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<p>The construction industry faces the challenge of decarbonization. Integrating manufacturing principles and Artificial Intelligence (AI) offers a promising pathway to reduce CO<sub>2</sub> emissions, specifically by integrating CO<sub>2</sub>-emission variables into AI-driven production schedules. However, transparency to users is essential, as human users remain ultimately responsible for production outcomes. This requirement can be met through Explainable AI (XAI), which aims to provide transparency for end users. However, defining an appropriate XAI approach requires understanding problem- and industry-specific variables. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this study examines the state of the art in XAI literature to identify research gaps and formulate actionable recommendations. The study provides insights for developing an XAI approach to support the decarbonization of housing manufacturing explicitly. The key findings highlight the need for user-centric and industry-specific frameworks and the importance of clearly defining the XAI-AI relationship. Finally, this research synthesizes these findings into a roadmap to guide future research on XAI for the decarbonization of housing manufacturing.</p>

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Explainable AI for CO2 emissions reduction in housing manufacturing: a review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月13日

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  • Miguel Mora,
  • Pingbo Tang

摘要

The construction industry faces the challenge of decarbonization. Integrating manufacturing principles and Artificial Intelligence (AI) offers a promising pathway to reduce CO2 emissions, specifically by integrating CO2-emission variables into AI-driven production schedules. However, transparency to users is essential, as human users remain ultimately responsible for production outcomes. This requirement can be met through Explainable AI (XAI), which aims to provide transparency for end users. However, defining an appropriate XAI approach requires understanding problem- and industry-specific variables. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this study examines the state of the art in XAI literature to identify research gaps and formulate actionable recommendations. The study provides insights for developing an XAI approach to support the decarbonization of housing manufacturing explicitly. The key findings highlight the need for user-centric and industry-specific frameworks and the importance of clearly defining the XAI-AI relationship. Finally, this research synthesizes these findings into a roadmap to guide future research on XAI for the decarbonization of housing manufacturing.