Disparate perceptions about uncertainty consideration and disclosure practices in environmental assessment and opportunities for improvement

The need to better address uncertainties in environmental assessment (EA) is well known, but less known is how those involved in, or affected by, EA processes understand and perceive uncertainties and how uncertainties are considered and disclosed. Based on a survey of 77 Canadian EA practitioners, regulators, and interest groups, this paper explores uncertainties in the EA process, uncertainty consideration and disclosure in EA practice and decision-making, and opportunities for improved disclosure. Nearly 80% of participants indicated that all EAs contain uncertainty; however, uncertainty disclosure was described as poor. Only 15% indicated that uncertainties are sufficiently acknowledged in practice and, when disclosed, considered by decision makers. Perceptions about uncertainty differed significantly between those who conducted EAs compared to those potentially affected by development, suggesting that either communication about uncertainty is poor, or participants' understandings about what is considered ‘good’ practice are very different. Almost half of the participants believe that there is overconfidence in impact predictions and mitigation measures, and the majority indicated that if uncertainties were more openly reported then EA would be a better tool for informing decisions. Most participants did not believe that EAs that openly disclose uncertainties lack credibility; and contrary to proponents' tendencies to limit disclosure, participants perceived limited risk of disclosure in terms of project approval. The majority of participants did not believe that there was sufficient guidance available on how to report uncertainties, or on how to use that information in decision-making. Results indicate a substantial need to better understand how uncertainties are viewed and dealt with in EA; the importance of uncertainty disclosure and consideration in EA; and the risks and benefits of uncertainty disclosure to proponents, decision makers, and the public. We identify several opportunities for improving the practice of uncertainty consideration and disclosure.     

Optimal Expansion Strategy for a Sewer System under Uncertainty

Because of fast urban sprawl, land use competition, and the gap in available funds and needed funds, municipal decision makers and planners are looking for more cost-effective and sustainable ways to improve their sewer infrastructure systems. The dominant approaches have turned to planning the sanitary sewer systems within a regional context, while the decentralized and on-site/cluster wastewater systems have not overcome the application barriers. But regionalization policy confers uncertainties and risks upon cities while planning for future events. Following the philosophy of smart growth, this paper presents several optimal expansion schemes for a fast-growing city in the US/Mexico borderlands—the city of Pharr in Texas under uncertainty. The waste stream generated in Pharr is divided into three distinct sewer sheds within the city limit, including south region, central region, and north region. The options available include routing the wastewater to a neighboring municipality (i.e., McAllen) for treatment and reuse, expanding the existing wastewater treatment plant (WWTP) in the south sewer shed, and constructing a new WWTP in the north sewer shed. Traditional deterministic least-cost optimization applied in the first stage can provide a cost-effective and technology-based decision without respect to associated uncertainties system wide. As the model is primarily driven by the fees charged for wastewater transfer, sensitivity analysis was emphasized by the inclusion of varying flat-rate fees for adjustable transfer schemes before contracting process that may support the assessment of fiscal benefits to all parties involved. Yet uncertainties might arise from wastewater generation, wastewater reuse, and cost increase in constructing and operating the new wastewater treatment plant simultaneously. When dealing with multiple sources of uncertainty, the grey mixed integer programming (GIP) model, formulated in the second stage, can further allow all sources of uncertainties to propagate throughout the optimization context, simultaneously leading to determine a wealth of optimal decisions within a reasonable range. Both models ran for three 5-year periods beginning in 2005 and ending in 2020. The dynamic outputs of this analysis reflect the systematic concerns about integrative uncertainties within this decision analysis, which enable decision makers and stakeholders to make all-inclusive decisions for sanitary sewer system expansion in an economically growing region.     

An Optimization Model for Integrated Coastal Management: Development and a Case Study Using Italy’s Comacchio Municipality

In this paper, I develop an optimization model for integrated coastal management in which decisions arise from an area-based algorithm that minimizes predicted damage caused by beach erosion and inshore flooding, while accounting for economic, social, and environmental losses. The model favors the involvement of stakeholders in coastal management, but does not use complicated assessment procedures for non-economic indicators or relative weights to combine economic, social, and environmental indicators. Instead, the integration between economic activities or properties and the environmental status and landscape is represented objectively and non-linearly by referring to initial and sustainability conditions, combined with budgetary and environmental constraints. The model successfully accounted for both human and environmental dynamics by depicting delayed effects, neighborhood externalities, and feedback effects. It calculated a single optimal value for each integrated coastal management strategy, which permitted the support of future decisions and the evaluation of past decisions. The model’s insights were based on reliable estimates, with reliability determined by calculating the confidence level. The model was successfully applied to Italy’s Comacchio coastal municipality, where it revealed the priorities for optimal beach nourishment, dune fixation, and residential and holiday housing development based on budget constraints, beach losses, flood damage, pollution impacts, and land-use constraints.     

