GCAM-USA v5.3_water_dispatch: Integrated modeling of subnational U.S. energy, water, and land systems within a global framework

Abstract. This paper describes GCAM-USA v5.3_water_dispatch, an open source model that represents key interactions across economic, energy, water, and land systems in a consistent global framework, with subnational detail in the United States. GCAM-USA divides the world into 31 geopolitical regions outside the United States (U.S.) and represents the U.S. economic and energy systems in 51 state-level regions (50 states plus the District of Columbia). The model also includes 235 water basins and 384 land-use regions; 23 of each fall at least partially within the United States. GCAM-USA offers a level of process and temporal resolution rare for models of its class and scope, including detailed subnational representation of U.S. water demands and supplies and sub-annual operations (day/night for each month) in the U.S. electric power sector. GCAM-USA can be used to explore how changes in socioeconomic drivers, technological progress, or policy impact demands for, and production of, energy, water, and crops at a subnational level in the United States, while maintaining consistency with broader national and international conditions. This paper describes GCAM-USA’s structure, inputs, and outputs, with emphasis on new model features. Four illustrative scenarios encompassing varying socioeconomic and energy system futures are used to explore subnational changes in energy, water, and land-use outcomes. We conclude with information about how public users can access the model.


description is very 'on the surface', and doesn't allow readers to fully understand how the model structure may affect its results.In this respect, the manuscript could also benefit from a broader discussion of the results.Many of my below comments can be addressed through an appendix with model details (ideally some sort of table with a description of many of the methodological details and assumptions of the model -Table 1 in Bauer et al. (2020) may offer some inspiration, though that focused specifically on bioenergy), as well as an expanded discussion section.More specifically: (2) The authors thank the reviewer for the valuable feedback.We address each specific point below.The reference to the Bauer et al. ( 2020) Table 1 is helpful and appreciated.We have added large tabular overview of GCAM-USA as Table SM1 that draws heavily from the Integrated Assessment Modelling Consortium (IAMC) wiki's (https://www.iamcdocumentation.eu/index.php/IAMC_wiki)reference card format and also informed by Bauer et al. (2020) Table 1.(3) There are no changes to manuscript corresponding to this comment.
(2) The authors thank the reviewer for the helpful comment.We adjusted the existing sentence and added another to clarify the foresight and solution approach of GCAM and GCAM-USA.More information regarding GCAM's market structure and solution approach is provided in the subsequent paragraph.
(3) Edits to manuscript "GCAM is a dynamic recursive, partial equilibrium model which captures key interactions between global economic, energy, water, land, and climate systems by simultaneously solving for equilibrium (price where demand equals supply) in energy, water, agriculture, and emissions markets.The model is myopic (not forward looking) and produces cost-effective solutions by clearing markets, although it does not optimize around any target function."--------------------(1) Concerning the projections for energy demand, on line 399 it states that "final energy demands increase as efficiency improvements are slower than increases in demand for end-use energy services".Some explanation is needed for how the demand in energy services is dealt with.Specifically for buildings in developed regions there is largely a stagnation in energy demand so it is a bit surprising that final energy demand increases faster than efficiency over the entire 21st century.
(2) The authors thank the reviewer for the helpful comment, which raises two important points.In response to the first point regarding the need for more explanation about how energy service demands are represented, a paragraph was added to section 2.2.2 Energy (within 2.2 Overview of GCAM-USA) describing how final energy demands are determined.In response to the second point about demand stagnation for building energy in developed regions, the reviewer's comment highlights that the sentence on line 399 needed further elaboration.The key drivers in the GCAM-USA Reference scenario are population growth and increasing per-capita service demands (such as per-capita floorspace or kilometers traveled) which, combined with conservative assumptions about future energy efficiency, result in slow but steady growth in total final energy demands over the century.These dynamics vary by end-use sector.We have added a clarifying clause in the manuscript as well as a Supplementary Note discussing these dynamics.
