SEARCH · Search NASA

Results for “GCAM modeling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Knowledge Graph for End-to-End Traceability of an Integrated Human-Earth System Model

Integrated human-Earth system models inform energy-water-land system dynamics and policies, yet their results are difficult to trace through input-data, model structure, scenario configurations, and solved outputs. Because this information is siloed across disconnected artifacts, process-based IAMs have historically lacked a unified, queryable representation. Such lack of traceability prevents researchers from systematically isolating the multi-sector drivers of complex outcomes (such as tracing water-scarcity results back to distant energy-system dynamics) or conducting holistic uncertainty attribution across hundreds of interacting parameters. To address this concern, our work documents the software engineering process of a knowledge graph that unifies these four layers for the Global Change Analysis Model (GCAM-USA_Reference scenario, GCAM v9.1). The graph was built as a relational property graph in DuckDB from the run’s own artifacts: the input-preparation dependency map (gcamdata chunk map), the model’s XML input files, the run configuration, and the results database (BaseX), successfully mapping the model’s declared structure. The resulting graph comprises 204,321 nodes and 1,687,814 edges across 16 node types and 15 edge types, with approximately 16.3 million time-series values stored separately to maintain structural efficiency. To ensure representation fidelity, every edge carries an epistemic-status annotation recording the warrant for the relationship (structural, provenance, dependency, or model-derived), and a machine-readable provenance ledger classifying the origin of every schema element. Evaluation against a fixed five-benchmark suite with locked baselines reports zero structural orphans, zero dangling edge endpoints, and 100% of output-producing technologies traceable to raw input files. Two interactive interfaces present the graph, including a serverless browser application built on DuckDB-Wasm. By establishing the first end-to-end provenance framework for an IAM, this work enables researchers and scientists to systematically audit complex policy scenarios, debug model structures, and trace policy-relevant outputs to their data origins in real time.

Artifical Intelligence↗

DOE OSTI2025

Long-term hydro-economic analysis tool for evaluating global groundwater cost and supply: Superwell v1.1

Abstract. Groundwater plays a key role in meeting water demands, supplying over 40 % of irrigation water globally, with this role likely to grow as water demands and surface water variability increase. A better understanding of the future role of groundwater in meeting sectoral demands requires an integrated hydro-economic evaluation of its cost and availability. Yet substantial gaps remain in our knowledge and modeling capabilities related to groundwater availability, recharge, feasible locations for extraction, extractable volumes, and associated extraction costs, which are essential for large-scale analyses of integrated human–water system scenarios, particularly at the global scale. To address these needs, we developed Superwell, a physics-based groundwater extraction and cost accounting model that operates at sub-annual temporal and at the coarsest 0.5° (≈50 km × 50 km) gridded spatial resolution with global coverage. The model produces location-specific groundwater supply–cost curves that provide the levelized cost to access different quantities of available groundwater. The inputs to Superwell include recent high-resolution hydrogeologic datasets of permeability, porosity, aquifer thickness, depth to water table, recharge, and hydrogeological complexity zones. It also accounts for well capital and maintenance costs, as well as the energy costs required to lift water to the surface. The model employs a Theis-based scheme coupled with an amortization-based cost accounting formulation to simulate groundwater extraction and quantify the cost of groundwater pumping. The result is a spatiotemporally flexible, physically realistic, economics-based model that produces groundwater supply–cost curves. We show examples of these supply–cost curves and the insights that can be derived from them across a set of scenarios designed to explore model outcomes. The supply–cost curves produced by the model show that most (90 %) nonrenewable groundwater in storage globally is extractable at costs lower than USD 0.57 m−3, while half of the volume remains extractable at under USD 0.108 m−3. The global unit cost is estimated to range from a minimum of USD 0.004 m−3 to a maximum of USD 3.971 m−3. We also demonstrate and discuss examples of how these cost curves could be used by linking Superwell's outputs with other models to explore coupled human–environmental system challenges, such as water resources planning and management, or broader analyses of multisectoral feedbacks.

