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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 estimati

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

Previous studies have projected a significant role for bioenergy in decarbonizing the global economy and helping realize international climate goals such as limiting global average warming to 2 ˚C or 1.5 ˚C. However, with substantial variability in bioenergy results and significant concerns about potential environmental and social implications, greater transparency and dedicated assessment of the underlying modeling and results and more detailed understanding of the potential role of bioenergy are needed. Stanford University’s Energy Modeling Forum (EMF) initiated a 33rd study (EMF-33) to explore the viability of large-scale bioenergy as part of a comprehensive climate management strategy. This special issue presents the papers of the EMF-33 study—a multi-year inter-model comparison project designed to understand and assess global, long-run biomass supply and bioenergy deployment potentials and related uncertainties. Using a novel scenario design with independent biomass supply and bioenergy demand protocols, EMF-33 separately elucidates and explores the modeling of biomass feedstock supplies and bioenergy technologies and their deployment—revealing, comparing, and assessing the modeling that is suggesting that bioenergy could be a key climate containment strategy. This introduction provides an overview of the EMF-33 study design and the overview, thematic, and individual modeling team papers and types of insights that make up this special issue. By providing enhanced transparency and new detailed insights, we hope to inform policy dialogue about the potential role of bioenergy and facilitate new research.

This paper explores the potential role of bioenergy coupled to carbon dioxide (CO2) capture and storage (BECCS) in long-term global scenarios. We first validate past insights regarding the potential use of BECCS in achieving climate goals based on results from 11 integrated assessment models (IAMs) that participated in the 33rd study of the Stanford Energy Modeling Forum (EMF-33). As found in previous studies, our results consistently project large-scale cost-effective BECCS deployment. However, we also find a strong synergistic nexus between CCS and biomass, with bioenergy the preferred fuel for CCS as the climate constraint increases. Specifically, the share of bioenergy that is coupled to CCS technologies increases since CCS effectively enhances the emissions mitigation capacity of bioenergy. For the models that include BECCS technologies across multiple sectors, there is significant deployment in conjunction with liquid fuel or hydrogen production to decarbonize the transportation sector. Using a wide set of scenarios, we find carbon removal to be crucial to achieving goals consistent with 1.5 °C warming. However, we find earlier BECCS deployment but not necessarily greater use in the long-term since ultimately deployment is limited by economic competition with other carbon-free technologies, especially in the electricity sector, by land-use competition (especially with food) affecting biomass feedstock availability and price, and by carbon storage limitations. The extent of BECCS deployment varies based on model assumptions, with BECCS deployment competitive in some models below carbon prices of 100 US$/tCO2. Without carbon removal, 2 °C is infeasible in some models, while those that solve find similar levels of bioenergy use but substantially greater mitigation costs. Overall, the paper provides needed transparency regarding BECCS’ role, and results highlight a strong nexus between bioenergy and CCS, and a large reliance on not-yet-commercial BECCS technologie

We present an overview of results from 11 integrated assessment models (IAMs) that participated in the 33rd study of the Stanford Energy Modeling Forum (EMF-33) on the viability of large-scale deployment of bioenergy for achieving long-run climate goals. The study explores future bioenergy use across models under harmonized scenarios for future climate policies, availability of bioenergy technologies, and constraints on biomass supply. This paper provides a more transparent description of IAMs that span a broad range of assumptions regarding model structures, energy sectors, and bioenergy conversion chains. Without emission constraints, we find vastly different CO2 emission and bioenergy deployment patterns across models due to differences in competition with fossil fuels, the possibility to produce large-scale bio-liquids, and the flexibility of energy systems. Imposing increasingly stringent carbon budgets mostly increases bioenergy use. A diverse set of available bioenergy technology portfolios provides flexibility to allocate bioenergy to supply different final energy as well as remove carbon dioxide from the atmosphere by combining bioenergy with carbon capture and sequestration (BECCS). Sector and regional bioenergy allocation varies dramatically across models mainly due to bioenergy technology availability and costs, final energy patterns, and availability of alternative decarbonization options. Although much bioenergy is used in combination with CCS, BECCS is not necessarily the driver of bioenergy use. We find that the flexibility to use biomass feedstocks in different energy sub-sectors makes large-scale bioenergy deployment a robust strategy in mitigation scenarios that is surprisingly insensitive with respect to reduced technology availability. However, the achievability of stringent carbon budgets and associated carbon prices is sensitive. Constraints on biomass feedstock supply increase the carbon price less significantly than excluding BECCS because c

