Emissions projections for US utilities through 2050

Decarbonizing the electricity sector is an increasingly urgent policy target in the United States, with increasing enactment and calls for zero-carbon electricity targets from city through federal levels (Trumbull et al 2019, Leon 2021, The White House 2021). As decarbonization policies begin to take shape, often leveraging both electricity sector decarbonization and increased electrification of historically fossil-based sectors (Williams et al 2021), understanding the mid-transition dynamics of electricity-related greenhouse gas (GHG) and other emissions is highly relevant for evaluating how interventions might reduce contributions to climate change (Vaishnav and Fatimah 2020). Although electrification has the potential to enable zero-GHG energy consumption, not all electricity is zero GHG, and other electricity-related emissions can contribute to large public health burdens (Penn et al 2017, Henneman et al 2019, Buonocore et al 2021). As such, understanding how the timing of electrification interacts with the timing of electricity decarbonization is highly relevant for evaluating overall environmental impact of policy interventions and the potential for burden shifting.

The spatiotemporal variability of electricity emissions factors is well known, as are the challenges of assigning generation to specific end users due to the complex physical and economic architecture of electricity systems (Weber et al 2010, Schivley et al 2018, de Chalendar et al 2019, Fell and Johnson 2021, von Wald et al 2021). Although regionalization of environmental impacts is common in evaluation methods like life cycle assessment (LCA), dynamics over time—particularly into the future—are rarely incorporated in a manner that reflects the transition from high to low GHG systems (though see (Ambrose et al 2020, Gençer et al 2020)). That is, while studies might include scenarios with future low-emissions conditions (Onat et al 2019), they do not generally account for the mid-transition where the existing system has been partially but not fully replaced. Like most equipment, power plants have lifespans that empirical data suggest are relatively stable despite having complex drivers (Davis and Socolow 2014), which enables asset-informed evaluation of potential future emissions (Grubert 2020a).

Given that most electricity sales are done via utilities, which supply power from specific generation assets (through ownership or markets), the emissions impact of a given electricity consumption activity largely depends on the utility's profile and the timing of electricity consumption. Timing is relevant at multiple scales, from hourly (Thind et al 2017, de Chalendar et al 2019) to asset lifespan. From a decarbonization perspective, understanding the potential future evolution of emissions factors for a given utility over a multidecadal time frame is particularly useful for evaluating how given interventions might affect emissions over their life cycles. Although prior work has mapped emissions to utilities (van Atten et al 2020), investigated changes in electricity emissions over time at regional (Schivley et al 2018) and utility (Alova 2020) levels, and performed LCA incorporating dynamic electricity sector emissions over time (Hertwich et al 2014), the ability to evaluate highly specific scenarios of future emissions for specific utilities, accounting for the existing generator asset base that utilities own and purchase power from, has been elusive. As such, this work aims to address a major literature gap by modeling future GHG emissions factors for US utilities that include committed emissions that would be expected if existing assets run about as long as similar assets have run in the past, over a time frame (∼30 years) that is relevant on an asset lifespan basis. The remainder of this letter describes how the utility-specific model was built, then presents results, validation, and an analytical example.

This research uses federally reported data on US electric utilities and generators to develop utility-specific projections of annual average GHG emissions per unit of retail electricity sales through 2050, in addition to utility-specific projections of five types of air emissions associated with owned assets. These projections are the outputs of an Excel-based model, three versions of which are available as supplementary data files 1A–1C of this letter (available online at stacks.iop.org/ERL/16/084049/mmedia). The difference among the three versions is the geographic scope of power markets from which a given utility can purchase electricity generated by power plants it does not own. Data File 1A, which is the basis of reported results in this letter unless stated otherwise, assigns each utility to its reported balancing authority for wholesale purchases. Data File 1B assigns each utility to its reported state, and Data File 1C assigns each utility to its reported North American Electric Reliability Corporation (NERC) region. These assignments are made based on reported balancing authority, state, and NERC region in Energy Information Administration (EIA) 861 (EIA 2021a). Note that some utilities operate in multiple balancing authorities, states, and NERC regions: given the structure of EIA 861, utilities in multiple balancing authorities are excluded from analysis (Data File 1A), but utilities in multiple states or NERC regions are assigned to the single reported locations, a potential source of error for retail power marketers in particular.

The modeling approach used here is adapted from the approach used in Grubert et al (2020) to project annual average GHG emissions factors for utilities in California. By also incorporating the multicriteria, generator-level modeling approach used in Grubert (2020a), this model is not only more comprehensive (covering all utilities in US states included in EIA 861) but also much higher resolution at the asset level, with generator- rather than plant-level specificity for (a) generation, (b) asset lifespan, (c) emissions (including nitrogen oxides, sulfur dioxide, and mercury in addition to carbon dioxide and, for natural gas units, upstream methane, with state-level resolution from Burns and Grubert (2021)), and (d) ownership. Unlike the California model in Grubert et al (2020), the current model does not assume policy compliance with legislated or voluntary GHG emissions targets, largely given the complexity of modeling interdependent compliance with rapidly changing policies and shared wholesale markets across states, regulatory environments, and utility internal policy. Instead, the current model includes utility-specific estimates of owned generation capacity that a zero-carbon target implemented in a given year would strand under the definition used in Grubert (2020a): requiring an asset to close earlier than would be expected absent policy, here defined as before it reaches a historically typical lifespan for its type.