Global Change Analysis Model (GCAM)↗

DOE OSTI2020

Dynamic projection of anthropogenic emissions in China: methodology and 2015–2050 emission pathways under a range of socio-economic, climate policy, and pollution control scenarios

Abstract. Future trends in air pollution and greenhouse gas (GHG)emissions for China are of great concern to the community. A set of globalscenarios regarding future socio-economic and climate developments, combiningshared socio-economic pathways (SSPs) with climate forcing outcomes asdescribed by the Representative Concentration Pathways (RCPs), was createdby the Intergovernmental Panel on Climate Change (IPCC). Chinese researchers have also developed various emission scenarios by considering detailed local environmental and climate policies. However, a comprehensive scenario set connecting SSP–RCP scenarios with local policies and representing dynamic emission changes under local policies is still missing. In this work, to fill this gap, we developed a dynamic projection model, the Dynamic Projection model for Emissions in China (DPEC), to explore China'sfuture anthropogenic emission pathways. The DPEC is designed tointegrate the energy system model, emission inventory model, dynamicprojection model, and parameterized scheme of Chinese policies. The modelcontains two main modules, an energy-model-driven activity rate projectionmodule and a sector-based emission projection module. The activity rateprojection module provides the standardized and unified future energyscenarios after reorganizing and refining the outputs from the energy systemmodel. Here we use a new China-focused version of the Global ChangeAssessment Model (GCAM-China) to project future energy demand and supply inChina under different SSP–RCP scenarios at the provincial level. Theemission projection module links a bottom-up emission inventory model, theMulti-resolution Emission Inventory for China (MEIC), to GCAM-China andaccurately tracks the evolution of future combustion and production technologiesand control measures under different environmental policies. We developedtechnology-based turnover models for several key emitting sectors (e.g.coal-fired power plants, key industries, and on-road transportationsectors), which can simulate the dynamic changes in the unit/vehicle fleetturnover process by tracking the lifespan of each unit/vehicle on an annualbasis. With the integrated modelling framework, we connected five SSP scenarios(SSP1–5), five RCP scenarios (RCP8.5, 7.0, 6.0, 4.5, and 2.6), and threepollution control scenarios (business as usual, BAU; enhanced controlpolicy, ECP; and best health effect, BHE) to produce six combined emissionscenarios. With those scenarios, we presented a wide range of China's futureemissions to 2050 under different development and policy pathways. We foundthat, with a combination of strong low-carbon policy and air pollutioncontrol policy (i.e. SSP1-26-BHE scenario), emissions of major airpollutants (i.e. SO 2 , NO x , PM 2.5 , and non-methane volatile organic compounds – NMVOCs) in China willbe reduced by 34%–66% in 2030 and 58%–87% in 2050 compared to 2015. End-of-pipe control measures are more effective for reducing air pollutant emissions before 2030, while low-carbon policy will play a more important rolein continuous emission reduction until 2050. In contrast, China's emissionswill remain at a high level until 2050 under a reference scenario without activeactions (i.e. SSP3-70-BAU). Compared to similar scenarios set from theCMIP6 (Coupled Model Intercomparison Project Phase 6), our estimates ofemission ranges are much lower than the estimates from the harmonized CMIP6 emissions dataset in2020–2030, but their emission ranges become similar in the year 2050.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

