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<p>Traditional Sustainable Development Goals (SDGs) evaluations, based on administrative units, lack the granularity required for effective urban interventions. This research emphasizes equity and sustainability, addressing how concepts like the 15-minute city often overlook accessibility barriers for all residents. A data-driven approach was used to generate building-level sustainability scores (BLSS) in Hong Kong, integrating equity within the 15-minute city framework. A novel simulation framework analyzes the impact of infrastructure development scenarios on BLSS across SDG indicators. Geospatial analysis calculates detailed sustainability scores, while statistical measures like Gini coefficients identify inequities and guide infrastructure improvements. Machine learning simulations explore future urban development scenarios, enabling proactive planning. BLSS, derived from a dataset covering over 40,000 residential buildings and 100 government departments, is scaled and aggregated to compare SDG indices at neighborhood, district, regional, and city-wide levels. This framework equips urban planners and policymakers with a robust tool for data-driven decision-making, fostering equitable and sustainable urban growth.</p>

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Actionable insights for equitable urban sustainability transition through building-level assessment of sustainable development goals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月5日

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  • Apoorva Maheshwari,
  • Shauhrat S. Chopra

摘要

Traditional Sustainable Development Goals (SDGs) evaluations, based on administrative units, lack the granularity required for effective urban interventions. This research emphasizes equity and sustainability, addressing how concepts like the 15-minute city often overlook accessibility barriers for all residents. A data-driven approach was used to generate building-level sustainability scores (BLSS) in Hong Kong, integrating equity within the 15-minute city framework. A novel simulation framework analyzes the impact of infrastructure development scenarios on BLSS across SDG indicators. Geospatial analysis calculates detailed sustainability scores, while statistical measures like Gini coefficients identify inequities and guide infrastructure improvements. Machine learning simulations explore future urban development scenarios, enabling proactive planning. BLSS, derived from a dataset covering over 40,000 residential buildings and 100 government departments, is scaled and aggregated to compare SDG indices at neighborhood, district, regional, and city-wide levels. This framework equips urban planners and policymakers with a robust tool for data-driven decision-making, fostering equitable and sustainable urban growth.