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<p>The construction sector represents approximately 40% of total CO<sub>2</sub> emissions and 30–40% of total energy consumption, presenting critical challenges for achieving international climate targets. While material selection fundamentally influences both environmental footprint and energy performance, existing frameworks employ fragmented approaches, such as isolated Multi-Criteria Decision Making (MCDM), Life Cycle Assessment (LCA), or Building Information Modeling (BIM) methodologies, that fail to optimize environmental, economic, and technical dimensions simultaneously. This study addresses these limitations by developing an integrated intelligent framework that synergistically combines multi-objective Particle Swarm Optimization (MO-PSO) with BIM-compatible energy simulation to identify optimal material configurations across diverse climate zones. The methodology was validated through a comprehensive case study of an educational building (3,600&#xa0;m²) evaluated under MENA (hot-arid), Miami (hot-humid), and Stockholm (cold) climate conditions. Results demonstrate that the optimized configuration incorporating Cross-Laminated Timber (3.2&#xa0;cm), phase change materials (Bio-based M182/Q23), and recycled aggregates with fly ash (7.9&#xa0;cm), achieves remarkable performance improvements: 50.37% reduction in CO<sub>2</sub> emissions, 29.25% decrease in cooling load, 26.81% reduction in heating load, and 11.34% improvement in total energy consumption compared to conventional construction. Sensitivity analysis reveals that wall assembly configuration (68–78% sensitivity index) has a 5–10 times greater impact than operational parameters, confirming that integrated envelope design optimization offers substantially greater decarbonization potential than building operation strategies. This comprehensive approach provides actionable pathways toward net-zero carbon buildings aligned with Sustainable Development Goals (SDGs).</p>

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Developing intelligent material selection framework for energy management and CO2 footprint reduction in wooden buildings: a case study

  • 期刊论文

  • 发表日期：

    2026年3月26日

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  • Jin Wang,
  • Li Yin,
  • Haibin He

摘要

The construction sector represents approximately 40% of total CO2 emissions and 30–40% of total energy consumption, presenting critical challenges for achieving international climate targets. While material selection fundamentally influences both environmental footprint and energy performance, existing frameworks employ fragmented approaches, such as isolated Multi-Criteria Decision Making (MCDM), Life Cycle Assessment (LCA), or Building Information Modeling (BIM) methodologies, that fail to optimize environmental, economic, and technical dimensions simultaneously. This study addresses these limitations by developing an integrated intelligent framework that synergistically combines multi-objective Particle Swarm Optimization (MO-PSO) with BIM-compatible energy simulation to identify optimal material configurations across diverse climate zones. The methodology was validated through a comprehensive case study of an educational building (3,600 m²) evaluated under MENA (hot-arid), Miami (hot-humid), and Stockholm (cold) climate conditions. Results demonstrate that the optimized configuration incorporating Cross-Laminated Timber (3.2 cm), phase change materials (Bio-based M182/Q23), and recycled aggregates with fly ash (7.9 cm), achieves remarkable performance improvements: 50.37% reduction in CO2 emissions, 29.25% decrease in cooling load, 26.81% reduction in heating load, and 11.34% improvement in total energy consumption compared to conventional construction. Sensitivity analysis reveals that wall assembly configuration (68–78% sensitivity index) has a 5–10 times greater impact than operational parameters, confirming that integrated envelope design optimization offers substantially greater decarbonization potential than building operation strategies. This comprehensive approach provides actionable pathways toward net-zero carbon buildings aligned with Sustainable Development Goals (SDGs).