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Keeping policy commitments

An organizational capability approach to local green housing equity

Bibliographic Data

ID4073929
AuthorsAaron Deslatte (0000-0001-5518-3949, Indiana University Bloomington, corresponding author), Serena Kim, Serena Y Kim (0000-0003-3839-3674, University of Denver), Christopher V Hawkins (0009-0005-8709-8892, University of Central Florida), Eric Stokan (0000-0001-8409-5016, University of Maryland, Baltimore County)
Year2024
Volume41
Issue1
Pages135-159
Publication date2024-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueReview of Policy Research (JOURNAL)
Journal identifiersISSN: 1541-132X • E-ISSN: 1541-1338
PublisherWiley (PUBLISHER • GB)
DOI10.1111/ropr.12499
OpenAlexW4289766678
LanguageEN
Citations received3
References cited71

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

Affordable housing · Business · Economic growth · Economics · Environmental economics · Local government · Political science · Public economics · Public relations · Stakeholder · Stakeholder engagement · Sustainability · Housing Market and Economics · Public Administration · Sustainable Building Design and Assessment · Urban Planning and Valuation

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Unique citing works3
Citations per year1
Citation span2023 - 2025 (3)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 3

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