Publications

Conservation interventions typically focus on protecting public goods, but they often also create private spillover co-benefits. For example, protecting open space may increase the values of adjacent properties and protecting a coral reef may increase fishing opportunities outside. These privately-captured co-benefits can confer substantial value, but are rarely tapped to help promote and expand conservation efforts. One reason, we argue, is that doing so is difficult: While co-beneficiaries are easily convinced of the benefits of the conservation intervention, they are not obliged to pay for it, and so usually free-ride and enjoy these benefits gratis. In this paper, we document and quantify the magnitude of co-benefits in the literature and identify the conditions under which co-benefits could be tapped to offset the cost of conservation for conservationists. In light of these conditions, we propose an approach that involves voluntary compensation for the provision of co-benefits to expand the total amount of resources available for conservation efforts. We show that taking advantage of these co-benefits lowers the cost of implementing conservation actions while being incentive compatible for all parties involved.

Trawling the seafloor can disturb carbon that took millennia to accumulate, but the fate of that carbon and its impact on climate and ecosystems remains unknown. Using satellite-inferred fishing events and carbon cycle models, we find that 55-60% of trawling-induced aqueous CO₂ is released to the atmosphere over 7-9 years. Using recent estimates of bottom trawling’s impact on sedimentary carbon, we found that between 1996-2020 trawling could have released, at the global scale, up to 0.34-0.37 Pg CO₂ yr^-1 to the atmosphere, and locally altered water pH in some semi-enclosed and heavy trawled seas. Our results suggest that the management of bottom-trawling efforts could be an important climate solution.

Crop residue burning (CRB) is a major source of air pollution in many parts of the world, especially Asia. Policymakers, practitioners, and researchers have invested in measuring the extent and impacts of burning and developing interventions to reduce its occurrence. However, any attempt to measure burning, in terms of its extent, impact, or the effectiveness of interventions to reduce it, requires data on where burning occurs. These data are challenging to collect in the field, both in terms of cost and feasibility, because crop-residue fires are short-lived, each covers only a small area, and evidence of burning disappears once fields are tilled. Remote sensing offers a way to observe fields without the complications of on-the-ground monitoring. However, the same features that make CRB hard to observe on the ground also make remote-sensing-based measurements prone to inaccuracies. The extent of crop burning is generally underestimated due to missing observations, while individual plots are often falsely identified as burned due to the local dominance of the practice, a lack of training data on tilled vs. burned plots, and a weak signal-to-noise ratio that makes it difficult to distinguish between the two states. Here, we summarize the current literature on the measurement of CRB and flag five common pitfalls that hinder analyses of CRB with remotely sensed data: inadequate spatial resolution, inadequate temporal resolution, ill-fitted signals, improper comparison groups, and inadequate accuracy assessment. We take advantage of data from ground-based monitoring of CRB in Punjab, India, to calibrate and validate analyses with PlanetScope and Sentinel-2 imagery and illuminate each of these pitfalls. We provide tools to assist others in planning and conducting remote sensing analyses of CRB and stress the need for rigorous validation.

Over the past two decades, sharks have been increasingly recognized among the world’s most threatened wildlife and hence have received heightened scientific and regulatory scrutiny. Yet, the effect of protective regulations on shark fishing mortality has not been evaluated at a global scale. Here we estimate that total fishing mortality increased from at least 76 to 80 million sharks between 2012 and 2019, ~25 million of which were threatened species. Mortality increased by 4% in coastal waters but decreased by 7% in pelagic fisheries, especially across the Atlantic and Western Pacific. By linking fishing mortality data to the global regulatory landscape, we show that widespread legislation designed to prevent shark finning did not reduce mortality but that regional shark fishing or retention bans had some success. These analyses, combined with expert interviews, highlight evidence-based solutions to reverse the continued overexploitation of sharks.

The world’s population increasingly relies on the ocean for food, energy production and global trade^1,2,3, yet human activities at sea are not well quantified^4,5. We combine satellite imagery, vessel GPS data and deep-learning models to map industrial vessel activities and offshore energy infrastructure across the world’s coastal waters from 2017 to 2021. We find that 72–76% of the world’s industrial fishing vessels are not publicly tracked, with much of that fishing taking place around South Asia, Southeast Asia and Africa. We also find that 21–30% of transport and energy vessel activity is missing from public tracking systems. Globally, fishing decreased by 12 ± 1% at the onset of the COVID-19 pandemic in 2020 and had not recovered to pre-pandemic levels by 2021. By contrast, transport and energy vessel activities were relatively unaffected during the same period. Offshore wind is growing rapidly, with most wind turbines confined to small areas of the ocean but surpassing the number of oil structures in 2021. Our map of ocean industrialization reveals changes in some of the most extensive and economically important human activities at sea.

