#Scenario Definitions

The scenarios modeled here are designed to show both a range of potential technology pathways to an economy-wide net-zero target and to illustrate the sensitivity of model results to specific assumptions about key technologies and other drivers. Figure 3 depicts the scenario space, organized into a matrix where the columns represent alternative technology cases and the rows represent alternative assumptions about policy and other drivers, including fuel prices and data center (DC) load growth. The scenario set includes several “reference” cases that do not impose a long-run constraint on emissions, as well as several net-zero scenarios that include a target for economy-wide net CO₂ emissions form the energy sector which declines linearly from 3.2 GtCO₂ in 2030 to zero by 2050 (see Figure 9). Net emissions are defined as the net balance between positive emissions from uncaptured combustion of fossil fuels and negative emissions from carbon dioxide removal (CDR) activities, including bioenergy with CO₂ capture and storage, direct air capture, and natural climate solutions (see Figure 4 of the 2022 report).

The technology cases, defined in Figure 4, are constructed based on alternative combinations of four key technology areas: advanced nuclear, electrolysis, CO₂ transport and storage, and bioenergy conversion and supply.^[1] The ‘Opt-Tech’ case reflects optimistic (generally lower cost or higher resource availability) assumptions in all four areas, while the ‘Ref-Tech’ case reflect moderate (generally higher cost or more limited resource availability) assumptions. In between these two cases are a set of individual technology sensitivity cases in which ‘Opt-Tech’ assumptions applied for each technology area individually, with ‘Ref-Tech’ assumptions for the others. Finally, the ‘Lim-Tech’ case reflects the most restrictive technology set in the analysis, with CCS technologies unavailable, due to some combination of technical and institutional barriers. A variant of this case considers a mixed scenario with limited CCS and bioenergy but optimistic costs for advanced nuclear and electrolysis.

Further details on each technology area are provided here.

The analysis considers as a starting point a ‘Reference 1.0’ scenario, which excludes the IRA, EPA rules, and DC load growth updates, although it does include other model updates. Comparing this scenario to the ‘Reference 2.0’ scenario illustrates the impact of these factors on system outcomes. Both cases use ‘Ref-Tech’ assumptions as shown in Figure 4. Two additional reference cases are included, one with ‘Opt-Tech’ assumptions, and another with higher fuel costs. While most scenarios assume commodity prices for gas and petroleum products based on AEO 2023 reference case, the scenarios labeled ‘High-Fuel’ uses commodity prices based on AEO’s Low Oil and Gas variant.

Most of the scenarios in this analysis include an economy-wide net-zero target. Net-zero scenarios are included for each technology case; for the ‘Ref-Tech’ technology case with ‘High-Fuel’ assumptions; and for the ‘Opt-Tech’ technology case with high DC load growth. One additional net-zero scenario, based on the ‘Opt-Tech’ technology case, considers additional constraints on region- and sector-level emissions. Whereas other net-zero scenarios include only a single target for economy-wide net CO₂ emissions, allowing flexibility to allocation positive and negative emissions across sectors, this ‘Lim-Flex’ scenario also targets direct reductions in each sector and region, limiting flexibility in a hypothetical carbon credit market under the economy-wide target. These additional regional and sectoral targets for 2050 are summarized here:

#Regional and Sectoral Targets in Lim-Flex Scenario

• Each region must achieve economy-wide net-zero (natural CDR credits allowed from outside region)
• Electric sector within each region must achieve net-zero (and no fossil gas without CCS)
• Buildings and transportation positive emissions in each region must be reduced 90% below 2020
• Industry positive emissions in each region must be reduced 75% below 2020 (unless infeasible due to regional industrial mix)

Table 1 provides a brief description of each scenario.