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Abstract In Sub-Saharan Africa (SSA), over 75% of households lack on-premises water access, requiring residents to spend time walking to collect water from outside their homes – a time burden that falls disproportionately on women and girls. Climate change is predicted to alter precipitation and temperature patterns in SSA, which could impact household water access. Here, we use spatial first differences to assess the causal effects of weather on water fetching walk time using household survey data (n = 979,759 observations from 31 countries) merged with geo- and temporally-linked precipitation and temperature data over time periods ranging from 7 to 365 days. We find increases in precipitation reduce water fetching times; a 1 cm increase in weekly rainfall over the past year decreases walking time by 3.5 min. Higher temperatures increase walk times, with a 1°C increase in temperature over the past year increasing walking time by 0.76 min. Rural household water fetching times are more impacted by recent weather compared to urban households; however, electricity access in rural communities mitigates the effect. Our findings suggest that future climate change will increase the water fetching burden in SSA, but that co-provision of electricity and water infrastructure may be able to alleviate this burden. 

Abstract International environmental initiatives, such as the Bonn Challenge and the UN Decade on Restoration, have prompted countries to put the management and restoration of forest landscapes at the center of their land use and climate policies. To support these goals, many governments are promoting forest landscape restoration and management through financial forestry incentives, a form of payment for ecosystem services. Since 1996, Guatemala has implemented a series of forestry incentives that promote active forest landscape restoration and management on private and communal lands. These programs have been widely hailed as a success with nearly 600 000 ha enrolled since 1998. However, there has been no systematic assessment of the effectiveness of these programs on preserving and restoring Guatemalan forests. This study evaluates the impacts of over 16 000 individual PES projects funded through two incentive programs using a synthetic control counterfactual. Overall, a program for smallholders resulted in lower rates of forest loss, while a program for industrial timber owners led to greater gains in forest cover. Across policies, we found dramatically higher forest cover increases from restoration projects (15% forest cover increase) compared to plantation and agroforestry projects (3%–6% increase in forest cover). Projects that protected natural forest also showed a 6% reduction in forest loss. We found forest cover increases to be under 10% of total enrolled area, although positive local spillovers suggest this is an underestimate. Restoration projects show the most promise at promoting forest landscape restoration, but these benefits need to be weighed against priorities like resilience and rural development, which may be better served by other projects. 

Crop production is among the most extensive human activities on the planet – with critical importance for global food security, land use, environmental burden, and climate. Yet despite the key role that croplands play in global land use and Earth systems, there remains little understanding of how spatial patterns of global crop cultivation have recently evolved and which crops have contributed most to these changes. Here we construct a new data library of subnational crop-specific irrigated and rainfed harvested area statistics and combine it with global gridded land cover products to develop a global gridded (5-arcminute) irrigated and rainfed cropped area (MIRCA-OS) dataset for the years 2000 to 2015 for 23 crop classes. These global data products support critical insights into the spatially detailed patterns of irrigated and rainfed cropland change since the start of the century and provide an improved foundation for a wide array of global assessments spanning agriculture, water resource management, land use change, climate impact, and sustainable development. 

In theories and metrics of product innovation, gender is invisible or ignored, and innovative products are presumed to be gender-neutral or agnostic. Yet, many ostensibly-innovative consumer products overlook the needs of women and gender non-conforming individuals, suggesting an implicit masculine framing. This research introduces a mixed-methods approach for analyzing gender scripts in product features and marketing, applied to a case study of the Apple Watch (2015–2024). Findings reveal a sustained reinforcement of gender norms: masculine-coded language and industrial design dominate how innovation is presented, even as objective technical improvements decline. In contrast, feminine-coded features, especially relational or user-centered ones, receive less emphasis in innovation framing. This work demonstrates how masculine value systems shape perceptions and theories of innovation and offers opportunities for future research on gender and design. 

Food insecurity is a perennial problem in much of the developing world, with gains against hunger backsliding in recent years and climate change predicted to accelerate this trend. Food insecurity is highly disruptive to rural livelihoods and can lead to dramatic shifts in food production strategies and resultant land use. However, studies to date have yet to outline the overarching patterns of land use change that can result from food insecurity. We elucidate the impact of food insecurity events between 2013 and 2020 in 25 low- and middle-income countries on resulting land use change and demographics. Using propensity score matching, we create a counterfactual and assess changes in forest cover, crop cover, population and nighttime luminosity between regions that experience food insecurity and comparable food-secure regions. Land use change theory, specifically the classical trajectories of agricultural intensification, land rent theory, and regime shifts help to explain observed land use trajectories. We find that food insecurity events lead to around a 4 % decline in population and a 3 % decline in cropped areas, alongside a 4 % increase in forest cover compared to control regions. Additionally, we show that drought-driven food insecurity drives impacts on land use and conflict-driven food insecurity shows greater impacts on population and nighttime luminosity. Food insecurity shocks result in an increase in population and crop cover in urban areas despite losses in adjoining rural land, suggesting that food insecurity drives local rural to urban migration. Furthermore, by assessing the impacts of discrete food insecurity events in three countries, we find that regional contexts mediate impacts by producing variable land use change trajectories. 

