{"id":9603,"date":"2024-04-24T20:51:14","date_gmt":"2024-04-24T17:51:14","guid":{"rendered":"https:\/\/www.cfwt.sua.ac.tz\/ecosystems\/?p=9603"},"modified":"2024-04-24T20:51:14","modified_gmt":"2024-04-24T17:51:14","slug":"using-models-to-study-climate-changes-economic-impact","status":"publish","type":"post","link":"https:\/\/www.cfwt.sua.ac.tz\/ecosystems\/news\/using-models-to-study-climate-changes-economic-impact","title":{"rendered":"Using models to study climate change’s economic impact"},"content":{"rendered":"
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Klaus Desmet<\/p>\n

Altshuler Professor of Cities, Regions and Globalization<\/span>\u00a0Southern Methodist University<\/span><\/p>\n

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Climate change is a spatial phenomenon. While it will be very costly at equatorial latitudes, where it is already very hot, it will have more benign effects at northern latitudes, where temperatures today are too cold to foster large concentrations of people and economic activity. This column discusses recent advances in the development of spatial integrated assessment to analyse the economic impact of global warming<\/a>. The authors find that while important progress has been made in developing dynamic climate assessment models<\/a>, much work remains to be done.<\/p>\n<\/div>\n<\/div>\n<\/article>\n<\/header>\n

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The average annual temperature range in the world is around 50\uf0b0C, which is much larger than the expected warming of the Earth due to anthropogenic climate change<\/a>. Under the business-as-usual scenario, the Intergovernmental Panel on Climate Change<\/a> (IPCC) projects that by the end of the century average temperatures will be between 1.4\uf0b0C to 4.5\uf0b0C above preindustrial levels. Since land in the colder northern latitudes is abundant and mostly unused for economic purposes, the immediate implication of these observations is that humanity could adapt to the economic consequences of climate change<\/a> by shifting the location of economic activity.<\/p>\n

Yet, changing the spatial distribution<\/a> of economic activity is costly. In addition to political barriers, there are many other costs associated with migrating. We could also adapt by shifting production to sectors that are less hard hit<\/a> by global warming. However, changing local specialisation is only possible if there is trade<\/a>, which allows people to produce a different basket of goods from the one they consume. Of course, trade is costly too (Conte et al. 2021). Apart from migration and trade costs, there are other frictions to moving economic activity across space. If coastal cities become permanently inundated because of rising oceans, or particular areas get hit by storms<\/a>, the physical infrastructure and the local knowledge embedded in these economic clusters are lost, and it takes time and additional investments for new clusters to emerge elsewhere. The bottom line is that the variety of spatial frictions to move goods, people, capital, technology, and any other factors of production should be central to evaluating the economic impact of global warming.<\/p>\n

The need for spatial integrated assessment models
\n<\/strong>There is solid evidence documenting both the spatial heterogeneity in the impact of climate change and the role of moving as an adaptation mechanism to rising temperatures (e.g. Docquier et al. 2019, Kahn 2021, Di Falco et al. 2022). Yet, while a vast number of reduced-form empirical studies have identified many key facts about the economic impact of global warming across space, we need structural models to evaluate the economic cost of climate change<\/a> across regions and over time, decompose the most relevant economic channels, and think about policy solutions. Statistically extrapolating into new economic configurations that we have not yet observed is problematic, especially given the long time horizons associated with climate change<\/a>. To make appropriate predictions, we need models that combine<\/a> everything we know about the workings of the world economy and its relation to climate.<\/p>\n

Ideally, these climate assessment models should be global<\/a>, have high spatial resolution, and be dynamic. A global approach is needed, because any local emissions<\/a> mix rapidly in the atmosphere to generate changes in global temperatures, which in turn affect local temperatures. That is, carbon emissions<\/a> are a global externality. The models should also have a high spatial resolution to accommodate both the large spatial heterogeneity in damages and the possibility of adaptation by moving economic activity across regions (sometimes over short distances, like with storms and coastal flooding). Finally, the models should be dynamic, since most of the impact of today’s emissions will be experienced in the future. In the last 30 years, William Nordhaus and others have pioneered the use of integrated assessment models<\/a> (IAMs) that combine aggregate economic models with the main insights of climate science to evaluate the economic impact of global warming. While some of these models focus on more than one region, they lack the necessary geographic resolution to quantify the role of spatial frictions in migration, investments, trade, and innovation<\/a>.<\/p>\n

As reviewed in Desmet and Rossi-Hansberg (2024), the last decade has seen significant progress in developing dynamic spatial integrated<\/a> assessment models (S-IAMs). The construction of these models has been aided by the burgeoning literature on quantitative spatial economics (Redding and Rossi-Hansberg 2017). This research has progressively produced more realistic and detailed models that incorporate empirically well-grounded spatial relationships. These high-resolution frameworks can be used to measure the impact of climate change<\/a> on local productivity, amenities, and natality rates. By introducing trade and migration as potential adaptation mechanisms, these models lead to more realistic assessments of the cost of different phenomena, such as coastal flooding. Although these frameworks are dynamic, often the forward-looking decisions of agents are simplified to a sequence of static decisions to keep the models computationally tractable. However, a new generation of models is now capable of introducing forward-looking agents that make both migration and capital accumulation decisions. This allows studying the role of climate change anticipation in shaping adaptation<\/a> responses.<\/p>\n

The importance of spatial heterogeneity
\n<\/strong>To illustrate the importance of spatial heterogeneity within these frameworks, we take the S-IAM of Cruz and Rossi-Hansberg (2024) and analyse how the economic impact of global warming would change if we were to ignore spatial variation in damages from rising temperatures. As shown in Figure 1, averaging damages across the world would lead us to underestimate real income in polar latitudes, whereas it would make us overestimate real income in equatorial latitudes. In other words, we would fail to fully capture losses in sub-Saharan Africa<\/a> by more than 6% in some areas, and we would underestimate the climate-induced increase in spatial inequality across the world.<\/p>\n

Figure 1<\/strong>\u00a0Present discounted value of real GDP when averaging damages, relative to baseline without averaging<\/p>\n

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