A life cycle assessment (LCA) of residential windows was conducted to evaluate the environmental impacts of those made with aluminum-clad wood, polyvinyl chloride (PVC) and fiberglass frames for sale in North America. The LCA was a case study in which specific-site data were used for the manufacturing stage of each window, while published data were adapted to complete the life cycle inventories using Sima Pro software. These inventories were grouped into impact categories and scaled based on IMPACT 2002+ version 2.1 characterization and damage factors. The damage modeling indicated that the life cycle impacts are dominated by the combustion of non-renewable energy resources. The PVC life cycle used the most non-renewable energy and caused the most damage owing to that window's shorter service life. The disposal of aluminum cladding in the municipal waste stream caused greater aquatic ecotoxicity for the wood window. The sensitivity analysis revealed that replacing the virgin material in aluminum cladding with recycled content improved the life cycle impacts of the wood window. Using fiberglass or PVC to clad the wood window also improved the environmental performance. Other potential improvements to the life cycle impacts of the three windows include improving energy efficiency, particularly during the secondary manufacturing.

With the increased interest in exploiting renewable energy sources for district heating applications, the economic comparison of viable options has been considered as an important step in making a sound decision. In this paper, the economic performance of several energy options for a district heating system in Vancouver, British Columbia, is studied. The considered district heating system includes a 10 MW peaking/backup natural gas boiler to provide about 40% of the annual energy requirement and a 2.5 MW base-load system. The energy options for the base-load system include: wood pellet, sewer heat, and geothermal heat. Present values of initial and operating costs of each system were calculated over 25-year service life of the systems, considering tax savings due to depreciation and operating costs, and salvage value of equipment and building and resale price of land in the cash flow analysis. It was shown that the natural gas boiler option provided less expensive energy followed by the wood pellet heat producing technologies, sewer heat recovery, and geothermal heat pump. Among wood pellet technologies, the grate burner was a less expensive option than powder and gasifier technologies. It was found that using natural gas as a fuel source for the peaking/backup system accounted for 37% of the heat production cost for the considered district-heating center. The results show that the cost of produced heat from wood pellet grate burner is well comparable to that of the natural gas boiler. Emissions of the systems are also calculated in this study. It is shown that the natural gas boiler for the base-load heat production would produce more than 4300 tonnes of GHG emission per year, while wood pellet burning systems are GHG neutral. Sensitivity analysis on various inputs to the economic model has been carried out. It was shown that 20% increase in capital cost of the natural gas base-load system or 1% decrease in wood pellet price inflation would make the wood pellet grate burner economically preferable to the natural gas boiler. Copyright © 2009 John Wiley & Sons, Ltd.

Transportation of forest products accounts as a major contributor to the total operational costs; hence, its optimization has become an important aspect in supply chain planning. Transportation optimization at the operational level includes decisions related to product flow, storage, pre-processing, and routing and scheduling of vehicles. The decisions and constraints in the model depend on the type of product that is transported. Earlier review articles on forest transportation optimization focused only on log transportation, while in this review paper, products such as logs, biomass, pulp and furniture are considered and their similarities and differences are highlighted. Most of the previous studies focused on optimizing the total cost of transportation, while environmental aspects of truck routing and scheduling in forestry were not considered. Uncertainties in parameters such as supply and demand quantities and transportation time were not explored fully in the models. In addition to storage and truck routing and scheduling, considering pre-processing (e.g. sorting, grinding, blending, bucking) decisions at forest sites, satellite yards and the mills in the models could be done in future studies. It is important that aspects related to truck configuration, type and capacity be considered in the models as there is limited accessibility of large trucks such as large chip vans to forest sites. Management practices such as just-in-time production and vendor-managed inventory systems could be considered in forest supply chain planning. Using big data and business analytics techniques are other new trends that could improve decision-making related to logistics and transportation planning in forestry.

Mathematical programming models have been used to optimize the design and management of forest bioenergy supply chains. A deterministic mathematical model is beneficial for making optimum decisions; however, its applicability to real-world problems may be limited because it does not capture all the complexities, including uncertainties in the parameters, in the supply chain. In this paper, a combination of Monte Carlo Simulation and optimization model is used to evaluate the impact of uncertainty in biomass quality, availability and cost, and electricity prices on the supply chain of a forest biomass power plant. The optimization model is a deterministic mixed integer non-linear model with monthly time steps over a 1-year planning horizon. Variability in biomass quality, i.e. moisture content (MC) and higher heating value (HHV), based on the historical data of a real case study is studied in detail and fitted probability distributions are used in the model, while for electricity prices different scenarios are considered. The results show that the impact of variability in the MC on profit is higher than that of uncertainty in HHV. It is observed that the annual profit ranges between $13.3 million and $17.9 million in the presence of all possible uncertainties while its average is $15.5 million. Uncertainty in biomass availability and cost and electricity price results in the risks of having annual profit of less than $14 million and low monthly storage levels.