(3) Edits to manuscript (section 2.2.2) Demands for final energy services are represented at the state-level in GCAM-USA utilizing the same demand functions as GCAM.Final demands include residential and commercial building floorspace and service outputs (space heating, space cooling, lighting, water heating, and various appliances), generic industrial energy service, use of energy carriers as feedstocks in industry, cement production (million tons), fertilizer production (million tons), passenger-kilometers traveled, (split between domestic travel and international aviation) and freight ton-kilometers shipped (split between domestic freight and international shipping).Within the building sector, service demands are a function of building floorspace and service demands per unit of floorspace.Building floorspace varies with population, income (per-capita GDP), and energy service prices, with exogenous satiation levels prescribing an upper-limit on per-capita floorspace.Building service demands per unit of floorspace depend on climate (for space heating and cooling), building envelope efficiency, service prices, and exogenous satiation (for a more detailed discussion, see Clarke et al. ( 2018)).Within industry, demand for nitrogen fertilizer is dictated by the agriculture sector, where technologies with low and high levels of fertilizer application compete for production shares of each crop.Demand for cement is driven by economic growth and modulated by price and income elasticities.Aggregate industry output is represented in generic terms as a function of income and the price of generic energy service and feedstock use.Fuels compete on costs for share of total energy with a low elasticity of substitution.Transportation service demands depends on income and services prices.Service prices among competing passenger transportation modes consider the value of time traveled, which is calculated from the wage rate (per-capita GDP divided by the number of working hours in a year), the mode's (exogenous) speed of travel, and an exogenous time value multiplier for each mode reflecting the valuation of people's time in transport and waiting times associated with in each mode.
Additional edits to manuscript (section 4.1) The GCAM-USA Reference scenario assumes a continuation, but not an expansion or strengthening, of current energy efficiency policies (e.g., building efficiency standards).In aggregate, total final energy demands increase as efficiency improvements are slower than increases in demand for end-use energy services (space heating and cooling, passenger and freight transportation, industrial energy use, etc.), although the balance between population and income driven demand growth and service efficiency vary by sector.(Supplementary Note 3 provides information on service and energy growth by sector for the GCAM-USA Reference scenario.)--------------------(1) On line 442 it is stated that agricultural productivity in the US increases betweeen 0% to 0.67% per year between 2015 and 2100.An explanation of what drives this productivity is needed.Is it completely exogenous?Increased irrigation?Economic pressures?Closing of yield gaps? Is it crop or livestock-specific?What exactly is included in agriculture?
(2) The authors thank the reviewer for the helpful question.Those agricultural productivity figures represent exogenously specified technical change which varies by crop, management practice (irrigated vs. rainfed, high vs. low fertilizer application), and year.This technological change represents factors like improved mechanization, crop breeding, etc. Changes in fertilizer use & irrigation practices are represented endogenously in the model and respond to changing economic forces; these management practice changes generate additional productivity changes beyond the numbers listed on line 442.We have added additional detail to clarify these questions in the manuscript (additions below in bold).
(3) Edits to manuscript Agricultural productivity increases gradually in the U.S. in the future, with crop-specific annual productivity growth rates ranging from 0% to 0.67% per year between 2015 and 2100.These productivity gains represent exogenously specified technical change which varies by crop, management practice (irrigated vs. rainfed, high vs. low fertilizer application), and year, reflecting factors like improved mechanization, crop breeding, etc. Alternate fertilizer use and irrigation practices are represented endogenously and respond dynamically to economic forces (commodity prices, land values, input costs, etc.) within the model; shifting management practices generate additional productivity changes beyond those listed above.
- -------------------(1) Table 1 offers a brief overview of the scenario design, however the explanations are woefully vague, especially for the "Transition" scenario.An appendix with some details of what parameters were changed in the model would be useful when interpreting the results.Judging by fig 2, fossil fuels still play an important (though not dominant role), so it would be good to get a better understanding of what exactly drives this scenario.Is there a carbon price?Ad-hoc assumption?Cost reduction in renewables?
(2) The authors thank the reviewer for the constructive comment.The scenarios in the paper are intentionally simple and intended to illustrate key model dynamics rather than develop detailed narratives of the future.A statement has been added to clarify this intention, as well as a sentence elaborating on how the Transition scenario was implemented.
(3) Edits to manuscript (section 5) These scenarios, outlined in Table 1, are intentionally simple and designed to illustrate key model behavior and capabilities across a range of potential futures; they are not intended to reflect "likely" outcomes or detailed narratives of future worlds.

