Integrated Assessment: an emerging methodology for complex issues

Over recent years Integrated Assessment (IA) has been presented as a new and innovative approach to modern decision making. In this paper we explore some of the key features that we consider characterise IA; we present IA as a framework that allows a flexible approach to uncertainty in decision making and to which a broad palette of expertise can contribute. The example of the development of an international agreement to reduce acidic deposition is used to illustrate the current practical status of an IA. The scientific process leading to this agreement has previously been described as IA but we argue that, although it marks a change from traditional approaches, further progress is still required to satisfy the requirements for a successful and durable IA.     

Exploring the use of computer models in participatory integrated assessment – experiences and recommendations for further steps

Integrated assessment (IA) can be defined as a structured process of dealing with complex issues, using knowledge from various scientific disciplines and/or stakeholders, such that integrated insights are made available to decision makers (J. Rotmans, Enviromental Modelling and Assessment 3 (1998) 155). There is a growing recognition that the participation of stakeholders is a vital element of IA. However, only little is known about methodological requirements for such participatory IA and possible insights to be gained from these approaches. This paper summarizes some of the experiences gathered in the ULYSSES project, which aims at developing procedures that are able to bridge the gap between environmental science and democratic policy making for the issue of climate change. The discussion is based on a total of 52 IA focus groups with citizens, run in six European and one US city. In these groups, different computer models were used, ranging from complex and dynamic global models to simple accounting tools. The analysis in this paper focuses on the role of the computer models. The findings suggest that the computer models were successful at conveying to participants the temporal and spatial scale of climate change, the complexity of the system and the uncertainties in our understanding of it. However, most participants felt that the computer models were less instrumental for the exploration of policy options. Furthermore, both research teams and participants agreed that despite considerable efforts, most models were not sufficiently user-friendly and transparent for being accessed in an IA focus group. With that background, some methodological conclusions are drawn about the inclusion of the computer models in the deliberation process. Furthermore, some suggestions are made about how given models should be adapted and new ones developed in order to be helpful for participatory IA. This revised version was published online in July 2006 with corrections to the Cover Date.     

Risk management and integrated assessment

Integrated Assessment (IA) is the pursuit of a research program generated by the limitations of traditional forms of risk management. This claim can be justified by the following argument. Over the last decades, analysts and practitioners have brought to maturity a large array of tools for risk management. Most of them rely on combining judgments of utility with judgments of probability. This is the approach of the Rational Actor Paradigm (RAP). With many environmental problems, however, RAP‐based tools have run into considerable practical and theoretical difficulties. In response to these difficulties, a series of alternative approaches to practical risk management and to the theoretical understanding of risk have been elaborated. They try to embed the rational choices of individual actors studied by RAP into a broader framework of social rationality. This task can be approached by distinguishing situations where an actor holds unambiguous judgments of preference and probability from situations characterized by ambivalent judgments. RAP can handle the former, but not the latter. Problems whose management requires a combination of widely differing scientific disciplines are especially likely to involve ambivalent judgments of probability. The study of such problems constitutes the research program of integrated assessment. It involves three main tasks: developing IA models which can represent ambivalent expectations and evaluations, developing IA models which use such representations to study non‐marginal changes of social systems, and developing procedures of participatory IA which enable researchers to engage in an iterative exchange with various stakeholders. This revised version was published online in August 2006 with corrections to the Cover Date.     

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.     