A window on urban sustainability: Integration of environmental interests in urban planning through ‘decision windows’

Sustainable urban development requires the integration of environmental interests in urban planning. Although various methods of environmental assessment have been developed, plan outcomes are often disappointing due to the complex nature of decision-making in urban planning, which takes place in multiple arenas within multiple policy networks involving diverse stakeholders. We argue that the concept of ‘decision windows’ can structure this seemingly chaotic chain of interrelated decisions. First, explicitly considering the dynamics of the decision-making process, we further conceptualized decision windows as moments in an intricate web of substantively connected deliberative processes where issues are reframed within a decision-making arena, and interests may be linked within and across arenas. Adopting this perspective in two case studies, we then explored how decision windows arise, which factors determine their effectiveness and how their occurrence can be influenced so as to arrive at more sustainable solutions. We conclude that the integration of environmental interests in urban planning is highly dependent on the ability of the professionals involved to recognize and manipulate decision windows. Finally, we explore how decision windows may be opened.     

Integrating ecosystem services into environmental impact assessment: An analytic–deliberative approach

Analytic–deliberative techniques have been suggested as a promising approach to ecosystem service (ES) valuation but are still at an experimental stage. This paper contributes to the development of ES valuation in the environmental impact assessment (EIA) procedure by introducing an analytic–deliberative approach to assessing restoration options for a regulated river in Finland. Multi-criteria decision analysis (MCDA) with a value-focused approach was applied and compared with a desktop application of the ES-focused MCDA approach. We found out that the concept of ES could bring added value to the assessment process by: 1) enabling the framing and valuing especially of provisioning services — final ecosystem services, such as salmon catch in a more understandable way for the stakeholders, and 2) taking into account ecosystem processes and supporting services more precisely. However, some potential dangers of using the ES-based approach could include ES's appearing as a distant mode of thinking to affected groups and other stakeholders, and neglect of the trade-offs between ES and other relevant value and impact categories. Thus, although the ES framework is promising, it should not form a rigid ‘checklist’ way of making assessments but should rather be used to widen perspectives about potential issues in linking ecosystem properties to human benefits and values. It is argued that by combining the ES framework with the interactive MCDA approach, we can form a comprehensive and integrated approach to incorporating ESs into EIA.     

A multicriteria analytical framework for sustainability evaluation under methodological uncertainties

Multicriteria analysis is widely used to perform sustainability evaluation for purposes such as decision making or progress assessment following a chosen sustainable development concept. The stages of multicriteria analysis include indicator selection, data characterization, normalization, weighting, and aggregation. However, due to the various framings of sustainability, non-equivalent approaches could be adopted at each stage, introducing methodological uncertainty. Consequently, depending on the methods chosen, divergent and conflicting conclusions or decisions may result. This paper addresses the issue of methodological uncertainties in sustainability evaluation by analyzing how methodological uncertainties arise during multicriteria analysis, then proposing an analytical framework that employs uncertainty and sensitivity analyses to quantify and manage the effects of methodological uncertainties. The consideration of methodological uncertainties in the structure of multicriteria sustainability evaluation shifts the analysis from deterministic to probabilistic, which is advantageous to arrive at robust and homogenized sustainability conclusions or decisions.     

A review of uncertainty research in impact assessment

This paper examines uncertainty research in Impact Assessment (IA) and the focus of attention of the IA scholarly literature. We do so by first exploring ‘outside’ the IA literature, identifying three main themes of uncertainty research, and then apply these themes to examine the focus of scholarly research on uncertainty ‘inside’ IA. Based on a search of the database Scopus, we identified 134 journal papers published between 1970 and 2013 that address uncertainty in IA, 75% of which were published since 2005. We found that 90% of IA research addressing uncertainty focused on uncertainty in the practice of IA, including uncertainty in impact predictions, models and managing environmental impacts. Notwithstanding early guidance on uncertainty treatment in IA from the 1980s, we found no common, underlying conceptual framework that was guiding research on uncertainty in IA practice. Considerably less attention, only 9% of papers, focused on uncertainty communication, disclosure and decision-making under uncertain conditions, the majority of which focused on the need to disclose uncertainties as opposed to providing guidance on how to do so and effectively use that information to inform decisions. Finally, research focused on theory building for explaining human behavior with respect to uncertainty avoidance constituted only 1% of the IA published literature. We suggest the need for further conceptual framework development for researchers focused on identifying and addressing uncertainty in IA practice; the need for guidance on how best to communicate uncertainties in practice, versus criticizing practitioners for not doing so; research that explores how best to interpret and use disclosures about uncertainty when making decisions about project approvals, and the implications of doing so; and academic theory building and exploring the utility of existing theories to better understand and explain uncertainty avoidance behavior in IA.     

