• A web-based framework to rapidly generate urban building energy models was proposed. • Key stakeholders and the workflow to translate open datasets to urban building energy models and actionable insights for carbon reduction were described. • The framework is scalable, able to construct urban building geometries, assign build simulation templates, and incorporate modern open-source machine learning libraries. • The framework was first tested in a pilot study, and subsequently validated with policymakers and researchers in eight cities around the world. Policymakers are struggling to understand what specific mixes of building retrofitting upgrades are necessary to achieve carbon emission targets. Urban building energy modeling (UBEM) is a bottom-up simulation method to develop policy measures for building stocks. However, the use of UBEM tools requires hard-to-find individuals with training in multiple domains and significant setup time exacerbated through a lack of standardized building use and construction databases. To address these challenges, this paper presents UBEM.io, a novel web-based framework to rapidly generate UBEMs in an automated and scalable fashion with clear handover points. UBEM.io is designed to help any city or municipality conduct low-cost, rapid energy and carbon emissions scenario studies. UBEM.io contributes to practice and research in multiple ways: automated generation of urban-scale building geometries based on widely available inputs; assignment of building simulation templates from a pre-populated library; matching of templates to individual buildings via archetypes; and visualization of simulation results for various carbon emissions reduction pathways. The framework was piloted in Evanston, IL (USA) to build an UBEM comprising 1,363 buildings. It was then successfully deployed with representatives from eight municipalities: Braga (Portugal), Cairo (Egypt), Dublin (Ireland), Florianopolis (Brazil), Kiel (Germany), Middlebury, VT (USA), Montreal (Canada), and Singapore.