Consequential life cycle assessment (CLCA) applied to systems under continuous evolution, such as cities, usually disregard interactions among local components, how policy decisions influence them, and the value of spatial data. As a result, local environmental impacts might not be well-considered. This paper investigates how a spatially explicit system dynamics (SD) modelling approach can overcome such limitation, contributing to the advancement of CLCA. First, a novel CLCA conceptual framework is presented combining consequential life cycle inventories, SD principles and the use of spatially explicit data. Second, its innovative value is demonstrated through a proof-of-concept SD-CLCA model. This model evaluates the environmental impacts of changes in electricity supply-demand in the market due to an increasing adoption of solar photovoltaic panels (SPV) in residential buildings. It allows traceability of both system changes and their environmental consequences. For demonstration purposes, the SD-CLCA model is applied to Lisbon municipality and the broader electricity market of Portugal. Results showcase how SD-CLCA models could provide a closer representation of the real effects of predicted changes in the electricity market due to different SPV adoption scenarios. They also illustrate that changes in gross domestic product, population and precipitation provide a diversified set of impact scores. As a methodological advancement, and to the net of a few shortcomings, the proposed SD-CLCA model is able to capture the complexity of cause-effect dynamics determining environmental impacts, which currently represents a research gap in LCA.

A spatiotemporally differentiated product system modelling framework for consequential life cycle assessment

Babi Almenar J.;
2022-01-01

Abstract

Consequential life cycle assessment (CLCA) applied to systems under continuous evolution, such as cities, usually disregard interactions among local components, how policy decisions influence them, and the value of spatial data. As a result, local environmental impacts might not be well-considered. This paper investigates how a spatially explicit system dynamics (SD) modelling approach can overcome such limitation, contributing to the advancement of CLCA. First, a novel CLCA conceptual framework is presented combining consequential life cycle inventories, SD principles and the use of spatially explicit data. Second, its innovative value is demonstrated through a proof-of-concept SD-CLCA model. This model evaluates the environmental impacts of changes in electricity supply-demand in the market due to an increasing adoption of solar photovoltaic panels (SPV) in residential buildings. It allows traceability of both system changes and their environmental consequences. For demonstration purposes, the SD-CLCA model is applied to Lisbon municipality and the broader electricity market of Portugal. Results showcase how SD-CLCA models could provide a closer representation of the real effects of predicted changes in the electricity market due to different SPV adoption scenarios. They also illustrate that changes in gross domestic product, population and precipitation provide a diversified set of impact scores. As a methodological advancement, and to the net of a few shortcomings, the proposed SD-CLCA model is able to capture the complexity of cause-effect dynamics determining environmental impacts, which currently represents a research gap in LCA.
2022
Consequential life cycle assessment (CLCA)
Decision making
Electricity mix
Geographic information system (GIS)
System dynamics (SD)
Urban sustainability
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1252458
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