In this work, a new modeling tool called DECAL (DEcarbonize CALifornia) is developed and used to evaluate what it will take to reach California's climate mandate of net-zero emissions by 2045. DECAL is a scenario-based model that projects emissions, society-wide costs, and resource consumption in response to user-defined inputs. DECAL has sufficient detail to model true net-zero pathways and reveal fine-grain technology insights. Using DECAL, we find the State can achieve 52 % of the emissions abatement needed to meet net-zero by 2045 using technologies that are already commercially available, such as electric vehicles, heat pumps, and renewable electricity & storage. While these technologies are mature, the speed and scale of deployment required will still pose significant practical challenges if not technical ones. In addition, we find that 25 % of emissions abatement will come from technologies currently at early-stage deployment, and 23 % from technologies at research scale, motivating the continued research & development of these technologies, including zero-emission heavy-duty vehicles, carbon capture & sequestration, clean industrial heating, low global warming potential refrigerants, and direct air capture. Significant carbon dioxide removal will also be needed for California to meet its net-zero target on time, at least 45 Mt/yr and more likely up to 75 Mt/yr by 2045. Accelerating deployment of mature technologies can further reduce the need for carbon removal, nevertheless, establishing enforceable carbon removal targets and conducting policy planning to make said goals a reality will be needed if California is to meet its net-zero by 2045 goal.

Abstract Food, energy, and water security are key criteria in analyses of complex human‐Earth system interactions but are rarely analyzed jointly in scenario analysis of global change. Here, we generate a scenario ensemble centered around these outcomes using a global‐scale multisector model that simulates critical interactions across multiple sectors. We consider a wide range of future uncertainties in both physical and societal factors, including population, economics, alternative scenarios of future carbon intensity, and land conservation. We evaluate each scenario by computing both regional and subregional resource security outcomes and identify the uncertainties and key drivers that lead to the outcomes of interest. We find that lower‐income subgroups face higher burdens and more uncertain outcomes, and that drivers of resource security outcomes are heterogeneous across regions. Ultimately, our approach can help identify scenarios that can facilitate high‐priority research on resource security outcomes, especially in multisector contexts.

Emissions reductions may be met with relatively small costs.

The Energy Modeling Forum 37 study is organized around carbon dioxide (CO2) mitigation scenarios reaching net-zero CO2 emissions by 2050 in the United States. This paper summarizes the potential contribution of bioenergy use in the electric power, transportation, industrial, and buildings sectors toward meeting that target based on model results. Thirteen modeling teams reported bioenergy consumption in the Reference and Net Zero scenarios. Consumption of bioenergy increased over time in the Reference scenario, from an average across models of 3.2 exajoules (EJ) in 2020 to 3.8 EJ in 2050. Average bioenergy consumption in 2050 increased further to 7.3 EJ in the Net Zero scenario. All scenarios that reach net-zero emissions required some form of carbon dioxide removal to offset emissions that are difficult to reduce. Carbon dioxide removal using bioenergy with CO2 capture and storage (BECCS) varies widely across models, up to 1000 Mt CO2 in 2050. Some models rely instead on direct air carbon capture and storage (DACCS), up to 2200 Mt CO2, and others use a combination of BECCS and DACCS. Model results show a strong inverse relationship between the amounts of BECCS and DACCS deployed. All modeling teams assumed a carbon sink from land use, land use change, and forestry, further offsetting a portion of emissions from fossil fuels and industry that are expensive to eliminate. Bioenergy consumption in 2050 decreased by an average of 1.5 EJ across eight models in a Net Zero+ scenario relative to the Net Zero scenario, due in part to a lower equilibrium carbon price resulting from optimistic cost assumptions for all energy technologies.

