Climate change is expected to increase the scarcity and variability of fresh water supplies in some regions with important implications for irrigated agriculture. By allowing for increased flexibility in response to scarcity and by incentivizing the allocation of water to higher value use, markets can play an important role in limiting the economic losses associated with droughts. Using data on water demand, the seniority of water rights, county agricultural reports, high-resolution data on cropping patterns, and agronomic estimates of crop water requirements, we estimate the benefits of market-based allocations of surface water for California’s Central Valley. Specifically, we estimate the value of irrigation water and compare the agricultural costs of water shortages under the existing legal framework and under an alternate system that allows for trading of water. We find that a more efficient allocation of curtailments could reduce the costs of water shortages by as much as $362 million dollars per year or 4.4% of the net agricultural revenue in California in expectation, implying that institutional and market reform may offer important opportunities for adaptation.
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Abstract Here we use remotely sensed land surface temperature measurements to explore the distribution of the United States’ urban heating burden, both at high resolution (within cities or counties) and at scale (across the whole contiguous United States). While a rich literature has documented neighborhood‐level disparities in urban heat exposures in individual cities, data constraints have precluded comparisons across locations. Here, drawing on urban temperature anomalies during extreme summer surface temperature events from all 1,056 US counties with more than 10 developed census tracts, we find that the poorest tracts (and those with lowest average education levels) within a county are significantly hotter than the richest (and more educated) neighborhoods for 76% of these counties (54% for education); we also find that neighborhoods with higher Black, Hispanic, and Asian population shares are hotter than the more White, non‐Hispanic areas in each county. This holds in counties with both large and small spreads in these population shares, and for 71% of all counties the significant racial urban heat disparities persist even when adjusting for income. Although individual locations have different histories that have contributed to race‐ and class‐based geographies, we find that the physical features of the urban environments driving these surface heat exposure gradients are fairly uniform across the country. Systematically, the disproportionate heat surface exposures faced by minority communities are due to more built‐up neighborhoods, less vegetation, and—to a lesser extent—higher population density.
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Abstract The direct impacts of climate change on crop yields and human health are individually well-studied, but the interaction between the two have received little attention. Here we analyze the consequences of global warming for agricultural workers and the crops they cultivate using a global economic model (GTAP) with explicit treatment of the physiological impacts of heat stress on humans’ ability to work. Based on two metrics of heat stress and two labor functions, combined with a meta-analysis of crop yields, we provide an analysis of climate, impacts both on agricultural labor force, as well as on staple crop yields, thereby accounting for the interacting effect of climate change on both land and labor. Here we analyze the two sets of impacts on staple crops, while also expanding the labor impacts to highlight the potential importance on non-staple crops. We find, worldwide, labor and yield impacts within staple grains are equally important at +3∘C warming, relative to the 1986–2005 baseline. Furthermore, the widely overlooked labor impacts are dominant in two of the most vulnerable regions: sub-Saharan Africa and Southeast Asia. In those regions, heat stress with 3∘C global warming could reduce labor capacity in agriculture by 30%–50%, increasing food prices and requiring much higher levels of employment in the farm sector. The global welfare loss at this level of warming could reach $136 billion, with crop prices rising by 5%, relative to baseline.
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Abstract Many studies have estimated the adverse effects of climate change on crop yields, however, this literature almost universally assumes a constant geographic distribution of crops in the future. Movement of growing areas to limit exposure to adverse climate conditions has been discussed as a theoretical adaptive response but has not previously been quantified or demonstrated at a global scale. Here, we assess how changes in rainfed crop area have already mediated growing season temperature trends for rainfed maize, wheat, rice, and soybean using spatially-explicit climate and crop area data from 1973 to 2012. Our results suggest that the most damaging impacts of warming on rainfed maize, wheat, and rice have been substantially moderated by the migration of these crops over time and the expansion of irrigation. However, continued migration may incur substantial environmental costs and will depend on socio-economic and political factors in addition to land suitability and climate.
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Abstract Annual carbon dioxide (CO2) emissions from the U.S. power sector decreased 24% from 2000 to 2018, while carbon intensity (CO2per unit of electricity generated) declined by 34%. These reductions have been attributed in part to a shift from coal to natural gas, as gas‐fired plants emit roughly half the CO2emissions as coal plants. To date, no analysis has looked at the coal‐to‐gas shift from the perspective of commitment accounting—the cumulative future CO2emissions expected from power infrastructure. We estimate that between 2000 and 2018, committed emissions in the U.S. power sector decreased 12% (six GtCO2), from 49 to 43 GtCO2, assuming average generator lifetimes and capacity factors. Taking into consideration methane leakage during the life cycle of coal and gas plants, this decrease in committed emissions is further offset (e.g., assuming a 3% leakage rate, there is effectively no reduction at all). Thus, although annual emissions have fallen, cumulative future emissions will not be substantially lower unless existing coal and gas plants operate at significantly lower rates than they have historically. Moreover, our estimates of committed emissions for U.S. coal and gas plants finds steep reductions in plant use and/or early retirements are already needed for the country to meet its targets under the Paris climate agreement—even if no new fossil capacity is added.
