BioEnergy’s role in a Sustainable fuTure: an assessment of environment, technology, supply chains and uncertainty

SCHEME: INTER

CALL: 2018

DOMAIN: SR - Environmental and Earth Sciences

FIRST NAME: Thomas

LAST NAME: Gibon

INDUSTRY PARTNERSHIP / PPP: No

INDUSTRY / PPP PARTNER:

HOST INSTITUTION: LIST

KEYWORDS: Bioenergy, biomass, energy scenarios, biopower, climate change mitigation, spatialisation, geographical information systems, uncertainty, life cycle assessment, global warming potential, carbon capture and storage, supply chain, land use, land-energy nexus, energy crops

START: 01-03-2019

END:

WEBSITE: https://www.list.lu

Submitted Abstract

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.

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