This paper explores the role of electricity in achieving economy-wide net-zero CO2 emissions by 2050 in the United States based on results from 17 models as part of the 37th Stanford Energy Modeling Forum (EMF-37). In the study's Net-Zero scenario, the models use diverse pathways to achieve net-zero emissions by 2050, with gross energy-related residual emissions ranging from 17.2 to 66.6 % of 2020 levels. Electricity consistently emerges as central to achieving net-zero, with models projecting rapid electrification of end-uses and rapidly declining CO2 intensity of electricity. However, the extent of electrification and the technology mix to decarbonize the power sector vary considerably across models. In the Net-Zero scenario, electricity is projected to evolve from similar to 20 % of final energy in 2020 to 17-63 % in 2050 across the models driven by electrification in all sectors-buildings, industry, and transportation-and, to a lesser extent by direct air capture. By 2050, total electricity consumption increases by 24-176 % (relative to 2020), accompanied by significant expansion in renewable electricity production. Together, solar and wind generation grows by 175-834 %, supplying 45-90 % of total electricity in 2050, with wind achieving slightly higher shares than solar. Electricity storage technologies are deployed at scale to support wind and solar generation. The electricity generation mix varies across models: some project almost complete reliance on renewables, while others see a substantial role for natural gas, often with carbon capture and storage. This paper synthesizes the rich diversity of modeling approaches and results, highlighting differing views on how key drivers of electricity demand and supply might evolve.

Many countries, subnational jurisdictions, and companies are setting net-zero emissions goals; however, questions remain about strategies to reach these targets, policy measures, technology gaps, and economic impacts. We investigate the potential policy implications of reaching economy-wide net-zero CO2 emissions across the United States by 2050 using results from a multi-model comparison with 14 energy-economic models. Model results suggest that achieving net-zero CO2 targets depends on policies that accelerate deployment of zero- and low-emitting technologies that have seen rapid cost reductions in recent years (including wind, solar, battery storage, and electric vehicles) as well as relatively nascent options (including carbon capture and storage, advanced biofuels, low-carbon hydrogen, advanced nuclear, and long-duration energy storage). While net-zero policies are likely to lower fossil fuel consumption, including considerable coal and petroleum reductions, achieving net-zero emissions does not necessarily mean phasing out all fossil fuels. Model results indicate that the Inflation Reduction Act’s energy and climate provisions amplify near-term decarbonization but that net-zero policies have larger impacts on long-run outcomes. Stringent climate policy can have large fiscal impacts on tax revenue and government spending—revenues from carbon pricing and subsidies for carbon removal range from 0.1 % to 3.7 % of GDP in 2050 across models. Each dollar per metric ton carbon price leads to a 0.06 % to 0.31 % reduction in economy-wide CO2 emissions relative to a reference scenario with current policies. Spending on energy across the economy decreases relative to today for many models under reference and net-zero policies, especially as a share of GDP, due primarily to end-use electrification and energy efficiency.

Transportation is currently the largest source of U.S. anthropogenic CO2 emissions, at about a third of the total. Achieving net-zero emissions by mid-century will require substantial reductions in transportation emissions across passenger and freight travel. Here we leverage a model intercomparison study to explore the role of transportation in scenarios achieving net-zero economy-wide CO2 emissions by 2050. We find the transport sector is poised to play the most significant role in reducing demand-side emissions, mostly driven by technology substitution, as modeling results suggest a limited role for mode shifting and for reduced use of personal car travel in the U.S. Among various technology solutions, models show agreement that passenger on-road vehicles will largely transition to electric vehicles (EVs), while solutions to decarbonize heavier travel modes are more diverse and include greater use of liquid biofuels and hydrogen. Research should continue to investigate the evolution of on-road electrification, the role of biofuels and hydrogen across heavier travel modes, and the role of mode shifting and travel behavior change to support personal transportation decarbonization at national and regional scales to temper the rapid growth in clean fuel and electricity demand.