Improving Efficiency Of Uncertainty Analysis In Complex Integrated Assessment Models: The Case Of The Rains Emission Module

Abstact Ever since the Regional Acidification Information and Simulation Model (RAINS) has been constructed, the treatment of uncertainty has remained an issue of major interest. In a recent review of the model performed for the Clean Air for Europe (CAFE) programme of the European Commission, a more systematic and structured uncertainty analysis has been recommended. This paper aims at contributing to the scientific debate how this can be achieved. Because of its complex structure on the one hand and limited research resources (time, computational capacities) on the other hand a full-blown uncertainty analysis in RAINS is hardly feasible. Therefore, all types of uncertainty require more efficient ways for uncertainty analysis. With respect to parameter uncertainty, we propose to focus research efforts for uncertainty analysis on key parameters. Among different approaches to select key parameters that have been discussed in the literature screening methods seem to be particularly appropriate for complex, deterministic Integrated Assessment models such as RAINS. Surprisingly, in Integrated Assessment modelling for air pollution problems of screening design have not been taken up so far. As a case study we consider the emission module of RAINS. We show that its structure allows for a straightforward and effective screening procedure     

THE TIGER IN THE DINING ROOM: DESIGNING AND EVALUATING INTEGRATED ASSESSMENTS OF ATMOSPHERIC CHANGE

Integrated assessments (IAs) and integrated assessment models (IAMs) arerecent responses to the inter-disciplinary challenges provided by complexglobal environmental issues such as atmospheric change. This paper discussesan array of integrated assessments, providing an overview of the role of IAsas bridges or foundations for epistemic communities. Formal as well associal, political, and ethical issues are presented. As well as a definition of anIA and an IAM, different forms and approaches of current or proposed IAsare reviewed. Particular stress is laid on the need to maintain the integrity ofthe diverse components of an IA. Finally, reference is made to the need tounderstand the underlying ethical and normative concerns that have promotedthe current interest in IA.     

growing pains: a conceptual framework for considering integrated assessments

Policy concerns related to the environment have grown in the past decadesfrom relatively local and well-defined problems to increasingly complex andglobal issues. With this has grown the need to develop the capacity tointegrate, reconcile, organize, and communicate knowledge across scientificdisciplines, as well as to make this knowledge available and useful for policy-makers. One response to this has been the growing field ofIntegrated Assessment (IA). Our goal in this paper is to reflect on the purpose and valueof IA in principle. We propose a conceptual framework within whichindividual IA studies, and the practice of IA as a whole, can be placed andevaluated. The framework addresses both the integrative nature of IAs andtheir policy usefulness, including a self-awareness of their role and capabilities. We illustrate several stages in the evolution of integrated assessments: fromlinear to more complex chains of analysis, from non-adaptive to perfectly-adaptive to realistically-adaptive agents, from simplistic to sophisticated to pluralistic consideration of alternative underlying development paths, from strictly quantitative to quantitative and qualitative analyses, from science-driven to policy-driven, and from analyses that dictate to users to those that involvethose users in the actual assessment process. By so doing, we argue for botha richer form and process of IA. Ultimately, we feel that it may also benecessary to reconsider the framing of the questions that IAs have been askedto address, perhaps leading to the consideration of the use of other forms of mandated science.     

Opening new institutional spaces for grappling with uncertainty: A constructivist perspective

In the context of an increasing reliance on predictive computer simulation models to calculate potential project impacts, it has become common practice in impact assessment (IA) to call on proponents to disclose uncertainties in assumptions and conclusions assembled in support of a development project. Understandably, it is assumed that such disclosures lead to greater scrutiny and better policy decisions. This paper questions this assumption. Drawing on constructivist theories of knowledge and an analysis of the role of narratives in managing uncertainty, I argue that the disclosure of uncertainty can obscure as much as it reveals about the impacts of a development project. It is proposed that the opening up of institutional spaces that can facilitate the negotiation and deliberation of foundational assumptions and parameters that feed into predictive models could engender greater legitimacy and credibility for IA outcomes.     

Integrated assessment modeling of global climate change: Transparent rational tool for policy making or opaque screen hiding value‐laden assumptions?