GCAM v4.3 SSP-RCP-GCM Output Products

Supplemental dataset to: Graham N.T., M.I. Hejazi, M. Chen, E. Davies, J.A. Edmonds, S.H. Kim, and S. Turner, et al. 2020. "Humans drive future water scarcity changes across all Shared Socioeconomic Pathways." Environmental Research Letters 15, no. 1:Article No. 014007. PNNL-SA-151297. doi:10.1088/1748-9326/ab639b This analysis makes deterministic classifications of how, when water resources are limited and constrained, the human and climate systems interact. The relative effects of both systems on water scarcity are quantified at global and basin scales across 15 global futures that include five different socioeconomic conditions (the Shared Socioeconomic Pathways, SSPs) and four different climatic conditions (the Representative Concentration Pathways, RCPs). These 15 scenarios are used to first analyze a 'Human Alone' component by isolating the human impact on future scarcity. This is accomplished by holding all climate variables to their 2005 levels while altering socioeconomic growth and technological change. General circulation model (GCM) derived climate impacts for five models are applied to establish 75 'Human and Climate' scenarios which allows for the quantification of climate impacts. Climatic impacts to water supply, agricultural productivity and change, hydropower availability, and building energy demands are applied from 5 different bias-corrected GCMs to make a suite of 75 climate runs. By subtracting the human derived impacts from the 'Human Alone' scenarios, from the 'Human and Climate' scenarios the climate impact is isolated. First, the drivers of future water scarcity are evaluated by isolating the impacts that both humans and climate have while accounting for feedbacks between humans, energy, and land. Secondly, the simultaneous impacts that human and climate systems have on water scarcity are analyzed by determining whether each system is increasing or decreasing scarcity in all global water basins. Below we describe the GCAM model, the scenario components, climate derived impacts, and the calculations of scarcity changes and attribution. This study uses the global change assessment model (GCAM), with inclusions of water constraints to both renewable and nonrenewable sources of water, to investigate the relative contributions of climate and human systems on water scarcity regionally and globally under a wide range of scenarios. GCAM links socioeconomics, the energy system, land-use change, climate, and the water sector. GCAM is a market-equilibrium model that allows for prices to be adjusted within each time step to ensure that the supply and demand of goods and services remains equilibrated at each time step allowing for simultaneous market clearing across sectors. This study accounts for a limited supply of water by employing cost resource curves across all 235 basins that follow a logit formulation to determine the share of each water source (renewable surface water, nonrenewable groundwater, and desalinated water) needed to meet the water demands within all basins. As depletion of various water sources increases the extraction price increases, which leads to compounding price increases on the goods and services that require higher-priced water sources.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

CERF: IM3 Projected Western US Power Plant Locations

Overview The Capacity Expansion Regional Feasibility (CERF) model is an open-source geospatial python package that provides new power plant locations at a 1km resolution. The model ingests U.S. state or regional-scale electricity system capacity expansion plans, such as those produced by the Global Change Analysis Model (GCAM-USA), and identifies feasible, site-specific locations for individual new power plants (renewable and non-renewable). CERF combines high-resolution geospatial suitability analyses with an economic algorithm that selects individual plant siting locations based on grid interconnection costs and the locational marginal value of new generation. The model incorporates a wide range of dynamic constraints and opportunities, such as protected lands, population density, existing infrastructure, and water availability. This dataset provides CERF power plant siting results for IM3 Phase 2 simulations across eight different scenarios for the Western US through 2055. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 CERF siting results in this dataset correspond to capacity expansion plans in the GCAM-USA IM3 Phase 2 simulation data and are available for each of the above scenarios. Data Details Temporal Range: 2015-2055 in 5-year timesteps. Note that 2015 is the experiment base year and 2020 and beyond represent model simulation years. Spatial Range: Plant locations are provided for the eleven states in the Western US including Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming. Spatial Resolution: 1 km-squared, provided in x and y coordinates Geospatial Projection: Albers Equal Area Conic (ESRI:102003) File Type: csv The dataset contains subdirectories for each of the eight scenarios described in the overview. Each scenario folder contains two subfolders with the following information: 1. Power Plant Data This directory contains a single .csv file of power plant locations for both pre-existing (non-CERF sited plants in operation in 2015) and new (CERF-sited) power plants across the temporal range along with additional CERF model output parameters for CERF-sited plants. Plant with a siting year earlier than 2020 correspond to facilities that are operational leading into the first timestep CERF simulation. For a more detailed description of CERF model output parameters, see the CERF model documentation. Note that the cerf_plant_id parameter is unique within each scenario file but not across scenario files. Parameter Descriptions scenario - Name of scenario cerf_plant_id - Unique siting identifier cerf_sited - If True, indicates that plant was sited by CERF model. If False, indicates pre-existing facility region_name - Name of region (state) tech_id - Technology ID tech_name - Full generation technology name inclusive of cooling type (if applicable) and additional characteristics tech_simple - Simplified generation technology type unit_size_mw - Power plant unit size (MW) xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) index - Index position in the flattend 2D array buffer_in_km - Exclusion buffer around site (km) sited_year - Year of siting retirement_year - Year of retirement lmp_zone - Locational marginal price (LMP) zone ID locational_marginal_price_usd_per_mwh - Locational marginal price ($/MWh) generation_mwh_per_year - Generation output (MWh/yr) operating_cost_usd_per_year - Cost of plant operations ($/yr) net_operational_value - Net operational value based on LMP and and operating costs ($/yr) interconnection_cost - Cost of interconnection for transmission & gas pipeline (if applicable) net_locational_cost -- Difference of interconnection cost and operating value ($/yr) capacity_factor_fraction - Capacity factor (fraction) carbon_capture_rate_fraction - Carbon capture rate (fraction) fuel_co2_content_tons_per_btu - Fuel CO2 content (tons/Btu) fuel_price_usd_per_mmbtu - Fuel price ($/MMBtu) fuel_price_esc_rate_fraction - Fuel price escalation rate (fraction) heat_rate_btu_per_kWh - Heat rate (Btu/kWh) lifetime_yrs - Technology lifetime for annuity (years) operational_life_yrs - Operational lifetime for retirement (years) variable_om_usd_per_mwh - Variable operation and maintenance costs of yearly capacity use ($/MWh) variable_om_esc_rate_fraction - Variable operation and maintenance costs escalation rate (fraction) carbon_tax_usd_per_ton - Carbon tax ($/ton) carbon_tax_esc_rate_fraction - Carbon tax escalation rate (fraction) 2. Storage Data This directory contains information on new and pre-existing energy storage facilities operational in each timestep along with various storage operational parameters. The 2015 timestep provides pre-existing energy storage data and corresponds with facilities that are operational leading into the first model simulation timestep. Note that coordinates in the storage files correspond to the interconnection point on the grid (substation location), not individual energy storage locations. Energy storage is added in a cumulative process at each given interconnection point. That is, each individual file provides the total operational storage capacity interconnected to the specified substation for the given timestep, inclusive of previously installed storage at that location and new storage installed in that timestep at that location. Parameters scenario - Name of scenario timestep - Simulation timestep name - Unique storage identifier s_typ - Type of energy storage technology (battery or pumped storage hydro) s_node - Node ID of interconnecting substation xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) charge_rate - Maximum charge rate (power capacity) of storage system (MW) discharge_rate - Maximum discharge rate (power capacity) of storage system (MW) duration - Duration of storage system (hours) max_SoC - Allowed maximum state of charge (energy capacity) of storage system (MWh) min_SoC -Allowed minimum state of charge (energy capacity) of storage system (MWh) charge_eff - Efficiency of charge (fraction between 0 and 1) discharge_eff - Efficiency of discharge (fraction between 0 and 1) Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program.

