2022, Rikani, Albano, Frieler, Katja, Schewe, Jacob

International migration patterns, at the global level, can to a large extent be explained through economic factors in origin and destination countries. On the other hand, it has been shown that global climate change is likely to affect economic development over the coming decades. Here, we demonstrate how these future climate impacts on national income levels could alter the global migration landscape. Using an empirically calibrated global migration model, we investigate two separate mechanisms. The first is through destination-country income, which has been shown consistently to have a positive effect on immigration. As countries' income levels relative to each other are projected to change in the future both due to different rates of economic growth and due to different levels of climate change impacts, the relative distribution of immigration across destination countries also changes as a result, all else being equal. Second, emigration rates have been found to have a complex, inverted U-shaped dependence on origin-country income. Given the available migration flow data, it is unclear whether this dependence-found in spatio-temporal panel data-also pertains to changes in a given migration flow over time. If it does, then climate change will additionally affect migration patterns through origin countries' emigration rates, as the relative and absolute positions of countries on the migration "hump" change. We illustrate these different possibilities, and the corresponding effects of 3°C global warming (above pre-industrial) on global migration patterns, using climate model projections and two different methods for estimating climate change effects on macroeconomic development.

2019, Schulz, Christian M., Ahrend, Klaus-Michael, Schneider, Gerhard, Hohendorf, Gerrit, Schellnhuber, Hans Joachim, Busse, Reinhard

[No abstract available]

2021, Zhao, Qi, Guo, Yuming, Ye, Tingting, Gasparrini, Antonio, Tong, Shilu, Overcenco, Ala, Urban, Aleš, Schneider, Alexandra, Entezari, Alireza, Vicedo-Cabrera, Ana Maria, Zanobetti, Antonella, Analitis, Antonis, Zeka, Ariana, Tobias, Aurelio, Nunes, Baltazar, Alahmad, Barrak, Armstrong, Ben, Forsberg, Bertil, Pan, Shih-Chun, Íñiguez, Carmen, Ameling, Caroline, De la Cruz Valencia, César, Åström, Christofer, Houthuijs, Danny, Dung, Do Van, Royé, Dominic, Indermitte, Ene, Lavigne, Eric, Mayvaneh, Fatemeh, Acquaotta, Fiorella, de'Donato, Francesca, Di Ruscio, Francesco, Sera, Francesco, Carrasco-Escobar, Gabriel, Kan, Haidong, Orru, Hans, Kim, Ho, Holobaca, Iulian-Horia, Kyselý, Jan, Madureira, Joana, Schwartz, Joel, Jaakkola, Jouni J. K., Katsouyanni, Klea, Hurtado Diaz, Magali, Ragettli, Martina S., Hashizume, Masahiro, Pascal, Mathilde, de Sousa Zanotti Stagliorio Coélho, Micheline, Valdés Ortega, Nicolás, Ryti, Niilo, Scovronick, Noah, Michelozzi, Paola, Matus Correa, Patricia, Goodman, Patrick, Nascimento Saldiva, Paulo Hilario, Abrutzky, Rosana, Osorio, Samuel, Rao, Shilpa, Fratianni, Simona, Dang, Tran Ngoc, Colistro, Valentina, Huber, Veronika, Lee, Whanhee, Seposo, Xerxes, Honda, Yasushi, Guo, Yue Leon, Bell, Michelle L., Li, Shanshan

Background: Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. Methods: In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature–mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature–mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. Findings: Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967–5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58–11·07) of all deaths (8·52% [6·19–10·47] were cold-related and 0·91% [0·56–1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60–87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000–03 to 2016–19, the global cold-related excess death ratio changed by −0·51 percentage points (95% eCI −0·61 to −0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13–0·31), leading to a net reduction in the overall ratio. The largest decline in overall excess death ratio occurred in South-eastern Asia, whereas excess death ratio fluctuated in Southern Asia and Europe. Interpretation: Non-optimal temperatures are associated with a substantial mortality burden, which varies spatiotemporally. Our findings will benefit international, national, and local communities in developing preparedness and prevention strategies to reduce weather-related impacts immediately and under climate change scenarios. Funding: Australian Research Council and the Australian National Health and Medical Research Council. © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license