This study explores the importance of bioenergy to potential future energy transformation and climate change management. Using a large inter-model comparison of 15 models, we comprehensively characterize and analyze future dependence on, and the value of, bioenergy in achieving potential long-run climate objectives. Model scenarios project, by 2050, bioenergy growth of 1 to 10 % per annum reaching 1 to 35 % of global primary energy, and by 2100, bioenergy becoming 10 to 50 % of global primary energy. Non-OECD regions are projected to be the dominant suppliers of biomass, as well as consumers, with up to 35 % of regional electricity from biopower by 2050, and up to 70 % of regional liquid fuels from biofuels by 2050. Bioenergy is found to be valuable to many models with significant implications for mitigation and macroeconomic costs of climate policies. The availability of bioenergy, in particular biomass with carbon dioxide capture and storage (BECCS), notably affects the cost-effective global emissions trajectory for climate management by accommodating prolonged near-term use of fossil fuels, but with potential implications for climate outcomes. Finally, we find that models cost-effectively trade-off land carbon and nitrous oxide emissions for the long-run climate change management benefits of bioenergy. The results suggest opportunities, but also imply challenges. Overall, further evaluation of the viability of large-scale global bioenergy is merited.

Energy efficiency is one of the main options for mitigating climate change. An accurate representation of various mechanisms of energy efficiency is vital for the assessment of its realistic potential. Results of a questionnaire show that the EMF27 models collectively represent known channels of energy efficiency reasonably well, addressing issues of energy efficiency barriers and rebound effects. The majority of models, including general equilibrium models, have an explicit end-use representation for the transportation sector. All participating partial equilibrium models have some capability of reflecting the actual market behavior of consumers and firms. The EMF27 results show that energy intensity declines faster under climate policy than under a baseline scenario. With a climate policy roughly consistent with a global warming of two degrees, the median annual improvement rate of energy intensity for 2010–2030 reaches 2.3 % per year [with a full model range of 1.3–2.9 %/yr], much faster than the historical rate of 1.3 % per year. The improvement rate increases further if technology is constrained. The results suggest that the target of the United Nations’ “Sustainable Energy for All” initiative is consistent with the 2-degree climate change target, as long as there are no technology constraints. The rate of energy intensity decline varies significantly across models, with larger variations at the regional and sectoral levels. Decomposition of the transportation sector down to a service level for a subset of models reveals that to achieve energy efficiency, a general equilibrium model tends to reduce service demands while partial equilibrium models favor technical substitution.

This paper explores the variation in Asian energy systems in the energy-economic and integrated assessment models participating in the Asian Modeling Exercise (AME). Consistent with previous studies, there is substantial variation in energy system configurations across the AME scenarios, a reflection of the inherent uncertainty in how countries might undertake climate mitigation and associated energy system decisions. The paper also explores the degree to which scenarios from individual models retain regional differences in energy system configurations over time. The scenarios are mixed in this regard. Some models tend to converge toward common energy systems that focus on the utilization of a common set of options across regions, while others retain strong differences among regions. A review of modeling approaches indicates that the participating models are constructed to represent many, but not all, of the real-world forces leading to regional variation. The models do not have the capability to represent some of the most important social and political factors that influence energy technology decision making, raising important questions about how decision makers should interpret regional variations across models, particularly over the medium to long term.

The International Energy Workshop (IEW) is an informal network of analysts concerned with international energy issues. Its goals are to compare energy projections and to understand the reasons for diverging views of future developments. The main activity of the IEW is iterative polling of energy consumption, production, and trade. The poll includes questions on crude oil prices and GDP in order to identify major determinants of energy demand and supply. The environmental impact of energy use is reflected in the IEW Poll to the extent that it includes items on energy-related carbon emissions. The poll results and other topics of current interest are discussed at annual meetings attended by many authors of energy projections and other individuals involved in the wide field of energy. The original idea to establish this international network was introduced by Alan Manne; Manne organized the first IEW meeting in December 1981 and has served as its senior co-director since then.

This report examines short- and long-term measures to reduce the consumption of petroleum jet fuels by the Air Force. Engine retrofits and aerodynamic modifications to existing aircraft can save significant quantities of jet fuel; however, savings in fuel expenditures are not enough to offset high initial costs of engine retrofits. If accomplished early in an aircraft's life cycle, relatively lower costs of modest aerodynamic modifications may be recoverable through savings in fuel expenditures. Synthetic JP fuels derived from oil shale or coal appear to be the most attractive future alternatives to petroleum jet fuels. If the foreign oil cartel maintains its price-setting effectiveness and synthetic fuels industry develops in the United States, development of an Air Force capability to interchangeable use fuels derived from crude oil, oil shale, or coal could be economically attractive and enhance the Air Force's position in the jet fuel marketplace. (Author)

Several large computer-based R and D planning models, developed over the past few years, are examined to determine why such models have not been adopted for full and regular use by R and D managers. The models consider, in various degrees, three major tasks of the R and D planning process: (1) identifying and ranking organizational objectives (such as desired operational capabilities), and (2) evaluating candidate R and D projects based on their contribution toward achieving the objectives, and (3) selecting preferred projects and allocating resources among them. Four models were selected for detailed analysis.