CERF↗

DOE OSTI2024

Core Model Proposal #389: Ammonia Trade and Miscellaneous GCAM 7.0 Bugfixes

This proposal re-structures the N fertilizer sector in the model, replacing the trade structure which was implemented before GCAM had the capability to trade secondary goods between regions, and re-naming the production commodity "ammonia" in order to better set up the model for representing ammonia as an energy carrier in subsequent work. Several bugfixes for GCAM 7.0 are also included in the proposal.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Integrated Assessment Modeling of Grid Resilience: GCAM-to-PCM Scoping

Deep decarbonization towards a net-zero emissions future and the associated growth in power demand, renewables, and changes in water availability will require significant evolution in grid planning. Many obstacles threaten the viability of near-term generation capacity build out, including the need for large-scale transmission expansion. Current very-long term planning tools do not adequately model physical representations of economic dispatch and system topology down to inter- and intra-regional transmission and distribution infrastructure. This project attempts to fill that gap.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

The impact of regional resources and technology availability on carbon dioxide removal potential in the United States

To achieve net zero carbon emissions by mid-century, the United States may need to rely on carbon dioxide removal (CDR) to offset emissions from difficult-to-decarbonize sectors and/or shortfalls in near-term mitigation efforts. CDR can be delivered using many approaches with different requirements for land, water, geologic carbon storage capacity, energy, and other resources. The availability of these resources varies by region in the U.S. suggesting that CDR deployment will be uneven across the country. Using the global change analysis model for the United States (GCAM-USA), we modeled six classes of CDR and explored their potential using four scenarios: a scenario where all the CDR pathways are available (Full Portfolio), a scenario with restricted carbon capture and storage (Low CCS), a scenario where the availability of bio-based CDR options is limited (Low Bio), and a scenario with constraints on enhanced rock weathering (ERW) capabilities (Low ERW). We find that by employing a diverse set of CDR approaches, the U.S. could remove between 1 and 1.9 GtCO 2 /yr by midcentury. In the Full Portfolio scenario, direct air carbon capture and storage (DACCS) predominates, delivering approximately 50% of CO 2 removal, with bioenergy with carbon capture and storage contributing 25%, and ERW delivering 11.5%. Texas and the agricultural Midwest lead in CDR deployment due to their abundant agricultural land and geological storage availability. In the Low CCS scenario, reliance on DACCS decreases, easing pressure on energy systems but increasing pressure on the land. In all cases CDR deployment was found to drive important impacts on energy, land, or materials supply chains (to supply ERW, for example) and these effects were generally more pronounced when fewer CDR technologies were available.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