2.1. Data

2.2. Implementation

Two different types of utility-specific results are derived in the Data Files. First, for each year 2021–2050, and for each utility, committed impacts associated with the generators that each utility owns (either fully or partially, based on EIA 860 Schedule 4) are reported, assuming that all generators that were operable as of 2019 continue to produce at their 2019 levels until their modeled retirement date. This modeled retirement date is either the end of the generator's empirical fuel- and prime mover-specific lifespan if that date is 2021 or later, following Grubert (2020a), or a randomly selected year between (2022, 2030) if the unit reached that age before 2021, to avoid the step change associated with simultaneous retirement of all assets past their typical lifespans present in Grubert et al (2020). Modeled committed impacts include projected generation, emissions (of carbon dioxide, CO₂; natural gas supply-associated methane, CH₄; nitrogen oxides, NO_x ; sulfur dioxide, SO₂; and mercury, Hg), and capacity of owned assets that would be stranded if a zero-carbon electricity deadline were implemented in any year between 2021 and 2050. Stranded asset estimates are made based on typical lifespan, not modeled retirement year. On the results sheet 'Utility Multicriteria Results', users can select a utility and view projected committed emissions from owned generating assets by year as a percentage of estimated 2021 emissions (figure 1).

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Second, for each year 2021–2050, and for each utility, annual GHG emissions associated with total retail sales are computed, with the option to display annual average GHG emissions factors per unit of retail sales. Utilities are assumed to fulfill retail sales obligations in the following sequence: (a) with electricity generated by generators that were operable as of 2019, have not yet reached their modeled retirement date in the projection year, and that the utility owns either wholly or partially (for partial ownership, outputs are allocated in direct proportion to ownership share—i.e. a utility that owns 40% of a generator is assigned 40% of that generator's outputs.); (b) with electricity generated by generators that were operable as of 2019, have not yet reached their modeled retirement date in the projection year, are not assigned to their owner's retail sales needs, and are in the utility's region (balancing authority (Data File 1A), state (Data File 1B), or NERC region (Data File 1C)), up to the utility's share of unmet need (projected retail sales less projected owned generation) across all utilities in the region; and (c) with electricity generated by generators that were not operable as of 2019, i.e. new build generation.

User inputs include the GHG intensity of new build generation, annual retail sales growth rate (default: 0.9%, the 2021–2050 annual average growth rate assumed in the 2021 Annual Energy Outlook reference case (EIA 2021b)), and Global Warming Potential (GWP) of methane (default: 36, the Fifth Assessment Report GWP-100 with climate-carbon feedback for fossil methane (Intergovernmental Panel on Climate Change 2014)). To view generator CO₂ emissions only, enter '0' as the methane GWP. New build generation GHG intensity is based either on directly entered annual GHG emissions factors (2021–2050) or on an annual fuel mix comprising user-entered shares of natural gas combined cycle (NGCC) units with and without carbon capture and storage (CCS), natural gas combustion turbine units with and without CCS, and zero-carbon generation. For natural gas units, users can adjust assumptions about efficiency, capture level, and methane emissions rate. Defaults are set to Annual Energy Outlook heat rates, 90% capture rate for CCS units, and national average methane leakage rate for natural gas (production through transmission and storage). Assumptions and calculations can be viewed in the Data Files. New build generation is modeled only as an emissions factor in a given year rather than as new assets with lifespans, meaning that the new build emissions factors do not explicitly account for future asset commitments.

Several considerations not included in the current model that were included in predecessor models are noted here for clarity. In addition to this model's lack of explicit policy modeling, it differs from the California model (Grubert et al 2020) by not including nonownership contracts for generation (largely because what was reasonable to identify manually for five utilities is not reasonable to find manually for all utilities), not including plant-level GHG emissions aside from CO₂, and not including relatively minor upstream methane emissions from coal extraction. Unlike the non-utility-specific generator model (Grubert 2020a), this model does not include labor or water use estimates, and emissions are modeled as either a generator-level match with eGRID or by applying Grubert (2020a) 2018-base year emissions intensities to 2019 generation at either the generator level or the 'Energy Source 1' fuel level, in that order. Note that although projected total non-GHG air emissions and emissions factors like those shown in Grubert (2020a) are not explicitly presented as results in the Data Files, all necessary information is embedded in the models, and such results can be viewed by altering lookups in a few locations. See Data Files for details, with further instructions available from the author upon request.

2.3. Validation

2.4. Analytical example: lifetime GHG emissions from electric vehicle charging

Figure 2 shows model results using the balancing authority model (Data File 1A) for five example utilities, each of which has declared a goal of reaching net-zero carbon by 2050 (St. John 2020).

