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This article examines the relationship between the political fragmentation of cities in metropolitan regions, the distribution of social vulnerability, and the city-level economic and social sustainability strategies they adopt. Strategies emerge from prevailing community norms, and polycentric governance arrangements can support conditions in which both economic and social sustainability strategies emerge as compliments, contrary to the concern that fragmentation spurs zero-sum competition. Combining surveys of U.S. cities with social vulnerability data and text analysis of planning documents, we find that greater fragmentation has a negative impact on the sustainable development strategies cities adopt. However, growth and sustainable development strategies tend to develop alongside social sustainability efforts to address human needs. We conclude that development strategies emerge in polycentric systems in relation to the degree of fragmentation which exists, and that subsequent work should continue to focus on identifying these entropic thresholds in order to effectively address lingering inequities. 

Abstract Affordable housing that incorporates sustainability goals into its design has the potential to address both health and economic disparities via enhanced energy‐efficiency, structural durability and indoor environmental quality. Despite the potential for these win‐win advances, survey data of U.S. local governments indicate these types of equity investments remain rare. This study explores barriers and pathways to distributional equity via energy‐efficient housing. Using archival city sustainability survey data collected during a period of heightened U.S. federal investment in local government energy‐efficiency programs, we combine machine learning (ML) and process‐tracing approaches for modeling the complex drivers and barriers underlying these decisions. First, we ask, how do characteristics of a city's organizational learning methods—its administrative structure, past experience with housing programs, resources, stakeholder engagement and planning—predict policy commitments to green affordable housing? Using ensemble ML methods, we find that three specific modes of organizational learning—past experience with affordable housing programs, seeking assistance from neighborhood groups and the technical expertise of professional green organizations—are the most impactful features in determining city commitments to constructing green affordable housing. Our second stage uses process‐tracing within a specific case identified by the ML models to determine the ordering of these factors and to provide more nuance on green‐housing policy implementation.