A Robust Optimization Model for an Invasive Species Management Problem

Invasive species pose a significant threat to global biodiversity. Managing invasive species often involves modeling the species’ spread pattern, estimating control costs and damage costs due to the invasion, designing control efforts, and accounting for uncertainties in model parameters. Dealing with uncertainty is arguably the most important part of the process, since biological, environmental, and economic factors can cause parameter values to vary greatly. Managers need decision tools that are robust to such limited or variable information. Here, we present a robust spatial optimization model to select treatment sites in a way that maximally reduces the size of an invasive population, given a constraint on financial resources. We develop an integer programming model that includes population dynamics and management costs over space and time. The model incorporates uncertainty in the available budget and the invasive spread rate as sets of discrete scenarios to determine a robust, cost-effective management plan in a novel way.     

Interval-Parameter Conditional Value-at-Risk Two-Stage Stochastic Programming Model for Management of End-of-Life Vehicles

The management of end-of-life vehicles conserves natural resources, provides economic benefits, and reduces water, air, and soil pollution. Sound management of end-of-life vehicles is vitally important worldwide thus requiring sophisticated decision-making tools for optimizing its efficiency and reducing system risk. This paper proposes an interval-parameter conditional value-at-risk two-stage stochastic programming model for management of end-of-life vehicles. A case study is conducted in order to demonstrate the usefulness of the developed model. The model is able to provide the trade-offs between the expected profit and system risk. It can effectively control risk at extremely disadvantageous availability levels of end-of-life vehicles. The formulated model can produce optimal solutions under predetermined decision-making risk preferences and confidence levels. It can simultaneously determine the optimal long-term allocation targets of end-of-life vehicles and reusable parts as well as capital investment, production planning, and logistics management decisions within a multi-period planning horizon. The proposed model can efficiently handle uncertainties expressed as interval values and probability distributions. It is able to provide valuable insights into the effects of uncertainties. Compared to the available models, the resulting solutions are far more robust.

     

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.     

Approaches for performing uncertainty analysis in large-scale energy/economic policy models

A number of key policy insights have emerged from the application of large-scale economic/energy models, such as integrated assessment models for climate change. These insights have been particularly powerful in those instances when they are shared by all or most of the existing models. On the other hand, some results and policy recommendations obtained from integrated assessment models vary widely from model to model. This can limit their usability for policy analysis. The differences between model results are mostly due to different underlying assumptions about exogenous processes, about endogenous processes and the dynamics among them, differences in value judgments, and different approaches for simplifying model structure for computational purposes. Uncertainty analyses should be performed for the dual purpose of clarifying the uncertainties inherent in model results and improving decision making under uncertainty. This paper develops a unifying framework for comparing the different types of uncertainty analyses through their objective functions, categorizes types of uncertainty analyses that can be performed on large models, and compares different approaches to uncertainty analysis by explaining underlying assumptions, suitability for different model types, and advantages and disadvantages. The appendix presents a summary of integrated assessment models for climate change that explicitly account for uncertainty.     

Modelling Environmental Risk Using the Upper Partial Moment: a Safety-First Approach

The objective of the paper is to model economic-environmental trade-offs using a safety-first approach. The novelty of the approach is in the use of the empirically estimated flexible production risk and environmental risk functions that create a clear link between the source of the pollution, the production risk due to input use and the environmental outcome. Simulated production data and an environmental indicator were used to estimate a production function and environmental response function for every production year. The response functions were incorporated into an upper partial moment model, which ensures compliance to the user-specified environmental goal by enforcing the environmental constraint. Production decisions are significantly impacted by the presence of an environmental constraint due to the substantial compliance costs. Any changes in the intensive and extensive margin made by risk-averse decision-makers impact the size of the compliance cost faced by producers. The presence of an environmental constraint impact production decisions significantly with substantial compliance costs. While, fertiliser application technique have very little effect on the size of compliance costs. The greatest difference in compliance cost is therefore due to changes in the use of fixed resources.     