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As figure 2 shows, projected emissions factors are highly sensitive to assumptions about the environmental intensity of new-build power as existing generators close. Divergence between the zero-carbon (solid lines) and NGCC (dashed lines) replacement power scenarios occurs largely in response to closure of owned assets.

Figure 3 shows power generation capacity operable as of 2019 that, based on fuel- and generator-specific lifespan estimates, would be at risk of stranding under a 2035 deadline for zero-carbon electricity.

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As validation, and to show differences among regionalizations, figure 4 compares outputs for California's largest five utilities from this 2019 base year, generator-level model's three regionalizations (Data Files 1A–C) with outputs from a 2018 base year, plant-level model published for California specifically, which uses an eGRID regionalization (Grubert et al 2020).

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As figure 4 shows, although trends are similar across all models, actual values differ. For the three regionalizations of this work's model (Data Files 1A–C), results reflect that California's large utilities are in different balancing authorities with varying GHG intensity of supply, in addition to an overall observation that California's NERC region (the Western Electricity Coordinating Council, WECC) has higher GHG intensity than the state (which has strict GHG rules) or relevant balancing authorities. Figure 4 also shows that utilities that purchase large shares of their electricity (e.g. Southern California Edison and San Diego Gas & Electric, see (Grubert et al 2020) figure 1) are more sensitive to wholesale assumptions. Other differences are explained by the fact that the California-specific model differs from this model in several relevant ways. First, the models use different base years. Second, as a plant- rather than generator-based model, resolution on ownership, retirement, and specific emissions is lower for the California-specific model. Third, this model underestimates access to hydropower, as hydropower contracts are complex and sellers (e.g. the Western Area Power Administration) operate across multiple regions. Finally, the California-specific model relies solely on eGRID for plant-level emissions estimates, resulting in a roughly 20% underestimate of 2018 California generator CO₂ emissions versus this model's roughly 2% overestimate of 2019 California generator CO₂ emissions (EIA 2021e). Notably, the validated policy, contract, and import-conscious values presented in Grubert et al (2020) are substantially lower than estimates in figure 4, reflecting the need for caution in using this model in contexts with significant policy-aware contracting and utilities with low asset ownership in particular. California is a particularly challenging case due to deregulation, policy, and import dynamics (Grubert et al 2020, von Wald et al 2021).

Figure 5 shows an analytical example of how utility-level annual average GHG emissions factors can be leveraged to evaluate life cycle impacts of electricity consuming activities. Specifically, figure 5 shows estimated lifetime GHG emissions from charging an electric vehicle on the utility systems shown in figure 2, using static 2021, dynamic 2021–2030 (with NGCC or zero-carbon new build), or 2031–2040 (with zero-carbon new build) annual average emissions factor projections.

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As figure 5 shows, results using dynamic emissions factors are meaningfully different from static emissions factors, demonstrating that life cycle environmental evaluations are sensitive to changing conditions in the electricity sector. Ideally, future evaluations will incorporate temporal dynamics both within (Miller et al 2020) and across years.

The GHG intensity of electricity is utility specific and changes over time. Particularly given recent policy emphasis on rapid decarbonization in the US electricity sector (Williams et al 2021), both understanding the potential evolution of emissions at decision-relevant scales (e.g. the power generator and utility levels) and creating tools that enable use of dynamic environmental characteristics for long-term analysis are relevant for evaluating possible futures. Understanding what emissions are committed by existing generation assets (Davis and Socolow 2014, Shearer et al 2020), or potentially stranded by policy (Fofrich et al 2020, Grubert 2020a), helps contextualize costs, difficulty, and environmental implications of alternative electricity system futures. In particular, clarity on how the assets utilities own are likely to affect their ability to meet decarbonization goals or requirements (Pomerantz and Kasper 2020) can highlight what actions might be necessary to meet those goals.

Existing models like the Annual Energy Outlook that do not account for asset lifespans in a manner consistent with historical observations risk portraying the system as much less mutable than it is on decadal time scales (Zacarias and Grubert 2021), further motivating the development of scenarios and modeling approaches that include explicit representation of historically consistent generator replacement. Understanding roughly when specific generators might close, particularly at the utility level, can inform recommendations for replacement investments that are consistent with utility, state, federal, and other emissions goals. Further, understanding potential future emissions intensity conditions for a given utility can inform sequencing of decarbonization efforts, including electrification, efficiency investments, and supply-side decarbonization. Efforts to characterize life cycle emissions associated with electrification likely over- or underestimate life cycle emissions by relying on existing or expected end-point future conditions alone, rather than accounting for dynamism over equipment lifetimes (figure 5; Ambrose et al 2020, Asdrubali et al 2020, Vaishnav and Fatimah 2020, Verma et al 2021). The model accompanying this letter is an attempt to improve access to reasonable projections for what these dynamics might be at utility and annual average resolution, with the expectation that future models might improve asset attribution and temporal resolution.

4.1. Limitations

This material is based upon work supported in part by the National Science Foundation under Grant CNS 1931980. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect those of the National Science Foundation. Thank you to Manasi Gali for assistance with preliminary data collection.

All data that support the findings of this study are included within the article (and any supplementary files).