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Achieving universal health care coverage—a key target of the United Nations Sustainable Development Goal number 3—requires accessibility to health care services for all. Currently, in sub-Saharan Africa, at least one-sixth of the population lives more than 2 h away from a public hospital, and one in eight people is no less than 1 h away from the nearest health center. We combine high-resolution data on the location of different typologies of public health care facilities [J. Maina et al., Sci. Data 6, 134 (2019)] with population distribution maps and terrain-specific accessibility algorithms to develop a multiobjective geographic information system framework for assessing the optimal allocation of new health care facilities and assessing hospitals expansion requirements. The proposed methodology ensures universal accessibility to public health care services within prespecified travel times while guaranteeing sufficient available hospital beds. Our analysis suggests that to meet commonly accepted universal health care accessibility targets, sub-Saharan African countries will need to build ∼6,200 new facilities by 2030. We also estimate that about 2.5 million new hospital beds need to be allocated between new facilities and ∼1,100 existing structures that require expansion or densification. Optimized location, type, and capacity of each facility can be explored in an interactive dashboard. Our methodology and the results of our analysis can inform local policy makers in their assessment and prioritization of health care infrastructure. This is particularly relevant to tackle health care accessibility inequality, which is not only prominent within and between countries of sub-Saharan Africa but also, relative to the level of service provided by health care facilities.

We thank Joe Aldy, Spencer Banzhaf, Geoffrey Heal, Charlie Kolstad, Robert Mendelsohn, and seminar participants at NBER, GSU, RFF, William and Mary, and Yale for helpful comments, and Kalee Burns, Bo Liu, and Drew Moxon for valuable research assistance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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We thank Derek Lemoine and seminar participants at Tennessee, Stanford, Harvard, and the Southern Economic Association annual meeting for comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

We examine the potential for climate change to impact fertility via adaptations in human behavior. We start by discussing a wide range of economic channels through which climate change might impact fertility, including sectoral reallocation, the gender wage gap, longevity, and child mortality. Then, we build a quantitative model that combines standard economic-demographic theory with existing estimates of the economic consequences of climate change. In the model, increases in global temperature affect agricultural and non-agricultural sectors differently. Near the equator, where many poor countries are located, climate change has a larger negative effect on agriculture. The resulting scarcity in agricultural goods acts as a force towards higher agricultural prices and wages, leading to a labor reallocation into this sector. Since agriculture makes less use of skilled labor, climate damage decreases the return to acquiring skills, inducing parents to invest less resources in the education of each child and to increase fertility. These patterns are reversed at higher latitudes, suggesting that climate change may exacerbate inequities by reducing fertility and increasing education in richer northern countries, while increasing fertility and reducing education in poorer tropical countries. While the model only examines the role of one mechanism, it suggests that climate change could have an impact on fertility, indicating the need for future work on this important topic.Climate change will have a substantial impact on the economy [1,2]. There is also a broad consensus that economic factors affect fertility [3][4][5]. Thus, climate change has the potential to affect fertility patterns. 9

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Direct air capture (DAC) technologies are promising but speculative. Their prospect as an affordable negative emissions option that can be deployed in large scale is particularly uncertain. Here, we report the results of an expert elicitation about the evolution of techno-economic factors characterizing DAC over time and across climate scenarios. This is the first study reporting technical experts' judgments on future costs under different scenarios, for two time periods, for two policy options, and for two different DAC technologies. Experts project CO2 removal costs to decline significantly over time but to remain expensive (median by mid-century: around 200 USD/tCO2). Nonetheless, the role of direct air capture in a 2°C policy scenario is expected to be significant (by 2050: 1.7 [0.2, 5.9] GtCO2)1. Projections align with scenarios from integrated assessment model (IAM) studies. Agreement across experts regarding which type of DAC technology might prevail is low. Energy usage and policy support are considered the most critical factors driving these technologies' future growth.