Integrative Assessment In Environmental Studies Research Paper

(a) What is Integrated Assessment?

(b) Why is Integrated Assessment necessary?

(c) How can Integrated Assessment be applied?

Finally, some recommendations are made for the future development of the emerging research area of Integrated Assessment.

1. What Is Integrated Assessment?

The term Integrated Assessment (IA) was used for the first time in the early 1970s, in particular in the USA and Europe. In Europe, IA has its roots in population–environment research, developing into integrated environmental research, often practiced by doing integrated environmental modeling. During the 1980s and 1990s, many integrated environmental models were developed (Hordijk 1991, Rotmans 1990, Rotmans and de Vries 1997). In the USA, IA was rooted in economic cost–benefit analyses of environmental problems. The impact of supersonic transport on stratospheric ozone and ground level ultraviolet levels increased the awareness that human activities could disrupt the global environment.

Subsequently, there has been increasing recognition and credibility for the rapidly evolving field of IA, illustrated, for example, by the establishment of the European Forum for Integrated Environmental Assessment (EFIEA) (Tol and Vellinga 1998) under the auspices of the European Commission. Multiple definitions of IA circulate (Weyant et al. 1996, Rotmans and Dowlatabadi 1998, Parson 1996), but these definitions have three elements in common: multior interdisciplinarity, structuring of knowledge, and decision-support. Using these commonalities, the following definition can be given:

IA is a multior interdisciplinary process of structuring knowledge elements from various scientific disciplines in such a manner that all relevant aspects of a social problem are considered in their mutual coherence for the benefit of decision-making.

Metaphorically IA can be viewed as making a puzzle. Everybody sees the separate pieces of the puzzle, but the real art is fitting them together in such a manner that a logical whole arises, which is more than the sum of its parts. The essence of IA is, however, that there is no optimal solution of fitting the scientific pieces of the puzzle together. Depending on the underlying perspective, a multitude of possible solutions exists, and there is no standard-recipe available on how to arrive at this set of possible solutions. This issue explains the heuristic character of IA, being a quest in triplicate: for causalities, coherence, and commonalities.

Although active participation is no prerequisite, more and more people in the IA community are convinced of the vital importance of involvement of stakeholders in the IA process. It is generally acknowledged that involvement of nonscientific and practical knowledge and expertise, valuation and preferences in the form of direct involvement of actors will enrich the process of IA.

This process can be represented in a simplified manner through a sort of demand supply mechanism, as depicted in Fig. 1. From society’s angle there is a need for information about complex social issues, the demand side. Science delivers facts, uncertainties and hypotheses with regard to these complex issues, the supply side. Demand and supply mechanisms come together in an active dialogue, which ultimately leads to integrated visions. In consultation with decision-makers these visions can be translated into decision-strategies. However, the supply side could also be delivered by social actors in the form of nonscientific expertise and judgment, whereas the demand side could be provided by scientists, for instance through prioritizing of research themes.

One of the more successful integrated assessments achieved to date is that of global climate change. Within the framework of the Intergovernmental Panel on Climate Change (IPCC, 1996), the integrated approach has been successful in both scientific and policy terms. In science, it has proven useful in addressing earth system functions and impacts, for example, the imbalance in the global carbon budget, integrated analysis of land use and land cover changes, and the climate impact on sulphate aerosols (Rotmans and Dowlatabadi 1998). In policy terms, the attempt was to underpin the discussion about the question: when do we have to undertake action and with what pace do we have to take what kind of measures?

1.1 Climate Change

An IA of climate change takes into account both the socioeconomic and biogeochemical aspects of the climate problem. The most important cause of global climate change is the large-scale increase in the demand for energy and land food. The driving forces behind this are economic growth, population growth and changes in life-style. This all leads to changes in the composition of the atmosphere and terrestrial biosphere, resulting in changes in global and regional climate. Major social impacts are the effects on the economic sectors such as agriculture, water management, coastal defense, health care, and on natural ecosystems in terms of land degradation, erosion, decreasing vitality, and change in spatial composition.

An IA model of climate change aims to simulate the intricate dynamics of the human-climate system, in which numerous interactions and feedbacks occur, as shown in Fig. 2. Also, such a model allows for rapid calculation and evaluation of various preventive (e.g., an energy tax regime) or adaptive (e.g., infrastructural adaptations) measures.

