---
title: Our Approach
description: A data-informed approach to understanding and articulating a region’s readiness to develop an economy that generates and sustains quality green jobs.
---

###### Regional solutions for growing quality green jobs

---

## **Our Approach**

- #### Contents

### **Informing an Approach for Regional Action**

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)

 

- #### Climate patterns
  
   
  
  | ** Indicator ** | Variable  |
  | --- | --- |
  | **Historical 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 frequency  |  |
  | **Risk 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 risk  |  |
  | **Economic 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 exposure  |  |
  | **Annual 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
  
   
  
  | ** Indicator ** | Variable  |
  | --- | --- |
  | **Community 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](https://info.jff.org/language-matters) for more information. 
- #### Local labor market
  
   
  
  | ** 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
  
   
  
  | ** 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 and Readiness Icons -02](https://info.jff.org/hs-fs/hubfs/Risk%20and%20Readiness%20Icons%20-02.jpg?width=150&height=150&name=Risk%20and%20Readiness%20Icons%20-02.jpg)**Risk** reflects a county’s vulnerability to climate-related challenges based on its climate patterns and social vulnerability indicators.

**![Risk and Readiness Icons -01](https://info.jff.org/hs-fs/hubfs/Risk%20and%20Readiness%20Icons%20-01.jpg?width=150&height=150&name=Risk%20and%20Readiness%20Icons%20-01.jpg)Readiness **signifies a county’s preparedness to address such challenges based on their local labor market and political landscape indicators.

Each county was then categorized into one of four distinct types according to their risk level and their readiness to act on green transformational opportunities—[Critical](https://info.jff.org/assessing-regional-readiness-for-action?hs_preview=KwcEIVtq-150882346027#critical), [Primed](https://info.jff.org/assessing-regional-readiness-for-action?hs_preview=KwcEIVtq-150882346027#primed), [Exposed](https://info.jff.org/assessing-regional-readiness-for-action?hs_preview=KwcEIVtq-150882346027#exposed), and [Early](https://info.jff.org/assessing-regional-readiness-for-action?hs_preview=KwcEIVtq-150882346027#early). 

![Critical](https://info.jff.org/hs-fs/hubfs/Critical.png?width=120&height=120&name=Critical.png "Critical")

##### Critical

![Primed](https://info.jff.org/hs-fs/hubfs/Primed.png?width=120&height=120&name=Primed.png "Primed")

##### Primed

![Exposed](https://info.jff.org/hs-fs/hubfs/Exposed.png?width=120&height=120&name=Exposed.png "Exposed")

##### Exposed

![Early](https://info.jff.org/hs-fs/hubfs/Early.png?width=120&height=120&name=Early.png "Early")

##### 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](https://www.weather.gov/mob/katrina) 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.](https://guides.lib.lsu.edu/Hurricanes/KatrinaCommunities#:~:text=Thousands%20were%20left%20homeless%2C%20and,thousands%20of%20homes%20were%20flooded) 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.](https://guides.lib.lsu.edu/Hurricanes/KatrinaCommunities#:~:text=Thousands%20were%20left%20homeless%2C%20and,thousands%20of%20homes%20were%20flooded) 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](https://www.washingtonpost.com/weather/2021/08/28/hurricane-katrina-orleans-rebuilt-photos/). 

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](https://iopscience.iop.org/article/10.1088/2516-1083/aca9b4/pdf). 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](https://www.forbes.com/sites/energyinnovation/2023/07/16/renewables-and-storage-got-texas-grid-through-this-heat-wave-but-the-state-legislature-still-hasnt-fixed-its-underlying-problems/?sh=6e05f3825a6d), 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.  

##### **[Our Approach](https://info.jff.org/our-method?hs_preview=sajEMdqw-150653952141)**

##### **[Our Approach](https://info.jff.org/our-method?hs_preview=sajEMdqw-150653952141)**

![Group Collaboration Post It Notes](https://info.jff.org/hs-fs/hubfs/Group%20Collaboration%20Post%20It%20Notes.jpg?width=2000&name=Group%20Collaboration%20Post%20It%20Notes.jpg)

##### **[Assess Your Region](https://info.jff.org/assessing-regional-readiness-for-action?hs_preview=KwcEIVtq-150882346027)**

##### **[Assess Your Region](https://info.jff.org/assessing-regional-readiness-for-action?hs_preview=KwcEIVtq-150882346027)**

![Office Space Collaboration](https://info.jff.org/hs-fs/hubfs/Office%20Space%20Collaboration.jpg?width=2000&name=Office%20Space%20Collaboration.jpg)

##### **[Recommendations](https://info.jff.org/recommendations-and-actions-for-regional-leaders)**

##### **[Recommendations](https://info.jff.org/recommendations-and-actions-for-regional-leaders)**

![Team Meeting Conversation](https://info.jff.org/hs-fs/hubfs/Team%20Meeting%20Conversation.jpg?width=2000&name=Team%20Meeting%20Conversation.jpg)

##### **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

![](https://px.ads.linkedin.com/collect/?pid=3117020&fmt=gif) ![](https://px.ads.linkedin.com/collect/?pid=3117020&fmt=gif) ![](https://px.ads.linkedin.com/collect/?pid=3117020&fmt=gif)

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