This chapter describes the way scenario analysis is used as a convenient tool to envision the future of land use and cover. The main messages of large-scale scenarios and their insights into plausible global and continental-scale trends are described in the chapter. Scenario analysis is the procedure by which scenarios are developed, compared, and evaluated. Scenario analysis does not eliminate the uncertainties about the future, but it does provide a means to represent current knowledge in the form of consistent, conditional statements about the future. There is a variety of ways of classifying land scenarios. One way is to distinguish between qualitative and quantitative scenarios. Scenarios with a greater extent of agricultural land result from assumptions about high population growth rates together with low but steady economic growth, which combine to stimulate large increases in food demand.
This chapter discusses the pros and cons of qualitative and quantitative scenarios and the way they fulfill the different requirements of scenario developers and users. It also describes major international scenario exercises in which combined scenarios have been used. This international experience is distilled into a general procedure for combining qualitative and quantitative scenarios called the “story and simulation” (SAS) approach. In the chapter, the successes and drawbacks of this approach are pointed out and some ideas are presented for producing more scientifically sound scenarios. The qualitative storylines provide an understandable vehicle for communicating the messages of the scenarios and can express the more complex dimensions and interconnectedness of environmental problems. The quantitative scenarios provide a consistency check to the different assumptions of the qualitative scenarios and the numerical data often needed in environmental studies. To capitalize on their advantages, qualitative and quantitative scenarios have been combined in recent international scenario exercises.
This chapter describes environmental scenario analysis. As a methodology, it can be summarized as the process of building scenarios, comparing them, and evaluating their expected consequences. Scenario analysis evolved from strategic studies conducted during World War II and became a popular method for studying the future in the 1960s. Environmental scenario analysis has been used to examine many different scales and types of problems ranging from global sustainability to very specific environmental issues, such as changes in emissions, air quality, or land cover in a specific district or region. As compared to large-scale field experiments, scenario analysis has the potential to be more comprehensive, flexible, and perhaps less expensive. Scenarios can depict different future time steps and periods in the evolution of the environment. They can incorporate a virtually unlimited number of environmental compartments and their interactions as well as the complex interactions between society and the environment. Many examples demonstrate that scenario analysis has become a common and useful tool in many future-oriented environmental studies and assessments. But the current practise of environmental scenario analysis has serious deficiencies that are discussed in the chapter.
This chapter discusses the potential of and the requirements for participatory scenario planning as a new part of a modern approach to environmental management. Scenario planning is a method with high potential that has not yet received a significant amount of attention in environmental policy development and resources management. This may be attributed to the fact that the tradition of resources management and of dealing with environmental problems is characterized by a command and control approach. Scenario planning and group model building techniques are quite common in business management where the prime target of management has always been the social system. However, the increasing awareness of the complexity of environmental problems and societal responses has led to increasing support of polycentric governance and has promoted the development of more flexible and adaptive management approaches. The chapter describes the role of social learning processes and the need to develop methods combining formal analysis and subjective perceptions.
Environmental assessments make use of a wide range of different approaches and methods for identifying concerns, analyzing problems, and testing possible response options. This chapter discusses some of the information available about experiences made in the development and analysis of environmental scenarios. From an environmental perspective, the Intergovernmental Panel of Climate Change describes scenarios as "images of the future or alternative futures that are neither projections nor forecasts." While the Millennium Ecosystem Assessment recently defined scenarios as "plausible and often simplified descriptions of the way the future may develop based on a coherent and internally consistent set of assumptions about key driving forces and relationships." Scenario analysis is a broader concept encompassing both scenario development and the analysis of scenarios. Scenario analysis is a procedure covering the development of scenarios, the comparison of scenario results, and the evaluation of their consequences. The goal of environmental scenario analysis is to anticipate future developments of nature and society and to evaluate strategies for responding to these developments. Environmental scenarios can be developed and analyzed for a host of different purposes. These can be clustered into three categories: education and public information, science and research, and decision support and strategic planning.
This chapter discusses a large array of applications of different types of environmental scenarios. It reviews different types of surprises that are considered for inclusion in environmental scenarios. The selection of a particular scenario and surprise depends on many factors: the bounding and complexity of the issue, the objectives of the scenario development and use, the client or intended user of the scenario, and many others. Given the large number of possible combinations, it is not practical or simply impossible to give detailed guidance for choosing the scenario type and the surprises to be included. Therefore, some general guidance about the compatibility of different kinds of surprises into environmental scenarios according to their purpose and their function are presented in the chapter. The chapter also presents some guidance about what could be effective ways to think creatively about the various surprise types in the scenario creation/analysis process
Scenario analysis has become a common and useful tool in many future-oriented environmental studies and assessments. This chapter presents a survey of environmental scenarios and scenario exercises undertaken over the past few decades. It discusses a few ways in which to characterize scenarios, focusing on (1) the driving forces and key uncertainties explored, (2) the nature of the end states—that is, the archetypes they reflect, and (3) the logic behind the scenarios and the scenario exercises, including their purpose, process, and substance. The value of doing so is to see the many ways in which scenario analysis has and can be used. The scenario studies begin with a particular geographic focus. They are integrated in that they address a number of interrelated issues. The chapter focuses on scenario studies for which the environment is either the central focus or one of the primary foci.
