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DDC: 690 × Type: article × Subject: Agriculture × Subject: Climate change ×

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    Revisiting economic burdens of malaria in the face of climate change: a conceptual analysis for Ethiopia
    (Bradford : Emerald, 2020) Yalew, Amsalu Woldie
    Purpose: Climate change affects the geographic and seasonal range of malaria incidence, especially, in poor tropical countries. This paper aims to attempt to conceptualize the potential economic repercussions of such effects with its focus on Ethiopia. Design/methodology/approach: The paper is conceptual and descriptive in its design. It first reviews existing literature and evidence on the economic burdens of malaria, and the impacts of climate change on malaria disease. It then draws the economic implications of the expected malaria risk under the future climate. This is accompanied by a discussion on a set of methods that can be used to quantify the economic effects of malaria with or without climate change. Findings: A review of available evidence shows that climate change is likely to increase the geographic and seasonal range of malaria incidence in Ethiopia. The economic consequences of even a marginal increase in malaria risk will be substantial as one considers the projected impacts of climate change through other channels, the current population exposed to malaria risk and the country’s health system, economic structure and level of investment. The potential effects have the potency to require more household and public spending for health, to perpetuate poverty and inequality and to strain agricultural and regional development. Originality/value: This paper sheds light on the economic implications of climate change impacts on malaria, particularly, in Agrarian countries laying in the tropics. It illustrates how such impacts will interact with other impact channels of climate change, and thus evolve to influence the macro-economy. The paper also proposes a set of methods that can be used to quantify the potential economic effects of malaria. The paper seeks to stimulate future research on this important topic which rather has been neglected. © 2020, Amsalu Woldie Yalew.
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    Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios
    (Bristol : IOP Publ., 2021) Mueller, Christoph; Franke, James; Jaegermeyr, Jonas; Ruane, Alex C.; Elliott, Joshua; Moyer, Elisabeth; Heinke, Jens; Falloon, Pete D.; Folberth, Christian; Francois, Louis
    Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.