{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/308019"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/308019","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Bringing economic inequality into coupled economic-environmental models","abstract":"Coupled economic-environmental models are cross-disciplinary tools that explore how economic activity interacts with the environment and vice versa. This thesis focuses on models that estimate the economic consequences of climate change, different greenhouse gas emissions pathways, and mitigation efforts. Such models typically examine interactions with gross domestic product (GDP). Within-country inequality rarely features, despite evidence from other disciplines suggesting that climate change may cause significant distributional effects within countries. The thesis uses input-output analysis (IOA) to explore how climate change may affect within-country income inequality via two case studies. The first uses a prominent integrated assessment model (IAM), the Climate Framework for Uncertainty, Negotiation and Distribution (FUND), to estimate impacts for seven countries, Egypt, Ethiopia, India, Mexico, the United States, Vietnam and Zambia, while the second uses a collection of impact studies for Alaska. Long-term predictions are not feasible for complex, nonlinear systems, so scenario analysis is instead used to explore which sectors, if any, may produce sizeable inequality effects, if results are consistent across countries, if any household groups appear particularly vulnerable, if inequality effects could negate the benefits of average income growth for certain households, and if there is evidence to suggest that poorer or more unequal countries may be more vulnerable to climate change inequality effects. 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The first uses a prominent integrated assessment model (IAM), the Climate Framework for Uncertainty, Negotiation and Distribution (FUND), to estimate impacts for seven countries, Egypt, Ethiopia, India, Mexico, the United States, Vietnam and Zambia, while the second uses a collection of impact studies for Alaska. Long-term predictions are not feasible for complex, nonlinear systems, so scenario analysis is instead used to explore which sectors, if any, may produce sizeable inequality effects, if results are consistent across countries, if any household groups appear particularly vulnerable, if inequality effects could negate the benefits of average income growth for certain households, and if there is evidence to suggest that poorer or more unequal countries may be more vulnerable to climate change inequality effects. A broad range of types and degrees of climate change costs and benefits are considered. 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