The transition to low-carbon electricity is crucial for meeting global climate goals. However, given the uneven spatial distribution and temporal variability of renewable resources, balancing the supply and demand of electricity will be challenging when relying on close to 100% shares of renewable energy. Here, we use an electricity planning model with hourly supply-demand projections and high-resolution renewable resource maps, to examine whether transcontinental power pools reliably meet the growing global demand for renewable electricity and reduce the system cost. If all suitable sites for renewable energy are available for development, transcontinental trade in electricity reduces the annual system cost of electricity in 2050 by 5–52% across six transcontinental power pools compared to no electricity trade. Under land constraints, if only the global top 10% of suitable renewable energy sites are available, then without international trade, renewables are unable to meet 12% of global demand in 2050. Introducing transcontinental power pools with the same land constraints, however, enables renewables to meet 100% of future electricity demand, while also reducing costs by up to 23% across power pools. Our results highlight the benefits of expanding regional transmission networks in highly decarbonized but land-constrained future electricity systems.

Much of the developed world has adopted substantial, complex agricultural subsidy schemes in an attempt to produce desired rural livelihood and environmental outcomes. Understanding how farmers adjust their production activity in response to farm subsidies is crucial for setting optimal agricultural policy. Whereas standard economic theory suggests that farmers largely adjust production levels in response to prices and marginal subsidy rates, recent work in consumer behavior suggests that average (dis-)incentives may play a relevant role. We use a unique panel covering all farms applying for subsidies in Norway and a flexible deep-learning method to exploit kinks in the subsidy scheme to answer whether farmers respond more to average or marginal subsidies. In contrast to the standard economic theory of production, we find suggestive empirical evidence that farmers respond more to changes in average payments than to changes in marginal payments. We anticipate that our findings on the relevance of average payment levels for farmers' decision making may inspire further theoretical and empirical inquiries into agricultural policy effects. The study also highlights how novel deep-learning tools can be applied for detailed policy analysis and what advantages and challenges come with it. We believe that this approach has substantial potential for analysts and policymakers to evaluate and predict the impacts of policy options.

Climate change is shifting the structure and function of global forests, underscoring the critical need to predict which forests are most vulnerable to a hotter and drier future. We analyzed 6.6 million tree rings from 122 species to assess trees’ sensitivity to water and energy availability. We found that trees growing in wetter portions of their range exhibit the greatest drought sensitivity. To test how these patterns of drought sensitivity influence vulnerability to climate change, we predicted tree growth through 2100. Our results suggest that drought adaptations in arid regions will partially buffer trees against climate change. By contrast, trees growing in the wetter, hotter portions of their climatic range may experience unexpectedly large adverse impacts under climate change.

More system-specific and finer-scale analyses are necessary to better understand how spatio-temporal variability in geophysical forces affects grid planning. Moreover, we need a broader focus on the multi-sectoral implications of decarbonization efforts, including the societal consequences of grid management decisions. Importantly, all these efforts are challenged by the computational requirements of existing power system models, which often limit our ability to characterize uncertainty and scale analyses across larger domains.

Designing effective spatial management strategies is challenging because marine ecosystems are highly dynamic and opaque, and extractive entities such as fishing fleets respond endogenously to ecosystem changes in ways that depend on ecological and policy context. We present a modelling framework, marlin, that can be used to efficiently simulate the bio-economic dynamics of marine systems in support of both management and research. We demonstrate marlin's capabilities by focusing on two case studies on the conservation and food production impacts of marine protected areas (MPAs): a coastal coral reef and a pelagic tuna fishery. In the coastal coral reef example, we explore how heterogeneity in species distributions and fleet preferences can affect distributional outcomes of MPAs. In the pelagic case study, we show how our model can be used to assess the climate resilience of different MPA design strategies, as well as the climate sensitivity of different fishing fleets. This paper demonstrates how intermediate complexity simulation of coupled bio-economic dynamics can help communities predict and potentially manage trade-offs among conservation, fisheries yields and distributional outcomes of management policies affected by spatial bio-economic dynamics.