US energy system transitions under cumulative emissions budgets

Cumulative emissions budgets are increasingly being used by decision makers and analysts to understand emissions reductions and associated transitions in the context of long-term goals such as limiting global mean temperature increase over the century to 1.5 or 2°C (IPCC, 2014). While previous studies have explored the implications of cumulative emissions budgets for the global economy, few studies have conducted regional and national-level analyses (Iyer et al., 2015; Riahi et al., 2015). This paper explores cumulative emissions budgets through 2050 consistent with the 1.5 and 2°C long-term temperature goals in the context of the United States. We employ a state-level model of the United States embedded within a global human-Earth system model (GCAM-USA) to study the implications of such budgets for the U.S. energy system (Iyer et al., 2017a; Iyer et al., 2017b). Our results show that achieving the stringent cumulative emissions budgets entails accelerated deployment of energy conserving technology, almost complete decarbonization of the power sector and increased electrification of buildings and industrial end-use sectors and decarbonization of transport employing a combination of electrification and the substitution of bioenergy for fossil fuels by the mid-century. We also find substantial state-level differences in the relative roles of these decarbonization strategies. Furthermore, our results highlight that increased ambition in the near-term will be valuable in setting the stage for smoother transformations in the future to achieve stringent cumulative emissions budgets consistent with long-term temperature goals established by the international community.

Feijoo, Felipe↗

DOE OSTI2024

Impacts of Forest Management-Induced Productivity Changes on Future Land Use and Land Cover Change

Anthropogenic land use and land cover change (LULCC) is projected to continue in the future. However, the influence of forest management on forest productivity change and subsequent LULCC projections remains under-investigated. This study explored the impacts of forest management-induced change in forest productivity on LULCC throughout the 21st century. Specifically, we developed a framework to softly couple the Global Change Analysis Model and Global Timber Model to consider forest management-induced forest productivity change and projected future LULCC across the five Shared Socioeconomic Pathways (SSPs). We found future increases in forest management intensity overall drive the increase of forest productivity. The forest management-induced forest productivity change shows diverse responses across all SSPs, with a global increase from 2015 to 2100 ranging from 3.9% (SSP3) to 8.8% (SSP1). This further leads to an overall decrease in the total area with a change of land use types, with the largest decrease under SSP1 (-7.5%) and the smallest decrease under SSP3 (-0.7%) in 2100. Among land use types, considering forest management-induced change significantly reduces the expansion of managed forest and also reduces the loss of natural land in 2100 across SSPs. This suggests that ignoring forest management-induced forest productivity change underestimates the efficiency of wood production, overestimates the managed forest expansion required to meet the future demand, and consequently, potentially introduces uncertainties into relevant analyses, for example, carbon cycle and biodiversity. Thus, we advocate to better account for the impacts of forest management in future LULCC projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Historical and Future Global Irrigation Energy Consumption by Fuel and Region

Irrigation energy use is a significant component of agricultural production costs, contributing directly to the energy and emissions intensity of crop production and ultimately to food prices. Understanding the existing structure of irrigation energy consumption help achieve food-energy-water security and environmental goals. We present a comprehensive global data set detailing country-level irrigation energy consumption, emphasizing the comparative use of electric, diesel, and emerging solar pumps. To our knowledge, no such data set exists. We draw from a literature review to develop a logistic transformed regression model to estimate the shares of fuel sources for irrigation across countries over historical years to construct a global data set of country-level irrigation energy consumption by multiple fuel sources. Additionally, we compare our estimates of irrigation energy use with agricultural energy use as reported by the International Energy Agency and other external sources. We then use this data to project future irrigation energy use with the Global Change Analysis Model, which is a multisector dynamics model, to showcase the usage of this data set. Projections under the reference scenario show a global shift in fuel types for irrigation pumping, while patterns vary across regions, with India and Pakistan leading in solar-powered irrigation growth and countries like the USA and China continuing to rely primarily on grid electricity. This data set provides a resource to understand the role of irrigation fuel choices within the broader energy sector, as well as the connected agricultural, land use, and water sectors under alternative future scenarios, enabling informed decision making toward efficient agricultural practices.

