Abstract
AbstractThe 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.
Highlights
Biomass is an important renewable energy source in climate change mitigation strategies, for sectors relying on energy‐dense liquid fuels, such as aviation, shipping, and long‐haul trucking (Rose et al, 2014; World Wildlife Fund, 2011)
The conventional approach to quantify the climate change mitigation value of bioenergy is based on cradle‐to‐ grave life‐cycle assessment (LCA) of greenhouse gas (GHG) emission fluxes (GHG‐LCA), often using the 100‐year global warming potential as an equivalency metric to convert non‐ CO2 emissions into CO2 equivalents (equivalency metrics are commonly referred to as normalized metrics or characterization factors)
To the best of our knowledge, the Dynamic Relative Climate Impact (DRCI) approach is the first which enables consistent comparison of the climate impact of bioenergy systems with different time‐dependent emission profiles, while offering the flexibility to compare the effects of value judgments regarding the impact category, analytical time horizon, and equivalency metric
Summary
Biomass is an important renewable energy source in climate change mitigation strategies, for sectors relying on energy‐dense liquid fuels, such as aviation, shipping, and long‐haul trucking (Rose et al, 2014; World Wildlife Fund, 2011). The conventional approach to quantify the climate change mitigation value of bioenergy is based on cradle‐to‐ grave life‐cycle assessment (LCA) of greenhouse gas (GHG) emission fluxes (GHG‐LCA), often using the 100‐year global warming potential as an equivalency metric to convert non‐ CO2 emissions into CO2 equivalents (equivalency metrics are commonly referred to as normalized (emission) metrics or characterization factors) This method is widely employed to compare system performance and to determine the compliance of bioenergy systems to sustainability standards or policies. To the best of our knowledge, the DRCI approach is the first which enables consistent comparison of the climate impact of bioenergy systems with different time‐dependent emission profiles, while offering the flexibility to compare the effects of value judgments regarding the impact category, analytical time horizon, and equivalency metric.
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