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This paper aims to provide a bibliometric analysis of publication trends on the themes of biomass and bioenergy worldwide. A wide range of studies have been performed in the field of the usage of biomass for energy production, in order to contribute to the green transition from fossil fuels to renewable energies. Over the past 20 years (from 2000 to 2019), approximately 10,000 articles have been published in the “Agricultural and Biological Sciences” field on this theme, covering all stages of production—from the harvesting of crops to the particular type of energy produced. Articles were obtained from the SCOPUS database and examined with a text mining tool in order to analyze publication trends over the last two decades. Publications per year in the bioenergy theme have grown from 91 in 2000 to 773 in 2019. In particular the analyses showed how environmental aspects have increased their importance (from 7.3% to 11.8%), along with studies related to crop conditions (from 10.4% to 18.6%). Regarding the use of energy produced, growing trends were recognized for the impact of biofuels (mentions moved from 0.14 times per article in 2000 to 0.38 in 2019) and biogases (from 0.14 to 0.42 mentions). Environmental objectives have guided the interest of researchers, encouraging studies on biomass sources and the optimal use of the energy produced. This analysis aims to describe the research evolution, providing an analysis that can be helpful to predict future scenarios and participation among stakeholders in the sector.

Pinios river basin constitutes the most important agricultural production area in Greece but contributes to the degradation of the quality and quantity of surface water and groundwater bodies. Bioenergy crops implemented as part of the existing cropping systems could be a novel and efficient mitigation strategy against water degradation, contributing to the production of energy through renewable sources. This study uses the Soil and Water Assessment Tool (SWAT) to first develop a representative model of Pinios river basin and evaluate its current state with respect to water availability and nitrate water pollution. A low-input perennial bioenergy crop, switchgrass, is then simulated closely to the Greek conditions to investigate its potential effects on water in three implementation scenarios: the installation and growth of switchgrass in the entire irrigated cropland, exclusively in irrigated sloping (slopes > 1.5%) cropland, and exclusively in irrigated non-sloping cropland. The simulated results demonstrate that under all scenarios, the water quality improvements with respect to the nitrate loads entering surface water and groundwater bodies were significant, with their reduction being directly affected by the extent to which switchgrass replaced resource-demanding conventional crops. Specifically, the reduction in the annual nitrate loads in the surface water under these three scenarios varied from 7% to 18% at the river basin scale, while in certain cropland areas, the respective reduction even exceeded a level of 80%. The potential to improve the water status was also considerable, as the implementation of the bioenergy crop reduced the irrigation water used annually in the basin by 10% (64 Mm3) when switchgrass replaced the conventional crops only on the sloping land and by almost 30% (187 Mm3) when it replaced them throughout the irrigated land. At the same time, significant biomass production above 18 t/ha/y applied in all of the simulations. This study also highlights the contribution of the bioenergy crop to the rehabilitation of the groundwater levels across the basin, with the possibility of increasing them by >50% compared to the baseline, implying that the adoption of switchgrass could be a promising means against water scarcity.

Most climate change mitigation scenarios rely on increased use of bioenergy to decarbonize the energy system. Here we use results from the 33rd Energy Modeling Forum study (EMF-33) to investigate projected international bioenergy trade for different integrated assessment models across several climate change mitigation scenarios. Results show that in scenarios with no climate policy, international bioenergy trade is likely to increase over time, and becomes even more important when climate targets are set. More stringent climate targets, however, do not necessarily imply greater bioenergy trade compared to weaker targets, as final energy demand may be reduced. However, the scaling up of bioenergy trade happens sooner and at a faster rate with increasing climate target stringency. Across models, for a scenario likely to achieve a 2 °C target, 10–45 EJ/year out of a total global bioenergy consumption of 72–214 EJ/year are expected to be traded across nine world regions by 2050. While this projection is greater than the present trade volumes of coal or natural gas, it remains below the present trade of crude oil. This growth in bioenergy trade largely replaces the trade in fossil fuels (especially oil) which is projected to decrease significantly over the twenty-first century. As climate change mitigation scenarios often show diversified energy systems, in which numerous world regions can act as bioenergy suppliers, the projections do not necessarily lead to energy security concerns. Nonetheless, rapid growth in the trade of bioenergy is projected in strict climate mitigation scenarios, raising questions about infrastructure, logistics, financing options, and global standards for bioenergy production and trade.

