Vassilis Daioglou

Projections of the availability and cost of residues from agriculture and forestry

By‐products of agricultural and forestry processes, known as residues, may act as a primary source of renewable energy. Studies assessing the availability of this resource offer little insight on the drivers and constraints of the available potential as well as the associated costs and how these may vary across scenarios. This study projects long‐term global supply curves of the available potential using consistent scenarios of agriculture and forestry production, livestock production and fuel use from the spatially explicit integrated assessment model IMAGE. In the projections, residue production is related to agricultural and forestry production and intensification, and the limiting effect of ecological and alternative uses of residues are accounted for. Depending on the scenario, theoretical potential is projected to increase from approximately 120 EJ yr−1 today to 140–170 EJ yr−1 by 2100, coming mostly from agricultural production. To maintain ecological functions approximately 40% is required to remain in the field, and a further 20–30% is diverted towards alternative uses. Of the remaining potential (approximately 50 EJ yr−1 in 2100), more than 90% is available at costs <10

Model collaboration for the improved assessment of biomass supply, demand, and impacts

2005 GJ−1. Crop yield improvements increase residue productivity, albeit at a lower rate. The consequent decrease in agricultural land results in a lower requirement of residues for erosion control. The theoretical potential is most sensitive to baseline projections of agriculture and forestry demand; however, this does not necessarily affect the available potential which is relatively constant across scenarios. The most important limiting factors are the alternative uses. Asia and North America account for two‐thirds of the available potential due to the production of crops with high residue yields and socioeconomic conditions which limit alternative uses.

Alternative pathways to the 1.5 °C target reduce the need for negative emission technologies

Existing assessments of biomass supply and demand and their impacts face various types of limitations and uncertainties, partly due to the type of tools and methods applied (e.g., partial representation of sectors, lack of geographical details, and aggregated representation of technologies involved). Improved collaboration between existing modeling approaches may provide new, more comprehensive insights, especially into issues that involve multiple economic sectors, different temporal and spatial scales, or various impact categories. Model collaboration consists of aligning and harmonizing input data and scenarios, model comparison and/or model linkage. Improved collaboration between existing modeling approaches can help assess (i) the causes of differences and similarities in model output, which is important for interpreting the results for policy‐making and (ii) the linkages, feedbacks, and trade‐offs between different systems and impacts (e.g., economic and natural), which is key to a more comprehensive understanding of the impacts of biomass supply and demand. But, full consistency or integration in assumptions, structure, solution algorithms, dynamics and feedbacks can be difficult to achieve. And, if it is done, it frequently implies a trade‐off in terms of resolution (spatial, temporal, and structural) and/or computation. Three key research areas are selected to illustrate how model collaboration can provide additional ways for tackling some of the shortcomings and uncertainties in the assessment of biomass supply and demand and their impacts. These research areas are livestock production, agricultural residues, and greenhouse gas emissions from land‐use change. Describing how model collaboration might look like in these examples, we show how improved model collaboration can strengthen our ability to project biomass supply, demand, and impacts. This in turn can aid in improving the information for policy‐makers and in taking better‐informed decisions.

Energy demand and emissions of the non-energy sector

Mitigation scenarios that achieve the ambitious targets included in the Paris Agreement typically rely on greenhouse gas emission reductions combined with net carbon dioxide removal (CDR) from the atmosphere, mostly accomplished through large-scale application of bioenergy with carbon capture and storage, and afforestation. However, CDR strategies face several difficulties such as reliance on underground CO2 storage and competition for land with food production and biodiversity protection. The question arises whether alternative deep mitigation pathways exist. Here, using an integrated assessment model, we explore the impact of alternative pathways that include lifestyle change, additional reduction of non-CO2 greenhouse gases and more rapid electrification of energy demand based on renewable energy. Although these alternatives also face specific difficulties, they are found to significantly reduce the need for CDR, but not fully eliminate it. The alternatives offer a means to diversify transition pathways to meet the Paris Agreement targets, while simultaneously benefiting other sustainability goals.Scenarios that constrain warming to 1.5 °C currently place a large emphasis on CO2 removal. Alternative pathways involving lifestyle change, rapid electrification and reduction of non-CO2 gases could reduce the need for such negative emission technologies.

