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

Year: 2026

Type: article

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.

Source: Buildings

DOI: 10.3390/buildings16020384

Authors: Min Chen, Shuqi Fan, Yuan Gao, Vishwa Akalanka Udaya Bandara Konara Mudiyanselage, Lili Zhang

Institutions: Nantong University, Tongji University

Language: English

Topic: Efficiency Analysis Using DEA

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDGs: Climate action, Industry, innovation and infrastructure, Sustainable cities and communities, Responsible consumption and production

Open Access status: gold

Estimated APC paid: $3,137