With companies, states, and countries targeting net-zero emissions around midcentury, there are questions about how these targets alter household welfare and finances, including distributional effects across income groups. This paper examines the distributional dimensions of technology transitions and net-zero policies with a focus on welfare impacts across household incomes. The analysis uses a model intercomparison with a range of energyeconomy models using harmonized policy scenarios reaching economy-wide, net-zero CO2 emissions across the United States in 2050. We employ a novel linking approach that connects output from detailed energy system models with survey microdata on energy expenditures across income classes to provide distributional analysis of net-zero policies. Although there are differences in model structure and input assumptions, we find broad agreement in qualitative trends in policy incidence and energy burdens across income groups. Models generally agree that direct energy expenditures for many households will likely decline over time with reference and netzero policies. However, there is variation in the extent of changes relative to current levels, energy burdens relative to reference levels, and electricity expenditures. Policy design, primarily how climate policy revenues are used, has first-order impacts on distributional outcomes. Net-zero policy costs, in both absolute and relative terms, are unevenly distributed across households, and relative increases in energy expenditures are higher for lowest-income households. However, we also find that recycled revenues from climate policies have countervailing effects when rebated on a per-capita basis, offsetting higher energy burdens and potentially even leading to net progressive outcomes. Model results also show carbon Laffer curves, where revenues from net-zero policies increase but then decline with higher stringencies, which can diminish the progressive effects of climate policies. We also illustrate how using annual income deciles for distributional analysis instead of expenditure deciles can overstate the progressivity of emissions policies by overweighting revenue impacts on the lowest-income deciles.

The Inflation Reduction Act (IRA) is regarded as the most prominent piece of federal climate legislation in the U.S. thus far. This paper investigates potential impacts of IRA on the power sector, which is the focus of many core IRA provisions. We summarize a multi-model comparison of IRA to identify robust findings and variation in power sector investments, emissions, and costs across 11 models of the U.S. energy system and electricity sector. Our results project that IRA incentives accelerate the deployment of low-emitting capacity, increasing average annual additions by up to 3.2 times current levels through 2035. CO 2 emissions reductions from electricity generation across models range from 47%–83% below 2005 in 2030 (68% average) and 66%–87% in 2035 (78% average). Our higher clean electricity deployment and lower emissions under IRA, compared with earlier U.S. modeling, change the baseline for future policymaking and analysis. IRA helps to bring projected U.S. power sector and economy-wide emissions closer to near-term climate targets; however, no models indicate that these targets will be met with IRA alone, which suggests that additional policies, incentives, and private sector actions are needed.

Shared community scenarios of societal and environmental system changes have underpinned a broad range of research and assessment studies over the past several decades. These scenarios have largely aimed to address specific questions within broad issue areas like climate change or biodiversity and generally provided information on the drivers of change. The consequences of those drivers, such as impacts on society and policy responses, have tended to be left to the research community to investigate, using scenarios of drivers as inputs to their studies, producing projections of a disparate set of relevant output metrics. While this approach has had many benefits, it has fallen short of producing a robust, comparable literature describing outcomes across studies in common metrics. We argue that new scenarios are needed that extend current approaches to be organized around common outcome metrics for the well-being and resilience of society and ecosystems. We propose an approach that would focus on agreed upon outcomes for well-being and resilience as well as critical drivers of change, cut across issues and scales in multiple sectors, and draw on new systematic methods of scenario generation and discovery to highlight scenarios that are most critical in understanding societal risks and responding to them. Research derived from this outcome-based scenario development approach would facilitate improved assessment of risks of and responses to a range of stressors and the multi-sector interactions they generate.