Competing uses of biomass for energy and chemicals: implications for long‐term global CO2 mitigation potential

The demand for fossil fuels for non-energy purposes such as production of bulk chemicals is poorly understood. In this study we analyse data on non-energy demand and disaggregate it across key services or products. We construct a simulation model for the main products of non-energy use and project the global demand for primary fuels used as feedstocks and the resulting carbon emissions until 2100. The model is then applied to estimate the potential emission reductions by increased use of biomass, a more ambitious climate policy and advanced post-consumer waste management. We project that the global gross demand for feedstocks more than triples from 30 EJ in 2010 to over 100 EJ in 2100, mainly due to the increased demand for high value chemicals such as ethylene. Carbon emissions increase disproportionately (from 160 MtC per year in 2010 to over 650 MtC per year in 2100) due to greater use of coal, especially in ammonia and methanol production. If biomass is used, it can supply a large portion of the required primary energy and reduce carbon emissions by up to 20% in 2100 compared to the reference development. Climate policy can further reduce emissions by over 30%. Post-consumer waste management options such as recycling or incineration with energy recovery do not necessarily reduce energy demand or carbon emissions.

Modeling the Effects of Future Growing Demand for Charcoal in the Tropics

Biomass is considered a low carbon source for various energy or chemical options. This paper assesses its different possible uses, the competition between these uses, and the implications for long‐term global energy demand and energy system emissions. A scenario analysis is performed using the TIMER energy system model. Under baseline conditions, 170 EJ yr−1 of secondary bioenergy is consumed in 2100 (approximately 18% of total secondary energy demand), used primarily in the transport, buildings and nonenergy (chemical production) sectors. This leads to a reduction of 9% of CO2 emissions compared to a counterfactual scenario where no bioenergy is used. Bioenergy can contribute up to 40% reduction in emissions at carbon taxes greater than 500/tC. As higher CO2 taxes are applied, bioenergy is increasingly diverted towards electricity generation. Results are more sensitive to assumptions about resource availability than technological parameters. To estimate the effectiveness of bioenergy in specific sectors, experiments are performed in which bioenergy is only allowed in one sector at a time. The results show that cross‐sectoral leakage and emissions from biomass conversion limit the total emission reduction possible in each sector. In terms of reducing emissions per unit of bioenergy use, we show that the use of bioelectricity is the most effective, especially when used with carbon capture and storage. However, this technology only penetrates at a high carbon price (>100/tC) and competition with transport fuels may limit its adoption.

Greenhouse gas emission curves for advanced biofuel supply chains

Global demand for charcoal is increasing mainly due to urban population in developing countries. More than half the global population now lives in cities, and urban-dwellers are restricted to charcoal use because of easiness of production, access, transport, and tradition. Increasing demand for charcoal, however, may lead to increasing impacts on forests, food and water resources, and may even create additional pressures on the climate system. Here we assess how different charcoal scenarios based on the Shared Socio-economic Pathways (SSP) relate to potential biomass supply. For this, we use the energy model TIMER to project the demand for fuelwood and charcoal for different socio-economic pathways for urban and rural populations, globally and for four tropical regions (Central America, South America, Africa and Indonesia). Second, we assess whether the biomass demands for each scenario can be met with current and projected forest biomass estimated with remote sensing and modeled Net Primary Productivity (NPP) using a Dynamic Global Vegetation Model (LPJ-GUESS). Currently one third of residential energy use is based on traditional bioenergy, including charcoal. Globally, biomass needs by urban households by 2100 under the most sustainable scenario, SSP1, are of 14.4 mi ton biomass for charcoal plus 17.1 mi ton biomass for fuelwood (31.5 mi ton biomass in total). Under SSP3, the least sustainable scenario, we project a need of 205 mi tons biomass for charcoal plus 243.8 mi ton biomass for fuelwood by 2100 (total of 450 mi ton biomass). Africa and South America contribute the most for this biomass demand, however, all areas are able to meet the demand. We find that the future of the charcoal sector is not dire. Charcoal represents a small fraction of the energy requirements, but its biomass demands are disproportionate and in some regions require a large fraction of forest. This could be because of large growing populations moving to urban areas, conversion rates, production inefficiencies, and regions that despite available alternative energy sources still use a substantial amount of charcoal. We present a framework that combines Integrated Assessment Models and local conditions to assess whether a sustainable sector can be achieved.

