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Sensitivity analysis of environmentally extended input–output models as a tool for building scenarios of sustainable development

Sensitivity analysis of environmentally extended input–output models as a tool for building scenarios of sustainable development

Ecological Economics 86: 148-155

There is an urgent need to develop scenarios and roadmaps for a more sustainable future than where business-as-usual is heading. This paper addresses the use of sensitivity analysis for analyzing environmentally extended input output (EEIO) models in order to develop cost-effective and comprehensive scenario building. Main components of resource use, emission intensity and final demand are extracted from the complete network of interactions contained in the input output tables of the national accounts. The method is demonstrated using a detailed Finnish EEIO-model (ENVIMAT). Based on the results, only 0.3% of the 23 103 interactions were found to have a significant effect on Finnish greenhouse gas emissions. The same parameters were also relevant for waste generation and land use, but not for gross domestic product. The identified main components were tested by structural decomposition. Actual development of greenhouse gas emissions from 2002 to 2005 was compared to that predicted by updating only the identified components. Based on the results, the development of greenhouse gas emissions could be predicted with high accuracy using only the identified main components. Generalizing the results, sensitivity analysis can assist in identifying the main components to be included in future scenarios for sustainable development. Sensitivity analysis can highlight relevant issues for sustainability scenario building. Less than 0.5% of all parameters in an EEIO model were found to be relevant. Same set of parameters contributed to climate change, GDP, land use and waste production. Narrow list of main components could explain temporal development of greenhouse gases in Finland.

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Accession: 036685593

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DOI: 10.1016/j.ecolecon.2012.11.021

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