2. Why IA?

The world around us is becoming increasingly integrated in its commercial and financial activities, while modern means of travel and communication are breaking down many of the traditional physical and cultural barriers between states. The complex dynamics of strongly interacting short and long-term processes on various scale levels force us to think and act in a more integrative manner. This increasing complexity means that major social problems can no longer be addressed from one perspective, one country, one state, one culture, one ministry, or one scientific discipline, requiring a new conception of planning. IA tries to provide the new tools for this new way of planning, by getting more grip on the increasing complexity of society: considering problems in the light of other problems, coupling different scale levels in time and space, interrelating different themes, addressing cross-sectoral trends and developments, among others. This integration follows from the growing understanding that the various pieces of the puzzle can no longer be examined in isolation.

Opponents may argue that IA is premature, and can lead to inappropriate confidence in questionable or misleading findings. However, the complexity of the issues demands an integrated approach to ensure that key interactions, feedbacks and effects are not inadvertently omitted from the analysis, leading to surprise consequences. Another argument against IA may be that we have insufficient knowledge of the interactions and level of coherence among the social-cultural, economic and environmental subsystems. This line of reasoning fails because the uncertainties within the subsystems are also large, and our complex society asks for studies that focus on the interface between the subsystems. Current knowledge indicates that sharp fluctuations in the dynamics of our society can be attributed to changes occurring at the cutting edge of the social subsystems, rather than within the subsystems themselves.

So overall, performing IA has a number of advantages. In general terms, IA can help to:

(a) put a complex problem in the broader context of other problems, by exploring the interrelations of the specific problem with other issues;

(b) assess potential response options to complex problems, be it in the form of cost–benefit and cost-effectiveness or some other analysis;

(c) identify, illuminate, and clarify the different types and sources of uncertainties in the cause–effect chain(s) of a complex problem;

(d) translate the concept of uncertainty into the concept of risk analysis, to assist in decision making under uncertainty; and

(e) set priorities for research topics, also by identifying and prioritizing decision-relevant gaps in knowledge.

3. What Is The Problem?

IA deals with causally linking those social-cultural, economic and ecological processes that are relevant in the light of the problem under concern. This means integration of physical, monetary, information and policy processes. These processes are unlike, with regard to time scale, spatial scale and dynamics, e.g., many economic processes operate at a relatively short time scale, determined by the financial capital invested. Demographic processes play at a longer term of at least one generation, whereas the effect of many ecological processes becomes visible at a time scale of several generations, if not centuries. IA, therefore, seeks to couple: (a) long-term processes with shortterm processes; (b) higher scale processes with lower scale processes; and (c) equilibrium processes with nonequilibrium processes.

The process of up and down-scaling between different levels in time, space and complexity is called aggregation. The level of aggregation of an IA refers to the spatial and temporal resolution and the level of complexity used in the assessment. In general, the optimal spatial and temporal scale at which an IA operates strongly depends on the nature and scope of the problem.

The real problem of IA is that there is no unifying theory that indicates unequivocally how this analytical process of aggregation should happen. Also, the price of integration is simplification, in order to integrate disciplinary pieces into an overall framework. As a consequence, a manifold of integration routes could lead to an integrated knowledge framework. This explains why IA has been a practical, intuitive and heuristic activity so far. It also underlines the need for procedures and guidelines for testing the quality of the integration process.

4. Methods For IA

The methodological approaches used at this time to do IA are relatively immature. In general, two types of methods can be distinguished: analytical methods and participatory methods. While analytical methods are often rooted in natural sciences, participatory methods stem from social sciences. The group of analytical methods is reasonably well defined and basically includes model analysis, scenario analysis, and risk analysis. Their commonality is that they provide analytical frameworks for representing and structuring scientific knowledge in an integrated manner. A plethora of participatory methods, however, exist, varying from expert panels and delphi methods, to gaming, policy exercises and focus groups. They have in common the aim to involve nonscientists as stakeholders in the process, where the assessment effort is driven by stakeholder–scientist interactions. Here, we will only briefly discuss IA models and IA participatory approaches. For a comprehensive and elaborative description of IA methods, refer to Rotmans (1998).

