High-resolution land value maps reveal underestimation of conservation costs in the United States

Landscape-scale predictions of future grassland conversion to cropland or development

Grassland conservation planning often focuses on high-risk landscapes, but many grassland conversion models are not designed to optimize conservation planning because they lack multidimensional risk assessments and are misaligned with ecological and conservation delivery scales. To aid grassland conservation planning, we developed landscape-scale models at relevant scales that predict future (2021-2031) total and proportional loss of unprotected grassland to cropland or development. We developed models for 20 ecoregions across the contiguous United States by relating past conversion (2011-2021) to a suite of covariates in random forest regression models and applying the models to contemporary covariates to predict future loss. Overall, grassland loss models performed well, and explanatory power varied spatially across ecoregions (total loss model: weighted group mean R2 = 0.89 [range: 0.83-0.96], root mean squared error [RMSE] = 9.29 ha [range: 2.83-22.77 ha]; proportional loss model: weighted group mean R2 = 0.74 [range: 0.64-0.87], RMSE = 0.03 [range: 0.02-0.06]). Amount of crop in the landscape and distance to cities, ethanol plants, and concentrated animal feeding operations had high variable importance in both models. Total grass loss was greater when there were moderate amounts of grass, crop, or development (∼50%) in the landscape. Proportional grass loss was greater when there was less grass (∼<30%) and more crop or development (∼>50%). Some variables had a large effect on only a subset of ecoregions, for example, grass loss was greater when ∼>70% of the landscape was enrolled in the Conservation Reserve Program. Our methods provide a simple and flexible approach for developing risk layers well suited for conservation that can be extended globally. Our conversion models can support conservation planning by enabling prioritization as a function of risk that can be further optimized by incorporating biological value and cost.

Systematic conservation prioritization with the prioritizr R package

Plans for expanding protected area systems (prioritizations) need to fulfill conservation objectives. They also need to account for other factors, such as economic feasibility and anthropogenic land-use requirements. Although prioritizations are often generated with decision support tools, most tools have limitations that hinder their use for decision-making. We outlined how the prioritizr R package (https://prioritizr.net) can be used for systematic conservation prioritization. This decision support tool provides a flexible interface to build conservation planning problems. It can leverage a variety of commercial (e.g., Gurobi) and open-source (e.g., CBC and SYMPHONY) exact algorithm solvers to identify optimal solutions in a short period. It is also compatible with a variety of spatially explicit (e.g., ESRI Shapefile, GeoTIFF) and nonspatial tabular (e.g., Microsoft Excel Spreadsheet) data formats. Additionally, it provides functionality for evaluating prioritizations, such as assessing the relative importance of different places selected by a prioritization. To showcase the prioritizr R package, we applied it to a case study based in Washington state (United States) for which we developed a prioritization to improve protected area coverage of native avifauna. We accounted for land acquisition costs, existing protected areas, places that might not be suitable for protected area establishment, and spatial fragmentation. We also conducted a benchmark analysis to examine the performance of different solvers. The prioritization identified 12,400 km2 of priority areas for increasing the percentage of species' distributions covered by protected areas. Although open source and commercial solvers were able to quickly solve large-scale conservation planning problems, commercial solvers were required for complex, large-scale problems.. The prioritizr R package is available on the Comprehensive R Archive Network (CRAN). In addition to reserve selection, it can inform habitat restoration, connectivity enhancement, and ecosystem service provisioning. It has been used in numerous conservation planning exercises to inform best practices and aid real-world decision-making.

Electrified Solar Zero Liquid Discharge: Exploring the Potential of PV-ZLD in the US

Current brine management strategies are based on the disposal of brine in nearby aquifers, representing a loss in potential water and mineral resources. Zero liquid discharge (ZLD) is a possible strategy to reduce brine rejection while increasing the resource recovery from desalination plants. However, ZLD substantially increases the energy consumption and carbon footprint of a desalination plant. The predominant strategy to reduce the energy consumption and carbon footprint of ZLD is through the use of a hybrid desalination technology that integrates renewable energy. Here, we built a computational thermodynamic model of the most mature electrified hybrid technology for ZLD powered by photovoltaic (PV). We examine the potential size and cost of ZLD plants in the US. This work explores the variables (geospatial and design) that most influence the levelized cost of water and the second law efficiency. There is a negative correlation between minimizing the LCOW and maximizing the second-law. And maximizing the second-law, the states that more brine produces, Texas is the location where the studied system achieves the lowest LCOW and high second-law efficiency, while California is the state where the studied system is less favorable. A multiobjective optimization study assesses the impact of considering a carbon tax in the cost of produced water and determines the best potential size for the studied plant.

