Policy-makers in the world's poorest countries are often forced to make decisions based on limited data. Consider Angola, which recently conducted its first postcolonial census. In the 44 years that elapsed between the prior census and the recent one, the country's population grew from 5.6 million to 24.3 million, and the country experienced a protracted civil war that displaced millions of citizens. In situations where reliable survey data are missing or out of date, a novel line of research offers promising alternatives. On page 790 of this issue, Jean et al.(1) apply recent advances in machine learning to high-resolution satellite imagery to accurately measure regional poverty in Africa.

There have been dramatic advances in understanding the physical science of climate change, facilitated by substantial and reliable research support. The social value of these advances depends on understanding their implications for society, an arena where research support has been more modest and research progress slower. Some advances have been made in understanding and formalizing climate-economy linkages, but knowledge gaps remain [e.g., as discussed in (1, 2)]. We outline three areas where we believe research progress on climate economics is both sorely needed, in light of policy relevance, and possible within the next few years given appropriate funding: (i) refining the social cost of carbon (SCC), (ii) improving understanding of the consequences of particular policies, and (iii) better understanding of the economic impacts and policy choices in developing economies.

This research brief is based on a paper from the journal Nature, published on-line on October 21, 2015, entitled “Global non-linear effect of temperature on economic production.” The paper, led by Stanford University’s Marshall Burke, provides the first evidence that economic activity in all regions is coupled to the global climate and establishes a new empirical foundation for modelling economic loss in response to climate change.

David Lobell’s recent research indicates that negative impacts to the global agriculture system are much more likely, more severe and wider-ranging in the face of human-caused climate change. Temperature increases are the main drier behind these far-reaching impacts.. There are several pathways toward adaptation, though none of them appears to completely offset the losses. Research highlighted in this brief offers insights for institutions and decisionmakers concerned with protecting food security and international stability throughout the coming decades.

Climate change can reduce crop yields and thereby threaten food security. The current measures used to adapt to climate change involve avoiding crops yield decrease, however, the limitations of such measures due to water and other resources scarcity have not been well understood. Here, we quantify how the sensitivity of maize to water availability has increased because of the shift toward longer-maturing varieties during last three decades in the Chinese Maize Belt (CMB). We report that modern, longer-maturing varieties have extended the growing period by an average of 8 days and have significantly offset the negative impacts of climate change on yield. However, the sensitivity of maize production to water has increased: maize yield across the CMB was 5% lower with rainfed than with irrigated maize in the 1980s and was 10% lower (and even >20% lower in some areas) in the 2000s because of both warming and the increased requirement for water by the longer-maturing varieties. Of the maize area in China, 40% now fails to receive the precipitation required to attain the full yield potential. Opportunities for water saving in maize systems exist, but water scarcity in China remains a serious problem.

In this paper we discuss the scope of the adaptation challenge facing world agriculture in the coming decades. Due to rising temperatures throughout the tropics, pressures for adaptation will be greatest in some of the poorest parts of the world where the adaptive capacity is least abundant. We discuss both autonomous (market driven) and planned adaptations, distinguishing: (a) those that can be undertaken with existing technology, (b) those that involve development of new technologies, and (c) those that involve institutional/market and policy reforms. The paper then proceeds to identify which of these adaptations are currently modeled in integrated assessment studies and related analyses at global scale. This, in turn, gives rise to recommendations about how these models should be modified in order to more effectively capture climate change adaptation in the farm and food sector. In general, we find that existing integrated assessment models are better suited to analyzing adaptation by relatively well-endowed producers operating in market-integrated, developed countries. They likely understate climate impacts on agriculture in developing countries, while overstating the potential adaptations. This is troubling, since the need for adaptation will be greatest amongst the lower income producers in the poorest tropical countries. This is also where policies and public investments are likely to have the highest payoff. We conclude with a discussion of opportunities for improving the empirical foundations of integrated assessment modeling with an emphasis on the poorest countries.

Quantitative estimates of the impacts of climate change on economic outcomes are important for public policy. We show that the vast majority of estimates fail to account for well-established uncertainty in future temperature and rainfall changes, leading to potentially misleading projections. We reexamine seven well-cited studies and show that accounting for climate uncertainty leads to a much larger range of projected climate impacts and a greater likelihood of worst-case outcomes, an important policy parameter. Incorporating climate uncertainty into future economic impact assessments will be critical for providing the best possible information on potential impacts.