Advanced Air Mobility (AAM) operations are expected to transform air transportation while challenging current air traffic management practices. By introducing a novel market-based mechanism, we address the problem of on-demand allocation of capacity-constrained airspace to AAM vehicles with heterogeneous and private valuations. We model airspace and air infrastructure as a collection of contiguous regions (or sectors) with constraints on the number of vehicles that simultaneously enter, stay, or exit each region. Vehicles request access to airspace with trajectories spanning multiple regions at different times. We use the graph structure of our airspace model to formulate the allocation problem as a path allocation problem on a time-extended graph. To ensure that the cost information of AAM vehicles remains private, we introduce a novel mechanism that allocates each vehicle a budget of “air-credits” (an artificial currency) and anonymously charges prices for traversing the edges of the time-extended graph. We seek to compute a competitive equilibrium that ensures that: (i) capacity constraints are satisfied, (ii) a strictly positive resource price implies that the sector capacity is fully utilized, and (iii) the allocation is integral and optimal for each AAM vehicle given current prices, without requiring access to individual vehicle utilities. However, a competitive equilibrium with integral allocations may not always exist. We provide sufficient conditions for the existence and computation of a fractional-competitive equilibrium, where allocations can be fractional. Building on these theoretical insights, we propose a distributed, iterative, two-step algorithm that: (1) computes a fractional competitive equilibrium, and (2) derives an integral allocation from this equilibrium. We validate the effectiveness of our approach in allocating trajectories for the emerging urban air mobility service of drone delivery. 

Problem definition: Inventory management problems with periodic and controllable resets occur in the context of managing water storage in the developing world and dynamically optimizing endcap promotion duration in retail outlets. In this paper, we consider a set of sequential decision problems in which the decision maker must not only balance holding and shortage costs but discard all inventory before a fixed number of decision epochs with the option for an early inventory reset. Methodology/results: Finding optimal policies for these problems through dynamic programming presents unique challenges because of the nonconvex nature of the resulting value functions. Moreover, this structure cannot be readily analyzed even with extended convexity definitions, such as K-convexity. Managerial implications: Our key contribution is to present sufficient conditions that ensure the optimal policy has an easily interpretable structure, which generalizes the well-known [Formula: see text] policy from the operations management literature. Furthermore, we demonstrate that, under these rather mild conditions, the optimal policy exhibits a four-threshold structure. We then conclude with computational experiments, thereby illustrating the policy structures that can be extracted in various inventory management scenarios. Funding: This work was supported by the National Science Foundation [Grant CMMI-1847666] and the Division of Graduate Education [Grant DGE-2125913]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0318 . 

A large fraction of total healthcare expenditure occurs due to end-of-life (EOL) care, which means it is important to study the problem of more carefully incentivizing necessary versus unnecessary EOL care because this has the potential to reduce overall healthcare spending. This paper introduces a principal-agent model that integrates a mixed payment system of fee-for-service and pay-for-performance in order to analyze whether it is possible to better align healthcare provider incentives with patient outcomes and cost-efficiency in EOL care. The primary contributions are to derive optimal contracts for EOL care payments using a principal-agent framework under three separate models for the healthcare provider, where each model considers a different level of risk tolerance for the provider. We derive these optimal contracts by converting the underlying principal-agent models from a bilevel optimization problem into a single-level optimization problem that can be analytically solved. Our results are demonstrated using a simulation where an optimal contract is used to price intracranial pressure monitoring for traumatic brain injuries. 

The rapid growth of electric vehicles (EVs) is driving the expansion of charging infrastructure globally. As charging stations become ubiquitous, their substantial electricity consumption can influence grid operation and electricity pricing. Naturally, some groups of charging stations, which could be jointly operated by a company, may coordinate to decide their charging profile. While coordination among all charging stations is ideal, it is unclear if coordination of some charging stations is better than no coordination. In this paper, we analyze this intermediate regime between no and full coordination of charging stations. We model EV charging as a non-cooperative aggregative game, where each station’s cost is determined by both monetary payments tied to reactive electricity prices on the grid and its sensitivity to deviations from a desired charging profile. We consider a solution concept that we call C-Nash equilibrium, which is tied to a coalition C of charging stations coordinating to reduce their costs. We provide sufficient conditions, in terms of the demand and sensitivity of charging stations, to determine when independent (aka uncoordinated) operation of charging stations could result in lower overall costs to charging stations, coalition and charging stations outside the coalition. Somewhat counter to common intuition, we show numerical instances where allowing charging stations to operate independently is better than coordinating a subset of stations as a coalition. Jointly, these results provide operators of charging stations insights into how to coordinate their charging behavior, and open several research directions.