The development of spatiotemporal simulation methods for the strategic assessment of ecologically sustainable bioenergy supplies.

摘要

Energy derived from biological materials, or bioenergy, is a potential solution to climate change, energy security, and ecological sustainability, three issues closely related to human dependence on fossil energy. Bioenergy is widely available, compatible with existing energy infrastructure, and less carbon intensive than fossil fuels. It is seen as a promising source to meet future energy demands, however the feasibility of large-scale bioenergy has not been adequately studied.;An example case was analyzed to demonstrate the functionality of the methods using the province of Nova Scotia, Canada. A series of scenario tests indicate that bioenergy could meet a modest portion of the region's primary energy requirements, but aggressive targets result in forest depletion within 100 years. Land-use constraints, moderately productive forests, and a lack of agricultural bioenergy production combined to limit supply potential in the case study example.;Nutrient measurement capability was added to the simulation tool to provide an additional measure of bioenergy system performance. Over time, bioenergy related nutrient removal, specifically soil base cation depletion can threaten to limit forest productivity. However, the extent of this effect on long-term bioenergy supply remains poorly understood.;The methods and software described in the thesis provide scientists, resource managers, and policy-experts useful techniques for exploring strategic-level bioenergy supply questions using a transparent, reproducible, and empirically-based approach.;This thesis uses systems analysis techniques to develop models and simulation software capable of providing strategic information on sustainable biomass supplies from forest and agricultural systems. A grid-based, spatiotemporal analysis technique was used to estimate bioenergy supply under various land-use, ecological, and management constraints. The simulation tool allows analysis of large spatial data-sets to support strategic level decision-making in resource management and policy formulation.