Most environmental, ecological, and human processes exhibit characteristic scales, which are also called “grain.” A characteristic scale can be defined as the typical extent or duration over which a process has impacts. If the impact of processes is assessed at scales significantly smaller than their characteristic scale, then there is a very large danger of misinterpreting a system's behavior. One important general scale issue is the “scaling” issue—that is, the way variables and their values are translated from one scale to another. Some variables can be scaled in a very straightforward way. These variables are scale-independent, additive, or linearly scaled. The chapter discusses qualitative–quantitative scenarios that are considered by some to be the most powerful tool for communication between science and policy-making. This type of scenarios combines narration, in the form of storylines, with quantitative interpretations of the storylines that are mainly done by mathematical modeling. In scenario development, the term “large scale” means having a numerically greater extent or duration than something with a “small scale.”
Progress in computer capabilities has substantially influenced research in air quality modelling, a very complex and multidisciplinary area. It covers remote sensing, land use impacts, initial and boundary conditions, data assimilation techniques, chemical schemes, comparison between measured and modelled data, computer efficiency, parallel computing, coupling with meteorology, long-range transport impact on local air pollution, new satellite data assimilation techniques, real-time and forecasting and sensitivity analysis. This contribution focuses on providing a general overview of the state of the art in air quality modelling from the point of view of the “user community,” which includes policy makers, urban planners and environmental managers. It also tries to bring to the discussion key questions, such as where are the greatest uncertainties in emission inventories and meteorological fields, how well do air quality models simulate urban aerosols, and what are the next generation developments in models to answer new scientific and management questions.
Uncertainty pervades all aspects of environmental policy making. Numerous typologies and techniques have been developed to conceptualise, classify, assess (qualitatively and quantitatively), propagate, control, reduce and communicate uncertainty. Assessments made using these tools are a necessary but insufficient condition for reducing uncertainty in environmental decision making. In this chapter we discuss how uncertainty is translated into decisions. Since this entails numerous value judgements and tradeoffs which are sensitive to how policy problems are framed, we argue that perceptions of uncertainty cannot be viewed independently of the (quality of) the policy process that it intends to inform. Thus, uncertainty management should not be limited to the elicitation of preferences and value judgements under uncertainty. Rather, it should be embedded within policy-making processes more generally, including learning, surfacing tacit assumptions and scrutinising beliefs and knowledge.
The main aim of this chapter is to air questions about the future of adaptive management (AM) of natural resources, and more specifically about what approaches may be feasible which have not yet been explored well. The method adopted is to compare the histories, ideas, strengths and limitations of AM, control engineering and Bayesian analysis, which have superficial similarities, significant differences and perhaps lessons for each other. Questions arising in these comparisons include: – What factors limit or prevent application of the principles of feedback control and AM in natural-resource management (NRM)? Do the apparent similarities in problems allow approaches developed in control engineering to be applied in NRM, or are there fundamental differences? Do social-political-economic-biophysical realities prevent a systematic approach to NRM, employing techniques portable from problem to problem? Do short-term accountability, short-term funding and difficulties in measuring outcomes prevent managers from implementing policies embodying the principles of AM, with its focus on monitoring, adaptation to evolving situations and attention to long-term results which may not be clear in the short term? – In what sense is AM adaptive? If the rules which derive management actions from observed behaviour of the system are changed in the light of experience, AM is adaptive according to the usage of the word in control engineering, but not if management actions, but not management rules, are modified as time goes on. What light does the chequered history of adaptive control throw on the prospects for genuinely adaptive AM? – Do the ideas of robust control offer anything for NRM? For instance, does maximising the worst-case benefit, or optimising subject to bounds on some aspects of performance, make sense? – Are there roles in NRM for receding-horizon control based on predictive models, determination of future actions by constrained numerical optimisation of model response and model revision according to observed behaviour, as in robust schemes such as Model Predictive Control? Do multiple and conflicting criteria prevent their use? – How do the probabilistic (Bayesian) and bound-based alternatives for specifying uncertainty lend themselves to realistic use in NRM? – What does the Bayes updating paradigm offer for NRM? In this chapter, Sections 11.1 Adaptive Management and Feedback Control , 11.6 Conclusions Preceding the Workshop review the most relevant aspects of AM, control engineering and Bayesian analysis. These sections are closely based on the position paper for Workshop 1 of the Summit on Environmental Modelling and Software in Burlington in 2006. The appendix summarises the ensuing workshop proceedings, which consisted of three short presentations to give practical substance to the topics, followed by a free-flowing discussion only loosely mediated by the convenors. Because of the informal and at times complicated nature of the discussion, no attempt has been made to attribute opinions to individual participants.
