Towards a Spatio-temporal Life Cycle Analysis: a Novel Approach to Consider Local and Regional Inventories
Date: 26 June 2017
University of Exeter
PhD in Mathematics
Neglecting dynamic information can impact the results of Life Cycle Assessment studies and wrong conclusions, e.g. in policy making, may be drawn. The long-term vision can be described as the development of a complete Life Cycle Assessment study using one comprehensive spatio-temporal Life Cycle Assessment approach to not only calculate ...
Neglecting dynamic information can impact the results of Life Cycle Assessment studies and wrong conclusions, e.g. in policy making, may be drawn. The long-term vision can be described as the development of a complete Life Cycle Assessment study using one comprehensive spatio-temporal Life Cycle Assessment approach to not only calculate regional and local inventories, but also include a dynamical Life Cycle Impact Assessment to determine environmental impact caused by the regional and spatial inventories. This thesis aims to take steps towards this vision by concentrating on spatio-temporal Life Cycle Inventories. Two spatio-temporal Life Cycle Inventory methods are proposed within this Thesis, both are based on the Enhanced Structural Path Analysis method. The first one includes temporal and spatial information on a local scale and comprises landscape characteristics. Therewith, the dispersion of inventories can be modelled. Results of a case study analysing wheat production show the spatio-temporal dispersion for the example emission of salt in flowing water. The second method uses regional datasets from Ecoinvent to calculate inventories over the entire life cycle. The framework was applied to calculate the CO2-inventories of a 5 MW offshore Wind Turbine over its entire life cycle stages. Results are presented in dynamical emissions maps, showing the accumulation of emissions according to the regional occurrence within the life cycle stages over time. Furthermore, the Enhanced Structural Path Analysis is considered for an optimisation analysis. Limitations of the method are mathematically proven. This includes results that the sum and maximum of the Inventory vector as well as Lifetime of Atmospheric CO2 models can either not be minimised or have restricted optimal solutions. The cumulative output, including and excluding Lifetime of Atmospheric CO2 models are minimal for every inventory vector time-series. The output peaks of the Inventory are minimal if the final demand vector is uniformly distributed. The results are confirmed by simulations for using the 5 MW offshore Wind Turbine case study. The thesis demonstrates that it is possible to implement spatio-temporal information into Life Cycle Assessments. We believe that the proposed methods will improve the traditional Life Cycle Assessments and will help to introduce a framework for a dynamical approach.
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