An environmental risk assessment framework for enhanced oil recovery solutions from offshore oil and gas industry

Environmental risk assessments are necessary to understand the risk associated with enhanced oil recovery (EOR) solutions and to provide decision support for choosing the best technology and implementing risk-reducing measures. This study presents a review of potentially relevant environmental/ecological risk assessment (ERA) guidelines and, based on this review, proposes an initial suggestion of an ERA framework for understanding the environmental impacts from EOR solutions. We first shortlist the important elements necessary for conducting an ERA of EOR solutions from the selected guidelines. These elements are then used to build the suggested ERA framework for produced water discharges, drilling discharges and emissions to air from EOR solutions, which is the primary objective of the present study. Furthermore, the emphasis is placed on identifying the knowledge gaps that exist for conducting ERA of EOR processes. In order to link the framework with the current best environmental practices, a review of environmental policies applicable to the marine environment around the European Union (EU) was conducted. Finally, some major challenges in the application of ERA methods for novel EOR technologies, i.e. uncertainties in the ERA due to lack of data and aggregation of risk from different environmental impacts, are discussed in detail. The frameworks suggested in this study should be possible to use by relevant stakeholders to assess environmental risk from enhanced oil recovery solutions.     

Partial information and complex development decisions: Illustrations from infrastructure projects

This study assesses the theoretical and practical limitations of evaluations of infrastructure projects. Existing tools used in such evaluations typically exhibit two main limitations. Firstly, they tend to accommodate only two dimensions at once and rely on extensive use of averages for additional dimensions. Secondly, they mostly produce a narrow (project exclusive) or information that is too general, thereby precluding both broader and more detailed interpretations at once.Development decisions so informed are consequently based on partial information. Too often this has produced unsatisfactory outcomes for some stakeholders along with increased risks and heightened uncertainties. This is particularly the case for infrastructure projects.This paper proposes an alternative approach to development decision-making called ‘complex stakeholder perception mapping’ (CSPM). CSPM's evaluations inclusively involve a spectrum of insights and perception maps which offer flexibility, combined with complex perceptions of multiple stakeholders on multiple aspects of development.Applicability of CSPM is demonstrated using four perceptual dimensions in infrastructure development evaluation. Different perception combinations and individual group perceptions generate patterns which indicate aspects and groups that need attention. This approach generates rigorous interpretations of information at both the micro and macro level of the project environment, thereby supporting well-informed strategic decision making to advance project outcomes.     

A Bayesian approach for incorporating uncertainty and data worth in environmental projects

A data worth model is presented for the analysis of alternative sampling schemes in a special project where decisions have to be made under uncertainty. This model is part of a comprehensive risk analysis algorthm with the acronym BUDA. The statistical framework in BUDA is Bayesian in nature and incorporates both parameter uncertainty and natural variability. In BUDA a project iterates among the analyst, the decision maker, and the field work. As part of the analysis, a data worth model calculates the value of a data campaign before the actual field work, thereby allowing the identification of an optimum data collection scheme. A goal function which depicts the objectives of a project is used to discriminate among different alternatives. A Latin hypercube sampling scheme is used to propagate parameter uncertainties to the goal function. In our example the uncertain parameters are the parameters which describe the geostatistical properties of saturated hydraulic conductivity in a Molasse environment. Our results indicated that failing to account for parameter uncertainty produces unrealistically optimistic results, while ignoring the spatial structure can lead to an inefficient use of the existing data.     

Deliberative assessment in complex socioecological systems: recommendations for environmental assessment in drylands

Desertification is a complex process, characterised not only by a damaged ecology but also by conflict over access to scarce resources and trade-offs between the needs of multiple stakeholders at multiple scales. As such, orthodox approaches to environmental assessment in drylands, which rely solely on ecological expertise, are gradually losing legitimacy and greater attention is being given to integrated and participatory assessment approaches, which draw on multiple sources of knowledge in order to accurately describe complex socioecological processes. Moreover, there is growing recognition that successful management of desertification requires a strategy that can accommodate the multiple and often competing needs of contemporary and future stakeholders. In light of these conceptual advances, this paper highlights seven key criteria that dryland environmental assessments must meet: (1) accurately understand complex socioecological system processes, (2) focus on slow variables, (3) integrate multiple scales of analysis, (4) integrate multiple stakeholder perspectives and values, (5) ensure that future generations are fairly represented, (6) ensure that less powerful stakeholders are fairly represented and (7) integrate local and scientific knowledge. The virtues and challenges of deliberative environmental assessments, a novel subset of participatory environmental assessment approaches which places emphasis on social learning, argumentation and critical reflection, are considered in relation to each of these requirements. We argue that deliberative approaches have the potential to achieve accurate, progressive and integrated assessment of dryland environments.     