4.1 IA Models

IA models are computer simulation frameworks that try to describe quantitatively, as much as possible, the cause and effect relationships of a specific issue and of the inter-linkages and interactions among different issues. Current projects in IA modeling build on a tradition started in the early 1970s, by the Club of Rome (Meadows et al. 1972).

The next generation of IA models explicitly addressed environmental issues, such as acidification (Hordijk 1991) and climate change (Rotmans 1990, Nordhaus 1992). Recent overviews of IA modeling activities in the field of climate change can be found in Weyant et al. (1996) and Rotmans and Dowlatabadi (1998). The latter distinguish between macroeconomic-oriented models, which represent relatively simple, parameterized decision-analytic formulations of complex problems, and biosphere-oriented models, which represent a more comprehensive, process oriented description of a complex problem. Most macroeconomic-oriented models are neoclassical models based on an equilibrium framework, using traditional economic concepts regarding optimization and capital accumulation, largely ignoring environmental dynamics.

Biosphere-oriented models, however, focus on a systems-based description of the geophysical and biogeochemical processes and feedbacks, but do not adequately represent the socioeconomic system. The Dynamic Integrated Climate Economy (DICE) model is a well-known exponent of the macroeconomic oriented school, whereas the Integrated Model to Assess the Greenhouse Effect (IMAGE) model (Rotmans 1990, Alcamo 1994) is representative for the biosphere-oriented school. Meanwhile, some attempts are underway to combine the best of both worlds, yielding a hybrid of the two categories above. Examples of such hybrid IA models are ICAM (Dowlatabadi and Morgan 1993), Global Change Assessment Model (GCAM) (Edmonds et al. 1994), and TARGETS (Rotmans and de Vries 1997).

IA models have the advantage that they are flexible and rapid simulation tools, which can easily explore interactions, feedback mechanisms, and uncertainties. Above all, they are tools to communicate complex scientific issues to decision makers, disciplinary scientists, stakeholders, and the general public.

4.2 Participatory Methods For IA

‘Participatory methods’ is an umbrella term describing approaches for assessment in which nonscientists, such as policy people, stakeholders or even lay people, play an active role. Participatory methods for IA differ with regard to the profile of the participants, the goal of participation and the degree of participation. Three classes of methods exist: dialogue-methods, policy exercises, and mutual learning methods. The dialoguemethod is applied in cases where the intended users are considered as a source of information necessary for the analysts to perform the assessment. Within this class, we can discriminate between continuous dialogues, and cases in which the dialogue takes place in a specific phase of the assessment. In the first case, the role of the intended users can be described as that of co-designer. In case the dialogue takes place in the design phase, the role of the intended users is to contribute to the design principles by sketching their wants and needs. This type of dialogue can be described as a user-platform.

Policy exercises build upon the tradition of simulation games. A policy exercise can be described as a flexibly structured process designed as an interface between scientists and policy makers, in which a complex system is represented by a simpler one with relevant behavioral similarity, and from which decision making is part through human participants (Parson and Fisher-Vanden 1997). In general, a game is set up that represents a negotiation process in which the different teams are responsible for a certain country or region. Policy exercises quite often make use of computer support. A computer model can be used as a consulting device or as tool to convert the negotiated agreements into a new ‘state of the world.’

The principle behind mutual learning approaches is that participation of stakeholders and citizens enriches the assessment by a multiplicity of perspectives, skills, and competence. The participants are considered as co-producers of knowledge. Two forms of mutual learning can be distingished: the focus group approach, in which scientists play the role of facilitator and observer, and the interactive approach, in which scientists are actively involved as participants. In the first case, special groups are composed typically consisting of citizens, policy makers or other stakeholders who are provided with scientific input. The assessments of these groups are then used in a broader IA process. The ULYSSES project involves IA focus groups with lay people (Kasemir et al. 1997).

A major weakness pertaining to participatory methods is that the use of it in IA is in its infancy, with the result that there are no (as yet) established procedures and work-packages for setting up participatory processes.

5. Quo Vadis IA?

In order to meet the high expectations that many people have of IA , a number of steps need to be taken. First, the existing tools and instruments for IA should be improved. Much of the criticism on IA has to do with the opaque structure of the IA methods used, and the fact that these methods are often technocratic abstractions of reality.