A low-carbon energy transition is essential for mitigating climate change, but can also cause energy justice and equity impacts on Black, Indigenous, and People of Color (BIPOC), low-income, and other frontline communities. Examples include exacerbating energy burden, inaccessibility and unaffordability of low-carbon energy and electric end-use technologies, property value loss and displacement from renewable energy siting, and unequal health benefits and employment losses from fossil fuel retirement. To avoid perpetuating historical and creating new injustices, an equitable and just energy transition will require careful planning and execution. To this end, measuring and evaluating the effects of existing and proposed programs and policies aimed at decarbonizing energy systems is critical. However, methods and metrics for evaluating equity effects vary across disciplines and transitions, making it challenging to identify effective evaluation strategies. This paper presents a comprehensive review of the equity implications of low-carbon energy transitions and identifies key metrics that have been used across disciplines to quantify energy injustices and equity impacts. We focus on four key low-carbon energy transitions: (1) renewable energy deployment; (2) fossil fuel infrastructure retirement; (3) transportation electrification; and (4) residential building decarbonization. We classify energy justice and equity metrics into the dimensions of health, access, and livelihood, and construct an analytical framework that supports policymakers, planners and other stakeholders in identifying important equity considerations and quantitatively evaluating the effects of decarbonization initiatives. While our framework can serve as a starting point for evaluating justice and equity impacts of energy transitions, further work is needed to address the limitations of existing metrics and additional evaluation methods will be critical to effect energy transitions that are truly equitable.

Many countries sell fishing rights to foreign nations and fishers. AlthoughAfrican coastal waters are among the world’s most biologically rich, Africancountries earn much less than their peers from selling access to foreign fishers.African countries sell fishing access individually (in contrast to some Pacificcountries who sell access as a bloc). We develop a bilateral oligopoly model tosimulate the effects of an African fish cartel. The model shows that wieldingmarket power entails both ecological and economic dimensions. Africa wouldsubstantially restrict access catch, which raises biomass by 16%. But this alsoconfers economic benefits to all African nations, raising profits by an averageof 23%. These benefits arise because market power shifts from foreign buyersto African sellers. While impediments to sustainable development like cor-ruption are hard to change in the medium-term, deeper African integration isan already-emerging solution to African countries’ economic and ecologicalchallenges.

Natural climate solutions (NCS) are recognized as an important tool for governments to reduce greenhouse gas emissions and remove atmospheric carbon dioxide. Using California as a globally relevant reference, we evaluate the magnitude of biological climate mitigation potential from NCS starting in 2020 under four climate change scenarios. By mid-century NCS implementation leads to a large increase in net carbon stored, flipping the state from a net source to a net sink in two scenarios. Forest and conservation land management strategies make up 85% of all NCS emissions reductions by 2050, with agricultural strategies accounting for the remaining 15%. The most severe climate change impacts on ecosystem carbon materialize in the latter half of the century with three scenarios resulting in California ecosystems becoming a net source of carbon emissions under a baseline trajectory. However, NCS provide a strong attenuating effect, reducing land carbon emissions 41–54% by 2100 with total costs of deployment of 752–777 million USD annually through 2050. Rapid implementation of a portfolio of NCS interventions provides long-term investment in protecting ecosystem carbon in the face of climate change driven disturbances. This open-source, spatially-explicit framework can help evaluate risks to NCS carbon storage stability, implementation costs, and overall mitigation potential for NCS at jurisdictional scales.

Agricultural supply chains play a crucial role in supporting food security in Africa. However, high-resolution supply chain information is often not available, which hinders our ability to determine which interventions in food supply chains would most enhance food security. In this study, we develop a high-resolution supply chain model for essential staple crops in Zambia, aiming to estimate how improvements in transportation infrastructure would impact food security. Specifically, we simulate district-level monthly consumption, trade flows, and storage for maize and cassava in Zambia. We then conduct a counterfactual case study with low transportation costs, discovering that reducing transaction costs leads to higher aggregate net agricultural revenue and aggregate net expenditure. These results indicate that transportation investments are more beneficial to suppliers than to consumers, with implications for household food security in smallholder agriculture. Our study highlights the potential for infrastructure investments to improve food security.

Although it is increasingly recognized that aspirations drive economic behavior and outcomes, it is not fully understood how aspirations are formed (or eroded). However, it has been theoretically established that aspirations are socially constructed and formed under an aspiration window. An aspiration window refers to a cognitive zone of similar individuals based on age, gender, caste, geography, religion, ethnicity, and other social (self-help) groups. In these groups, individuals learn from each other through interaction and experimentation. We examine the relationship between group membership and aspirations. As a proxy for group membership, we use agricultural cooperatives that abound in many developing countries and have been associated with productivity and welfare gains. Given that farmers interact and are exposed to role models in these cooperatives, we also investigate the relationship between exposure to role models and aspirations. We show a positive association between cooperative membership and aspirations as well as between exposure to role models and aspirations. Interacting cooperative membership with exposure to role models, we find a larger association between cooperative membership and aspirations, highlighting the relevance of exposure to role models in these cooperatives. Given the growing evidence regarding the relevance of aspirations in achieving various developmental outcomes, our study highlights some entry points in improving aspirations.