Global Change Analysis Model (GCAM)↗

DOE OSTI2021

The surprisingly inexpensive cost of state-driven emission control strategies

Traditionally, analysis of the costs of cutting greenhouse gas emissions has assumed that governments would implement idealized, optimal policies such as uniform economy-wide carbon taxes. Yet actual policies in the real world, especially in large federal governments, are often highly heterogeneous and vary in political support and administrative capabilities within a country. While the benefits of heterogeneous action have been discussed widely for experimentation and leadership, little is known about its costs. Focusing on the United States, we represent plausible variation (by more than a factor of 3) in the stringency of state-led climate policy in a process-based integrated assessment model (GCAM-USA). For a wide array of national decarbonization targets, we find that the nationwide cost from heterogeneous subnational policies is only one-tenth higher than nationally uniform policies. Such results hinge on two critical technologies (advanced biofuels and electricity) for which inter-state trade ameliorates the economic efficiencies that might arise with heterogeneous action.

Peng, Wei↗

DOE OSTI2024

The Role of Biofuels and Biomass Feedstocks for Decarbonizing the U.S. Economy by 2050 - (DECARB) Decarbonizing Energy Through Collaborative Analysis of Routes and Benefits

Utilizing biomass resources, such as cellulosic biomass and waste, can greatly contribute to decarbonization efforts in the U.S. economy. The U.S. bioenergy sector includes corn ethanol production, biodiesel, renewable diesel production, and the utilization of biomass wastes for electricity generation being the primary applications. Within the electricity sector, biopower can play a crucial role as a stable low-carbon resource. Enhancing the electricity mix's diversity could enhance grid reliability. If the issues regarding hot gas cleanup can be resolved, flexible biopower resources like biomass gasification facilities could complement the integration of variable renewable energy sources due to their quick ramp-up and ramp-down times. The criticality of bioenergy deployment lies in its ability to decarbonize hard-to-electrify sectors, such as aviation, where alternative decarbonization options may not be viable in the short term. Moreover, bioenergy has the potential to be converted into process heating, building materials, and plastics, which are not considered in this study. A set of pathways was carefully chosen to represent viable options for converting ample herbaceous and woody cellulosic feedstocks into fuels, chemicals, and electricity in this study. In conclusion, biomass pathways provide flexibility by generating various types of bioenergy and bioproducts, including electricity, hydrogen, liquid fuels, biochemicals, and bioplastics. When paired with carbon dioxide capture and storage (CCS), specific bioenergy approaches can effectively extract carbon dioxide from the atmosphere, thus providing an effective decarbonization option for the transportation sector.

09 BIOMASS FUELS↗

DOE OSTI2023

Core Model Proposal #338: Regional Forest Markets and Crop Trade Updates

The main purpose of this proposal is to update the forest (i.e., Roundwood) trade modeling in GCAM from HOV (Heckscher-Ohlin Vanek) based fully integrated world market to segmented regional markets using the logit-based Armington approach. We also improve agricultural trade-related data, update trade (Armington) parameters based on literature information, and allow elastic forest demand responses. This CMP is an extension of previous GCAM agricultural trade CMPs (289 & 312).

