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The objective of this paper is to assess the impact of proposed Canadian climate change mitigation policies (discounting and carbon pricing) on cost, energy use, and greenhouse gas (GHG) emissions in the single-objective design/expansion optimization of the Amherstview water distribution system in Amherstview, Ontario, Canada. The single-objective optimization problem is solved with the elitist genetic algorithm (EGA). The optimization approach is used in a parametric analysis to examine the impact of discounting and carbon pricing on GHG reductions for cement-mortar ductile iron and polyvinyl chloride pipe materials. Preliminary results indicate that the discount rate and carbon prices investigated had no significant influence on energy use and GHG mass in the Amherstview system and did not meet the emission-reduction targets set by the Canadian government. This result was attributed to a number of factors, including adequately installed hydraulic capacity in the Amherstview system, the use of a time-declining GHG emission intensity factor, and the scope of the expansion problem. [ABSTRACT FROM AUTHOR]

The construction industry contributes significantly to global environmental loads with massive amounts of construction and demolition waste (CDW) ending up in landfills. To address the need for efficient CDW management, this research proposes a new decision support framework for managing construction waste generated during end-of-life activities for building projects. The framework monetizes potential environmental savings from different recovery options (e.g., reuse, recycle, and so on) and uses multiobjective optimization to determine the optimal quantity of material to undergo each material recovery scenario. The framework uses parametric weights to consider stakeholders' preferences and their appreciation of environmental benefits compared with costs. A case study of a renovation project in Waterloo, Ontario, Canada, is used to demonstrate how the proposed framework can divert concrete and glass waste from the landfill. For this particular project, savings of 200 GJ of embodied energy, 22 m3 of water, and over 12 t of greenhouse gases can be realized from optimal recovery planning using the proposed framework. This study concludes that decision support systems should be used well in advance of end-of-life activities to evaluate trade-offs for recovery planning activities effectively. [ABSTRACT FROM AUTHOR]