Additional edits to manuscript (section 5)
The energy transition is implemented via a price on carbon dioxide emissions from the energy system of roughly $22 / tCO2 (2015 USD), beginning in 2025 and escalating at 5% per year thereafter.
- -------------------(1) Along the same lines, I recognize that the manuscript presents a new model, and not necessarily a new scenario analysis, but since results are presented, some discussion is needed.Is the transition scenario based completely on improved supply options?If so, there needs to be a discussion as the "socioeconomic drivers" do not cover all "lifestyle aspects" which govern energy demand.Currently it is difficult to derive policy advice or compare the results with other scenario analyses since the projections seem to be little more than tinkering with the model (which is fine, but what was tinkered-with should be clear to the reader).An in-depth explanation and discussion of the scenario design would be helpful.
(2) The authors thank the reviewer for the constructive comment.The reviewer is correct that more clarification of what is altered in the scenarios is needed.The Transition scenario includes on a price on carbon dioxide emissions from the energy system; no changes to technology options, technology characteristics, or consumer behavior/preferences are included in the scenario.Some of these dimensions have been explored in other papers.For example, Ou et al. ( 2021) examined the impact of renewable technology costs on US capacity expansion.
A sentence has been added to clarify the Transition scenario design (additions below in bold).As mentioned above, the scenarios in the paper are intentionally simple for the purposes of illustrating key model dynamics and thus not as rich as many other scenario analyses.
(3) Edits to manuscript The Transition scenario does not include any new or improved technology options that are not available in the Ref scenario (only shifting technology deployment in response to the carbon price); it also does not explicitly reflect any "lifestyle" changes which could impact future energy demand (although end-use energy consumption responds endogenously to changes in energy prices).
- -------------------(1) On line 499 the projected ranges of electrification of building energy services across different states are presented.Can you please explain how the model methodology leads to this result?I guess it has to do with the heating/cooling dichotomy of different locations, but it would be good to point this (or any other relevant dynamic) to the reader.Similarly, for line 520 and the transport sector.
(2) The authors thank the reviewer for the helpful suggestion.An explanation of the model methodology / dynamics that lead to these results have been added to the manuscript.
(3) Edits to manuscript (section 6.1)The wide range of building electrification rates is driven by several factors.Differences in the energy service profile of the buildings sector vary by region; for example, southern states require more air conditioning (powered by electricity) and little heating, while northern states require little cooling but substantial space heating (often provided by gas, heating oil, or biomass, as well as some electricity).Historically estimated differences in fuel preferences for different services (e.g., electricity vs. gas (or other fuels) for space heating, water heating, cooking, etc.) by state are carried forward and influence future technology choices (GCAM-USA's calibration routine is discussed in Sect.6.5.), as do regionally differentiated fuel prices (oil, gas, coal, and electricity prices all vary by grid region).
This range in building electrification tightens substantially in the Transition scenario.Electrification of end-use sectors is a key emission reduction strategy in response to the carbon price in the Transition scenario; buildings, as the most electrified sector in the Ref scenario, tends towards the upper end of electrification, with electricity accounting for 79% of building energy consumption in New York and 97% in Florida.With fossil fuel technologies facing a substantial price on the CO2 emissions they generate in the Transition scenario and electric options available for every building service, deep electrification of the buildings sector occurs across states by 2100, although some differences remain due to variations in regional preferences, fuel prices, and turnover rates of existing equipment stock.
Additional edits to manuscript (section 6.1) GCAM-USA's industry sector is represented at a more aggregate level than buildings, but the same basic factorsstate-specific fuel preferences, capital stock accumulation, and regionally differentiated fuel pricesdrive differences in industrial fuel mix across the states.Additionally, some states, such as those with large petrochemical sectors (e.g., Louisiana, Texas), use much of the energy in industry as feedstocks, which lowers the share of electricity in their industrial energy mix.
- -------------------(1) Lines 524 to 527 highlight a very interesting calibration problem the authors have faced.This is worth discussing at depth since it likely affects the results significantly.In fact, it would be great if a sensitivity analysis was conducted, where different "consumer preferences" are tested.Alternatively, different "consumer preference" parameterization could be used across the Reference-Transition scenarios, thus expanding the "transition scenario" design.
