Regional solutions for growing quality green jobs
Our Approach
To support regional leaders, we’ve outlined a data-informed approach to understanding and articulating a region’s readiness to develop an economy that generates and sustains quality green jobs. By leveraging publicly available data at the county level, we identified trends that can inform a regional approach for a more sustainable tomorrow.
Our Methods
Our methodology was designed to provide a holistic understanding of U.S. economic and climate resilience. We took into account the risks from climate change as well as the readiness to make a transformative shift toward green job opportunities while maintaining an overarching focus on equitable workforce transitions.
We focused our analysis on four dimensions that impact economic and climate resilience:
![iStock-1278858409](https://info.jff.org/hs-fs/hubfs/iStock-1278858409.jpg?width=2000&name=iStock-1278858409.jpg)
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Climate patterns
Source: National Risk Index, FEMA.
Indicator
VariableHistorical frequency of
adverse weather
Coastal flooding frequency
Cold wave frequency
Drought frequency
Heat wave frequency
Hurricane frequency
Riverine flooding frequency
Tornado frequency
Wildfire frequency
Winter weather frequencyRisk of adverse weather
Coastal flooding risk
Cold wave risk
Drought risk
Heat wave risk
Hurricane risk
Riverine flooding risk
Tornado risk
Wildfire risk
Winter weather riskEconomic activity exposed
to adverse weather
Coastal flooding exposure
Cold wave exposure
Drought exposure
Heat wave exposure
Hurricane exposure
Riverine flooding exposure
Tornado exposure
Wildfire exposure
Winter weather exposureAnnual economic loss
from adverse weather
Coastal flooding annual loss
Cold wave annual loss
Drought annual loss
Heat wave annual loss
Hurricane annual loss
Riverine flooding annual loss
Tornado annual loss
Wildfire annual loss
Winter weather annual loss -
Social vulnerability
Sources: National Risk Index, FEMA;* Agency for
Toxic Substances and Disease Registry, CDC;# US Census Bureau.^
Indicator
VariableCommunity health
Community resilience score*
Community risk factor score*Resilience
Population with income below 150% poverty rate#
Population with housing-cost burden#Marginalization
Percent disabled^
Percent single-parent households^
Percent minority^
Percent with limited English proficiency^Vulnerability
Percent without high school diploma^
Percent uninsured#
Percent unemployed^Note: JFF strives to use equitable and inclusive language in all our published content. When we share insights or data from individuals or organizations, such as federal agencies whose language choices differ from our own, we use their terms to preserve accuracy. See our Language Matters Guide for more information.
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Local labor market
Source: The Burning Glass Institute.
Indicator
Variable
Prevalence of green skills
Green skills share
in job postings
Demand for green skills
Green skills rate
in job postings
Growth in prevalence of green skills
Change in green skills share in
job postings, 2018-2022
Growth in demand for green skills
Change in green skills rate in
job postings, 2018-2022 -
Political landscape
Sources: Yale Climate Opinion Maps*, C40 Cities#, Center for Climate and Energy Solutions%, state legislatures^.
Indicator
Variable
Support for local officials’ green initiatives
Percent surveyed that think local officials should do more to fight climate change*State policy opportunities
Number of mayors in the state that have committed to local climate action plans#
Number of state action plans to address climate change%Number of greenhouse gases emission targets in state legislature^
Number of renewable energy efficiency incentives in state legislature^
Support for green business practices
Percent surveyed who think local businesses and business practices contribute to global warming*
Global warming experienced by constituents
Percent surveyed who are experiencing global warming*
Drawing from a diverse set of data repositories, including U.S. census records, climate observations, job market trends, and community surveys, we leveraged machine learning methodologies, such as factor analysis, to identify the key variables for each dimension. We selected counties as our unit of analysis for a level of actionable granularity that could be scaled up into a regional approach.
Following that analysis, we determined risk and readiness scores for each county relative to the average of counties across the nation. We used weighted averages to express both risk and readiness, which allow for all the determinants to be factored into both scores while also balancing their relative impact on the region.
Risk reflects a county’s vulnerability to climate-related challenges based on its climate patterns and social vulnerability indicators.
Readiness signifies a county’s preparedness to address such challenges based on their local labor market and political landscape indicators.
![Critical Critical](https://info.jff.org/hs-fs/hubfs/Critical.png?width=120&height=120&name=Critical.png)
Critical
![Primed Primed](https://info.jff.org/hs-fs/hubfs/Primed.png?width=120&height=120&name=Primed.png)
Primed
![Exposed Exposed](https://info.jff.org/hs-fs/hubfs/Exposed.png?width=120&height=120&name=Exposed.png)
Exposed
![Early Early](https://info.jff.org/hs-fs/hubfs/Early.png?width=120&height=120&name=Early.png)
Early
Assessing counties in relation to one another not only provides a nuanced comparative understanding of each county’s economic and climate resilience but also serves as a vital compass for identifying strategies that match their unique characteristics.
Why a Data-Informed Approach?
In the United States, efforts to strengthen resilience to climate change are often shaped by a crisis response to extreme weather disasters rather than by proactive planning. The nation’s costliest natural disaster to date was Hurricane Katrina in 2005. In terms of community impact, 40% of the 1.5 million residents in Louisiana, Mississippi, and Alabama who were forced to evacuate did not return to their homes. In the city of New Orleans specifically, nearly 100,000 residents lost their jobs in the months following the storm, with thousands more left homeless, the majority of whom were Black. As with many climate emergencies, Hurricane Katrina had disproportionate impacts on people of color and people from low-income households. However, the hurricane’s devastating legacy also shaped the way New Orleans and the surrounding regions later responded to and prepared for extreme weather through investment in jobs that helped to strengthen and improve flood-protection systems.
More recently, the record-setting polar vortex that hit Texas and surrounding southern states in 2021 also left many regions in shock. Over 13 million people lost electricity and 250 people died due to the extreme weather, which also caused nearly $100 billion in economic damages. Despite the mixed commitment and, at times, opposition to climate preparedness steps at the state level, the impacts of this storm spurred discussions about implementing stronger measures to enhance the state’s energy infrastructure, investing more in renewable energy resources, and subsequently expanding green jobs that will help these regions be better prepared for more extreme weather in the future.
Crises like hurricanes and polar vortexes can accelerate these climate-readiness steps in the short term, highlighting the need to act quickly and build economic resilience for unexpected extreme weather events. In these cases, such reactive planning can even build momentum toward lasting change. But recognizing your region’s lower readiness earlier can potentially lead to more significant, and even life-saving outcomes. Using available tools and strategies to examine local political readiness and social vulnerability can put regions on a path to proactive planning for creating a sustainable economy with quality green jobs and more effective responses to a wide range of climate emergencies. Weighing the different factors as they apply to your local area can support tailored strategies to achieve maximum impact where it is most needed. Significant, sometimes systemic, changes in the service of communities can be difficult, but there is always a first step to be taken—and data can help.
Contributors:
Alessandro Conway, Julia Delgado, Lee Domeika, Molly Dow, Meena Naik, Marymegan Wright
Special Thanks To:
Raymond Barbosa, Sarah Bennett, Bryan Egan, Carol Gerwin, Tyler Nakatsu, Carlin Praytor