Decarbonization of the Building Sector Crucial for Global Carbon Neutrality

Strategic Insights -

The building sector is responsible for a significant portion of total greenhouse gas emissions worldwide, accounting for 37% of global emissions. While operational carbon emissions have decreased due to energy efficiency advancements, embodied carbon emissions remain challenging to address due to their complexity across multiple building lifecycle stages. Researchers at Tsinghua University have conducted a systematic review of 7,731 publications to identify methodological innovations and effective reduction strategies. Their findings emphasize the need for hybrid machine learning-multi-criteria decision-making frameworks with enhanced explainability to deliver actionable insights for embodied carbon emissions reduction in diverse contexts.

Key Takeaways:

  • The building sector is crucial for achieving global carbon neutrality, as it constitutes 37% of total greenhouse gas emissions worldwide.
  • Embodied carbon emissions (ECE) remain challenging to address due to their complexity across multiple building lifecycle stages.
  • Conventional Life Cycle Assessment (LCA) faces limitations in dynamic scenarios, regional variability, and system boundary inconsistencies.
  • Machine learning (ML)-driven methodologies for dynamic assessment and reduction are pivotal in addressing ECE, leveraging three fundamental learning paradigms (supervised, unsupervised, and reinforcement learning) to automate predictive modeling, uncertainty quantification, and design optimization across the building lifecycle.
  • Multi-criteria decision-making (MCDM)-based frameworks are essential in resolving the carbon-performance-cost trilemma and reducing ECE while maintaining building performance.
  • Case studies from diverse geographical contexts highlight the necessity of context-sensitive strategies for localized decarbonization pathways.
  • Key challenges include data heterogeneity and standardization gaps, interpretability limitations of advanced ML algorithms, and geospatial adaptation of MCDM frameworks.
  • The research concluded that future research should prioritize the development of interpretable ML models, establishing unified protocols for ECE accounting across diverse contexts, and fostering cross-disciplinary partnerships.
  • The study's findings strengthen the need for hybrid ML-MCDM frameworks to address the complexity of ECE reduction in diverse contexts.

Statistics:

  • The building sector accounts for 37% of total greenhouse gas emissions worldwide.
  • The researchers conducted a systematic review of 7,731 publications.
  • 159 high-impact studies were ultimately analyzed.
  • The review employed bibliometric analysis and qualitative content analysis to identify methodological innovations and effective reduction strategies.
  • The study found a notable shift from traditional LCA approaches toward ML-driven methodologies for dynamic assessment and reduction.
  • Key findings emphasize the need for hybrid ML-MCDM frameworks with enhanced explainability that deliver actionable insights for ECE reduction in diverse contexts.

Sources:

  • Assessment and Reduction of Embodied Carbon Emissions In Buildings: a Systematic Literature Review of Recent Advances. Energy and Buildings, 2025;345.
  • Elsevier Science Sa, PO Box 564, 1001 Lausanne, Switzerland. (Elsevier - www.elsevier.com; Energy and Buildings - www.journals.elsevier.com/energy-and-buildings/)
  • Tsinghua University, Building Sciences Department, Beijing 100084, People's Republic of China.