(2) The authors thank the reviewer for the prescient comment and suggestion.Unfortunately, additional sensitivity scenarios or adjustments to the existing scenario design is beyond the scope of this model description paper.As mentioned previously, the scenarios in this paper are intentionally simple and intended to illustrate key model dynamics rather than develop detailed narratives of the future.
The reviewer is correct that this section highlights an interesting methodological issue that is worth exploring in more detail.To this end, we have added a supplementary note (Supplementary Note 4) where we elaborate on the default transportation assumptions in GCAM-USA v5.3_water_dispatch as well as how users can adjust these assumptions to create alternate scenarios for the transportation sector.
(3) Edits to manuscript Additional information about the reference transportation assumptions in GCAM-USA v5.3_water_dispatch, as well as how alternate assumptions of state-level policy or consumer preferences could be implemented, is provided in Supplementary Note 4.
- -------------------(1) On lines 597 to 600, the reduction in capacity factors for all technologies is highlighted in the transition scenarios, a very interesting result.Is it appropriate to interpret this as "increased electrification leads to increased redundancy of generating capital", and thus a disproportionate increase in electricity prices?I think this is something worth discussing further especially in connection to the to the model formulation.How does the methodology lead to this result?Is this something directly related to the improved electricity module which, as shown in Figure 1, accounts for load variation at different temporal aggregations?
(2) The authors thank the reviewer for the thoughtful comment.It is not necessarily correct to interpret this result as "increased electrification leads to increased redundancy of generating capital".As shown in Figure 4 of the original manuscript (note that figure numbers have changed in the revised manuscript as a new Figure 1 was added), national average electric capacity factors decrease across all scenarios, driven by greater penetration of intermittent renewable technologies (wind and solar), which have lower overall availability rates.The competition for investment in new generation capacity, based on levelized generation costs (inclusive of capital, operations and maintenance, fuel costs, and emissions penalties), tends to favor wind and solar in all scenarios; this is especially true in the Transition and High Growth + Transition scenarios where CO2 emissions face a carbon price.Rather than suggesting that increased electrification de-facto leads to increased redundancy of power sector capital, this suggests that intermittent renewables with lower capacity factors but also lower operating costs, no emissions (and associated costs), and rapidly improving capital costs, are most economical in this scenario.
Regarding the specific technologies show in Figure 5 (original manuscript) and discussed in lines 597 to 600 (original manuscript), capacity factors for fossil fuel (coal and gas) technologies without CCS capacity factors decrease in these scenarios because the price associated with their CO2 emissions makes them uneconomical to operate.In the new GCAM-USA electricity dispatch model structure, capacity is operated in order of least variable (operating) cost, which includes variable O&M, input (fuel and water) prices, and emissions penalties.Wind and solar capacity factors decline somewhat because resources are finite and heterogeneousthe resource representation in GCAM-USA assumes that the highest quality (highest capacity factor) resources are utilized first, so increased deployment entails exploiting lower quality resources and thus diminished capacity factors.The overall (national average) effect is small (2.2%/1.9%reduction in PV/onshore wind capacity factors in 2050 for the Transition scenario, relative to Ref), although some states see larger reductions.Not all technologies experience a reduction in capacity factor in the Transition scenario, although many do.A new Supplementary Figure SF3 presents national average electric technology capacity factors for all scenarios and model periods.Bioenergy with CCS capacity factors are stable in the Transition scenario; nuclear capacity factors decline through mid-century as intermittent renewables penetrate (nuclear has fuel costs and variable O&M costs which exceed those for solar and wind) but stabilizes from 2050 onward.Fossil technologies with CCS see steadily declining capacity factors over the course of the century as renewables continue to penetrate and carbon prices increase.
We have added a discussion of these dynamics to the manuscript (additions below in bold).- -------------------(1) Related to this, Supplementary Note 2 states that the dispatch of renewables depends on their supply curves, each containing a quantity of resource available at a specific capacity factor.This detail seems crucial so it would be good to get some insight on how these curves are constructed, and perhaps the curves should be presented as well.