Environmental decision making is complicated by the complexity of natural systems and the generally competing needs of multiple stakeholders. Modelling tools are often used to assist at various stages of the environmental decision-making process. If such models are to provide effective decision support, the uncertainties associated with all aspects of the decision-making process need to be taken into account explicitly, including those associated with data, models and human factors. However, as models become more complex to better represent integrated environmental, social and economic systems, achieving this goal becomes more difficult. Some of the important issues that need to be addressed in relation to the incorporation of uncertainty in environmental decision-making processes include: The development of appropriate risk-based performance criteria that are understood and accepted by a range of scientific disciplines. Risk-based criteria generally relate to the concept of likelihood, the likely magnitude and the likely duration of failure, where failure is defined as the inability of an environmental system to perform its desired function. However, the terminology used in various disciplines differs. Given the increase in the use of integrated models, and the resulting collaboration between people from different disciplines, there is a need to develop a common lexicon, or at least a shared understanding of the meaning of the terminology used. The development of methods for quantifying the uncertainty associated with human input (see Chapter 6). This includes the development of uncertainty analysis methods that are able to cater for subjective and non-quantitative factors, human decision-making processes (which may be influenced by political and other external factors), and uncertainties associated with the model development process itself. The development of approaches and strategies for increasing the computational efficiency of integrated models, optimisation methods, and methods for estimating risk-based performance measures. Examples include the use of efficient Monte Carlo sampling techniques (e.g. Latin hypercube sampling) or first- and second-order approximations (e.g. first- and second-order reliability methods), the use of innovative sensitivity analysis methods to skeletonise complex integrated models and the replacement of computationally expensive process models with data-driven metamodels (e.g. artificial neural networks). The development of integrated software frameworks that enable all sources of uncertainty to be incorporated in the environmental decision-making process (see Chapter 7).
Earth system modelling has taken on increasing importance over the past several years. These models are being used to address an increasing number of environmental and global change problems of societal concern. Perhaps most commonly known is the application to possible greenhouse-gas induced warming. Other compelling problems include the climatic effects of land use changes, aerosols (including sulphate emissions, and smoke from biomass burning), changing trace gas fluxes, interactions and feedbacks with the global carbon cycle and the impacts of changing nutrient fluxes to Earth's ecosystems. While these models have produced many important and exciting results, they are far from perfect, both in terms of the physical processes they attempt to represent and the computational resources required to run them. This chapter focuses on the key challenges that currently confront Earth system modellers in terms of both model development, and how these models can be applied to key outstanding scientific questions of global change.
Current uncertainty in quantifying the global carbon budget remains a major contributing source of uncertainty in reliably projecting future climate change. Furthermore, quantifying the global carbon budget and characterizing uncertainties have emerged as critical to a successful implementation of United National Framework Convention on Climate Change and its Kyoto Protocol. Beyond fundamental quantification, attribution of the processes responsible for the so-called ‘residual terrestrial uptake’ is important to the carbon cycle communities’ ability to simulated the future response of the terrestrial biosphere to climate change and intentional sequestration activities. This paper’s objective is to describe the efforts of the workshop participants and their approaches to model-data fusion enabling continued advances in the solution of quantifying carbon cycling and the terrestrial mechanisms at work.
By copying information from sources and distributing it to new destinations we do not lose information at the sources. Nevertheless, exchange of information is still restricted by patent law, as well as by institutional, cultural and traditional hurdles that create protective barriers hindering the free flow of this valuable commodity. We believe that one of the greatest challenges we face in creating a new research paradigm will be building the community modeling and information sharing culture. How do we get engineers and scientists to put aside their traditional modes of doing business? How do we provide the incentives that will be required to make these changes happen? How do we get our colleagues to see that the benefits of sharing resources far outweigh the costs? We argue that timely sharing of data and information is not only in the best interest of the research community, but that it is also in the best interest of the scientist who is doing the sharing.
Uncertainty pervades all aspects of environmental policy making. Numerous typologies and techniques have been developed to conceptualise, classify, assess (qualitatively and quantitatively), propagate, control, reduce and communicate uncertainty. Such assessments are a necessary but insufficient condition for reducing uncertainty in environmental decision making. In this paper we discuss how uncertainty is translated into decisions. Since this entails numerous value judgements and trade-offs which are sensitive to how policy problems are framed, we argue that perceptions of uncertainty cannot be viewed independently of the (quality of) the policy process that it intends to inform. Thus, uncertainty management should not be limited to the elicitation of preferences and value judgements under uncertainty. Rather, it should be embedded within policy making processes more generally, including learning, surfacing tacit assumptions and scrutinising beliefs and knowledge.