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Impacts of water scarcity on agricultural production and electricity generation in the Middle East and North Africa

Incorporating the interdependencies between water, energy and food (WEF) within an integrated approach of planning and management could help nations worldwide to address sustainability concerns. This is a topic of great importance for the Middle East and North Africa (MENA) region, where water is a very limited resource. In this study, we develop an analytical framework to analyze the water-energy-food nexus in the MENA region to inform the formulation of integrated strategies for water, energy and food activities. Our approach is based on an integrated assessment model for the MENA region, which explicitly represents WEF sectors within an economic framework, in tandem with a set of relevant scenarios addressing three key dimensions (socioeconomics, climate and water-management). Using this framework, our study analyzes the current and projected status of water resources in the region, and the potential implications for the agriculture and electricity sectors. Our scenarios demonstrate that water scarcity worsens by the end of the 21st century in most MENA countries, mostly due to growing demands. The impacts of growing scarcity on agriculture are significant, with production projected to drop by 60 percent by 2050 in some countries. On the other hand, and to a lesser extent, water-saving technologies and fuel-switching in the power sector play a key role in mitigating the effects of water scarcity on electricity generation in some parts of the MENA region. Our analysis then underscores the need to reduce the dependence of MENA’s agricultural and energy sectors on water, and transition to renewable energies to reduce water scarcity.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Evaluating long-term model-based scenarios of the energy system

Energy-economic models are used to provide science-based decision support in a variety of contexts. Analyses using these tools often involve defining a “reference” scenario, which serves as a counterfactual against which alternative scenarios are compared. Evaluating scenarios, including reference scenarios, is critical for establishing the credibility of the analyses these models support. We propose a framework for evaluating energy system scenarios which consists of three parts – a qualitative storyline, quantitative metrics, and evaluation criteria. We apply this framework to the reference scenario for GCAM-USA, a version of the global human-Earth system model GCAM (Global Change Assessment Model) with state-level detail in the United States, focusing on the evolution of the electric power sector. We develop new visual analytic tools to facilitate the evaluation of model outcomes in 51 sub-national regions, and demonstrate how scenario performance can be tracked and compared across four quantifications of the GCAM-USA reference scenario.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Core Model Proposal #350: Detailed Natural Gas Trade

This core model proposal adds detail to GCAM's natural gas trade model by separating gas trade between Liquefied Natural Gas (LNG) and six regional gas pipeline networks. This entails creating new trade markets for global LNG and the six regional pipeline networks, and modifying the regional natural gas sector in each region to reflect the competition between LNG and pipelines within imported natural gas, and between different pipeline networks. The nesting subsector capability (CMP #299 Infinite Subsector Nesting) was used to represent this additional level of competition on the consumption side.

03 NATURAL GAS↗

DOE OSTI2021

Dynamic Linking of Upstream Energy and Freight Demands for Bio and Fossil Energy Pathways in the Global Change Analysis Model

Comprehensive study of the environmental impacts associated with demand for an energy resource or carrier in any one sector requires a full consideration of the direct and indirect impacts on the rest of the regional and global energy system. It is important to consider the energy that is consumed in producing primary energy resources and energy consumed in transporting these energy resources internationally and domestically in order to produce a complete picture of these impacts. Biofuels are especially complex since they have feedbacks not just to the energy system but also to regional and global crop markets. Different modeling and analysis strategies have been applied to consider these impacts, which occur “upstream” of final energy consumption, with some success. Traditional life cycle models allow for rich technological detail in linking upstream impacts of energy demand with downstream final consumption; however, they cannot by themselves represent dynamic economic feedbacks across multiple sectors and regions over time. Computable general equilibrium (CGE) modeling does account for economic feedback among all sectors and regions; however, CGE modeling does not lend itself to physical representation of technological detail. Partial equilibrium (PE) modeling is a heterogeneous category describing economic models that focus on a subset of the economy, and they differ in coverage and ease in incorporating more sectors and economic links. In this study, we present a strategy for dynamically including the direct and indirect impacts of energy demand with physical and technology detail by explicitly adding these upstream energy and transportation links to GCAM, a PE model of global energy, land use, and emissions. We incorporate the following inter-sectoral linkages: energy inputs to crop production, energy inputs to fossil resource production, and freight transport requirements of energy and agricultural commodities. We assess the implications of explicitly including these links by measuring the global impacts of increased corn ethanol demand in the United States with and without these links included. Although the net global impact of the upstream links on energy and emissions are relatively modest in the scenarios we studied, the inclusion of these links illustrates interesting trade-offs in energy and transportation demand among fossil fuel and agriculture sectors. These sectoral interactions suggest that this level of modeling detail could be important in evaluating future analytical questions.

Sampedro Martinez de Estivariz, Jon↗