(2) The authors thank the reviewer for the helpful suggestion.Plots of the resource curves for onshore wind and PV in each state are now included in Supplementary Note 2 (as Figure SN2.1).A brief discussion of how GCAM-USA's renewable resource curves are generated is also included in Supplementary Note 2 (additions below in bold).
(3) Edits to Supplementary Info The resource curves for solar PV and onshore wind in each state are presented below as Figure SN2.1.These curves are developed utilizing data from the Regional Energy Deployment System (ReEDS) modelspecifically resource potential, grid connection costs, and hourly capacity factor for multiple "resource regions" and resource classes within each state.The hourly capacity factor data are aggregated to annual average capacity factors, mapped to resource potential, and sorted from highest capacity to lowest capacity factor, generating the resource curves depicted in Figure SN2.1.(The same hourly capacity factor data are used to generate the segment capacity factorsi.e., the capacity factor specific to each monthly day/night dispatch segmentfor each resource and state.)Grid connection costs are included in the model as a separate cost component (considered in investment decisions only).Concentrating solar power and offshore wind resources are represented in the same way (also using data from ReEDS), although not every state has these resources.
- -------------------(1) Concerning the projections of agricultural water withdrawals mentioned on lines 267, is it correct to understand that irrigation rates are directly coupled to population and GDP growth (state or national?).Or are the intermediate drivers such as competition for land, food prices, etc.? These methodological choices should be crystal clear to allow the reader the fully appreciate the results.
(2) The authors thank the reviewer for the question.(We assume the reviewer intended to refer to line 627, rather than line 267.)It is not the case that irrigation rates are directly coupled to population and GDP growth.Irrigation rates are a function of cost competition between irrigated and rainfed production for each crop and land region.Irrigated agriculture entails higher costs (equipment + water costs) begetting greater yields.Agricultural water withdrawals are a function of total agricultural demand and these irrigation vs. rainfed choices across many crops.A discussion of these dynamics has been added to the manuscript (additions below in bold).
(3) Edits to manuscript Growth in agricultural water withdrawals in the Ref scenario is driven primarily by cropland expansion (see Figure 10) to meet increasing food demand caused by growing population and GDP.Nationally, cropland irrigation shares (the percentage of cropland that is irrigated as opposed to rainfed) for food and feed crops increase marginally over time across scenarios (16.4% in 2015, 17.4-19.6% in 2100), albeit with substantial variation across basins and crop types.Irrigation water demands are influenced by several factors, including agricultural land area (driven by food demand and modulated by competition between cropland and alternate land uses), competition between different crop types (which have different profit rates and water requirements), and competition among production strategies (irrigated vs. rainfed and high vs. low fertilizer application) based on their costs (including water and fertilizer prices), yields, and profitability (crop prices).Irrigated agriculture entails higher costs (equipment + water costs) but achieves higher yields.
(2) The authors thank the reviewer for the helpful question.Bioenergy crops can be produced with the same four production strategies (combinations of irrigated vs. rainfed and high vs. low fertilizer application) as other crops.Bioenergy irrigation shares range between 8-11% across scenarios and model periods, but bioenergy cropland expands tremendously in the Transition scenario while traditional (food and feed) cropland only slightly exceeds historical levels in 2100.Some additional detail has been added to the manuscript (additions below in bold).
(3) Edits to manuscript Bioenergy crops can be produced with the same four production strategies (combinations of irrigated vs. rainfed and high vs. low fertilizer application) as other crops.Irrigation shares are generally lower for bioenergy crops than they are for traditional (food and feed) crops (between 8-11% across scenarios and model periods), but bioenergy cropland expands tremendously in the Transition scenario while traditional cropland grows minimally, as described in Sect.6.1.As a result, about 71% of the increase in irrigation water withdrawals in 2100 (relative to 2015) are attributable to bioenergy crops rather than food and feed crops.
- -------------------(1) Figure 10 excludes "dedicated energy crops" for the percentage change of cropland.Is there a justification for their exclusion?Does it skew the results?I think this would be an interesting result to show.
