Authors

Rachel Shen, Z. Quinn Shen, Keyang Yan

Research Question

Can locally generated rooftop solar electricity help offset New York City's growing energy demand for cooling under future climate conditions? This project combines geospatial analytics, climate projections and urban morphology data to show where rooftop PV delivers the greatest benefit and where other adaptation or energy strategies may be needed.

Background

New York City has set ambitious climate goals, including deploying 1,000 MW of solar capacity by 2030 and reaching carbon neutrality by 2050. Rooftop solar is expected to play a central role in cutting emissions, lowering electricity costs and increasing grid resilience. At the same time, climate change is projected to bring more frequent and intense hot weather, raising cooling demand and straining the electricity system during summer peaks.

Rooftop solar potential and future energy demand are usually studied separately, but effective planning requires understanding how they interact. Inspired by the European Space Agency's Green Transition Information Factories (GTIFs), the project shows how Earth Observation, climate projections and urban data can become decision-ready information for planners, policymakers and utilities.

Methodology

The project involves:

• Estimating building-scale solar potential using 3D urban morphology and solar radiation modeling that accounts for building geometry and shading.
• Deriving atmospheric conditions from the Copernicus Atmosphere Monitoring Service and meteorological information from reanalysis datasets and weather stations.
• Using CMIP6 climate model projections to examine how future climate may affect both solar production and cooling needs.
• Quantifying future cooling demand with Cooling Degree Days (CDD).
• Jointly analyzing rooftop PV generation and projected cooling demand to evaluate the contribution of distributed solar.
• Integrating geospatial, climate, satellite and socioeconomic data using GIS, Python, Google Earth Engine and data science techniques, and exploring machine learning and spatial clustering methods.

Deliverables

• Geospatial Framework: A workflow assessing rooftop PV potential and future cooling demand across New York City.
• Spatial Assessment: A spatially explicit analysis of where rooftop PV can deliver the greatest benefits and where additional strategies are needed.
• Decision-Support Information: GTIF-inspired outputs that help planners, policymakers and utility stakeholders prioritize investments.

Data Sources

• 3D urban morphology / building geometry data
• Copernicus Atmosphere Monitoring Service (atmospheric conditions)
• Reanalysis datasets and weather station records (meteorology)
• CMIP6 climate model projections
• Satellite observations
• Socioeconomic indicators