Land-use emissions play a critical role in land-based mitigation for Paris climate targets

Most climate change mitigation scenarios that are consistent with the 1.5–2 °C target rely on a large-scale contribution from biomass, including advanced (second-generation) biofuels. However, land-based biofuel production has been associated with substantial land-use change emissions. Previous studies show a wide range of emission factors, often hiding the influence of spatial heterogeneity. Here we introduce a spatially explicit method for assessing the supply of advanced biofuels at different emission factors and present the results as emission curves. Dedicated crops grown on grasslands, savannahs and abandoned agricultural lands could provide 30 EJBiofuel yr−1 with emission factors less than 40 kg of CO2-equivalent (CO2e) emissions per GJBiofuel (for an 85-year time horizon). This increases to 100 EJBiofuel yr−1 for emission factors less than 60 kgCO2e GJBiofuel−1. While these results are uncertain and depend on model assumptions (including time horizon, spatial resolution, technology assumptions and so on), emission curves improve our understanding of the relationship between biofuel supply and its potential contribution to climate change mitigation while accounting for spatial heterogeneity.Here emission curves are developed for advanced biofuel supply chains to enhance understanding of the relationship between biofuel supply and its potential contribution to climate change mitigation while accounting for landscape heterogeneity.

Using Dynamic Relative Climate Impact curves to quantify the climate impact of bioenergy production systems over time

Scenarios that limit global warming to below 2 °C by 2100 assume significant land-use change to support large-scale carbon dioxide (CO2) removal from the atmosphere by afforestation/reforestation, avoided deforestation, and Biomass Energy with Carbon Capture and Storage (BECCS). The more ambitious mitigation scenarios require even greater land area for mitigation and/or earlier adoption of CO2 removal strategies. Here we show that additional land-use change to meet a 1.5 °C climate change target could result in net losses of carbon from the land. The effectiveness of BECCS strongly depends on several assumptions related to the choice of biomass, the fate of initial above ground biomass, and the fossil-fuel emissions offset in the energy system. Depending on these factors, carbon removed from the atmosphere through BECCS could easily be offset by losses due to land-use change. If BECCS involves replacing high-carbon content ecosystems with crops, then forest-based mitigation could be more efficient for atmospheric CO2 removal than BECCS.Land-based mitigation for meeting the Paris climate target must consider the carbon cycle impacts of land-use change. Here the authors show that when bioenergy crops replace high carbon content ecosystems, forest-based mitigation could be more effective for CO2 removal than bioenergy crops with carbon capture and storage.

Global energy sector emission reductions and bioenergy use: overview of the bioenergy demand phase of the EMF-33 model comparison

The climate impact of bioenergy is commonly quantified in terms of CO2 equivalents, using a fixed 100‐year global warming potential as an equivalency metric. This method has been criticized for the inability to appropriately address emissions timing and the focus on a single impact metric, which may lead to inaccurate or incomplete quantification of the climate impact of bioenergy production. In this study, we introduce Dynamic Relative Climate Impact (DRCI) curves, a novel approach to visualize and quantify the climate impact of bioenergy systems over time. The DRCI approach offers the flexibility to analyze system performance for different value judgments regarding the impact category (e.g., emissions, radiative forcing, and temperature change), equivalency metric, and analytical time horizon. The DRCI curves constructed for fourteen bioenergy systems illustrate how value judgments affect the merit order of bioenergy systems, because they alter the importance of one‐time (associated with land use change emissions) versus sustained (associated with carbon debt or foregone sequestration) emission fluxes and short‐ versus long‐lived climate forcers. Best practices for bioenergy production (irrespective of value judgments) include high feedstock yields, high conversion efficiencies, and the application of carbon capture and storage. Furthermore, this study provides examples of production contexts in which the risk of land use change emissions, carbon debt, or foregone sequestration can be mitigated. For example, the risk of indirect land use change emissions can be mitigated by accompanying bioenergy production with increasing agricultural yields. Moreover, production contexts in which the counterfactual scenario yields immediate or additional climate impacts can provide significant climate benefits. This paper is accompanied by an Excel‐based calculation tool to reproduce the calculation steps outlined in this paper and construct DRCI curves for bioenergy systems of choice.