On the Role of Fines as an Environmental Enforcement Tool

This paper provides a theoretical analysis of enforcement and compliance decisions when the enforcement process involves significant interaction between a source of violation and enforcer. We show that the comparative static effects of a fine on the probability of a violation consist of a direct effect, which refers to the effect of an increase in the fine on the expected cost of a violation holding the probabilities of enforcementrelated decisions constant, and an indirect effect, which refers to the effect of the fine on the probability of a violation through its effect on the probabilities of enforcement actions taken by the regulator. We show that, in the absence of the indirect effects, an increase in the fine unambiguously reduces the probability of a violation and hence should lead to lower pollution (as expected). However, if the indirect effects are positive and large, an increase in the fine can actually reduce the likelihood that a firm will comply with environmentalregulations. Thus, the increased fines do not necessarily increase compliance incentives.     

Using Multiple Watershed Models to Predict Water,Nitrogen, and Phosphorus Discharges to the Patuxent Estuary1

Boomer, Kathleen M.B., Donald E. Weller, Thomas E. Jordan, Lewis Linker, Zhi‐Jun Liu, James Reilly, Gary Shenk, and Alexey A. Voinov, 2012. Using Multiple Watershed Models to Predict Water, Nitrogen, and Phosphorus Discharges to the Patuxent Estuary. Journal of the American Water Resources Association (JAWRA) 1‐25. DOI: 10.1111/j.1752‐1688.2012.00689.x Abstract: We analyzed an ensemble of watershed models that predict flow, nitrogen, and phosphorus discharges. The models differed in scope and complexity and used different input data, but all had been applied to evaluate human impacts on discharges to the Patuxent River or to the Chesapeake Bay. We compared predictions to observations of average annual, annual time series, and monthly discharge leaving three basins. No model consistently matched observed discharges better than the others, and predictions differed as much as 150% for every basin. Models that agreed best with the observations in one basin often were among the worst models for another material or basin. Combining model predictions into a model average improved overall reliability in matching observations, and the range of predictions helped describe uncertainty. The model average was not the closest to the observed discharge for every material, basin, and time frame, but the model average had the highest Nash–Sutcliffe performance across all combinations. Consistently poor performance in predicting phosphorus loads suggests that none of the models capture major controls. Differences among model predictions came from differences in model structures, input data, and the time period considered, and also to errors in the observed discharge. Ensemble watershed modeling helped identify research needs and quantify the uncertainties that should be considered when using the models in management decisions.     

Empirical modeling of atmospheric deposition in mountainous landscapes.

Atmospheric deposition has long been recognized as an important source of pollutants and nutrients to ecosystems. The need for reliable, spatially explicit estimates of total atmospheric deposition (wet + dry + cloud) is central, not only to air pollution effects researchers, but also for calculation of input-output budgets, and to decision makers faced with the challenge of assessing the efficacy of policy initiatives related to deposition. Although atmospheric deposition continues to represent a critical environmental and scientific issue, current estimates of total deposition have large uncertainties, particularly across heterogeneous landscapes such as montane regions. We developed an empirical modeling approach that predicts total deposition as a function of landscape features. We measured indices of total deposition to the landscapes of Acadia (121 km2) and Great Smoky Mountains (2074 km2) National Parks (USA). Using approximately 300-400 point measurements and corresponding landscape variables at each park, we constructed a statistical (general linear) model relating the deposition index to landscape variables measured in the field. The deposition indices ranged over an order of magnitude, and in response to vegetation type and elevation, which together explained approximately 40% of the variation in deposition. Then, using the independent landscape variables available in GIS data layers, we created a GIS-relevant statistical nitrogen (N) and sulfur (S) deposition model (LandMod). We applied this model to create park-wide maps of total deposition that were scaled to wet and dry deposition data from the closest national network monitoring stations. The resultant deposition maps showed high spatial heterogeneity and a four- to sixfold variation in "hot spots" and "cold spots" of N and S deposition ranging from 3 to 31 kg N x ha(-1) x yr(-1) and from 5 to 42 kg S x ha(-1) x yr(-1) across these park landscapes. Area-weighted deposition was found to be up to 70% greater than NADP plus CASTNET monitoring-station estimates together. Model-validation results suggest that the model slightly overestimates deposition for deciduous and coniferous forests at low elevation and underestimates deposition for high-elevation coniferous forests. The spatially explicit deposition estimates derived from LandMod are an improvement over what is currently available. Future research should test LandMod in other mountainous environments and refine it to account for (currently) unexplained variation in deposition.     