Impacts of climate and land use change on terrestrial carbon storage: A multi-scenario case study in the Yellow River Basin (1992-2050)

Currently, socioeconomic development and climate change pose new challenges to the assessment and management of terrestrial carbon storage (CS). Accurate prediction of future changes in land use and CS under different climate scenarios is of great significance for regional land use decision-making and carbon management. Taking the Yellow River Basin (YRB) in China as the study area, this study proposed a framework integrating the land use harmonization2 (LUH2) dataset, the patch-generating land use simulation (PLUS) model, and the integrated valuation of ecosystem services and trade-offs (InVEST) model. Under this framework, we systematically analyzed the spatiotemporal evolution characteristics of land use and their impact on CS in the YRB from 1992 to 2050. The results showed that (1) CS was highest in forestland and lowest in construction land, with a spatial distribution of high in the south and low in the north. From 1992 to 2020, construction land, forestland, and grassland increased while cropland decreased, reducing the total CS by 74.04 Tg. (2) From 2020 to 2050, under SSP1-2.6 scenario, forestland increased by 158.87 %; under SSP2-4.5 scenario, unused land decreased by 65.55 %; and under SSP5-8.5 scenario, construction land increased by 13.88 %. By 2050, SSP1-2.6 scenario exhibited the highest CS (8105.25 Tg), followed by SSP2-4.5 scenario (7363.61 Tg), and SSP5-8.5 scenario was the lowest (7315.86 Tg). (3) Forestland and construction land were the most critical factors affecting the CS. Shaanxi and Shanxi had the largest CS in all scenarios, and Qinghai had a huge carbon sink potential under SSP1-2.6 scenario. Scenario modeling demonstrated that future climate and land-use changes would have significant impacts on terrestrial CS in the YRB, and green development pathways could strongly contribute to meeting the dual‑carbon target. Overall, this study provides a scientific basis for promoting low-carbon development, land-use optimization, and ecological civilization construction in YRB, China.

Understanding variation in impacts from private protected areas across regions and protection mechanisms to inform organizational practices

Private land protection is an important and growing tool to address biodiversity loss and climate change. Thus, better empirical evidence on the effectiveness of private land protection and organizational practices, such as targeting of lands for protection and choice of protection mechanism (i.e., fee simple land acquisition and conservation easements), is needed. We addressed this gap by estimating the impacts of The Nature Conservancy's (TNC) (a large nongovernmental organization with relatively decentralized management) conservation land acquisitions and easements from 1988 to 2016 in three regions of the United States (Mid-Atlantic, New England and New York, and California). We estimated impact in terms of avoided conversion by comparing natural land cover on 3179 protected parcels with matched unprotected parcels. Nineteen of 21 ecoregional plans used threats of agriculture and development to identify priorities for protection. When regions and protection mechanisms were pooled, on average there was no evidence of avoided conversion from 1988 to 2016. Accounting for mechanisms, TNC land acquisitions avoided conversion and easements did not. TNC's easements on parcels acquired by conservation partners did avoid conversion. Limitations of these results include focus on a single measure of impact, inability to capture future avoided conversion, and low land cover change accuracy in California. Our results suggest that private land protection managers who seek to avoid land conversion in the near to medium term should increase focus on areas with higher threats. Special attention should be paid to strengthening accountability and the role of partners, improving or clarifying how easements are used, and facilitating the flow of resources to work with the greatest potential impact.

