The purpose of this paper is to focus on tracing GHG emissions across the supply chain industries associated with the US residential, commercial and industrial building stock and provides optimized GHG reduction policy plans for sustainable development.
A two-step hierarchical approach is developed. First, Economic Input-Output-based Life Cycle Assessment (EIO-LCA) is utilized to quantify the GHG emissions associated with the US residential, commercial and industrial building stock. Second, a mixed integer linear programming (MILP) based optimization framework is developed to identify the optimal GHG emissions’ reduction (percent) for each industry across the supply chain network of the US economy.
The results indicated that “ready-mix concrete manufacturing”, “electric power generation, transmission and distribution” and “lighting fixture manufacturing” sectors were found to be the main culprits in the GHG emissions’ stock. Additionally, the majorly responsible industries in the supply chains of each building construction categories were also highlighted as the hot-spots in the supply chains with respect to the GHG emission reduction (percent) requirements.
The decision making in terms of construction-related expenses and energy use options have considerable impacts across the supply chains. Therefore, regulations and actions should be re-organized around the systematic understanding considering the principles of “circular economy” within the context of sustainable development.
Although the literature is abundant with works that address quantifying environmental impacts of building structures, environmental life cycle impact-based optimization methods are scarce. This paper successfully fills this gap by integrating EIO-LCA and MILP frameworks to identify the most pollutant industries in the supply chains of building structures.
Mohamed Abdul Ghani, N.M.A., Egilmez, G., Kucukvar, M. and S. Bhutta, M.K. (2017), "From green buildings to green supply chains: An integrated input-output life cycle assessment and optimization framework for carbon footprint reduction policy making", Management of Environmental Quality, Vol. 28 No. 4, pp. 532-548. https://doi.org/10.1108/MEQ-12-2015-0211Download as .RIS
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