Assessing cumulative impacts within state environmental review frameworks in the United States

Cumulative impact assessment (CIA) is the process of systematically assessing a proposed action's cumulative environmental effects in the context of past, present, and future actions, regardless of who undertakes such actions. Previous studies have examined CIA efforts at the federal level but little is known about how states assess the cumulative impacts of nonfederal projects. By examining state environmental review statutes, administrative rules, agency-prepared materials, and a national survey of the administrators of state environmental review programs, this study identifies the legal and administrative frameworks for CIA. It examines current CIA practice, discusses the relationship between CIA policy and its implementation, and explores the opportunities for improvement. The results of the study show that twenty-nine state environmental review programs across twenty-six states required the assessment of cumulative environmental impacts. More than half of these programs have adopted specific procedures for implementing their policies. Some programs assessed cumulative impacts using a standard review document, and others have created their own documentations incorporated into applications for state permits or funding. The majority of programs have adopted various scales, baselines, significance criteria, and coordination practices in their CIA processes. Mixed methods were generally used for data collection and analysis; qualitative methods were more prevalent than quantitative methods. The results also suggest that a program with comprehensive and consistent environmental review policies and procedures does not always imply extensive CIA requirements and practices. Finally, this study discusses the potential for improving existing CIA processes and promoting CIA efforts in states without established environmental review programs.     

The precision and accuracy of U.S. environmental impact statements

A post-project audit was conducted of 239 impacts forecast in an representative cross-section of 29 U.S. environmental impact statements. Results must be qualified because of the imprecision of EIS forecasts and the quality of data available to a retrospective evaluator. Relatively few forecasts were found to be inaccurate and even fewer unanticipated impacts were identified. However, only 30% of the impacts were unqualifiedly close to their forecasts, with almost as many rated accurate principally by virtue of the vagueness of the forecasts.     

Not so Black and White: environmental justice and cumulative impact assessments

A growing number of scientific studies in recent years have investigated disparate exposure to ecological hazards in American society. Working from an environmental justice perspective, this body of research consistently reveals that poor communities of color are most likely to bear a disproportionate burden of negative externalities. These studies utilize a wide range of research methodologies, including various indicators of ecological hazards (e.g., proximity to waste sites, industrial emissions, ambient air quality), but few, if any, utilize composite measures to approximate cumulative environmental impact. Consequently, the environmental justice (EJ) literature is characterized by a failure to effectively measure overall impact from an extensive range of ecological hazards. Limitations on available data make this a serious problem for present and future studies. We argue that cumulative measures of environmental impact can play an important role in furthering our understanding of environmental injustices in the United States. In this study of Massachusetts, we develop and implement such a cumulative measure of negative environmental impacts. By controlling for the density and severity of ecological hazardous sites and facilities within every community in the state, we demonstrate that exposure patterns take a generally linear distribution when analyzed by race and class. So, while our results reaffirm previous findings that low-income communities and communities of color bear significantly greater ecological burdens than predominantly White and more affluent communities, our findings also suggest that environmental injustices exist on a remarkably consistent continuum for nearly all communities. In other words, as the minority population and lower-income composition of a community increases, correspondingly, so does cumulative exposure to environmental hazards. In this respect, communities which are more racially mixed and of moderate income status that are not typically identified as meeting EJ criteria (in demographic terms) also face more significant ecological hazards. Thus, the strict bifurcation of communities into categories of Environmental Justice and Non-Environmental Justice is problematic, and poses a serious dilemma for policy makers, public health officials, and community activists. To overcome this challenge requires the adoption of a cumulative environmental justice impact assessment (CEJIA), which in addition to the demographic characteristics of a community, also takes into account the total environmental burden and related health impacts upon residents. Furthermore, through the adoption of the precautionary principle, source reduction, and alternative forms of “cleaner” production, environmental justice advocates must work for policies which reduce the environmental threat for the full range of communities, as well as their own.     

