC evaluation model combining effectiveness and efficiency. • Used BP neural network and three-stage DEA to assess effectiveness and efficiency. • LCC effectiveness rises steadily, while efficiency shows fluctuations over time. • Shanghai, Guangzhou excel; Taiyuan, Hefei lag in effectiveness and efficiency. Global economic growth has led to substantial carbon dioxide emissions, positioning urban low-carbon transformation as a crucial strategy for addressing climate change. A scientific evaluation of low-carbon city (LCC) performance is vital for effective implementation. However, existing studies predominantly focus on assessing LCCs from a singular perspective of either effectiveness or efficiency, often neglecting a comprehensive consideration of both. To address this gap, this study employs Back Propagation (BP) Neural Network and three-stage Data Envelopment Analysis (DEA) models to conduct an empirical assessment of LCC performance in 35 mega-cities in China from both effectiveness and efficiency dimensions. The findings reveal that: (1) The dual-perspective evaluation method effectively reflects LCC performance from both process and outcome aspects; (2) In some regions, effectiveness and efficiency yield consistent results, indicating both are either high or low; conversely, in other regions, they exhibit complementarity, with instances of high effectiveness coupled with low efficiency, or vice versa; (3) Temporal analysis indicates a continuous improvement in LCC effectiveness over the study period, while efficiency demonstrates considerable fluctuations; (4) Spatial analysis highlights that cities like Shanghai, Guangzhou, and Shenzhen excel, whereas Taiyuan, Hefei, and Zhengzhou lag behind. This research offers essential policy insights for the construction of LCCs.