Robert J. Johnston Archives

Conservation Incentives and the Socio-Spatial Dynamics of Water Sustainability

Disputes over scarce water resources are common worldwide and there is a growing interest in voluntary incentives (e.g., payments offered to water users) as a strategy for reducing conflicts. Incentive-based programs hold promise, but uncertainties remain regarding how state and non-state environmental organizations may implement them. Efficient and effective implementation requires strategic allocation of financial…
Next Generation Choice Experiment Architecture for Spatially-Explicit Agricultural Conservation and Ecosystem Service Valuation

The USDA spends more than $5 billion annually on conservation programs to enhance environmental quality, ecosystem services and agricultural sustainability. Yet credible information on economic (and particularly non-market) benefits is often lacking, particularly for heterogeneous conservation practices that occur over large spatial scales. Current economic valuation methods are challenged by the individualized and spatially heterogeneous…
Coupled Prediction of Residential Fertilizer Use and Nitrogen Loads to Long Island Sound: An Integrated Targeting Tool for Nitrogen-Reduction Behavior Change Campaigns

Non-point sources account for approximately 60% of nitrogen loading in Long Island Sound (LIS) and residential lawn fertilizer has been among the most difficult of these sources to reduce. In response, policymakers and other stakeholders have proposed behavior-change campaigns to promote lawn practices that reduce fertilizer use. However, the potential effect of these efforts on…
Reducing Non-Point Source Nitrogen Loads from Residential Septic Systems: Identifying Barriers and Opportunities for Large-Scale Water Quality Improvements

Residential septic systems are a primary source of nitrogen loading to nearby water bodies which can negatively impact water quality. Collaborating with researchers at University of Connecticut, University of Maryland, and University of Miami, this interdisciplinary project will develop a generalizable framework and integrated, spatially-explicit economic-behavioral-hydrological model to inform the design and targeting of programs…
Evaluating Social, Economic and Environmental Outcomes of Community-Based Coastal Adaptation Engagements: An Integrated Economics and Machine-Learning Framework

Coastal communities face compound hazards due to elements such as sea-level rise, increased frequency and intensity of extreme weather, flooding, changing morphology, heat, drought, and human development. Amidst these challenges, public and private organizations have developed public engagement and capacity-building programs to help support adaptation planning and implementation that meet community goals, support social welfare…
Integrating Locally-Weighted Meta-Regression and Machine Learning to Capture Spatial Complexity in Multi-Scale Benefit Transfers

The USDA spends more than $5 billion per year on conservation to enhance environmental quality, ecosystem services and agricultural sustainability. The biophysical impacts of these programs (e.g., on soil retention and water quality) are relatively well understood and can be estimated using standard modeling approaches. Yet the economic benefits of these programs remain unknown, and…

George Perkins Marsh Institute

Conservation Incentives and the Socio-Spatial Dynamics of Water Sustainability

Next Generation Choice Experiment Architecture for Spatially-Explicit Agricultural Conservation and Ecosystem Service Valuation

Coupled Prediction of Residential Fertilizer Use and Nitrogen Loads to Long Island Sound: An Integrated Targeting Tool for Nitrogen-Reduction Behavior Change Campaigns

Reducing Non-Point Source Nitrogen Loads from Residential Septic Systems: Identifying Barriers and Opportunities for Large-Scale Water Quality Improvements

Evaluating Social, Economic and Environmental Outcomes of Community-Based Coastal Adaptation Engagements: An Integrated Economics and Machine-Learning Framework

Integrating Locally-Weighted Meta-Regression and Machine Learning to Capture Spatial Complexity in Multi-Scale Benefit Transfers

George Perkins Marsh Institute

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