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The justification and targeting of conservation policy rests on reliable measures of public and private benefits from competing land uses. Advances in Earth system observation and modeling permit the mapping of public ecosystem services at unprecedented scales and resolutions, prompting new proposals for land protection policies and priorities. Data on private benefits from land use are not available at similar scales and resolutions, resulting in a data mismatch with unknown consequences. Here I show that private benefits from land can be quantified at large scales and high resolutions, and that doing so can have important implications for conservation policy models. I developed high-resolution estimates of fair market value of private lands in the contiguous United States by training tree-based ensemble models on 6 million land sales. The resulting estimates predict conservation cost with up to 8.5 times greater accuracy than earlier proxies. Studies using coarser cost proxies underestimate conservation costs, especially at the expensive tail of the distribution. This has led to underestimations of policy budgets by factors of up to 37.5 in recent work. More accurate cost accounting will help policy makers acknowledge the full magnitude of contemporary conservation challenges and can help improve the targeting of public ecosystem service investments.

Protected areas are intended to conserve biodiversity by restricting human activities within their boundaries. However, such restrictions are difficult to enforce fully in many tropical parks. Improving regulatory enforcement requires an understanding of prevailing challenges to detection and sanctioning activities. Drawing from empirical field research in 15 Colombian parks, I show that current enforcement efforts may be insufficient to deter most priority threats. For long-term infractions, such as agriculture, livestock grazing, and construction, sanctioning violators is challenging, whereas for furtive infractions, such as logging and hunting, it may be difficult to detect violators. Investment in staff, equipment and infrastructure may fail to increase enforcement capacity and yield positive conservation outcomes unless accompanied by resolution of land tenure, clarification of use rights, improved patrolling strategies and protection of park guards.

Climate change impacts threaten the stability of the US housing market. In response to growing concerns that increasing costs of flooding are not fully captured in property values, we quantify the magnitude of unpriced flood risk in the housing market by comparing the empirical and economically efficient prices for properties at risk. We find that residential properties exposed to flood risk are overvalued by US $121–US$ 237 billion, depending on the discount rate. In general, highly overvalued properties are concentrated in counties along the coast with no flood risk disclosure laws and where there is less concern about climate change. Low-income households are at greater risk of losing home equity from price deflation, and municipalities that are heavily reliant on property taxes for revenue are vulnerable to budgetary shortfalls. The consequences of these financial risks will depend on policy choices that influence who bears the costs of climate change.Natural hazards exacerbated by climate change pose serious risks to property markets in the United States. Ignoring these risks could create instability in housing values. This research shows the magnitude of unpriced flood risk and who stands to lose from housing prices that reflect climate risks.

Protected areas in tropical countries are managed under different governance regimes, the relative effectiveness of which in avoiding deforestation has been the subject of recent debates. Participants in these debates answer appeals for more strict protection with the argument that sustainable use areas and indigenous lands can balance deforestation pressures by leveraging local support to create and enforce protective regulations. Which protection strategy is more effective can also depend on (i) the level of deforestation pressures to which an area is exposed and (ii) the intensity of government enforcement. We examine this relationship empirically, using data from 292 protected areas in the Brazilian Amazon. We show that, for any given level of deforestation pressure, strictly protected areas consistently avoided more deforestation than sustainable use areas. Indigenous lands were particularly effective at avoiding deforestation in locations with high deforestation pressure. Findings were stable across two time periods featuring major shifts in the intensity of government enforcement. We also observed shifting trends in the location of protected areas, documenting that between 2000 and 2005 strictly protected areas were more likely to be established in high-pressure locations than in sustainable use areas and indigenous lands. Our findings confirm that all protection regimes helped reduce deforestation in the Brazilian Amazon.

Planning for cost-effective conservation requires reliable estimates of land costs, spatially-differentiated at high resolution. Nolte (2020) provides a county-by-county, parcel-level estimation approach that dramatically improves estimates of fair market value for undeveloped land across the contiguous Unites States. Much undeveloped land of conservation interest is under threat of conversion to agricultural use or is already agricultural. This paper demonstrates the value of accounting for additional variables that affect agricultural productivity and demand for undeveloped land, as well as the benefit of modeling at scales corresponding to regional agricultural markets. We find that countywide median home value, climatic variables, and several parcel-level soil type variables contribute substantially to predictive power. Enlarging the set of predictors and the geographical scale of modeling improves accuracy by approximately 15 percent and, relative to a more restricted modeling benchmark adapted from Nolte (2020), extends coverage into 376 counties occupying 1.35 million km 2 . To assess the practical benefits of our modeling approach, we simulate the protection of 30 percent of US lands via government purchasing, modeled after the Biden administration’s “30x30” initiative. Using our proposed modeling strategy, the purchasing agency saves approximately $15 million per year, or 4 percent of the USDA’s annual land easement budget.

A major reduction in global deforestation is needed to mitigate climate change and biodiversity loss. Recent private sector commitments aim to eliminate deforestation from a company’s operations or supply chain, but they fall short on several fronts. Company pledges vary in the degree to which they include time-bound interventions with clear definitions and criteria to achieve verifiable outcomes. Zero-deforestation policies by companies may be insufficient to achieve broader impact on their own due to leakage, lack of transparency and traceability, selective adoption and smallholder marginalization. Public–private policy mixes are needed to increase the effectiveness of supply-chain initiatives that aim to reduce deforestation. We review current supply-chain initiatives, their effectiveness, and the challenges they face, and go on to identify knowledge gaps for complementary public–private policies.

