The recent IPCC reports show that bioenergy can play a critical role to supply renewable energy (especially sustainable transport fuels for aviation, shipping, and heavy duties) and meet climate change mitigation targets. At the same time, there are concerns that future bioenergy use will cause significant ecological damages, supply chain emissions and emissions induced by land use competition. Many of the factors are complex and/or highly uncertain. How much biomass will be available at low or no environmental risks depend on future socio-economic changes, governance, technology improvements, and lifestyle changes. This project evaluates the role of bioenergy in the context of 1.5 ºC and 2 ºC climate targets. The goal is to create new tools to improve sustainability studies of future bioenergy systems and identify strategies that can maximize climate benefits and reduce environmental trade-offs. The research activities include both methodological advancements, by combining Life Cycle Assessment (LCA) with Integrated Assessment Models (IAMs) and dynamic land use models into an open-source research tool, and applications to relevant case studies for the Norwegian, European and global context.
The initial part of the project has produced advanced routines and approaches for the integration of IAM outcomes into an LCA framework, a method called prospective-LCA. Future scenarios of land use and energy systems representing of different future socio-economic pathways have been produced, and connected to background LCA databases. This novel analytic setting allows to analyse a large number of land use scenarios, biomass feedstocks, and processing technologies into a future-oriented context, with explicit regional variations. For example, it makes possible to assess how a future cleaner energy system or projected technological and societal changes affect the LCA results of bioenergy systems. A large set of land use scenarios and biomass feedstock potentials have been compiled, using agro-ecological models to estimate yields of energy crops (and agricultural requirements from farming operations), as well as projections of biomass residues availability from agriculture and forestry. These estimates of resource potentials take into account different sustainability thresholds, which are defined to maintain key levels of ecosystem services. High resolution data are available for Norway and Europe, while the rest of the world is covered at a lower resolution.
The new analytic framework has been tested and applied to case studies published into international scientific journals, and include a mix of feedstocks, technologies, and applications. For example, the production of biofuels from Norwegian forest residues for deep-sea shipping under existing and future techno-economic and policy scenarios can reduce emissions relative to fossil fuels from 65% to 87%, depending on the type of technology and timeframe considered. Mitigation and energy potentials are lower in other European countries, owing to a more carbon-intensive energy system and more limited resource availability. Climate benefits of biofuels from dedicated energy crops are smaller than those from residues, primarily because of the emissions from the agricultural phase, but when they are grown on marginal land or degraded cropland they help to restore multiple ecosystem services, and generally improve soil quality.
Another analysis specific to the Norwegian transport system shows that biofuels contribute to climate change mitigation even in presence of high rates of vehicle fleet electrification (up to 30% of today’s road transport emissions by 2030), especially because of their use in trucks and vans and in the remaining cars with internal combustion engines. Overall, it indicates that complementary and integrated strategies combining high electrification rates of the vehicle fleet with targeted applications of biofuels can increase the mitigation of future road transport emissions.
In the last year of the project, research activities will be devoted to finalize two core scientific publications and associated dissemination and outreach. One publication will summarize and explain the main methodology developed in the project, and by making the corresponding model code openly available in a general setting (i.e., not targeted to a case study) it will make results ready for use in other applications and/or for further improvements. The other publication will combine all the knowledge produced in the project to investigate the climate change mitigation potential of biofuel production in a Norwegian context from different feedstocks, technology conversions, and alternative applications (aviation, shipping, and heavy duty), so to inform where the largest benefits are achieved and how to deploy sustainable fuels in Norway.
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Most climate change mitigation scenarios are profoundly dependent on future large-scale deployment of purpose-grown bioenergy crops. At the same time, there are widespread concerns that these bioenergy crops will bring about significant ecological damage, supply chain emissions, and emissions induced by land use. Also widespread are concerns that the bioenergy crops will compete with food crops. Such impacts and dynamics are currently poorly understood and/or highly uncertain. This project will evaluate the role of bioenergy in a sustainable future. It will combine life cycle assessment (LCA) and dynamic land use-energy scenario modelling in order to evaluate co-benefits and adverse side-effects of global bioenergy deployment across different environmental impact indicators, and perform comparative environmental assessments of a diverse set of bioenergy technology alternatives. This, in turn, will help identify what future optimal bioenergy deployment pathways should look like, and to identify possible win-win strategies. An interlinked and mutually reinforcing objective is to lift a scenario-based LCA model to a new state-of-the-art level of functionality, utility and quality. Achieving this will be a three-fold approach: developing sets of practical computer routines systematizing the generation of life cycle inventories reflecting regional variation and future changes; feeding back the regionally and temporally explicit inventories into existing processes in an LCA database; and undertaking scenario analyses in LCA with proper uncertainty characterization.