Applications of Turbidity Monitoring to Forest Management in California

Many California streams have been adversely affected by sedimentation caused by historic and current land uses, including timber harvesting. The impacts of timber harvesting and logging transportation systems on erosion and sediment delivery can be directly measured, modeled, or inferred from water quality measurements. California regulatory agencies, researchers, and land owners have adopted turbidity monitoring to determine effects of forest management practices on suspended sediment loads and water quality at watershed, project, and site scales. Watershed-scale trends in sediment discharge and responses to current forest practices may be estimated from data collected at automated sampling stations that measure turbidity, stream flow, suspended sediment concentrations, and other water quality parameters. Future results from these studies will provide a basis for assessing the effectiveness of modern forest practice regulations in protecting water quality. At the project scale, manual sampling of water column turbidity during high stream flow events within and downstream from active timber harvest plans can identify emerging sediment sources. Remedial actions can then be taken by managers to prevent or mitigate water quality impacts. At the site scale, manual turbidity sampling during storms or high stream flow events at sites located upstream and downstream from new, upgraded, or decommissioned stream crossings has proven to be a valuable way to determine whether measures taken to prevent post-construction erosion and sediment production are effective. Turbidity monitoring at the project and site scales is therefore an important tool for adaptive management. Uncertainty regarding the effects of current forest practices must be resolved through watershed-scale experiments. In the short term, this uncertainty will stimulate increased use of project and site-scale monitoring.     

Water resource management and climate change adaptation: a holistic and multiple criteria perspective

Anthropogenic climate change is likely to significantly increase human exposure to droughts and floods. It will also alter seasonal patterns of water availability and affect water quality and the health of aquatic ecosystems with various implications for social and economic wellbeing. Policy development for water resource adaptation needs to allow for a holistic and transparent analysis of the probable consequences of policy options for the wide variety of water uses and users, and the existing ecosystem services associated with any stream basin. This paper puts forward an innovative methodological framework for planning development-compatible climate policies drawing on multi-criteria decision analysis and an implicit risk-management approach to the economics of climate change. Its objectives are to describe how the generic methodology could be tailored for analysis of long-range water planning and policy options in developing countries, and to describe the place of climate change considerations in water governance and planning processes. An experimental thought-exercise applying the methodology to water policy development in Yemen provides further insights on the complexity of water adaptation planning. It also highlights the value of conducting sensitivity analysis to explore the implications of multiple climate scenarios, and the importance of accounting for policy portfolios rather than individual policy options. Rather than constituting a tool that can generate clear measures of optimal solutions in the context of adaptation to uncertain climate futures, we find that this approach is best suited to supporting comprehensive and inclusive planning processes, where the focus is on finding socially acceptable paths forward.     

Determining the drivers of population structure in a highly urbanized landscape to inform conservation planning

Understanding the environmental contributors to population structure is of paramount importance for conservation in urbanized environments. We used spatially explicit models to determine genetic population structure under current and future environmental conditions across a highly fragmented, human‐dominated environment in Southern California to assess the effects of natural ecological variation and urbanization. We focused on 7 common species with diverse habitat requirements, home‐range sizes, and dispersal abilities. We quantified the relative roles of potential barriers, including natural environmental characteristics and an anthropogenic barrier created by a major highway, in shaping genetic variation. The ability to predict genetic variation in our models differed among species: 11–81% of intraspecific genetic variation was explained by environmental variables. Although an anthropogenically induced barrier (a major highway) severely restricted gene flow and movement at broad scales for some species, genetic variation seemed to be primarily driven by natural environmental heterogeneity at a local level. Our results show how assessing environmentally associated variation for multiple species under current and future climate conditions can help identify priority regions for maximizing population persistence under environmental change in urbanized regions.