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Sustainable Decision-Making based on Life Cycle Sustainability Assessment

By: Murat KucuKvar, Nuri C. Onat, Burak Sen

Summarized Description:

Framework for the application of LCSA-based MCDM

In the balanced weighting case (i.e. when environmental and socio-economic indicators have equal importance) under Scenario 1 (i.e., assuming existing power infrastructure), hybrid electric vehicles have the largest fleet share, comprising 91% of the optimal U.S. passenger car fleet. Similarly, under Scenario 2 (i.e., assuming 100% solar energy infrastructure), the optimal U.S. passenger car fleet consists entirely (100%) of plug-in hybrid electric vehicles, with 16 km of all-electric range.

Optimum distributions of alternative fuel vehicles given different weightings under both scenarios

Furthermore, the trade-off relationships between the sustainability dimensions have also been quantified and visualized. Such quantification can be used to set upper limits for environmental impacts given their positive socio-economic impacts, thereby giving decision-makers more control over the potential environmental, social, and economic consequences.

Figure. Trade-off curves plotted for the relationships between the sustainability dimensions

Zooming into the figure above, it can be seen that increasing GHG emissions after a certain point will have decreasing economic returns. For example, while an economic activity (e.g., driving) that will increase GHG emissions per 100 km from 16.7 kg to 17.1 kg CO2-eq. will provide $1 increase in GDP, the same economic activity will provide only a 20 cent increase in GDP per 100km given that the emissions are further increased from 17.1 kg to 17.4 kg CO2-eq.

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