Unequal considerations of justice in municipal adaptation planning: an assessment of US climate plans over time and by context

ABSTRACT

Climate adaptation planning is increasingly approached locally through a social justice lens to ensure the needs of the most vulnerable are addressed. This study aims to identify trends in how recognitional, distributional, and procedural justice are considered within climate adaptation plans over time and across socio-demographic contexts. We coded these forms of justice in 101 climate adaptation plans and related documents published in the United States between 2010 and 2021 and conducted a series of regressions to understand patterns over time and across contexts. Newer plans more commonly addressed each type of justice, with a marked shift in plans published after 2017. More recent plans addressed new elements of recognitional justice (e.g. historical marginalisation, racial justice), a broader scope of distributional justice approaches (e.g. more strategies related to greenspaces, food, and green jobs), and more procedural justice-related initiatives to engage marginalised residents in adaptation. Plans from more Republican-leaning communities considered recognitional and distributional justice to a lesser degree than those from more Democratic-leaning areas. Plans by larger communities were more likely to address procedural justice and include strategies for monitoring the impacts to marginalised people. Plans from communities with a larger percentage of residents living in poverty addressed distributional justice more often and acknowledged more injustices faced by marginalised groups more often. We observed no trends in the treatment of procedural justice related to racial demographics or income. We discuss potential reasons for these trends and their implications.

KEYWORDS:

• Climate change
• social justice
• climate adaptation plans
• recent trends
• contextual variables

Data availability statement

The data that support the results of this study are publicly available at the Harvard Dataverse through this link: https://doi.org/10.7910/DVN/PRKACP

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Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This material is supported by the National Science Foundation under award numbers 1810851 and 1811534.