Evaluation of Carbon Emission Efficiency in the Construction Industry Based on the Super-Efficient Slacks-Based Measure Model: A Case Study at the Provincial Level in China

被引:1
|
作者
Zhang, Jun [1 ]
Zhang, Ying [1 ]
Chen, Yunjie [1 ]
Wang, Jinpeng [1 ]
Zhao, Lilin [1 ]
Chen, Min [1 ]
机构
[1] Nantong Univ, Sch Transportat & Civil Engn, Nantong 226019, Peoples R China
关键词
construction industry; carbon emission efficiency; super-efficient SBM model; carbon emission reduction potential; ML index model; CO2; EMISSION; ENERGY EFFICIENCY; UNDESIRABLE OUTPUTS; POTENTIAL OUTLOOK; BUILDING SECTOR; PRODUCTIVITY; PERFORMANCE; INPUT; REDUCTION; INTENSITY;
D O I
10.3390/buildings13092207
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Rapid urbanization and an increasing carbon footprint have underscored the need for sustainable practices in the construction industry. With the aim of prioritizing global sustainable development, the measurement of carbon emission efficiency in the construction industry (CEECI) has emerged as a critical indicator. Nevertheless, a comprehensive exploration of carbon emission efficiency within the Chinese construction sector remains limited, despite the pressing demand to mitigate carbon emissions. To address this research gap, this study aims to provide valuable policy recommendations for effectively reducing carbon emissions. We conducted a thorough assessment of both the total carbon emissions and the carbon emission intensity in 30 provinces and cities across China from 2010 to 2020. Utilizing the slacks-based measure (SBM) model with non-desired outputs, we evaluated the static CEECI, including the spatial correlation analysis and the evaluation of the carbon reduction potential in the construction industry (CRPCI). Additionally, the dynamic CEECI was quantified using the Malmquist-Luenberger (ML) index model, followed by an index decomposition analysis. The findings reveal several noteworthy insights: (1) There exists a positive correlation between carbon emissions in the construction industry and the economic scale. Generally, less developed areas (e.g., central and western regions of China) exhibit higher levels of carbon emission intensity (CEICI), while more developed areas (e.g., eastern regions of China) demonstrate lower levels of CEICI. (2) The CEECI across various provinces and cities demonstrates a clear spatial positive autocorrelation, while the CRPCI exhibits a negative correlation with the CEECI, with larger CRPCI values observed predominantly in western China. (3) Technological progress (MLTC) emerges as a crucial factor influencing the CEECI in our dynamic analysis. These findings offer valuable insights for policymakers to develop focused strategies to effectively mitigate carbon emissions nationwide.
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页数:22
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