One of the principal tools used in the integrated assessment (IA) of environmental science, technology and policy problems is integrated assessment models (IAMs). These models are often comprised of many sub‐models adopted from a wide range of disciplines. A multi‐disciplinary tool kit is presented, from which three decades of IA of global climatic change issues have tapped. A distinction between multi‐ and inter‐disciplinarity is suggested, hinging on the synergistic value added for the latter. Then, a hierarchy of five generations of IAMs are proposed, roughly paralleling the development of IAMs as they incorporated more components of the coupled physical, biological and social scientific disciplines needed to address a “real world” problem like climatic change impacts and policy responses. The need for validation protocols and exploration of predictability limits is also emphasized. The critical importance of making value‐laden assumptions highly transparent in both natural and social scientific components of IAMs is stressed, and it is suggested that incorporating decision‐makers and other citizens into the early design of IAMs can help with this process. The latter could also help IA modelers to offer a large range of value‐containing options via menu driven designs. Examples of specific topics which are often not well understood by potential users of IAMs are briefly surveyed, and it is argued that if the assumptions and values embedded in such topics are not made explicit to users, then IAMs, rather than helping to provide us with refined insights, could well hide value‐laden assumptions or conditions. In particular, issues of induced technological change, timing of carbon abatement, transients, surprises, adaptation, subjective probability assessment and the use of contemporary spatial variations as a substitute for time evolving changes (what I label “ergodic economics”) are given as examples of problematic issues that IA modelers need to explicitly address and make transparent if IAMs are to enlighten more than they conceal. A checklist of six practices which might help to increase transparency of IAMs is offered in the conclusions. Incorporation of decision‐makers into all stages of development and use of IAMs is re‐emphasized as one safeguard against misunderstanding or misrepresentation of IAM results by lay audiences.     

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Economic development, variation in weather patterns and natural disasters focus attention on the management of water resources. This paper reviews the literature on the development of mathematical programming models for water resource management under uncertainty between 2010 and 2017. A systematic search of the academic literature identified 448 journal articles on water resource management for examination. Bibliometric analysis is employed to investigate the methods that researchers are currently using to address this problem and to identify recent trends in research in the area. The research reveals that stochastic dynamic programming and multistage stochastic programming are the methods most commonly applied. Water resource allocation, climate change, water quality and agricultural irrigation are amongst the most frequently discussed topics in the literature. A more detailed examination of the literature on each of these topics is included. The findings suggest that there is a need for mathematical programming models of large-scale water systems that deal with uncertainty and multiobjectives in an effective and computationally efficient way.     

Regionalization of land use impact models for life cycle assessment: Recommendations for their use on the global scale and their applicability to Brazil

Life Cycle Assessment (LCA) is the main technique for evaluate the environmental impacts of product life cycles. A major challenge in the field of LCA is spatial and temporal differentiation in Life Cycle Impact Assessment (LCIA) methods, especially impacts resulting from land occupation and land transformation. Land use characterization modeling has advanced considerably over the last two decades and many approaches have recently included crucial aspects such as geographic differentiation. Nevertheless, characterization models have so far not been systematically reviewed and evaluated to determine their applicability to South America. Given that Brazil is the largest country in South America, this paper analyzes the main international characterization models currently available in the literature, with a view to recommending regionalized models applicable on a global scale for land use life cycle impact assessments, and discusses their feasibility for regionalized assessment in Brazil. The analytical methodology involves classification based on the following criteria: midpoint/endpoint approach, scope of application, area of data collection, biogeographical differentiation, definition of recovery time and reference situation; followed by an evaluation of thirteen scientific robustness and environmental relevance subcriteria. The results of the scope of application are distributed among 25% of the models developed for the European context, and 50% have a global scope. There is no consensus in the literature about the definition of parameters such biogeographical differentiation and reference situation, and our review indicates that 35% of the models use ecoregion division while 40% use the concept of potential natural vegetation. Four characterization models show high scores in terms of scientific robustness and environmental relevance. These models are recommended for application in land use life cycle impact assessments, and also to serve as references for the development or adaptation of regional methodological procedures for Brazil.     

