Luis Manuel Guerreiro Alves Arroja
Programme - Projetos de Investigação Científica e Desenvolvimento Tecnológico - 2014 (PTDC/AAG-MAA/6234/2014)
Execution dates - 2016-06-01 - 2020-05-31 (48 Months)
Funding Entity - FCT - Fundação para a Ciência e a Tecnologia; FEDER - Fundo Europeu de Desenvolvimento Regional
Funding for CESAM - 86696 €
Total Funding - 199836 €
Proponent Institution - Universidade de Aveiro
Associação para o Desenvolvimento da Aerodinâmica Industrial (ADAI)
Instituto de Engenharia de Sistemas e Computadores de Coimbra (INESCC)
Bioenergy (energy from biomass) has been proposed as a solution to several pressing concerns such as energy security, climate change and rural development. However, accelerating growth in bioenergy demand has been accompanied by a growing concern about the environmental, economic and social impacts, such as deforestation, related food competition, and land conflicts. The assessment of the environmental performance of bioenergy systems has hence become an important focus of research and debate within the scientific community.
This project addresses the challenge of developing a methodology to establish a framework for the Life Cycle Assessment of (LCA) of bioenergy systems sustainability, able to inform industry actors, policy makers and stakeholders, and to support bioenergy systems management. The chain modelling of the production of biomass and its use as an energy carrier will encompass cultivation and harvesting, transport, conversion to bioenergy products and co-products, not neglecting disposal/treatment of residues and the production and use of subsidiary inputs (e.g., agrochemicals and transport fuels). The methodology aims to innovate and advance the state of the art along three interrelated lines:
1) The LCA will account for the indirect Land Use Changes (LUC) effects, occurring when pressure on agriculture due to the displacement of previous activity or by use of the biomass induces LUC on other lands. Such indirect effects have been neglected to a great extent in past studies, which focus on direct effects. This calls for a consequential modelling of the selected bioenergy systems, which will be also compared with the type of attributional models prevailing among existing bioenergy life cycle modelling assessments. The consequential life cycle modelling will consider market mechanisms and handle co-products by system expansion. The modelling framework to be developed and the cases to be studied will allow assessing and discussing differences in the results, contributing to the ongoing debate on how to model and assess indirect LUC for bioenergy systems. New insights about modelling options in LCA will also be generated, namely on the choice between attributional and consequential modelling.
2) The assessment of bioenergy alternatives will be based on the complementary use of Multi-Criteria Decision Analysis (MCDA) and LCA, following the recent trends of using LCA as a means to obtain a subset of the criteria to be considered in MCDA. This allows integrating criteria such as local environmental impacts, besides economic or social ones. MCDA structuring tools will be used to define a coherent family of criteria representing the interests of stakeholders and the informed public. Modern MCDA aggregation tools will be used, namely methods that deal with partial information on preference parameters. This will improve the current practice (often formally incorrect) of using equal “weights” or weights obtained by default and contributes to obtaining robust conclusions. While performing MCDA to obtain results for the specific cases to be studied, the team aims at developing also the methodological state of the art in combining LCA and MCDA.
3) Uncertainty analysis will be embedded throughout all models to be developed, an important aspect since there is considerable uncertainty regarding the type, scale and timing of indirect LUC. Published bioenergy LC studies seldom consider uncertainty comprehensively. Published results vary quite widely, not only due to differences in data and scenarios, but also due to different normative choices in the modelling procedures. In contrast, this project will model parameter uncertainty and variability, as well as scenario uncertainty, related to normative choices in the modelling procedure. Furthermore, uncertainty about MCDA preference-related parameters will also be addressed. Techniques to address these different types of uncertainties will include robustness analysis and Monte Carlo simulation. Methodological innovations are expected in addressing different types of uncertainties and in using uncertainty analysis to focus the collection and elicitation of information on what is more relevant to the decision process, based on successive refinements of the models.