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<p>This study adopts a life-cycle approach to analyze carbon emissions in Shandong’s construction sector. It integrates emission factors, decoupling analysis, LMDI decomposition, extended STIRPAT modeling, and machine learning to identify key drivers and trends. The results show that emissions rose from 115.98 MtCO₂ in 2005 to 315.08 MtCO₂ in 2021, with building material production and building operation as the main sources. LMDI shows per capita GDP as the main driver, with land-use efficiency reducing emissions. The ridge-corrected STIRPAT model suggests that population size, labor productivity, and carbon intensity significantly increase emissions, whereas area efficiency helps suppress them. In forecasting, the STIRPAT-PCA-WOA-SVR model demonstrated the best accuracy, with R² reaching 0.98 and MAPE at 2.21%. This study underscores the importance of advancing decarbonization in both the material supply chain and residential operations. At the policy level, it calls for accelerating the promotion and application of low-carbon building materials, expediting the renovation and electrification of existing buildings, and systematically integrating nature-based solutions into urban planning to achieve both emission reduction and enhanced livability. The findings provide scientific evidence and practical insights to support the green transition in Shandong and other carbon-intensive regions.</p>

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Drivers and forecasting of carbon emissions in the construction sector of Shandong Province, China: a life cycle perspective

  • 期刊论文

  • 发表日期：

    2026年2月28日

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  • Xuanhao Zhang,
  • Kexin Zhao,
  • Yubo Jin,
  • Meng Yuan,
  • Xinshuo Zhang,
  • Chuanqi Liu

摘要

This study adopts a life-cycle approach to analyze carbon emissions in Shandong’s construction sector. It integrates emission factors, decoupling analysis, LMDI decomposition, extended STIRPAT modeling, and machine learning to identify key drivers and trends. The results show that emissions rose from 115.98 MtCO₂ in 2005 to 315.08 MtCO₂ in 2021, with building material production and building operation as the main sources. LMDI shows per capita GDP as the main driver, with land-use efficiency reducing emissions. The ridge-corrected STIRPAT model suggests that population size, labor productivity, and carbon intensity significantly increase emissions, whereas area efficiency helps suppress them. In forecasting, the STIRPAT-PCA-WOA-SVR model demonstrated the best accuracy, with R² reaching 0.98 and MAPE at 2.21%. This study underscores the importance of advancing decarbonization in both the material supply chain and residential operations. At the policy level, it calls for accelerating the promotion and application of low-carbon building materials, expediting the renovation and electrification of existing buildings, and systematically integrating nature-based solutions into urban planning to achieve both emission reduction and enhanced livability. The findings provide scientific evidence and practical insights to support the green transition in Shandong and other carbon-intensive regions.