Generative AI for ESG Reporting, Net-Zero Planning and Sustainability Analytics

The aim of this workshop is to help participants understand how AI, digital twins, simulation, and dashboards can be used to support climate-resilient sustainability planning for buildings, campuses, industrial facilities, and urban infrastructure.

• Introduce the concept of digital twins for sustainable buildings, campuses, industrial systems, and urban facilities.
• Explain how AI supports climate-resilient infrastructure planning and sustainability analytics.
• Help participants identify key sustainability indicators such as energy use, carbon impact, water consumption, resource efficiency, and operational performance.
• Build understanding of climate-risk indicators such as heat stress, flooding risk, air quality, extreme weather, and infrastructure disruption.
• Demonstrate how simulation tools can be used for climate-impact and resource-flow analysis.
• Enable participants to perform basic sustainability scenario analysis using Python and Google Colab.
• Introduce QGIS-based spatial visualization for climate-risk and sustainability mapping.
• Guide participants in creating dashboard indicators for energy demand, carbon impact, resilience score, climate risk, and operational efficiency.

• Students and postgraduate learners in engineering, environmental science, sustainability, data science, urban planning, and related fields
• PhD scholars and researchers working in sustainability, climate resilience, smart infrastructure, ESG, energy systems, and environmental analytics
• Academicians and faculty members interested in AI-enabled sustainability education and research
• Sustainability professionals, ESG analysts, and net-zero planning professionals
• Civil, environmental, energy, industrial, and infrastructure engineers
• Urban planners, facility managers, and smart city professionals
• Data science and AI learners interested in real-world sustainability and climate-risk applications

• Explain the role of digital twins in climate-resilient sustainability planning.
• Identify physical system components, virtual model components, data layers, and decision layers in a digital twin framework.
• Prepare a sustainability and climate-risk indicator matrix for a building, campus, or industrial facility.
• Use Python and Google Colab for basic climate and sustainability data analysis.
• Understand how simulation can support sustainability planning and climate-impact assessment.
• Build basic climate-impact scenarios such as heatwave stress, energy-demand variation, flooding disruption, or operational load changes.
• Interpret simulation outputs for resilience and sustainability decision-making.
• Use QGIS for spatial climate-risk and sustainability visualization.