(2) The authors thank the reviewer for the question.Bioenergy crops were excluded from Figure 10 (original manuscript) to emphasize the way food production shifts across basins within the scenarios.Additionally, there is no dedicated bioenergy cropland in the USA in 2015 (the model base year and base year for the percentage changes in Figure 10).Bioenergy cropland expansion in GCAM-USA tends to occur in marginal areas (bioenergy cropland expansion in the Transition scenario entails conversion of other arable land, pastureland, and to a lesser extent grassland and shrubland).The reviewer is correct that excluding dedicated energy crops from the figure paints an incomplete picture of total agricultural land.On the other hand, including dedicated energy crops in the figure can skew the picture of changes in croplandareas with relatively little agriculture historically (e.g.areas with limited water availability or marginal land quality) may see large percentage increases in cropland for dedicated energy crops while traditional cropland expands only modestly.
The reviewer is correct that both dynamics are interestingwe have added a new supplementary figure (SF6) showing absolute and percentage changes in traditional cropland, energy cropland, and total (food + feed + energy cropland).The supplementary figure is highlighted in the text.
(3) Edits to manuscript Bioenergy crops are excluded from Figure 11 to emphasize the way food production shifts across basins within the scenarios.There is no dedicated bioenergy cropland in 2015 (the model base year and base year for the percentage changes in Figure 11).Supplementary Figure SF6 presents absolute and percentage changes in traditional (food + feed) cropland, dedicated bioenergy cropland, and total cropland.In the discussion below, cropland is used to refer to traditional (food + feed) cropland, excluding dedicated energy crops.
- -------------------(1) Lines 755 onwards give a useful overview of the model calibration.Some more methodological details here would be interesting.Perhaps in an appendix.Are these calibration parameters the "share weights" mentioned on line 780, or are is the forcing simply a model "starting point"?How, is this forcing applied in the projections period?Is there a transition from forced-to-free?Or are the "forcing parameters" maintained?
(2) The authors thank the reviewer for the pertinent question.The reference to GCAM-USA being "calibrated to match historical data" does indeed relate to logit share weights mentioned on line 780.A comment has been added to clarify this connection.
Additional detail on the calibration process has been added to the manuscript.We've also added a supplementary table outlining how the calibrated share weight parameters for key sectors are applied in future model periods.Additions are below in bold.
(3) Edits to manuscript (section 6.5) GCAM-USA is initialized over five historical periods (1975,1990,2005,2010,2015) and calibrated (by estimating estimate logit share weight parameter values) to match historical data (calibration is discussed in more detail below).
Additional edits to manuscript (section 6.5)  SM5 (supplementary materials) provides an overview of how the calibrated share weights parameters for key sectors are applied in future model periods.
(2) The authors thank the reviewer for the question.Technological improvement is included and exogenously specified.A sentence has been added to the manuscript to clarify this point (additions below in bold).
(3) Edits to manuscript Technology costs in the logit equation include exogenous non-energy cost inputs and, endogenous energy and water prices (combined with exogenous conversion efficiencies), and emissions or other policy costs; non-energy cost and efficiency assumptions reflect exogenous technological improvement for most technologies/sectors. -------------------- (1) Lines 864 to 885 list some future improvements for the model.For improvement (1) is this really a priority?While I'm not knowledgeable on the topic, I would expect that there is a lot of inter-state trade for resources and electricity (famously not for Texas though), so i would expect state-level resource endowments (except for land) to provide limited extra insights.I could very well be wrong, in which case a justification for this decision would be very welcome.
(2) The authors thank the reviewer for the question.The reviewer is correct that inter-state trade for resources and electricity limit the impact of these resources' regional distribution on energy consumption.However, representing state-level fossil resource endowments and production is valuable because it permits the accounting of associated energy consumption and emissions at the state level, and thus facilitates the exploration of changes in regional patterns of resource production under alternate future scenarios, such as those with heterogenous state-level policies.
Currently, hydropower capacity is exogenously prescribed for each state and model period (with hydropower capacity and capacity factors fixed at 2019 levels for all future model periods).However, hydropower capacity and generation could vary in the future under different socioeconomic, technology, policy, or water availability scenarios, which could play out differentially among the states and impact the generation and final energy mix for a given state and those with which it trades electricity.
These explanations have been added to item 1 in the list of potential model developments (additions below in bold).
