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We present a novel data set of subnational economic output, Gross Regional Product (GRP), for more than 1500 regions in 77 countries that allows us to empirically estimate historic climate impacts at different time scales. Employing annual panel models, long-difference regressions and cross-sectional regressions, we identify effects on productivity levels and productivity growth. We do not find evidence for permanent growth rate impacts but we find robust evidence that temperature affects productivity levels considerably. An increase in global mean surface temperature by about 3.5°C until the end of the century would reduce global output by 7–14% in 2100, with even higher damages in tropical and poor regions. Updating the DICE damage function with our estimates suggests that the social cost of carbon from temperature-induced productivity losses is on the order of 73–142$/tCO2 in 2020, rising to 92–181$/tCO2 in 2030. These numbers exclude non-market damages and damages from extreme weather events or sea-level rise.
Macro-economic assessments of climate impacts lack an analysis of the distribution of daily rainfall, which can resolve both complex societal impact channels and anthropogenically forced changes(1-6). Here, using a global panel of subnational economic output for 1,554 regions worldwide over the past 40 years, we show that economic growth rates are reduced by increases in the number of wet days and in extreme daily rainfall, in addition to responding nonlinearly to the total annual and to the standardized monthly deviations of rainfall. Furthermore, high-income nations and the services and manufacturing sectors are most strongly hindered by both measures of daily rainfall, complementing previous work that emphasized the beneficial effects of additional total annual rainfall in low-income, agriculturally dependent economies(4,7). By assessing the distribution of rainfall at multiple timescales and the effects on different sectors, we uncover channels through which climatic conditions can affect the economy. These results suggest that anthropogenic intensification of daily rainfall extremes(8-10) will have negative global economic consequences that require further assessment by those who wish to evaluate the costs of anthropogenic climate change.
Elevated annual average temperature has been found to impact macro-economic growth. However, various fundamental elements of the economy are affected by deviations of daily temperature from seasonal expectations which are not well reflected in annual averages. Here we show that increases in seasonally adjusted day-to-day temperature variability reduce macro-economic growth independent of and in addition to changes in annual average temperature. Combining observed day-to-day temperature variability with subnational economic data for 1,537 regions worldwide over 40 years in fixed-effects panel models, we find that an extra degree of variability results in a five percentage-point reduction in regional growth rates on average. The impact of day-to-day variability is modulated by seasonal temperature difference and income, resulting in highest vulnerability in low-latitude, low-income regions (12 percentage-point reduction). These findings illuminate a new, global-impact channel in the climate–economy relationship that demands a more comprehensive assessment in both climate and integrated assessment models.
Modeling loss-propagation in the global supply network: The dynamic agent-based model acclimate
(2017)
World markets are highly interlinked and local economies extensively rely on global supply and value chains. Consequently, local production disruptions, for instance caused by extreme weather events, are likely to induce indirect losses along supply chains with potentially global repercussions. These complex loss dynamics represent a challenge for comprehensive disaster risk assessments. Here, we introduce the numerical agent-based model acclimate designed to analyze the cascading of economic losses in the global supply network. Using national sectors as agents, we apply the model to study the global propagation of losses induced by stylized disasters. We find that indirect losses can become comparable in size to direct ones, but can be efficiently mitigated by warehousing and idle capacities. Consequently, a comprehensive risk assessment cannot focus solely on first-tier suppliers, but has to take the whole supply chain into account. To render the supply network climate-proof, national adaptation policies have to be complemented by international adaptation efforts. In that regard, our model can be employed to assess reasonable leverage points and to identify dynamic bottlenecks inaccessible to static analyses. (C) 2017 Elsevier B.V. All rights reserved.
Both climate-change damages and climate-change mitigation will incur economic costs. While the risk of severe damages increases with the level of global warming (Dell et al., 2014; IPCC, 2014b, 2018; Lenton et al., 2008), mitigating costs increase steeply with more stringent warming limits (IPCC, 2014a; Luderer et al., 2013; Rogelj et al., 2015). Here, we show that the global warming limit that minimizes this century's total economic costs of climate change lies between 1.9 and 2 ∘C, if temperature changes continue to impact national economic growth rates as observed in the past and if instantaneous growth effects are neither compensated nor amplified by additional growth effects in the following years. The result is robust across a wide range of normative assumptions on the valuation of future welfare and inequality aversion. We combine estimates of climate-change impacts on economic growth for 186 countries (applying an empirical damage function from Burke et al., 2015) with mitigation costs derived from a state-of-the-art energy–economy–climate model with a wide range of highly resolved mitigation options (Kriegler et al., 2017; Luderer et al., 2013, 2015). Our purely economic assessment, even though it omits non-market damages, provides support for the international Paris Agreement on climate change. The political goal of limiting global warming to “well below 2 degrees” is thus also an economically optimal goal given above assumptions on adaptation and damage persistence.