The Energy Modeling Forum (EMF) 37 study on deep decarbonization and high electrification analyzed a set of scenarios that achieve economy-wide net-zero carbon dioxide (CO2) emissions in North America by mid-century, exploring the implications of different technology evolutions, policies, and behavioral assumptions affecting energy supply and demand. For this paper, 16 modeling teams reported resulting emissions projections, energy system evolution, and economic activity. This paper provides an overview of the study, documents the scenario design, provides a roadmap for complementary forthcoming papers from this study, and offers an initial summary and comparison of results for net-zero CO2 by 2050 scenarios in the United States. We compare various outcomes across models and scenarios, such as emissions, energy use, fuel mix evolution, and technology adoption. Despite disparate model structure and sources for input assumptions, there is broad agreement in energy system trends across models towards deep decarbonization of the electricity sector coupled with increased end-use electrification of buildings, transportation, and to a lesser extent industry. All models deploy negative emissions technologies (e.g., direct air capture and bioenergy with carbon capture and storage) in addition to land sinks to achieve net-zero CO2 emissions. Important differences emerged in the results, showing divergent pathways among end-use sectors with deep electrification and grid decarbonization as necessary but not sufficient conditions to achieve net zero. These differences will be explored in the papers complementing this study to inform efforts to reach net-zero emissions and future research needs.

Economy-wide emissions drop 43 to 48% below 2005 levels by 2035 with accelerated clean energy deployment.

This paper presents a framework for pricing the climate resilience of an energy infrastructure project through assessing the value of its required debt and equity investments. Integrating climate scenarios into an asset valuation model provides useful and specific insights for risk management, but there is a lack of academic and market tools that effectively address this need. The critical barrier is that climate-related risks (physical and transition) are typically indirect variables in the cash flow calculation, and they should be computed based on the direct variables such as revenue, capital expenditures, operating expenses, and financing costs. The implementation of this framework shows how to delineate climate-related risks that are asset-specific and transforms them into financial risks. Using cash flow simulation and scenario analysis, it estimates an energy infrastructure asset’s probability of default due to climate-related risks and the size and timing of the losses for any given default. To demonstrate the framework's application, we simulate the price climate-related risks of a utility-scale electricity generation facility powered by natural gas.

The expected growth in the demand for passenger and freight services exacerbates the challenges of reducing transport GHG emissions, especially as commercial low-carbon alternatives to petroleum fuels are limited for shipping, air and long-distance road travel. Biofuels can offer a pathway to significantly reduce emissions from these sectors, as they can easily substitute for conventional liquid fuels in internal combustion engines. In this paper, we assess the potential of bioenergy to reduce transport GHG emissions through an analysis leveraging various integrated assessment models and scenarios, as part of the 33rd Energy Modeling Forum study (EMF-33). We find that bioenergy can contribute a significant, albeit not dominant, proportion of energy supply to the future transport sector: in scenarios aiming to keep the temperature increase below 2 °C by the end of the twenty-first century, models project that in 2100 bioenergy can provide on average 42 EJ/yr (ranging from 5 to 85 EJ/yr) for transport (compared to 3.7 EJ in 2018), mainly through lignocellulosic fuels. This makes up 9–62% of final transport energy use. Only a small amount of bioenergy is projected to be used in transport through electricity and hydrogen pathways, with a larger role for biofuels in road passenger transport than in freight. The association of carbon capture and storage (CCS) with bioenergy technologies (BECCS) is a key determinant in the role of biofuels in transport, because of the competition for biomass feedstock to provide other final energy carriers along with carbon removal. Among models that consider CCS in the biofuel conversion process the average market share of biofuels is 21% in 2100 (ranging from 2 to 44%), compared to 10% (0–30%) for models that do not. Cumulative direct emissions from the transport sector account for half of the emission budget (from 306 to 776 out of 1,000 GtCO2). However, the carbon intensity of transport decreases as much as other energy sectors in 2100 when accounting for process emissions, including carbon removal from BECCS. Lignocellulosic fuels become more attractive for transport decarbonization if BECCS is not feasible for any energy sectors. Since global transport service demand increases and biomass supply is limited, its allocation to and within the transport sector is uncertain and sensitive to assumptions about political as well as technological and socioeconomic factors.

