Christoph Nolte

Public land acquisitions (PLAs) are a promising conservation instrument, combining the permanence of protected areas with the voluntary, compensatory structure of payments for ecosystem services, yet causal evidence on their effectiveness remains limited. Colombia’s Article 111 mandate, which requires departments to allocate 1% of revenue to land acquisition for watershed protection, has produced one of the world’s largest and longest-running PLA programs, enabling rigorous evaluation. We estimate the impact of 2,512 PLAs in the Colombian Andes on parcel-level forest cover from 2001 to 2021 using covariate matching and staggered difference-in-differences. PLAs have a statistically significant, time-accumulating effect on forest cover, increasing from 0.16 percentage points after one year of protection to 2.53 percentage points after 15 years. Effects are driven primarily by forest regrowth rather than avoided deforestation, consistent with program targeting and active reforestation on acquired parcels. Benchmarking against the literature, estimated effect sizes fall within the range reported for protected areas and payments for ecosystem services. These results indicate that PLAs can generate measurable conservation gains even when implemented in relatively low-threat landscapes. Crucially, impacts emerge slowly and accumulate over time, implying that evaluations based on short post-treatment windows will systematically underestimate their effectiveness.

Definitions of tropical dry forest (TDF) and tropical moist forest (TMF) typically rely on coarse spatial resolution climate envelopes that are unable to capture the detailed landscape dynamics essential for understanding their conservation status and protection needs. In addition, TDF and TMF are often separated by short distances, complicating their spatial delimitation using coarse spatial resolution methods. TDF typically exhibits marked seasonality prompted by dry months that determine its deciduous leaf phenology, contrasting starkly with the evergreen phenology of TMF. We present a methodology that leverages those distinct leaf phenologies to map TDF and TMF in Colombia. Our methods use a training dataset derived from a coarse climate envelope representing the conditions where TDF and TMF are expected to occur. This dataset is then used to train a machine learning model in which spatio-temporal features derived from the Continuous Change Detection and Classification algorithm (CCDC) applied to 20-year Landsat time series are used as predictors to capture the differences in leaf phenology between these two forest types. The resulting dataset shows the annual probability of pixels with tree cover being TDF or TMF over the last two decades. We validated our methodology using field plot data and ancillary datasets. Our methodology is flexible to allow accommodating custom parameters to define sub-regional climate envelopes or incorporating data from different optical sensors (e.g. Sentinel-2, Harmonized Landsat Sentinel data).

Protected area (PA) assessments rarely evaluate socio-economic and environmental impacts relative to competing land uses, limiting understanding of socio-environmental trade-offs from efforts to protect 30% of the globe by 2030. Here we assess deforestation and poverty outcomes (fiscal income, income inequality, sanitation and literacy) between 2000 and 2010 of strict PAs (SPAs), sustainable-use PAs (SUPAs) and Indigenous territories (ITs) compared with different land uses (agriculture and mining concessions) across ~5,500 census tracts in the Brazilian Legal Amazon. ITs reduced deforestation relative to all alternative land uses (48–83%) but had smaller socio-economic benefits compared with other protection types and land uses (18–36% depending on outcome), indicating that Indigenous communities experience socio-economic trade-offs. By contrast, SUPAs, and potentially SPAs, did not reduce deforestation relative to small-scale agriculture (landholdings <10 ha) but did so relative to larger agricultural landholdings (70–82%). Critically, these reductions in deforestation frequently occurred without negative socio-economic outcomes. By contrast, ITs and SUPAs protected against deforestation from mining, but at the cost of smaller improvements in income and inequality. Our results suggest that although PAs in the Brazilian Legal Amazon substantially reduced deforestation without compromising local socio-economic development, efforts to secure Indigenous rights need additional interventions to ensure these communities are not further disadvantaged.

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.

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 km². 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.

The National Park Service (NPS) is expected to calculate the economic benefits of national parks for use when evaluating policy and management options of the parks (Loomis 2002). We estimate the marginal willingness to pay (MWTP) to live close to a national park that is capitalized into house prices for 18 national parks. We find that estimated effects can be significant and are heterogeneous across different types of parks. However, these effects can also be attributed to other factors in the housing market which brings the significance of these estimated impacts into question.

We used the quasi-experimental difference-in-differences method to estimate the impact that the Endangered Species Act’s (ESA’s) critical-habitat (CH) regulation had on developed and undeveloped parcel prices throughout the United States between 2000 and 2019. At the national level we find that, on average, the prices of parcels in CH areas were not statistically different from the prices of similar nearby parcels not in CH. However, limiting our analysis to specific subsets of CH areas, we find mixed results. Previous empirical estimates have consistently found that the regulation reduces parcel prices. We offer several potential explanations for our contradictory results.

This study uses Zillow’s Transaction and Assessment Database to investigate variation in hedonic price effects of water clarity on single-family houses throughout the United States. We consider five spatial scales and estimate models using different sample selection criteria and model specifications. The results indicate considerable spatial heterogeneity within and across the four U.S. census regions. However, we also find heterogeneity resulting from different types of investigator decisions, including sample selection and modeling choices. Thus, it is necessary to use practical knowledge to consider the limits of market areas and to investigate the robustness of estimation results to investigator choices.

Policies that improve drought resilience have heterogenous effects on agricultural land values. We examine the value of surface water transfers and groundwater resources in a hedonic analysis of agricultural parcels in California. We find that parcels in irrigation districts, that engage in inter-basin surface water transfers, experience increase in value in subsequent years, but the gains are short-lived and unevenly distributed. Value of groundwater stock is higher in parcels outside irrigation districts. We also find that policies to restrict groundwater substitution-based transfers, that aimed to reduce groundwater open-access externalities, have not been a deterrent against transfers in all areas.

We discuss data quality and modeling issues inherent in the use of nationwide property data to value environmental amenities. By example of ZTRAX, a U.S.-wide real estate database, we identify challenges and propose guidance for: (1) the identification of arm’s-length sales, (2) the geo-location of parcels and buildings, (3) temporal linkages between transaction, assessor, and parcel data, (4) the identification of property types, such as single-family homes and vacant lands, and (5) dealing with missing or mismeasured data for standard housing attributes. We review current practice and show that how researchers address these issues can meaningfully influence research findings.