Search for: All records

Implicit in cities' decisions to engage in climate actions is the expectation that these efforts will result in significant greenhouse gas emissions reductions. Little research, however, has investigated this presumed cause and effect relationship in a manner that is both empirical and generalizable. This, in turn, challenges the design of evidence-based policy recommendations. We apply a difference-in-differences (DiD) approach to examine the impact that two frequently recommended capacity-building interventions—completing a greenhouse gas emissions (GHG) inventory and employing dedicated sustainability staff—have on the fossil fuel carbon dioxide (FFCO2) emissions of the cities that have undertaken them. This is enabled by the construction of a unique dataset that combines city-level FFCO2 emission estimates in 2010 and 2015 with multi-year survey based data indicating the adoption of relevant local policy actions. Findings offer evidence that the completion of a community-wide emissions inventory facilitates local action that, even in a relatively short timeframe, significantly reduces emissions from onsite residential sources. 

Although they play a key role in shaping local efforts, there is limited research on how city officials define sustainability as it is practiced in their communities. To address this limitation and contribute to planning research, we leveraged a unique nationwide data set of sustainability definitions provided by the sustainability lead in more than 400 U.S city governments. Our study’s qualitative analysis of these statements complements existing research by exploring emerging themes on how sustainability is perceived and practiced at the local level. Results indicated that practitioners’ conceptualizations reflected five general orientation categories: action, aspiration, emotion, process and organization, and progress. We evaluated the association of these general orientation categories with cities’ administrative arrangements, political environments, resources, and capacities. Findings suggested that supportive contexts were associated with city sustainability staff defining sustainability in aspirational terms, as opposed to emphasizing progress or discrete actions. 

Although many U.S. municipalities have adopted climate protection and sustainability as explicit objectives, they are not among their traditional responsibilities. As a result, compared to policies focused around core functions, those related to sustainability may be at greater risk of retrenchment or change in times of crisis. This research examines how the COVID-19 pandemic has impacted local governments’ sustainability efforts. Using data from a nation-wide survey, we examine the degree to which the pandemic has affected programmatic priorities, resources, and operations related to sustainability. Findings indicate that the pandemic hurt the implementation of sustainability initiatives in almost half of U.S. cities. At the same time, many cities increased the priority of economic and social sustainability initiatives in response to the pandemic. Cities which have formally included sustainability principles into a city plan appear more sensitive to COVID-induced challenges to their program operations. 

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.