Comparison of environmental indicator sets using a unified indicator classification framework |
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| Affiliation: | 1. Centro de Investigaciones del Mar y la Atmósfera, Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina;2. Departamento de Ecología Genética y Evolución, Universidad de Buenos Aires Pabellón II piso 2, Ciudad Universitaria, Buenos Aires, C1428EGA, Argentina;1. Department of Natural Resources, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran;2. Department of Biology and Biochemistry, University of Houston, USA;3. Geography Department, State University of New York College at Cortland, USA;1. Faculty of Veterinary Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4Z6, Canada;2. Centre for Coastal Health, 900 Fifth Street, Nanaimo, British Columbia, V9R 5S5, Canada;3. Canadian Wildlife Health Cooperative, Western College of Veterinary Medicine, University of Saskatchewan, 52 Campus Drive, Saskatoon, Saskatchewan, S7N 5B4, Canada;4. National Livestock Development Board, Narahenpitiya, Colombo 5, Sri Lanka;1. Vilnius Gediminas Technical University, Vilnius, Lithuania;2. University of Moratuwa, Moratuwa, Sri Lanka;3. Machine Intelligence Research Labs, Auburn, WA, USA;4. Tallinn University of Technology, Tallinn, Estonia;5. Lithuanian Institute of Agrarian Economics, Lithuania |
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| Abstract: | Environmental indicator sets (EIS) are tools to monitor and assess sustainability, and many environmental organizations have embraced their use. Due to the large number of EIS, it is a challenge to compare and reconcile their differences and gain a comprehensive view of their utility. To compare EIS, the first step is to classify their component indicators, for which several frameworks exist. Among the most widely used, is the causal-chain framework, also referred to as PSR after its categories of Pressure, State and Response. Other frameworks classify indicators by subject, yet none is widely applied. Aiming to compare EIS, we first proposed a unified classification criteria for indicators using PSR and five subject categories (i.e., biodiversity and ecosystem health, E; natural resources, N; physical and chemical contamination, C; human environment, H; and general, G). Then, we used these classification criteria to describe and compare fourteen existing environmental indicator sets. Finally, we compared EIS based on their production characteristics and goals. Across the fourteen EIS, we analyzed 706 indicators (which represent ∼1200 variables) and selected 16 and 79 keywords for classification in the PSR and ENCHG categories respectively. We found on average that the ratio of categories in the causal chain framework was 2.5S:1.5P:1R, while we observed a large variability across EIS. For the subject categories, C-E-N were nearly equally represented among EIS, and better represented than H-G. Also, the evaluated EIS showed a polarization between C-H and E categories that we interpreted as a human vs. natural-ecosystem welfare focus. Finally, we identified three broad categories of EIS based primarily on the organization that produced them, non-governmental organizations, governmental organizations, and international organizations. Our results can contribute to the design and implementation of scientifically robust and representative EIS, which are key to incorporate environmental data to policymaking in the search of sustainability. |
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| Keywords: | Environmental indicator sets Sustainability index Causal chain framework |
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