A new assessment framework to forecast land use and carbon storage under different SSP-RCP scenarios in China

The vision of achieving "carbon neutrality" has created new requirements for the projection of land use and land cover (LULC), as well as the carbon storage (CS) of terrestrial ecosystem. Global-scale LULC scenario assessments with coarser resolution introduces uncertainties to national and regional-scale studies, which in turn has a negative impact on CS analysis based on land use perspective. Therefore, we proposed a new framework for scenario-based assessment that integrates the global-scale Land Use Harmonization (LUH2) dataset, Patch-generating Land Use Simulation (PLUS) model, and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, which we called LUH2-PLUS-InVEST (LPI) model. Our aim is to investigate the potential impacts of the combinations of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) on China's future LULC and CS. By calibrating the demands, we generated structural predictions that were consistent with the actual land use. Furthermore, we explored the spatial heterogeneity of potential land use changes using 500 m × 500 m downscale simulations. Additionally, we developed a quantitative evaluation of CS from a spatiotemporal perspective and made recommendations on potential ecological threats. Our findings indicate that the basic characteristics of LULC and CS are determined by the natural context and that the prospects of land use distribution and carbon sequestration capacity are influenced by global emission pressure, regional competition, and China's unique development pattern. The results demonstrate that the LUH2-PLUS-INVEST model can provide an effective method for modeling the feedbacks of LULC and CS to the climate-society system.

Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates

We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the Army Corps of Engineers, each deciding regulation for one water resource. Under a 2006 Supreme Court ruling, the Clean Water Act protects two-thirds of US streams and more than half of wetlands; under a 2020 White House rule, it protects less than half of streams and a fourth of wetlands, implying deregulation of 690,000 stream miles, 35 million wetland acres, and 30% of waters around drinking-water sources. Our framework can support permitting, policy design, and use of machine learning in regulatory implementation problems.

Prioritizing the Best Potential Regions for Brine Concentration Systems in the USA Using GIS and Multicriteria Decision Analysis

We propose a methodology for identifying and prioritizing the best potential locations for brine concentration facilities in the contiguous United States. The methodology uses a geographic information system and multicriteria decision analysis (GIS-MCDA) to prioritize the potential locations for brine concentration facilities based on thermodynamic, economic, environmental, and social criteria. By integrating geospatial data with a computational simulation of a real brine concentration system, an objective weighting method identifies the weights for 13 subcriteria associated with the main criteria. When considering multiple dimensions for decision making, brine concentration facilities centered in Florida were consistently selected as the best location, due to the high second-law efficiency, low transportation cost, and high capacity for supplying municipal water needs to nearby populations. For inland locations, Southeast Texas outperforms all other locations for thermodynamic, economic, and environmental priority cases. A sensitivity analysis evaluates the consistency of the results as the priority of a main criterion varies relative to other decision-making criteria. Focusing on a single subcriterion misleads decision making when identifying the best location for brine concentration systems, identifying the importance of the multicriteria methodology.

Energy Management and Economic Considerations of Intermittent Photovoltaic-Driven Electrochemical Ammonia Production

As the energy sector shifts from fossil fuels to renewable energy, there is a need for long-duration energy storage solutions to handle the intermittency of renewable electricity. Electrofuels, or fuels synthesized from excess electricity, are an emerging medium poised to meet long-duration energy storage requirements. Ammonia as an electrofuel is potentially ideal because ammonia has a relatively low liquefaction pressure, indicating that ammonia can be easily stored and transported. Here, we develop a framework to optimize the electrochemical production of ammonia powered by intermittent photovoltaic power. We also explore various buyback policies to understand the impact that policy has on the cost of intermittent ammonia and optimal sizing ratios. The optimal ratio of the photovoltaic to the electrolyzer is ∼3.7 MWPV/MWELEC for a system that is completely powered by renewable photovoltaic power and operates intermittently. The optimal ratio of the photovoltaic to the electrolyzer is ∼3.3 MWPV/MWELEC for a system that uses photovoltaics in conjunction with grid electricity and operates continuously. For the purchase price at the avoided cost of electricity, the optimal ratio of the solar panel to the electrolyzer increases to ∼4 MWPV/MWELEC for a system that can only sell to the grid and ∼5 MWPV/MWELEC for a system that can buy and sell electricity to the grid at the avoided cost. Optimizing energy management by setting auxiliary battery size limits is essential to reducing ammonia costs, and the optimal battery size decreases as the buyback price of electricity increases. Finally, we find that systems connected to the grid and operating continuously have emissions comparable to the Haber-Bosch process because of the current emissions tied to the United States electricity generation. Thus, unless the grid is completely decarbonized, it is essential to create electrofuels that rely minimally on grid electricity.

