Development pathways for low carbon cities in China: A dual perspective of 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 predo...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24013050 |
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| author | Xiangrui Xu Lu Chen Xiaoyun Du Qiaojing Chen Renpeng Yuan |
| author_facet | Xiangrui Xu Lu Chen Xiaoyun Du Qiaojing Chen Renpeng Yuan |
| author_sort | Xiangrui Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2c9d07e04f0f4ed5b867e32a4e69d74b |
| institution | DOAJ |
| issn | 1470-160X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-2c9d07e04f0f4ed5b867e32a4e69d74b2025-08-20T02:49:05ZengElsevierEcological Indicators1470-160X2024-12-0116911284810.1016/j.ecolind.2024.112848Development pathways for low carbon cities in China: A dual perspective of effectiveness and efficiencyXiangrui Xu0Lu Chen1Xiaoyun Du2Qiaojing Chen3Renpeng Yuan4School of Spatial Planning and Design, Hangzhou City University, Hangzhou, Zhejiang, ChinaSchool of Management Science and Real Estate, Chongqing University, Chongqing, China; Corresponding author at: School of Management Science and Real Estate, Chongqing University, Chongqing 400045, China.School of Management, Zhengzhou University, Henan, ChinaSchool of Systems Engineering, National University of Defense Technology, Changsha, Hunan, ChinaSchool of Systems Engineering, National University of Defense Technology, Changsha, Hunan, ChinaGlobal 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.http://www.sciencedirect.com/science/article/pii/S1470160X24013050Low Carbon City (LCC)LCC effectivenessLCC efficiencyLCC evaluation model |
| spellingShingle | Xiangrui Xu Lu Chen Xiaoyun Du Qiaojing Chen Renpeng Yuan Development pathways for low carbon cities in China: A dual perspective of effectiveness and efficiency Ecological Indicators Low Carbon City (LCC) LCC effectiveness LCC efficiency LCC evaluation model |
| title | Development pathways for low carbon cities in China: A dual perspective of effectiveness and efficiency |
| title_full | Development pathways for low carbon cities in China: A dual perspective of effectiveness and efficiency |
| title_fullStr | Development pathways for low carbon cities in China: A dual perspective of effectiveness and efficiency |
| title_full_unstemmed | Development pathways for low carbon cities in China: A dual perspective of effectiveness and efficiency |
| title_short | Development pathways for low carbon cities in China: A dual perspective of effectiveness and efficiency |
| title_sort | development pathways for low carbon cities in china a dual perspective of effectiveness and efficiency |
| topic | Low Carbon City (LCC) LCC effectiveness LCC efficiency LCC evaluation model |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24013050 |
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