Abstract 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.

Bioenergy is expected to play an important role in long-run climate change mitigation strategies as highlighted by many integrated assessment model (IAM) scenarios. These scenarios, however, also show a very wide range of results, with uncertainty about bioenergy conversion technology deployment and biomass feedstock supply. To date, the underlying differences in model assumptions and parameters for the range of results have not been conveyed. Here we explore the models and results of the 33rd study of the Stanford Energy Modeling Forum to elucidate and explore bioenergy technology specifications and constraints that underlie projected bioenergy outcomes. We first develop and report consistent bioenergy technology characterizations and modeling details. We evaluate the bioenergy technology specifications through a series of analyses—comparison with the literature, model intercomparison, and an assessment of bioenergy technology projected deployments. We find that bioenergy technology coverage and characterization varies substantially across models, spanning different conversion routes, carbon capture and storage opportunities, and technology deployment constraints. Still, the range of technology specification assumptions is largely in line with bottom-up engineering estimates. We then find that variation in bioenergy deployment across models cannot be understood from technology costs alone. Important additional determinants include biomass feedstock costs, the availability and costs of alternative mitigation options in and across end-uses, the availability of carbon dioxide removal possibilities, the speed with which large scale changes in the makeup of energy conversion facilities and integration can take place, and the relative demand for different energy services.

Bioenergy is considered an important component within the European Union (EU) energy transition to meet mid‐century climate targets. Model assessments that have highlighted the role of bioenergy in decarbonising EU energy systems fail to account for the fact that mitigation strategies and bioenergy supply take place within a global decarbonisation effort. Thus, they do not account for inter‐regional competition for the resource base that Europe may face. This study shows how bioenergy can contribute to EU climate targets, highlighting its possible role within the energy system and developments required to facilitate its scale‐up. We use the global integrated assessment model IMAGE 3.2 to project bioenergy demand, sectoral deployment, feedstock, and inter‐regional import for Europe to 2050. Employing a global model allows for projections of EU decarbonisation strategies consistent with global climate targets and captures the effects of biomass production and consumption in other world regions. Bioenergy is projected to account for up to 27% of total primary energy demand, increasing from the current 5EJ to 18EJ/yr. To match this demand, the model projects imports of biomass to increase from 4% of its current supply to 60%. Bioenergy could provide up to 1GtCO2 or 40% of the overall mitigation needed by the EU in 2050. This is based on large‐scale use for power production, with the transport, industry and buildings sectors getting smaller shares. By 2050 it is projected that 55% of total EU bioenergy use is coupled with Bioenergy with carbon capture and storage (BECCS). Bioenergy supply comes primarily for agricultural and forestry residues, as these sources have low upstream greenhouse gas emissions. However, as demand increases, energy crops are increasingly used, especially in the provision of advanced liquid fuels. The results show that one route for achieving an EU energy transition is based on rapid deployment of BECCS and the mobilisation of sustainable imports of second‐generation feedstocks. This study uses the global integrated assessment model IMAGE 3.2 to explore the possible future role of bioenergy in Europe and its GHG mitigation potential to 2050.

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.

