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Labor pathways to achieve net-zero emissions in the United States by mid-century [1]

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Date: 2023-06-01

In order to stabilize the global mean temperature, net emissions of greenhouse gases must approach zero (Davis et al., 2018). While technological pathways to achieve deep decarbonization are well-researched (Haley et al., 2019; Jenkins et al., 2021; Larson et al., 2020; University of California Berkley, 2020), studies of labor market pathways and the distributional effects are sparse, despite labor playing a critical role in achieving a net-zero emissions goal (E. N. Mayfield, n.d.). The supply, productivity, and disposition of labor, in addition to public and private support and concern regarding employment, has the potential to accelerate or constrain the rate of decarbonization. Moreover, there is mounting policy and political discourse regarding just transitions, and embedding social equity goals into climate policy (Bergquist et al., 2020; Chapman et al., 2018; Henry et al., 2020a). Whereas just transitions and related policies have historically referred to labor transitions for communities and workers in declining industries, the term has evolved in the context of net-zero energy system transitions. Just transitions in recent discourse incorporate aspects of climate, energy, and environmental equity and justice, and include elements related to the distribution of societal costs, risks, and benefits, such as jobs and air pollution (Healy et al., 2019; Mayfield, 2022; Newell and Mulvaney, 2013; Sovacool and Dworkin, 2015). In the broader context of environmental policy processes and infrastructure planning in the United States (U.S.), principles of equity and justice have historically been treated as ancillary, if considered at all, rather than as policy objectives (E. N. Mayfield et al., 2019b).

The historical linkage between labor and energy transitions, in addition to the current state of the broader U.S. labor market, provide necessary context for evaluating future labor pathways to achieve a net-zero emissions target. There are several historical examples of the interaction between labor markets and energy transitions in the U.S., namely the oil, gas, and coal booms-and-busts of the 19th and 20th centuries, and more recently, several regional boom-and-bust cycles in oil and gas production enabled by horizontal drilling and hydraulic fracturing of shales (Black et al., 2005; Marchand and Weber, 2017; E. N. Mayfield et al., 2019a). The energy sector is a significant employer, with 6.7 million energy-related jobs in the U.S. in 2018 comprising approximately 4% of the total labor force, and there is evidence of subsequent declines in energy employment during the coronavirus pandemic (E2, 2020; Energy Futures Initiative & National Association of State Energy Officials, 2019; Henry et al., 2020a; U.S. Bureau of Labor Statistics, 2019b). Both the near-term salience of employment during economic recovery periods and the long-term demand for labor in the energy sector will likely influence the course of a net-zero emissions energy transition.

Here, we develop and demonstrate the D ecarbonization E mployment and E ne r gy S ystems (DEERS) model – a data-driven modeling framework for estimating labor market pathways of large-scale, low-carbon energy-supply infrastructure development. The DEERS model is designed as a tool to inform regional and national workforce and infrastructure planning and policy-making in the U.S. This modeling framework is distinct, but complementary to, traditional employment modeling approaches as described in the Methods section.

The model simulates the distribution of labor effects over time and across economic sectors, resource sectors, occupations, and geography for multi-decadal energy-supply system transition scenarios. The model is used to estimate employment and wages, as well as experience, education, and training requirements, across domestic energy supply chains. Leveraging current, publicly-available energy activity and labor market data, we apply a combination of simulation, regression-based, and bottom-up estimation approaches for incumbent fossil fuel resources and emerging low carbon resources. We also incorporate time-variant factors, such as labor productivity and wage inflation, which are especially important in the context of emerging labor markets and long-term transitions. The DEERS model is adaptable to different energy system contexts and readily coupled with regional and downscaled macro-energy system modeling outputs. It can also be used to explore modifiable workforce and infrastructure planning and policy decisions, such as siting domestic manufacturing facilities, creating just transition funds, and changing fossil fuel exports over time.

In this study, we apply the DEERS model to alternative, spatially downscaled energy system transition pathways to achieve net-zero emissions in the U.S. by 2050, which were developed as part of the Net Zero America (NZA) study (Larson et al., 2020). NZA implements a macro-energy system optimization model to select alternative techno-economic pathways, each of which meet a linearly decreasing net-zero emissions constraint over time and are cost-minimized subject to a range of end-use demand electrification and renewable deployment assumptions (Williams and Jones, 2021). These macro-energy system model outputs are then spatially downscaled, using detailed infrastructure siting models. Here, we model labor pathways associated with the following four transition scenarios: 1) high electrification by 2050 and without renewable deployment constraints (E+), 2) moderate electrification by 2050 and without renewable deployment constraints (E−), 3) high electrification by 2050 and with renewable deployment constrained to current capacity expansion rates (E + RE-), and 4) high electrification by 2050 and with 100% of energy derived from renewable sources by 2050 (E + RE+). We also model a reference scenario (REF) that assumes no new policies and has no specified emissions reduction targets. Additional details, as well as descriptions of supplementary scenarios, are provided in the Methods section and Supplemental Information (SI).

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[1] Url: https://www.sciencedirect.com/science/article/abs/pii/S0301421523001015?dgcid=rss_sd_all

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