Public perceptions and scientific uncertainty: The management of risky decisions

As part of an overall program of managing uncertainty in environmental decision making, the Ministry of the Environment of The Netherlands commissioned Environmental Resources Limited (ERL), based in London, to undertake a detailed study of highly publicized but scientifically controversial decisions. The objective was to determine how such decisions may be better managed. Through the examination of 18 selected cases. ERL investigators developed a clear profile of what they termed risky decisions, and identified determining their acceptability as government policies. Combining lessons from the case studies with a detailed survey of management approaches and decision-making techniques, ERL devised a strategy for handling risky decisions more effectively. The Dutch government is now planning a series of risky decision workshops for senior officials, scientists, and policy advisors.     

An inquiry into the potential of scenario analysis for dealing with uncertainty in strategic environmental assessment in China

Strategic environmental assessment (SEA) inherently needs to address greater levels of uncertainty in the formulation and implementation processes of strategic decisions, compared with project environmental impact assessment. The range of uncertainties includes internal and external factors of the complex system that is concerned in the strategy. Scenario analysis is increasingly being used to cope with uncertainty in SEA. Following a brief introduction of scenarios and scenario analysis, this paper examines the rationale for scenario analysis in SEA in the context of China. The state of the art associated with scenario analysis applied to SEA in China was reviewed through four SEA case analyses. Lessons learned from these cases indicated the word “scenario” appears to be abused and the scenario-based methods appear to be misused due to the lack of understanding of an uncertain future and scenario analysis. However, good experiences were also drawn on, regarding how to integrate scenario analysis into the SEA process in China, how to cope with driving forces including uncertainties, how to combine qualitative scenario storylines with quantitative impact predictions, and how to conduct assessments and propose recommendations based on scenarios. Additionally, the ways to improve the application of this tool in SEA were suggested. We concluded by calling for further methodological research on this issue and more practices.     

Fragmentary provisions for uncertainty disclosure and consideration in EA legislation,regulations and guidelines and the need for improvement

The consideration and disclosure of uncertainties is fundamental to a credible EA process, but little is known about the nature and type of requirements and guidance available to proponents, practitioners and decision makers about how to deal with uncertainties. This paper examines the provisions for considering and disclosing uncertainties in EA. Methods are based on a comparative review of uncertainty provisions in EA legislation, regulations and guidance documents under Canadian federal, provincial and territorial jurisdictions. Results show 10 types of provisions applied at different stages of the EA process with considerable jurisdictional variability and incoherence. The most common provision was that decision makers can request that project proponents provide more information, followed by the preparation of contingency plans, and that practitioners document their assumptions about data reliability. Most of these provisions were found in guidelines, versus legislation or regulations; and most addressed impact management, with very few provisions for addressing uncertainty during EA review and decision making. Current practices of uncertainty (non)disclosure and (non)consideration in EA can be explained, in part, by the superficial nature and limited extent of the requirements and guidance made available to EA practitioners, proponents, and decision makers. The existing requirements placed on proponents and practitioners to disclose and consider uncertainties are necessary, but insufficient. Stronger, more coherent and transparent requirements for those tasked with EA review and decision making to consider uncertainty information when disclosed, and the development of practical guidance on how to do so, are needed.     

A game theory perspective on environmental assessment: What games are played and what does this tell us about decision making rationality and legitimacy?

Game theory provides a useful theoretical framework to examine the decision process operating in the context of environmental assessment, and to examine the rationality and legitimacy of decision-making subject to Environmental Assessment (EA). The research uses a case study of the Environmental Impact Assessment and Sustainability Appraisal processes undertaken in England. To these are applied an analytical framework, based on the concept of decision windows to identify the decisions to be assessed. The conditions for legitimacy are defined, based on game theory, in relation to the timing of decision information, the behaviour type (competitive, reciprocal, equity) exhibited by the decision maker, and the level of public engagement; as, together, these control the type of rationality which can be brought to bear on the decision. Instrumental rationality is based on self-interest of individuals, whereas deliberative rationality seeks broader consensus and is more likely to underpin legitimate decisions. The results indicate that the Sustainability Appraisal process, conducted at plan level, is better than EIA, conducted at project level, but still fails to provide conditions that facilitate legitimacy. Game theory also suggests that Sustainability Appraisal is likely to deliver ‘least worst’ outcomes rather than best outcomes when the goals of the assessment process are considered; this may explain the propensity of such ‘least worst’ decisions in practise. On the basis of what can be learned from applying this game theory perspective, it is suggested that environmental assessment processes need to be redesigned and better integrated into decision making in order to guarantee the legitimacy of the decisions made.