Carbon offsets are widely promoted as a strategy to lower the cost of emission reductions, but recent findings suggest that offsets may not causally reduce emissions by the amount claimed. In a compliance market, offsets increase net emissions if they do not reflect real emission reductions beyond the baseline scenario. Few studies have examined the additionality of forest carbon offsets within California’s U.S. Forest Projects compliance offset protocol, one of the largest forest offset programs in the world. Here we examine additionality in California’s offset protocol. Since 2012, most of California’s offset credits (84%) have been awarded to improved forest management projects. Using a database of improved forest management project characteristics, locations, and remotely sensed forest disturbance data indicative of management activity, we find that projects have been primarily allocated to forests with high carbon stocks (127% higher than regional averages) and low historical disturbance (28% less disturbance than regional averages since 1985). A matching and panel regression analysis failed to show additionality, as project creation did not significantly lower disturbance rates 3 and 5 years after project implementation relative to similar non-project lands. These results indicate that California’s forest offset protocol may contribute to an increasingly large carbon debt.

Scholars have made great advances in modeling and mapping ecosystem services, and in assigning economic values to these services. This modeling and valuation scholarship is often disconnected from evidence about how actual conservation programs have affected ecosystem services, however. Without a stronger evidence base, decision makers find it difficult to use the insights from modeling and valuation to design effective policies and programs. To strengthen the evidence base, scholars have advanced our understanding of the causal pathways between conservation actions and environmental outcomes, but their studies measure impacts on imperfect proxies for ecosystem services (e.g., avoidance of deforestation). To be useful to decision makers, these impacts must be translated into changes in ecosystem services and values. To illustrate how this translation can be done, we estimated the impacts of protected areas in Brazil, Costa Rica, Indonesia, and Thailand on carbon storage in forests. We found that protected areas in these conservation hotspots have stored at least an additional 1,000 Mt of CO ₂ in forests and have delivered ecosystem services worth at least $5 billion. This aggregate impact masks important spatial heterogeneity, however. Moreover, the spatial variability of impacts on carbon storage is the not the same as the spatial variability of impacts on avoided deforestation. These findings lead us to describe a research program that extends our framework to study other ecosystem services, to uncover the mechanisms by which ecosystem protection benefits humans, and to tie cost-benefit analyses to conservation planning so that we can obtain the greatest return on scarce conservation funds. Significance Research shows how the potential services from ecosystem conservation can be modeled, mapped, and valued; however, this integrative research has not been systematically applied to estimate the actual impacts of programs on the delivery of ecosystem services. We bridge this divide by showing how protected areas in Brazil, Costa Rica, Indonesia, and Thailand store carbon and deliver ecosystem services worth at least $5 billion. Impacts on carbon are associated with poverty exacerbation in some settings and with poverty reduction in others. We describe an agenda to improve conservation planning by ( i ) studying impacts on other ecosystem services, ( ii ) uncovering the mechanisms through which conservation programs affect human welfare, and ( iii ) more comprehensively comparing costs and benefits of conservation impacts.

Anthropogenic climate change is projected to drive increases in climate extremes and climate-sensitive ecosystem disturbances such as wildfire with enormous economic impacts. Understanding spatial and temporal patterns of risk to property values from climate-sensitive disturbances at national and regional scales and from multiple disturbances is urgently needed to inform risk management and policy efforts. Here, we combine models for three major climate-sensitive disturbances (i.e., wildfire, climate stress-driven tree mortality, and insect-driven tree mortality), future climate projections of these disturbances, and high-resolution property values data to quantify the spatiotemporal exposure of property values to disturbance across the contiguous United States (US). We find that property values exposed to these climate-sensitive disturbances increase sharply in future climate scenarios, particularly in existing high-risk regions of the western US, and that novel exposure risks emerge in some currently lower-risk regions such as the southeast and Great Lakes regions. Climate policy that drives emissions towards low-to-moderate climate futures avoids large increases in disturbance risk exposure compared to high emissions scenarios. Our results provide an important large-scale assessment of climate-sensitive disturbance risk to property values to help inform land management and climate adaptation efforts.

Large‐scale estimations of flood losses are often based on spatially aggregated inputs. This makes risk assessments vulnerable to aggregation bias, a well‐studied, sometimes substantial outcome in analyses that model fine‐grained spatial phenomena at coarse spatial units. To evaluate this potential in the context of large‐scale flood risk assessments, we use data from a high‐resolution flood hazard model and structure inventory for over 1.3 million properties in Massachusetts and examine how prominent data aggregation approaches affect the magnitude and spatial distribution of flood loss estimates. All considered aggregation approaches rely on aggregate structure inventories but differ in whether flood hazard is also aggregated. We find that aggregating only structure inventories slightly underestimates overall losses (−10% bias), and when flood hazard data is spatially aggregated to even relatively small spatial units (census block), statewide aggregation bias can reach +366%. All aggregation‐based procedures fail to capture the spatial covariation of inputs distributions in the upper tails that disproportionately generate total expected losses. Our findings are robust to several key assumptions, add important context to published risk assessments and highlight opportunities to improve flood loss estimation uncertainty quantification.