This study assesses climate-related financial risks on energy infrastructure investments. We conduct an asset-level and forward-looking risk assessment on three downstream energy assets: natural gas, coal, and solar photovoltaic power plants. We first identify climate risk factors (physical and transition) that an asset is highly exposed to with its specific asset type, geographic location, time frame, and financing structure and build plausible climate risk scenarios using single or multiple risk factors. We then project an energy asset’s cash flow and estimate the asset’s probability of default under the built scenarios. We compare the financial impacts of varying climate risk scenarios by analyzing the time and size of the losses due to the given default. We observe climate-related financial risks that are systematic and idiosyncratic: some scenarios affect certain energy assets negatively and others positively, while others negatively affect multiple asset types simultaneously. Our comparative case study results also show that renewable energy investments are likely to be more resilient to climate change than fossil fuel-based energy assets.

Policymakers of emissions trading systems (ETSs) around the world are faced with the challenges of how to allocate carbon allowances. On one hand, the free allocation would undermine the efficiency and fairness of ETSs, while on the other hand, a precipitous shift to auctions would put enormous economic pressure on covered entities. The consignment auction is supposed to combine the free allocation and the auction. However, few studies have investigated the consignment auction's impacts on ETSs and commodity markets from a quantitative perspective. In this paper, an agent-based model is proposed, considering both the primary and secondary carbon markets, the commodity market and the behavior of firms. Simulation results based on China's data show that, compared with the free allocation, the consignment auction can achieve a higher and more stable carbon price without significantly increasing the cost borne by firms, increase the liquidity of ETSs, and stimulate the technological advances of firms. Compared with the auction, the consignment auction reduces the volatilities of the commodity market induced by ETSs and facilitates the smooth operation of ETSs. Our results also suggest that the consignment auction is particularly suitable for the early stage of ETSs, while the auction remains the best allocation method as the ETS matures, as it significantly promotes long-term technological advances.

Bioenergy is projected to have a prominent, valuable, and maybe essential, role in climate management. However, there is significant variation in projected bioenergy deployment results, as well as concerns about the potential environmental and social implications of supplying biomass. Bioenergy deployment projections are market equilibrium solutions from integrated modeling, yet little is known about the underlying modeling of the supply of biomass as a feedstock for energy use in these modeling frameworks. We undertake a novel diagnostic analysis with ten global models to elucidate, compare, and assess how biomass is supplied within the models used to inform long-run climate management. With experiments that isolate and reveal biomass supply modeling behavior and characteristics (costs, emissions, land use, market effects), we learn about biomass supply tendencies and differences. The insights provide a new level of modeling transparency and understanding of estimated global biomass supplies that informs evaluation of the potential for bioenergy in managing the climate and interpretation of integrated modeling. For each model, we characterize the potential distributions of global biomass supply across regions and feedstock types for increasing levels of quantity supplied, as well as some of the potential societal externalities of supplying biomass. We also evaluate the biomass supply implications of managing these externalities. Finally, we interpret biomass market results from integrated modeling in terms of our new understanding of biomass supply. Overall, we find little consensus between models on where biomass could be cost-effectively produced and the implications. We also reveal model specific biomass supply narratives, with results providing new insights into integrated modeling bioenergy outcomes and differences. The analysis finds that many integrated models are considering and managing emissions and land use externalities of supplying biomass and estimating that environmental and societal trade-offs in the form of land emissions, land conversion, and higher agricultural prices are cost-effective, and to some degree a reality of using biomass, to address climate change.

Accurately quantifying greenhouse gas (GHG) emissions is essential for climate policy implementation but challenging in the case of electricity transfers across regulatory jurisdictions. Regulating emissions associated with delivered electricity is further complicated by contractual arrangements for dynamic electricity transfers that confound emission accounting approaches rooted in the physics of grid operations. Here, we propose a novel consumption-based accounting methodology to reconcile the nominal and the physical flows of electricity from generators to consumers. We also compare capacity factor-based and regression-based approaches for estimating default emission factors, in the absence of fully specified nominal electricity flows. As a case study, we apply this approach to assess the methods by which California regulators quantify specified and unspecified electricity imports and their associated GHG emissions. Collectively, these efforts illustrate principles for a comprehensive, empirical accounting framework that could inform efforts to improve the accuracy and consistency of policies regulating regional electricity transfers.