Expanding the coverage and accuracy of parcel-level land value estimates

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.

Valuing water quality in the United States using a national dataset on property values

High-quality water resources provide a wide range of benefits, but the value of water quality is often not fully represented in environmental policy decisions, due in large part to an absence of water quality valuation estimates at large, policy relevant scales. Using data on property values with nationwide coverage across the contiguous United States, we estimate the benefits of lake water quality as measured through capitalization in housing markets. We find compelling evidence that homeowners place a premium on improved water quality. This premium is largest for lakefront property and decays with distance from the waterbody. In aggregate, we estimate that 10% improvement of water quality for the contiguous United States has a value of $6 to 9 billion to property owners. This study provides credible evidence for policymakers to incorporate lake water quality value estimates in environmental decision-making.

Gridded land use data for the conterminous United States 1940-2015

Multiple aspects of our society are reflected in how we have transformed land through time. However, limited availability of historical-spatial data at fine granularity have hindered our ability to advance our understanding of the ways in which land was developed over the long-term. Using a proprietary, national housing and property database, which is a result of large-scale, industry-fuelled data harmonization efforts, we created publicly available sequences of gridded surfaces that describe built land use progression in the conterminous United States at fine spatial (i.e., 250 m × 250 m) and temporal resolution (i.e., 1 year - 5 years) between the years 1940 and 2015. There are six land use classes represented in the data product: agricultural, commercial, industrial, residential-owned, residential-income, and recreational facilities, as well as complimentary uncertainty layers informing the users about quantifiable components of data uncertainty. The datasets are part of the Historical Settlement Data Compilation for the U.S. (HISDAC-US) and enable the creation of new knowledge of long-term land use dynamics, opening novel avenues of inquiry across multiple fields of study.

Risks to global biodiversity and Indigenous lands from China's overseas development finance

China has become one of the world's largest lenders in overseas development finance. Development projects, such as roads, railways and power plants, often drive biodiversity loss and infringe on Indigenous lands, yet the risks implicit in China's overseas development finance are poorly understood. Here we examine the extent to which projects financed by China's policy banks between 2008 and 2019 occur within and adjacent to areas where large-scale investment can present considerable risks to biodiversity and Indigenous peoples. Further, we compare these risks with those posed by similar projects financed by the World Bank, previously the world's largest source of development finance. We found that 63% of China-financed projects overlap with critical habitats, protected areas or Indigenous lands, with up to 24% of the world's threatened birds, mammals, reptiles and amphibians potentially impacted by the projects. Hotspots of the risks are primarily distributed in northern sub-Saharan Africa, Southeast Asia and parts of South America. Overall, China's development projects pose greater risks than those of the World Bank, particularly within the energy sector. These results provide an important global outlook of socio-ecological risks that can guide strategies for greening China's development finance around the world.

Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States

The collection, processing, and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth's surface. While satellite-based Earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially explicit settlement data for the United States that extend back to the early 19th century and are consistently enumerated at fine spatial and temporal granularity (i.e. 250m spatial and 5-year temporal resolution). We create these time series using a large, novel building-stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at an unprecedented spatial and temporal resolution. The datasets are part of the Historical Settlement Data Compilation for the United States (https://dataverse.harvard.edu/dataverse/hisdacus, last access: 25 January 2021) and are available at https://doi.org/10.7910/DVN/YSWMDR (Uhl and Leyk, 2020a), https://doi.org/10.7910/DVN/SJ213V (Uhl and Leyk, 2020b), and https://doi.org/10.7910/DVN/J6CYUJ (Uhl and Leyk, 2020c).

High-resolution land value maps reveal underestimation of conservation costs in the United States

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.