Setting boundaries of participation in environmental impact assessment

Public participation processes are touted as an effective way to increase the capacity and legitimacy of environmental assessment and the regulatory process that rely on them. Recent changes to the Canadian environmental assessment process narrowed the criteria for who can participate in environmental assessments from any who were interested to those who were most directly affected. This article examines the potential consequences of this change by exploring other areas of Canadian regulatory law where a similar directed affected test has been applied. This new standard risks institutionalizing the long-understood representational bias confronted by more diffuse interest like environmental protection. Restricting participation to the “directly affected” is far too narrow a test for processes like environmental assessment that are designed to determine the public interest.     

A case study predicting environmental impacts of urban transport planning in China

Predicting environmental impacts is essential when performing an environmental assessment on urban transport planning. System dynamics (SD) is usually used to solve complex nonlinear problems. In this study, we utilized system dynamics (SD) to evaluate the environmental impacts associated with urban transport planning in Jilin City, China with respect to the local economy, society, transport, the environment and resources. To accomplish this, we generated simulation models comprising interrelated subsystems designed to utilize changes in the economy, society, road construction, changes in the number of vehicles, the capacity of the road network capacity, nitrogen oxides emission, traffic noise, land used for road construction and fuel consumption associated with traffic to estimate dynamic trends in the environmental impacts associated with Jilin's transport planning. Two simulation scenarios were then analyzed comparatively. The results of this study indicated that implementation of Jilin transport planning would improve the current urban traffic conditions and boost the local economy and development while benefiting the environment in Jilin City. In addition, comparative analysis of the two scenarios provided additional information that can be used to aid in scientific decision-making regarding which aspects of the transport planning to implement in Jilin City. This study demonstrates that our application of the SD method, which is referred to as the Strategic Environmental Assessment (SEA), is feasible for use in urban transport planning.     

Planning theories and environmental impact assessment

The limited and sporadic interaction between EIA and planning theory has meant that EIA has largely failed to benefit from planning theory insights and lessons. Obstacles and dilemmas already encountered and addressed in planning theory are still hampering EIA theory building and practice. An overview of the characteristics, strengths, and limitations of five major planning theories—rationalism, pragmatism, socio-ecological idealism (SEI), political-economic mobilization (PEM), and communications and collaboration (CC) is presented in an effort to ameliorate this problem. EIA parallels to each planning theory and lessons for EIA are identified. Also addressed are the application of planning theories to different context types, overlaps, interconnections and middle ground concepts among theories, the integration of values, ethics, and postmodern perspectives and the potential to construct composite theories.     

Prediction of environmental impacts of quarry blasting operation using fuzzy logic

Blast-induced ground vibration is one of the most important environmental impacts of blasting operations because it may cause severe damage to structures and plants in nearby environment. Estimation of ground vibration levels induced by blasting has vital importance for restricting the environmental effects of blasting operations. Several predictor equations have been proposed by various researchers to predict ground vibration prior to blasting, but these are site specific and not generally applicable beyond the specific conditions. In this study, an attempt has been made to predict the peak particle velocity (PPV) with the help of fuzzy logic approach using parameters of distance from blast face to vibration monitoring point and charge weight per delay. The PPV and charge weight per delay were recorded for 33 blast events at various distances and used for the validation of the proposed fuzzy model. The results of the fuzzy model were also compared with the values obtained from classical regression analysis. The root mean square error estimated for fuzzy-based model was 5.31, whereas it was 11.32 for classical regression-based model. Finally, the relationship between the measured and predicted values of PPV showed that the correlation coefficient for fuzzy model (0.96) is higher than that for regression model (0.82).     

Underwater noise modelling for environmental impact assessment

Assessment of underwater noise is increasingly required by regulators of development projects in marine and freshwater habitats, and noise pollution can be a constraining factor in the consenting process. Noise levels arising from the proposed activity are modelled and the potential impact on species of interest within the affected area is then evaluated. Although there is considerable uncertainty in the relationship between noise levels and impacts on aquatic species, the science underlying noise modelling is well understood. Nevertheless, many environmental impact assessments (EIAs) do not reflect best practice, and stakeholders and decision makers in the EIA process are often unfamiliar with the concepts and terminology that are integral to interpreting noise exposure predictions. In this paper, we review the process of underwater noise modelling and explore the factors affecting predictions of noise exposure. Finally, we illustrate the consequences of errors and uncertainties in noise modelling, and discuss future research needs to reduce uncertainty in noise assessments.