New investments in conservation are needed to halt and reverse the rapid and extensive changes to ecosystems driven by growing human demands for natural resources. A major barrier is matching viable financing solutions to conservation projects. Recent conservation finance studies catalog available financing options but do not provide adequate guidance on which financing pathways are suitable for a particular conservation project. Studies in the natural capital literature identify activities that best serve the conservator's objectives but typically fail to address the question of how to pay for them. We attempt to bridge these literature sources by providing a framework for identifying the specific conditions that must be satisfied by a project in order for an existing financing mechanism to be viable. Notably, our framework quickly reveals financing approaches that can be eliminated. We demonstrate the utility of this approach through conservation case studies on establishment of native forests, coral reef restoration, oyster restoration, and island biosecurity.

Despite a 30-year long history, Renewable Portfolio Standards (RPS) remain controversial and debates continue to surround their efficacy in leading the low-carbon transition in the electricity sector. Contributing to the ongoing debates is the lack of definitive causal evidence on their impact on investments in renewable capacity and generation. This paper provides the most detailed analysis to date of the impact of RPSs on renewable electricity capacity investments and on generation. We use state-level data from 1990–2019 and recent econometric methods designed to address dynamic and heterogeneous treatment effects in a staggered adoption panel data design. We find that, on average, RPS policies increase wind generation capacity by 600–1200 MW, a 44% increase, but have no significant effect on investments in solar capacity. Additionally, we demonstrate that RPSs have slow dynamic effects: most of the capacity additions occur 5 years after RPS implementation. Estimates for wind and solar electricity generation mimic those for capacity investments. We also find similar results using an alternate treatment definition that allows states to meet their RPS requirements with pre-existing renewable generation and renewable generation from nearby states.

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

Remote sensing is developing at a rapid pace, with satellite-based Earth observation (EO) data being made available freely, openly, and at higher spatial, temporal, and spectral resolutions than ever before. This provides new opportunities for EO data to complement traditional survey data and improve the rigor and scope of impact evaluations. This document provides methodological guidance for the use of EO data in measuring the impacts of innovations and interventions on outcomes related to agriculture, natural resources, livestock, and the environment. Though the particular aim is to support incorporation of EO data into evaluation of One CGIAR activities, these guidelines are also relevant for a broad audience of researchers and practitioners from a variety of backgrounds interested in learning about the potential for using remote sensing to conduct impact evaluation. We showcase 11 case studies that use EO data for impact evaluation. These case studies— representing primarily a set of recent and ongoing projects sponsored by the CGIAR—highlight the range of use cases for EO data as well as several innovative methodological approaches to their analysis. Our discussion of these case studies draws from interviews with members of the implementing research teams. The lessons these researchers learned from their experiences demonstrate how the challenges of using EO data for impact evaluation can be identified and addressed in a variety of real-world research contexts. We provide strategic insights arising from this portfolio of case studies to help identify in which context remote sensing data can be relevant and appropriate for impact evaluation. We reflect on the skill set, background, and experience that can make up a successful research team that use EO for impact evaluation.

1. Calls for using marine protected areas (MPAs) to achieve goals for nature and people are increasing globally. While the conservation and fisheries impacts of MPAs have been comparatively well-studied, impacts on other dimensions of human use have received less attention. Understanding how humans engage with MPAs and identifying traits of MPAs that promote engagement is critical to designing MPA networks that achieve multiple goals effectively, equitably and with minimal environmental impact.
2. In this paper, we characterize human engagement in California's MPA network, the world's largest MPA network scientifically designed to function as a coherent network (124 MPAs spanning 16% of state waters and 1300 km of coastline) and identify traits associated with higher human engagement. We assemble and compare diverse indicators of human engagement that capture recreational, educational and scientific activities across California's MPAs.
3. We find that human engagement is correlated with nearby population density and that site “charisma” can expand human engagement beyond what would be predicted based on population density alone. Charismatic MPAs tend to be located near tourist destinations, have long sandy beaches and be adjacent to state parks and associated amenities. In contrast, underutilized MPAs were often more remote and lacked both sandy beaches and parking lot access.
4. Synthesis and applications: These results suggest that achieving MPA goals associated with human engagement can be promoted by developing land-based amenities that increase access to coastal MPAs or by locating new MPAs near existing amenities during the design phase. Alternatively, human engagement can be limited by locating MPAs in areas far from population centres, coastal amenities or sandy beaches. Furthermore, managers may want to prioritize monitoring, enforcement, education and outreach programmes in MPAs with traits that predict high human engagement. Understanding the extent to which human engagement impacts the conservation performance of MPAs is a critical next step to designing MPAs that minimize tradeoffs among potentially competing objectives.