Selection and modeling of sustainable development indicators: a case study of the Fraser River Basin, British Columbia

作者: Kent R Gustavson , Stephen C Lonergan , H.Jack Ruitenbeek

DOI: 10.1016/S0921-8009(98)00032-9

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摘要: Abstract A large number of groups in British Columbia and Canada as a whole have expended considerable effort to define sustainable development goals and, lesser extent, select indicators. Many hope that collection monitoring such indicators will provide important policy guidance decision-makers means for tracking development. This project was designed with the following goals: (i) could be linked an operational definition within Fraser River Basin (British Columbia); (ii) assess accessibility, quality relevance best available data developing indicators; (iii) apply modeling techniques discerning linkages among (iv) recommendations further indicator selection research. The major methodological conclusions reached were: it is link movements small slate individual single can rarely any specific goal; poor quality, inaccessibility irrelevance existing are pervasive constraints reliable modeling; most appropriate at aggregated spatial scales provinces or watersheds, while smaller ecosystem-based levels feasible but unreliable; linking use deterministic qualitative approaches useful projecting linkages, conventional statistical frequently inappropriate because unreliable non-commensurable data. has profound implications modeling. In contrast much current work, which relies on selecting detailed indicators, would more fruitful less costly focus attention selected classes (such economic, social, environmental human health indicators). precise specification each these consequence. Indicator work should larger scale systems, opposed ecosystem units. Such suited identifying trade-offs implications, rather than forecasting Data gathering efforts scaled down substantially informed, not unduly constrained, by model frameworks. Greater required frameworks incomplete sets information, quantitative structures external models.