The global shift towards green energy, necessary to mitigate climate change, poses a significant challenge: the potential displacement of millions of fossil fuel workers. The US alone faces the prospect of 1.7 million job losses. This necessitates a "Just Transition" – a policy framework aiming to provide support for affected workers to find new employment. Green jobs are often presented as a solution, with initiatives like the Biden administration's Inflation Reduction Act aiming to create millions of green jobs. However, the success of this strategy depends on two crucial factors: the transferability of skills from fossil fuel jobs to green jobs and the geographical co-location of displaced workers and new job opportunities. This paper investigates these factors, specifically examining whether fossil fuel extraction workers in the US possess the necessary skills for green jobs and whether they are located near existing or projected green energy production areas. This is critical because past large-scale labor market transitions have proven to be costly and difficult, highlighting the need for proactive policy solutions. The paper addresses a gap in existing research, which often lacks a broader examination of occupational portfolios and worker mobility.

Existing literature highlights the ethical and governance challenges associated with clean energy transitions, focusing on the impact on fossil fuel workers as a directly affected constituency. Studies have examined the potential for green jobs to absorb displaced workers, emphasizing the need for re-skilling initiatives. However, the geographical aspect of the transition has received less attention. While some studies point to a skill mismatch between fossil fuel occupations and green jobs, and the importance of geographical co-location, comprehensive analyses incorporating both skill transferability and geospatial mobility are lacking. The current work aims to address this gap by integrating data on worker skills, employment patterns, and projected growth in green sectors to provide a more holistic understanding of the challenges involved.

The study uses a multi-faceted approach combining data from various sources: 14 years of US Energy Information Administration (EIA) power plant data, Census Bureau job transition data (Job-to-Job flow origin-destination or J2JOD data), and Bureau of Labor Statistics (BLS) employment and skills data (including the O*NET database). The analysis proceeds in several stages. First, it assesses the skill similarity between fossil fuel extraction occupations and green occupations using Jaccard similarity, a metric measuring the overlap in required skills. Second, it analyzes the geographical co-location of current fossil fuel extraction workers and existing green energy power plants (solar, wind, hydro, biomass). Third, it incorporates BLS employment projections to forecast green job growth in different US regions up to 2029 and assesses the co-location of fossil fuel workers with projected green job growth. Finally, to understand worker mobility, it employs Poisson regression models on J2JOD data to examine the impact of skill similarity, distance, and regional employment on job transitions, including scenarios with various numbers of created green jobs and different job targeting strategies (geo-targeted vs. non-targeted). Random forest regression is used to predict green employment in 2029, incorporating historical employment data and other demographic and economic variables. The study employs a variety of statistical techniques to test for statistical significance and employs cross-validation to ensure robustness and evaluate predictive performance. The use of several data sets allows the researchers to corroborate their findings and understand the various dimensions of the transition.

The study reveals a significant mismatch between the location of fossil fuel workers and emerging green jobs. Key findings include: 1. **Skill Similarity:** Fossil fuel workers possess skills relatively similar to green occupations (average skillsim = 0.79), suggesting that extensive re-skilling may not be necessary for many workers. However, additional re-skilling could further enhance transition rates. 2. **Geographical Mismatch:** Current fossil fuel extraction workers are not co-located with existing green energy power plants. This lack of co-location is further exacerbated when considering projected green job growth in 2029, with the vast majority (98.97%) of extraction workers not located near projected green job growth. 3. **Worker Mobility:** Historical data shows that distance to employment is a more substantial barrier to job transitions than skill similarity. Even in idealized scenarios where skills perfectly match and green jobs are abundant, geographical distance significantly limits worker transitions. 4. **Policy Implications:** Geographically targeted job creation programs show promise in facilitating transitions. Simulations demonstrate that creating new green jobs in fossil fuel-intensive regions leads to substantially higher transition rates compared to distributing jobs proportionally to overall employment. This highlights the critical need to address geographical disparities in job creation rather than relying solely on the number of new jobs created. 5. **Alternative Industries:** Existing industries such as manufacturing and construction offer promising alternative avenues for absorbing displaced fossil fuel workers, exhibiting higher skill similarity and potential for transitions than green jobs in non-geo-targeted scenarios. However, geo-targeted green job creation can achieve similar or higher transition rates to manufacturing and construction sectors.

The findings underscore the critical role of geographical location in the success of a Just Transition for fossil fuel workers. While skill transferability is important, the lack of co-location between current workers and future job opportunities poses a significant hurdle. This challenges the assumption that simply creating green jobs will automatically result in a smooth transition for displaced workers. The study’s analysis reveals that the most effective strategy is to target job creation efforts towards regions currently employed in fossil fuel extraction. While skill similarity exists between many green jobs and fossil fuel jobs, this is not sufficient to overcome geographical barriers. The model’s sensitivity analyses, particularly concerning the impact of targeted versus non-targeted job creation, offer valuable insights for policy makers. The study suggests that policies focused on creating green jobs, while crucial, must consider the spatial distribution of both existing fossil fuel workers and projected green employment growth. Addressing this geographical mismatch is pivotal for realizing a truly just transition.

This research demonstrates that while fossil fuel workers possess skills relevant to green occupations, geographical distance presents the largest impediment to a successful Just Transition. Policies must prioritize geographically targeted job creation in fossil fuel-dependent regions, alongside re-skilling initiatives. Future research should investigate the social and economic factors influencing workers’ reluctance to relocate and explore alternative diversification strategies for fossil fuel-dependent communities. Failure to address the geographical mismatch could render large-scale investments in green jobs ineffective, leading to wasted resources and unmet social goals.

The study acknowledges several limitations. It assumes all fossil fuel workers desire to transition to green jobs, neglecting potential social barriers or individual preferences. The analysis assumes uniform latent mobility across fossil fuel workers, potentially obscuring sub-sector variations. The study also focuses on extraction workers, neglecting other fossil fuel sector roles. Furthermore, the analysis only considers a limited set of existing industries as potential destinations for transition. Finally, the study does not explicitly account for emerging and currently unclassified green occupations.