Environmental Modeling & Assessment 

About: Environmental Modeling & Assessment is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Climate change & Population. It has an ISSN identifier of 1420-2026. Over the lifetime, 1124 publications have been published receiving 23199 citations. The journal is also known as: Environmental modeling and assessment.

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

Abstract: 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

On the optimal control of carbon dioxide emissions: an application of FUND

Abstract: This paper presents the Climate Framework for Uncertainty, Negotiation and Distribution (FUND), an integrated assessment model of climate change, and discusses selected results. FUND is a nine-region model of the world economy and its interactions with climate, running in time steps of one year from 1990 to 2200. The model consists of scenarios for economy and population, which are perturbed by climate change and greenhouse gas emission reduction policy. Each region optimizes its net present welfare. Policy variables are energy and carbon efficiency improvement, and sequestering carbon dioxide in forests. It is found that reducing conventional air pollution is a major reason to abate carbon dioxide emissions. Climate change is an additional reason to abate emissions. Reducing and changing energy use is preferred as an option over sequestering carbon. Under non-cooperation, free riding as well as assurance behaviour is observed in the model. The scope for joint implementation is limited. Under cooperation, optimal emission abatement is (slightly) higher than under non-cooperation, but the global coalition is not self-enforcing while side payments are insufficient. Optimal emission control under non-cooperation is less than currently discussed under the Framework Convention on Climate Change, but higher than observed in practice.

Methods for IA: The challenges and opportunities ahead

Abstract: There is increasing recognition and credibility for the rapidly evolving field of Integrated Assessment (IA). Within the setting of the political arena it is accepted that IA can be supportive in the long-term policy planning process, while in the scientific arena more and more scientists do realise the complementary value of IA research. One of the best indicators for this increased recognition is the establishment of the European Forum on Integrated Environmental Assessment (EFIEA) by the European Commission DGXII. In spite of this growing appreciation for IA, the methodological basis of IA is still narrow, and lags behind the high expectations from the outside world. Broadening the basis of the methodologies underlying IA should therefore be one of the top priorities of the IA community. This paper deals with some ideas which could form a basis for an IA research agenda for the next 5–10 years. One of the problems of IA is still the many definitions and interpretations that circulate (Weyant et al. [118], Rotmans and Dowlatabadi [100], Parson [85–87], Ravetz [91], Jaeger et al. [49]). Notwithstanding this diversity, these definitions have two elements in common, i.e., interdisciplinarity and decision support. These two common elements make Integrated Assessment difficult to plan and even harder to conduct. Instead of coming up with another definition of IA, we simply focus on the above commonalities as points of departure for exploring challenges for the future. Thus irrespective of whatever definition is taken, IA can be described as

Mathematical methods for spatially cohesive reserve design

Abstract: The problem of designing spatially cohesive nature reserve systems that meet biodiversity objectives is formulated as a nonlinear integer programming problem. The multiobjective function minimises a combination of boundary length, area and failed representation of the biological attributes we are trying to conserve. The task is to reserve a subset of sites that best meet this objective. We use data on the distribution of habitats in the Northern Territory, Australia, to show how simulated annealing and a greedy heuristic algorithm can be used to generate good solutions to such large reserve design problems, and to compare the effectiveness of these methods.

Spatial attributes and reserve design models: A review

Abstract: A variety of decision models have been formulated for the optimal selection of nature reserve sites to represent a diversity of species or other conservation features. Unfortunately, many of these models tend to select scattered sites and do not take into account important spatial attributes such as reserve shape and connectivity. These attributes are likely to affect not only the persistence of species but also the general ecological functioning of reserves and the ability to effectively manage them. In response, researchers have begun formulating reserve design models that improve spatial coherence by controlling spatial attributes. We review the spatial attributes that are thought to be important in reserve design and also review reserve design models that incorporate one or more of these attributes. Spatial modeling issues, computational issues, and the trade-offs among competing optimization objectives are discussed. Directions for future research are identified. Ultimately, an argument is made for the development of models that capture the dynamic interdependencies among sites and species populations and thus incorporate the reasons why spatial attributes are important.