(3) Edits to manuscript State-level fossil resource endowments and production would permit the accounting of associated energy consumption and emissions at the state level and facilitates the exploration of changes in regional patterns of resource production under alternate scenarios.While hydropower production is exogenously prescribed for each state and model period (hydropower capacity and capacity factors are fixed at 2019 levels for all future periods), hydropower capacity and generation could vary in the future under different socioeconomic, technology, policy, or water availability scenarios, which could play out differentially among the states and impact the generation and final energy mix for a given state and its electricity trade partners.

( 3 )
Edits to manuscript Broadly, national average electric capacity factors decrease across all scenarios (Figure 4; original manuscript), driven by greater penetration of intermittent renewable technologies (wind and solar), which have lower maximum availability rates.The competition for investment in new generation capacity is based on levelized generation costs (inclusive of capital, operations and maintenance, fuel costs, and emissions penalties).Despite their lower capacity factors, wind and solar account for a large share of new installations in all scenarios due to their low operating costs, lack of emissions (and associated costs), and rapidly improving capital cost.Wind and solar are especially economical in the Transition and High Growth + Transition scenarios where fossil fuel technologies face a carbon price on their CO2 emissions.Wind and solar capacity factors themselves decline a bit over time across all scenarios (Supplementary Figure SF3 presents national average electric technology capacity factors for all scenarios and model periods); this reduction is a bit more pronounced in the Transition and High Growth + Transition scenarios (where wind and solar deployment is greatest).This is because wind and solar resource bases are finite and heterogeneously in quality; GCAM-USA assumes that the highest quality (highest capacity factor) resources are utilized first, so increased deployment over time entails exploiting lower quality resources and thus diminished capacity factors (Supplementary Note 2 explains this dynamic in greater detail).The overall (national average) reduction in capacity factor is small (2.2%/1.9%reduction in PV/onshore wind capacity factors in 2050 for the Transition scenario, relative to Ref), although some states see larger reductions.Capacity factors for fossil fuel (coal and gas) technologies without CCS also decrease in in the Transition and High Growth + Transition scenarios because the price associated with their CO2 emissions makes them less economical to operate (as noted above).In the new GCAM-USA electricity dispatch model structure, capacity is operated in order of least variable (operating) cost, including variable O&M, input (fuel and water) prices, and emissions penalties.However, not all technologies experience a reduction in capacity factor in the Transition scenario.Supplementary Figure SF3 presents national average electric technology capacity factors for all scenarios and model periods.Bioenergy with CCS capacity factors are stable in the Transition scenario; nuclear capacity factors decline through mid-century as intermittent renewable capacity increases (nuclear has fuel costs and variable O&M costs which exceed those for solar and wind) but stabilizes from 2050 onward.Fossil technologies with CCS see steadily declining capacity factors over the course of the century as renewable penetration grows and carbon prices steadily increase (making these as fossil plants with CCS more costly to operate, as these technologies still produce some residual CO2 emissions that escape capture).
Calvin et al. (2019).In short, technology shares within a nest are a function of the technology's cost, its share weight, the logit exponent, and the costs and share weights of other competing technologies in the nest.The exogenous logit exponent regulates the extent to which cost (or profit) dictates share; larger absolute logit exponent values lead to greater shares for the lowest cost (most profitable) technology, all else equal.Technology costs in the logit equation include exogenous non-energy cost inputs and, endogenous energy and water prices (combined with exogenous conversion efficiencies), and emissions or other policy costs; non-energy cost and efficiency assumptions reflect exogenous technological improvement for most technologies/sectors.During the calibration routine, GCAM uses the cost of each subsector or technology to estimate the (unobserved) logit share weight parameters, ensuring that historically observed outcomes are reproduced (Calvin et al., 2019).Technology shares are derived from the historical (calibration) data, leaving share weights as the unknown parameter in the logit equation that is solved for.These share weight parameters capture unobserved factors, including preferences, which impact economic choice but aren't explicitly represented in the model's choice indicator (cost).Share weights are typically held constant at their final (2015) calibration values or gradually converged to a common value in some future model period.Thus, the preferences captured by GCAM's calibration routine influence model decisions in future model periods (most strongly in initial model years).Table

Figure 9 :
Figure 9: I suspect the unit is not suppled to say EJ.If so, that is a very odd unit to use for land use.I would suggest MHa or an other unit of area.