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Waste-to-energy (WtE) is one of the municipal solid waste (MSW) management pathways that can reduce waste volumes while recovering energy. There has been considerable focus on using MSW for energy production; however, there are few integrated assessments of the optimal location and scale of the conversion of MSW to energy. This research develops a novel framework that integrates the analysis of candidate sites for MSW gasification facilities using a geographical information system (GIS) through a multi-criterion decision-making (MCDM) model with a mixed-integer linear programming (MILP) model to determine optimal scale and location. The most suitable candidate locations were identified through a GIS-based location-allocation analysis. A MILP model integrated with GIS was then used to determine the minimum per-unit electricity production costs by optimizing the numbers, locations, and sizes of the proposed WtE and preprocessing facilities. The key inputs to the optimization model are actual road and railway transportation distances, WtE gasification and preprocessing facility capital and installation costs, operation and maintenance costs, gate fee and tipping fees (the fee for disposal of ash at the landfill), and the municipal guidelines. The proposed integrated framework was applied in a case study for Alberta, a western province in Canada. The results indicate that at a 10% rate of return, electricity production is cost-competitive with current electricity prices. The per-unit cost of electricity production (COEP) was found to be only 16.14 $/MWh when the gate fee was treated as a revenue component. The optimal plant location was in Edmonton and the optimum plant size was 175 MW. Sensitivity and uncertainty analysis was conducted to understand the critical input parameters that impact the COEP. The results indicate that the COEP is most sensitive to changes in tipping fees, plant efficiency, capital cost, discount rate, and transportation cost. The developed framework is critical for investment decisions and policy development, and it can be applied globally with adjustments to data and assumptions. [Display omitted] • A novel integrated GIS-based optimization model is developed for MSW supply chain. • A GIS-based MCDM model is used to identify the candidate sites for WtE facilities. • ArcGIS network analysis is used to determine actual transportation distances. • The per unit COEP, 16.14 $/MWh, is competitive with the market price. • The proposed framework optimizes WtE facility numbers, locations, and sizes. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to develop a bottom-up life cycle assessment model to evaluate the greenhouse gas (GHG) emissions associated with the Canadian oil sands supply to China. Two pathways were considered. In pathway I, extracted bitumen is sent to Edmonton for upgrading and pipelined to the Westridge terminal near Vancouver, then shipped to a port in China. In pathway II, extracted bitumen is mixed with diluent, directly pipelined to the Westridge terminal, and shipped to China. The results from both the pipeline and the shipping models suggest that pathway I has better GHG emissions than pathway II on a per-barrel basis. GHG emissions are 2.4–3.5 times lower in pipelining synthetic crude oil (SCO) to Westridge than in pipelining dilbit, without and with a diluent return. The emissions range from 826 − 401 + 701 g CO 2 eq/bbl for SCO, 1819 − 719 + 1172 for dilbit without diluent return, and 2684 − 1065 + 1374 g CO 2 eq/bbl for dilbit with a diluent return. Shipping SCO has 15% fewer emissions than shipping dilbit. The shipping emissions are due to fuel use in the main engine. The emissions for shipping are 5536 − 914 + 966 g CO 2 /bbl for dilbit and 4034 − 822 + 899 g CO 2 /bbl for SCO. • GHG emission performances of Canadian oil sands supply to China were identified. • Pipeline GHG emissions range from 826 − 401 + 701 to 1819 − 719 + 1172 g CO 2 eq/bbl. • Shipping synthetic crude oil has 15% fewer GHG emissions than shipping dilbit. • Shipping GHG emissions ranges from 4034 − 822 + 899 to 5536 − 914 + 966 g CO 2 /bbl. • The synthetic crude oil supply pathway has a higher GHG performance than the dilbit. [ABSTRACT FROM AUTHOR]