Sustainable regional management (development) requires an understanding of interactions between the social, economic, and ecological systems within the boundaries of a region. In this paper, application of emergy (an environmental accounting method) for regional planning is discussed through a case study. Emergy (spelled with an "m") methodology is an environmental accounting technique that evaluates the energy system for the thermodynamics of an open system. Major renewable and non-renewable resource fluxes to a region, including energy, matter, human activities, and money can be converted to emergy by using corresponding transformity functions. As a case study, this paper discusses the emergy accounting of Canada and its provinces with various emergy-based indicators. Moreover, emergy maps were generated in a form of emergy geography. These maps are multi-dimensional illustrations that show resource consumption, emergy per person, and emergy density across Canada under two parameters: (1) the quantities of resources consumed and (2) the location of consumption. Emergy analysis also highlights concentrations of renewable and natural resources in Canada and distinguishes the provinces with the highest resource consumption. Analysis of emergy indicator for Canadian provinces shows that Alberta with the highest EYR (7.35) provides energy to the economy of Canada. However, ELR value of Alberta (8.5) indicates that the province's current economic approach is not sustainable as it relies mainly on non-renewable emergy inputs (mainly from fossil fuels). ELR of British Columbia and Manitoba indicates that these two provinces created a firm balance between emergy use of renewable and non-renewable resources. The characterizations of regions provided in this paper can be used for future land planning and management both in federal and provincial levels.
A fuzzy decision tree (FDT) based framework was developed to facilitate the selection of suitable oil spill response methods in the Arctic. Hypothetical oil spill cases were developed based on six identified attributes, while the suitability of three spill response methods (mechanical containment and recovery, use of chemical dispersants, and in-situ burning) for each spill case was obtained based on expert judgments. Fuzzy sets were used to address the associated uncertainties, and FDTs were then developed through generating: i) one decision tree for all three response methods (FDT-AP1) and ii) one decision tree for each response method and the development of linear regression models at terminal nodes (FDT-LR). The FDT-LR approach exhibited higher prediction accuracy than the FDT-AP1 approach. A maximum of 100% accurate predictions could be achieved for testing cases using it. On average, 75% of suitable oil spill response methods out of 10,000 performed iterations were predicted correctly.