How Certain Are Salmon Recovery Forecasts? A Watershed-scale Sensitivity Analysis

Complex relationships between landscape and aquatic habitat conditions and salmon (Oncorhynchus spp.) populations make science-based management decisions both difficult and essential. Due to a paucity of empirical data, models characterizing these relationships are often used to forecast future conditions. We evaluated uncertainties in a suite of models that predict possible future habitat conditions and fish responses in the Lewis River Basin, Washington, USA. We evaluated sensitivities of predictions to uncertainty in model parameters. Results were sensitive to 60% of model parameters but substantially so (|partial regression coefficients| >0.5) to <10%. We also estimated accuracy of several predictions using field surveys. Observations mostly fell within predicted ranges for riparian shade and fine-sediment deposition, but large woody debris estimates matched only half the time. We provide suggestions to modelers for improving model accountability, and describe how managers can incorporate prediction uncertainty into decision-making, thereby improving the odds of successful salmon habitat recovery.     

Inclusion of uncertainty in Environmental Impact Assessment in Greenland

Uncertainty is an inherent part of impact assessment (IA), and can vary in type and source. However, according to previous research, uncertainty is rarely explicitly acknowledged and handled in IA, indicating that it is a challenging issue in practice. This paper adds to the current research body a study of EIA in Greenland, which includes a document study of EIA reports as well as white papers and summaries from public hearings. The study findings are in line with previous results, finding a limited explicit acknowledgement of uncertainty, although uncertainty is indicated through implicit language use. The study also finds that various tools are applied, which could be used for handling uncertainty, including sensitivity analysis, monitoring and worst-case estimates. However, often these tools are not used systematically, and it is not transparent whether they are targeted at handling uncertainty. Regarding the examination of materials from hearing processes, there is little evidence that uncertainty is part of the discussions. These results initiate discussions of how choices of whether and how to acknowledge and handle uncertainty are made, and how consciously participants in the process make these choices.     

A modular approach to Integrated Assessment modeling

In this paper, we present a new approach to model coupling that probably forms the methodological basis of a new generation of Integrated Assessment models. This approach respects the knowledge and expertise that is embodied in existing models and encourages their gradual evolution. Modularity is the guiding principle. Our approach is distinguished by the way modules are coupled which is based on an interplay of a job control module, a numerical coupling module, and a couple of stand-alone functional modules. The numerical coupling module - the core component - serves to treat the feedbacks between the functional modules. A first implemented example that couples an economic and a climate module by means of a two-phase meta-optimization is presented here. The algorithm and mathematical structure behind are discussed as well as important features such as convergence behavior and reliability.     

Using Science to Assess Environmental Vulnerabilities

Beginning in 1995, the U.S. Environmental Protection Agency (U.S. EPA), Office of Research and Development has focused much of its ecological research in the Mid-Atlantic as part of the Mid-Atlantic Integrated Assessment (MAIA). The goal of MAIA is to improve the assessability of scientific information in environmental decision-making. Following the Environmental Monitoring and Assessment Program (EMAP) whose goal is to guide monitoring that effectively reflects current ecosystem condition and trends, MAIA's second, current, phase of research under the Regional Vulnerability Assessment (ReVA) program is designed to target risk management activities using available data and models. The papers presented here are from a conference held in May 2003 that presented results of research in this second phase of MAIA. The conference was organized into the following topics: 1. Assessing Current Impacts and Vulnerabilities 2. Forecasting Environmental Condition and Vulnerabilities 3. Developing Management Strategies to Optimize the Future, and 4. Assessing and Responding to Environmental Vulnerability.     

An approach to deal with uncertainty in energy and environmental planning: the MARKAL case

This paper presents a new concept to include uncertainty management in energy and environmental planning models developed in algebraic modeling languages. SETSTOCH is a tool for linking algebraic modeling languages with specialized stochastic programming solvers. Its main role is to retrieve from the modeling language a dynamically ordered core model (baseline scenario) that is sent automatically to the stochastic solver. The case presented herein concerns such a study realized with the IEAMARKAL model used by many research teams around the world.     

