Keeping policy commitments: An organizational capability approach to local green housing equity

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.

Indoor Environmental Quality (IEQ) and Sustainable Development Goals (SDGs): Technological Advances, Impacts and Challenges in the Management of Healthy and Sustainable Environments

“…Ahn et al [16] note that applying technologies, advanced methodologies, tools and high-performance workstations faces significant challenges, especially related to financial costs. In many places, these approaches are unfeasible due to the primary conditions in which people live in situations of vulnerability, making facilities such as air-conditioning systems inaccessible, for example [10,139,140]. Furthermore, implementing strategies aligned with the SDGs is not trivial, as Luerssen et al highlight [141].…”

“…According to Deslatte et al [10], governments in different nations are exploring methods to foster sustainability. For example, in the United States, investments have been directed toward renovating "green" or energy-efficient homes, addressing social inequalities and climate change, and promoting economic and infrastructural recovery.…”

Perception and performance in environmental policy

“…Machine learning techniques frequently find entry into policy process research further shown in the article by Deslatte et al (2024). Public policy research on climate change is often focused on policy measures in environmental and energy policy of which energy-efficient housing addresses many challenges at the same time.…”