Bioenergy is expected to have a prominent role in limiting global greenhouse emissions to meet the climate change target of the Paris Agreement. Many studies identify negative emissions from bioenergy generation with carbon capture and storage (BECCS) as its key contribution, but assume that no other CO2 removal technologies are available. We use a global integrated assessment model, TIAM‐UCL, to investigate the role of bioenergy within the global energy system when direct air capture and afforestation are available as cost‐competitive alternatives to BECCS. We find that the presence of other CO2 removal technologies does not reduce the pressure on biomass resources but changes the use of bioenergy for climate mitigation. While we confirm that when available BECCS offers cheaper decarbonization pathways, we also find that its use delays the phase‐out of unabated fossil fuels in industry and transport. Furthermore, it displaces renewable electricity generation, potentially increasing the likelihood of missing the Paris Agreement target. We found that the most cost‐effective solution is to invest in a basket of CO2 removal technologies. However, if these technologies rely on CCS, then urgent action is required to ramp up the necessary infrastructure. We conclude that a sustainable biomass supply is critical for decarbonizing the global energy system. Since only a few world regions carry the burden of producing the biomass resource and store CO2 in geological storage, adequate international collaboration, policies and standards will be needed to realize this resource while avoiding undesired land‐use change. National strategies highlighted in the Paris Agreement rely on bio‐electricity and bio‐heat without using bioenergy generation with carbon capture and storage (BECCS). Conversely, scientific evidence from Integrated Assessment Models suggests a critical role of BECCS in deep decarbonisation scenarios. Usually these models do not consider alternative carbon dioxide removal (CDR) options. Bridging this gap, we assess the role of biomass in global energy systems that also include CDR such as afforestation and direct air capture. We assess scenarios that look for maximum CO2 emissions reduction by 2100, while also accounting for actual biomass supply, CO2 storage availability, and policy developments.

Global bioenergy potentials have been the subject of extensive research and continued controversy. Due to vast uncertainties regarding future yields, diets and other influencing parameters, estimates of future agricultural biomass potentials vary widely. Most scenarios compatible with ambitious climate targets foresee a large expansion of bioenergy, mainly from energy crops that needs to be kept consistent with projections of agriculture and food production. Using the global biomass balance model BioBaM, we here present an assessment of agricultural bioenergy potentials compatible with the Food and Agriculture Organization's (2018) 'Alternative pathways to 2050' projections. Mobilizing biomass at larger scales may be associated with systemic feedbacks causing greenhouse gas (GHG) emissions, e.g. crop residue removal resulting in loss of soil carbon stocks and increased emissions from fertilization. To assess these effects, we derive 'GHG cost supply-curves', i.e. integrated representations of biomass potentials and their systemic GHG costs. Livestock manure is most favourable in terms of GHG costs, as anaerobic digestion yields reductions of GHG emissions from manure management. Global potentials from intensive livestock systems are about 5 EJ/yr. Crop residues can provide up to 20 EJ/yr at moderate GHG costs. For energy crops, we find that the medium range of literature estimates (∼40 to 90 EJ/yr) is only compatible with FAO yield and human diet projections if energy plantations expand into grazing areas (∼4-5 million km2) and grazing land is intensified globally. Direct carbon stock changes associated with perennial energy crops are beneficial for climate mitigation, yet there are-sometimes considerable-'opportunity GHG costs' if one accounts the foregone opportunity of afforestation. Our results indicate that the large potentials of energy crops foreseen in many energy scenarios are not freely and unconditionally available. Disregarding systemic effects in agriculture can result in misjudgement of GHG saving potentials and flawed climate mitigation strategies.

Climate change mitigation scenarios generated by integrated assessment models have been extensively used to support climate change negotiations on the global stage. To date, most studies exploring ensembles of these scenarios focus on the global picture, with more limited attention to regional metrics. A systematic approach is still lacking to improve the understanding of regional heterogeneity, highlighting key regional decarbonisation measures and their relative importance for meeting global climate goals under deep uncertainty. This study proposes a novel approach to gaining robust insights into regional decarbonisation strategies using machine learning techniques based on the IPCC SR1.5 scenario database. Random forest analysis first reveals crucial metrics to limit global temperature increases. Logistic regression modelling and the patient rule induction method are then used to identify which of these metrics and their combinations are most influential in meeting climate goals below 2 °C or below 1.5 °C. Solar power and sectoral electrification across all regions have been found to be the most effective measures to limit temperature increases. To further limit increase below 1.5 °C and not only 2 °C, decommissioning of unabated gas plants should be prioritised along with energy efficiency improvements. Bioenergy and wind power show higher regional heterogeneity in limiting temperature increases, with lower influences than aforementioned measures, and are especially relevant in Latin America (bioenergy) and countries of the Organisation for Economic Co-operation and Development and the Former Soviet Union (bioenergy and wind). In the future, a larger scenario ensemble can be applied to reveal more robust and comprehensive insights.