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Abstract Because human and environmental systems in the Anthropocene are increasingly coupled, hydrologists and economists often find themselves studying the same systems from different vantage points. Here we argue that synthesis across economics and hydrology can help address two pressing sociohydrologic challenges: actionable prediction and the generation of transferable knowledge from place‐based studies. Specifically, we review (1) empirical methods and (2) theoretical approaches from economics and connect the two through a proposed iterative framework. First, we find that empirical methods for statistical analysis of natural and quasi‐experiments in economics can be leveraged to distinguish causal relations from mere correlations in complex and data scarce systems, which can help address the challenge of sociohydrologic prediction. Second, we find that economic theories based on rational choice can be used to decipher known paradoxes in water resources, which can help address the challenge of sociohydrologic knowledge generation. In both empirical and theoretical domains, specialized knowledge in hydrology remains critical to properly applying techniques from economics to coupled human‐water systems. We propose that linkages between the two fields highlight a large potential for interaction.
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Abstract This paper explores the interplay between the biophysical and economic geographies of climate change impacts on agriculture. It does so by bridging the extensive literature on climate impacts on yields and physical productivity in global crop production, with the literature on the role of adaptation through international trade in determining the consequences of climate change impacts. Unlike previous work in this area, instead of using a specific crop model or a set of models, we employ a statistical meta-analysis that encompasses all studies available to the IPCC-AR5 report. This permits us to isolate specific elements of the spatially heterogeneous biophysical geography of climate impacts, including the role of initial temperature, differential patterns of warming, and varying crop responses to warming across the globe. We combine these climate impact estimates with the Global Trade Analysis Project model of global trade in order to estimate the national welfare changes that are decomposed into three components: the direct (biophysical impact) contribution to welfare, the terms of trade effect, and the allocative efficiency effect. We find that when we remove the spatial variation in climate impacts, the terms of trade impacts are cut in half. Given the inherent heterogeneity of climate impacts in agriculture, this points to the important role of trade in distributing the associated welfare impacts. When we allow the biophysical impacts to vary across the empirically estimated uncertainty range taken from the meta-analysis, we find that the welfare consequences are highly asymmetric, with much larger losses at the low end of the yield distribution. This interaction between the magnitude and heterogeneity of biophysical climate shocks and their welfare effects highlight the need for detailed representation of both in projecting climate change impacts.
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Despite major improvements in weather and climate modelling and substantial increases in remotely sensed observations, drought prediction remains a major challenge. After a review of the existing methods, we discuss major research gaps and opportunities to improve drought prediction. We argue that current approaches are top-down, assuming that the process(es) and/or driver(s) are known—i.e. starting with a model and then imposing it on the observed events (reality). With the help of an experiment, we show that there are opportunities to develop bottom-up drought prediction models—i.e. starting from the reality (here, observed events) and searching for model(s) and driver(s) that work. Recent advances in artificial intelligence and machine learning provide significant opportunities for developing bottom-up drought forecasting models. Regardless of the type of drought forecasting model (e.g. machine learning, dynamical simulations, analogue based), we need to shift our attention to robustness of theories and outputs rather than event-based verification. A shift in our focus towards quantifying the stability of uncertainty in drought prediction models, rather than the goodness of fit or reproducing the past, could be the first step towards this goal. Finally, we highlight the advantages of hybrid dynamical and statistical models for improving current drought prediction models. This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.more » « less
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Abstract Air quality associated public health co-benefit may emerge from climate and energy policies aimed at reducing greenhouse gas (GHG) emissions. However, the distribution of these co-benefits has not been carefully studied, despite the opportunity to tailor mitigation efforts so they achieve maximum benefits within socially and economically disadvantaged communities (DACs). Here, we quantify such health co-benefits from different long-term, low-carbon scenarios in California and their distribution in the context of social vulnerability. The magnitude and distribution of health benefits, including within impacted communities, is found to varies among scenarios which reduce economy wide GHG emissions by 80% in 2050 depending on the technology- and fuel-switching decisions in individual end-use sectors. The building electrification focused decarbonization strategy achieves ~15% greater total health benefits than the truck electrification focused strategy which uses renewable fuels to meet building demands. Conversely, the enhanced electrification of the truck sector is shown to benefit DACs more effectively. Such tradeoffs highlight the importance of considering environmental justice implications in the development of climate mitigation planning.more » « less