Enhancing Indigenous impact assessment: Lessons from Indigenous planning theory

Impact assessment faces a number of key challenges. One area singled out as needing attention is community-based impact assessment. Impact assessment related to Indigenous communities is a particular case in point. We lend an Indigenous voice to this conversation. The emerging field of Indigenous impact assessment is under-developed compared to other forms of IA, particularly in terms of its theoretical foundation. In the article we address that gap by highlighting the current conundrum in Indigenous practice, exemplified in the context of Aotearoa New Zealand. Then we base our deliberation in an Indigenous worldview, applying Indigenous planning theory to impact assessment. Indigenous planning theory has an explicit decolonising agenda that supports communities to reclaim traditional planning approaches. We provide key parameters for establishing a theoretical and practical space for Indigenous IA, and for conceptualising the relationship between Indigenous IA other forms of IA. We argue for a ‘third space’ that facilitates co-existence of different forms of IA activity rather than integration of Indigenous endeavours into broader IA types. In doing so, we demonstrate the potential of Indigenous planning theory to connect international dialogue and case studies on Indigenous impact assessment, and to broaden and deepen IA theory and practice.     

Mathematics for Scenarios of Biodiversity and Ecosystem Services

Balancing biodiversity conservation with food security and the preservation of a broader set of ecosystem services is among the greatest challenges of the century. The creation of the International Panel for Biodiversity and Ecosystem Services (IPBES), at the interface between decision support and scientific knowledge, is clearly in line with this ecological-economic perspective. IPBES particularly puts forward the development of model-based scenarios making sense economically and ecologically and promoting sustainability. The present paper provides generic modeling methods and tools to address such challenges. The paper argues that the framework of controlled dynamic systems under uncertainty together with ecoviability metrics are especially well suited. Such a modeling framework indeed makes it possible to simultaneously account for complex dynamics, indirect or indirect drivers, uncertainties along with multiple sustainability objectives. These general ideas are exemplified with scenarios relating to two applied fields: (i) fisheries and marine biodiversity and (ii) land-use and avifauna.     

Understanding why impact assessment fails; a case study of theory and practice from Wafi-Golpu,Papua New Guinea

From an instrumental or management perspective, impact assessment (IA) is a process of identifying impacts, finding solutions and achieving project approval. A recipient community, however, has a completely different perspective. For them the IA is about living with impacts, individually and collectively, perhaps over generations, and contested processes of self-determination, consultation and exclusion. IA practitioners live in a third space, usually bound to the proponent but also aware of responsibilities to communities and eco-systems. Seeking to better understand how IA is practiced and experienced, we explore the proposed Wafi-Golpu mine, located in the Morobe Province of Papua New Guinea. Determinably focused on local effects we situate the proposed mine within the context of the national mining experience and discuss how IA practices see local and/or Indigenous communities. We find that the Wafi-Golpu IA is blind to local ways of being and seeing the world, with an opaque and arbitrary assessment that reflects its technical and Western basis and bias. We finish with observations about the proposed Wafi-Golpu mine and IA that is relevant to the approval process, as well as making a decolonial, Southern contribution to IA theory and practice, extractive industry regulation and mining-affected communities elsewhere.     

Should different impact assessment instruments be integrated? Evidence from English spatial planning

This paper aims at providing empirical evidence to the question as to whether integration of different instruments is achieving its aim in supporting sustainable decision making, focusing on SEA inclusive sustainability appraisal (SA) and other impact assessments (IAs) currently used in English spatial planning. Usage of IAs in addition to SA is established and an analysis of the integration approach (in terms of process, output, and assessor) as well as its effectiveness is conducted. It is found that while integration enhances effectiveness to some extent, too much integration, especially in terms of the procedural element, appears to diminish the overall effectiveness of each IA in influencing decisions as they become captured by the balancing function of SA.