Second, new IA research methods have to be developed, which enable qualitative knowledge and quantitative data to be blended; but also methods that could handle different sorts and types of uncertainty; and finally methods that could operate at different aggregation levels in time, space and complexity. Regarding IA models, much attention has to be paid to actor-oriented IA models that describe the behavior of human or institutional actors in time and space by means of a set of dynamic behavior rules. Also, it is important to experiment as much as possible with different participatory methods in an IA context. In this way guidelines and procedures could be developed for the development and application of IA participatory methods.

Third, demand and supply of IA studies should be matched in a better way, as also propagated by Funtowicz and Ravetz (1994). At present, the majority of IA research is supply driven. In order to increase the number of demand-driven IA studies, the best of both analytical and participatory worlds should be combined. In this way the complementarity of both types of IA method could be used. The challenge then is to let nonscientists or stakeholders co-develop analytical IA methods.

Fourth, there is a need for IA-quality criteria to test the quality of IA research (Morgan and Dowlatabadi 1996). We distinguish here three types of quality criteria: analytical criteria, referring to calibration and validation of the used IA research methods, methodological criteria, which concern the quality of the used IA research method, and usability criteria, which refer to the extent to which the IA study is useful for policy. However, because of the rather heuristic and explorative character of many IA studies, these quality criteria should not be measured too stringently.

Finally, the time seems ripe for entering new problem areas from the perspective of IA. So far, it has been applied to only a few environmental problems, of which acidification and climate change are the most important. A real challenge for IA is now to broaden its field of application, such as transport and infrastructure issues, health care problems, technological development, the energy issue, and city planning (Rotmans 1998).

Bibliography:

1. Alcamo J 1994 IMAGE 2.0: Integrated modelling of global climate change. Water, Air, and Soil Pollution 76: 1–2
2. Dowlatabadi H, Morgan M G 1993 Integrated assessment of climate change. Science 259: 1813–4
3. Edmonds J, Wise M, et al. 1994 Advanced Energy Technologies and Climate Change: An Analysis Using the Global Change Assessment Model (GCAM). Air and Waste Management Meeting, Pittsburgh, PA
4. Funtowicz S O, Ravetz J R 1994 The worth of a songbird: Ecological economics as a post-normal science. Ecological Economics 10: 197–207
5. Hordijk L 1991 An Integrated Assessment Model for Acidification in Europe. Free University of Amsterdam, The Netherlands
6. IPCC 1996 Climate Change 1995: The Science of Climate Change. Cambridge University Press, Cambridge, UK
7. Kasemir B, Behringer J, et al. 1997 Focus Groups in Integrated Assessment: The ULYSSES Pilot Experience. Darmstadt University of Technology, Darmstadt, Germany
8. Meadows D H, Meadows D L et al. 1972 The Limits to Growth. Universe Books, New York
9. Morgan M G, Dowlatabadi H 1996 Learning from integrated assessment of climate change. Climatic Change 34: 337–68
10. Nordhaus W D 1992 The DICE model: Background and Structure of a Dynamic Integrated Climate Economy. Yale University, New Haven, CT
11. Parson E A, Fisher-Vanden K 1997 Integrated assessment of global climate change. Annual Review of Energy and the Environment 22
12. Rotmans J 1990 IMAGE: An Integrated Model to Assess the Greenhouse Eff Kluwer Academic, Dordrecht, The Netherlands
13. Rotmans J 1998 Methods for IA: The challenges and opportunities ahead. In: Rotmans J, Vellinga P (eds.) Environmental Modelling and Assessment, Special issue, pp. 155–79
14. Rotmans J, de Vries H J M (eds.) 1997 Perspectives on Global Change: The TARGETS Approach. Cambridge University Press, Cambridge, UK
15. Rotmans J, Dowlatabadi H 1998 Integrated assessment of climate change: Evaluation of methods and strategies. In: Rayner S, Malone E (eds.) Human Choice and Climate Change: An International Social Science Assessment. Battelle Press, Washington, DC
16. Tol R S J, Vellinga P 1998 The European forum on integrated environmental assessment. In Rotmans J, Vellinga P (eds.) Environmental Modelling and Assessment, Special issue, pp. 181–91
17. Weyant J, Davidson O et al. 1996 Integrated assessment of climate change: An overview and comparison of approaches and results. In: Bruce J P, Lee H, Haites E F (eds.) Economic and Social Dimensions of Climate Change. Cambridge University Press, IPCC, Cambridge, UK