Estimating future energy use and CO₂ emissions of the world's cities.

This paper develops a tool for estimating energy-related CO2 emissions from the world's cities based on regression models. The models are developed considering climatic (heating-degree-days) and urban design (land area per person) independent variables. The tool is applied on 3646 urban areas for estimating impacts on urban emissions of a) global transitioning to Electric Vehicles, b) urban density change and c) IPCC climate change scenarios. Results show that urban density decline can lead to significant increase in energy emissions (upto 346% in electricity & 428% in transportation at 2% density decline by 2050). Among the IPCC climate scenarios tested, A1B is the most effective in reducing growth of emissions (upto 12% in electricity & 35% in heating). The tool can further be improved by including more data in the regression models along with inclusion of other relevant emissions and climatic variables.