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January 18, 2026Buildings0 citationsOpen Access

Drivers of Carbon Emission Efficiency in the Construction Industry: Evidence from the Yangtze River Economic Belt

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MCM. ChenSFShuqi FanYGYuan Gao

Key Points

  • The aim is to examine the factors affecting carbon emission efficiency in the construction industry within the Yangtze River Economic Belt from 2010 to 2022.
  • Utilized the super-efficiency Slack-Based Measure model for efficiency analysis.
  • Applied the Malmquist–Luenberger index to measure changes in carbon emission efficiency.
  • Conducted spatial analysis using Moran's index to assess regional variations.
  • Developed a spatial econometric model to identify influencing factors affecting carbon emission efficiency.
  • Downstream, midstream, and upstream regions showed average carbon emission efficiency values of 1.10, 1.00, and 0.68 respectively.
  • Technological change was identified as the primary contributor to improvements in emission efficiency.
  • Resource redundancy affected carbon emission efficiency, particularly with energy redundancy rates exceeding 20%.
  • Significant positive spatial autocorrelation indicated that regions with high efficiency positively influence neighboring areas.

Abstract

Carbon emission reduction in the construction industry is pivotal for global carbon emission reduction, yet the lack of coordination mechanisms within the sector limits its effectiveness. This study examines the Yangtze River Economic Belt from 2010 to 2022, capturing the spatial and temporal evolution characteristics and key influencing factors of carbon emission efficiency in the construction industry (CEECI) to achieve coordinated emission reduction. Using the super-efficiency Slack-Based Measure (SBM) model and the Malmquist–Luenberger (ML) index, the study analyzes changes in CEECI, revealing significant regional variations: downstream, midstream, and upstream regions demonstrated average values of 1.10, 1.00, and 0.68, respectively. Resource redundancy is a major issue affecting CEECI, with energy redundancy rates exceeding 20%. The ML index indicates continuous improvement in CEECI, with technological change (TC) contributing the most to this improvement, as shown by index decomposition. Spatial analysis using Moran’s index (Moran’s I) revealed significant positive spatial autocorrelation, with distinct “high-high” (H-H) and “low-low” (L-L) clustering patterns, suggesting that regions with high CEECI positively influence their neighbors. Finally, we built a spatial econometric model to identify key influencing factors, including industrialization level, construction industry production level, energy consumption structure, human resources, and internal innovation levels, which directly or indirectly impact CEECI to varying degrees. These findings highlight the importance of regional coordination and targeted policy interventions to enhance carbon emission efficiency in the construction industry, addressing resource redundancy and leveraging technological advancements to contribute to global carbon reduction goals.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/696c77d4eb60fb80d13960b5https://doi.org/10.3390/buildings16020384
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