Process life cycle assessment (PLCA) is widely used to quantify environmental flows associated with the manufacturing of products and other processes. As PLCA always depends on defining a system boundary, its application involves truncation errors. Different methods of estimating truncation errors are proposed in the literature; most of these are based on artificially constructed system complete counterfactuals. In this article, we review the literature on truncation errors and their estimates and systematically explore factors that influence truncation error estimates. We classify estimation approaches, together with underlying factors influencing estimation results according to where in the estimation procedure they occur. By contrasting different PLCA truncation/error modeling frameworks using the same underlying input-output (I-O) data set and varying cut-off criteria, we show that modeling choices can significantly influence estimates for PLCA truncation errors. In addition, we find that differences in I-O and process inventory databases, such as missing service sector activities, can significantly affect estimates of PLCA truncation errors. Our results expose the challenges related to explicit statements on the magnitude of PLCA truncation errors. They also indicate that increasing the strictness of cut-off criteria in PLCA has only limited influence on the resulting truncation errors. We conclude that applying an additional I-O life cycle assessment or a path exchange hybrid life cycle assessment to identify where significant contributions are located in upstream layers could significantly reduce PLCA truncation errors.
Many phenomena of high relevance for economic development such as human capital, geography and climate vary considerably within countries as well as between them. Yet, global data sets of economic output are typically available at the national level only, thereby limiting the accuracy and precision of insights gained through empirical analyses. Recent work has used interpolation and downscaling to yield estimates of sub-national economic output at a global scale, but respective data sets based on official, reported values only are lacking. We here present DOSE — the MCC-PIK Database Of Sub-national Economic Output. DOSE contains harmonised data on reported economic output from 1,661 sub-national regions across 83 countries from 1960 to 2020. To avoid interpolation, values are assembled from numerous statistical agencies, yearbooks and the literature and harmonised for both aggregate and sectoral output. Moreover, we provide temporally- and spatially-consistent data for regional boundaries, enabling matching with geo-spatial data such as climate observations. DOSE provides the opportunity for detailed analyses of economic development at the subnational level, consistent with reported values.
The 2008-2010 food crisis might have been a harbinger of fundamental climate-induced food crises with geopolitical implications. Heat-wave-induced yield losses in Russia and resulting export restrictions led to increases in market prices for wheat across the Middle East, likely contributing to the Arab Spring. With ongoing climate change, temperatures and temperature variability will rise, leading to higher uncertainty in yields for major nutritional crops. Here we investigate which countries are most vulnerable to teleconnected supply-shocks, i.e. where diets strongly rely on the import of wheat, maize, or rice, and where a large share of the population is living in poverty. We find that the Middle East is most sensitive to teleconnected supply shocks in wheat, Central America to supply shocks in maize, and Western Africa to supply shocks in rice. Weighing with poverty levels, Sub-Saharan Africa is most affected. Altogether, a simultaneous 10% reduction in exports of wheat, rice, and maize would reduce caloric intake of 55 million people living in poverty by about 5%. Export bans in major producing regions would put up to 200 million people below the poverty line at risk, 90% of which live in Sub-Saharan Africa. Our results suggest that a region-specific combination of national increases in agricultural productivity and diversification of trade partners and diets can effectively decrease future food security risks.
Assessing global impacts of unexpected meteorological events in an increasingly connected world economy is important for estimating the costs of climate change. We show that since the beginning of the 21st century, the structural evolution of the global supply network has been such as to foster an increase of climate-related production losses. We compute first- and higher-order losses from heat stress-induced reductions in productivity under changing economic and climatic conditions between 1991 and 2011. Since 2001, the economic connectivity has augmented in such a way as to facilitate the cascading of production loss. The influence of this structural change has dominated over the effect of the comparably weak climate warming during this decade. Thus, particularly under future warming, the intensification of international trade has the potential to amplify climate losses if no adaptation measures are taken.