Predictive modeling of effects under global change

The status of computer simulation models from around the world for evaluating the possible ecological, environmental, and societal consequences of global change is presented in this paper. In addition, a brief synopsis of the state of the science of these impacts is included. Issues considered include future changes in climate and patterns of land use for societal needs. Models discussed relate to vegetation (e.g. crop), soil, bio-geochemistry, water, and wildlife responses to conventional, forecasted changes in temperature and precipitation. Also described are models of these responses, alone and interactively, to increased CO(2), other air pollutants and UV-B radiation, as the state of the science allows. Further, models of land-use change are included. Additionally, global multiple sector models of environment, natural resources, human population dynamics, economics, energy, and political relations are reviewed for integrated impact assessment. To the extent available, information on computer software and hardware requirements is presented for the various models. The paper concludes with comments about using these technologies as they relate to ecological risk assessment for policy decision analysis. Such an effort is hampered by considerable uncertainties with the output of existing models, because of the uncertainties associated with input data and the definitions of their dose-response relationships. The concluding suggestions point the direction for new developments in modeling and analyses that are needed for the 21st century.     

Science with Society in the Anthropocene

Interdisciplinary scientific knowledge is necessary but not sufficient when it comes to addressing sustainable transformations, as science increasingly has to deal with normative and value-related issues. A systems perspective on coupled human–environmental systems (HES) helps to address the inherent complexities. Additionally, a thorough interaction between science and society (i.e., transdisciplinarity = TD) is necessary, as sustainable transitions are sometimes contested and can cause conflicts. In order to navigate complexities regarding the delicate interaction of scientific research with societal decisions these processes must proceed in a structured and functional way. We thus propose HES-based TD processes to provide a basis for reorganizing science in coming decades.     

A Narrative Policy Approach to Environmental Conservation

Due to the urgency and seriousness of the loss of biological diversity, scientists from across a range of disciplines are urged to increase the salience and use of their research by policy-makers. Increased policy nuance is needed to address the science–policy gap and overcome divergent views of separate research and policy worlds, a view still relatively common among conservation scientists. Research impact considerations should recognize that policy uptake is dependent on contextual variables operating in the policy sphere. We provide a novel adaptation of existing policy approaches to evidence impact that accounts for non-evidentiary “societal” influences on decision-making. We highlight recent analytical tools from political science that account for the use of evidence by policy-makers. Using the United Kingdom’s recent embrace of the ecosystem approach to environmental management, we advocate analyzing evidence research impact through a narrative lens that accounts for the credibility, legitimacy, and relevance of science for policy.     

Atmospheric composition change research: Time to go post-normal?

For more than two decades a number of frameworks for scientific knowledge production are being proposed by science and technology researchers. They all advocate an extended involvement of non-specialists, in particular when it comes to knowledge production applicable to practical societal problems. We look to what extent these new frameworks have taken ground within a particular research community: the ACCENT Network of Excellence which coordinates European atmospheric chemistry and physics research applicable to air pollution and climate change. We did so by stimulating a debate through a “blog”, a survey and in-depth interviews with ACCENT scientists about the interaction between science, policy making and civil society, to which a great deal of ACCENT member contributed in writing or verbally. Most of them had interactions with policy makers and/or the general public, and they generally believe that interactions with spheres other than the scientific are needed. While such interactions give personal insight and satisfaction, they seem to have little impact on the goals and the practice of the scientific work itself. Extended frameworks of science production that go beyond the disciplinary mode seem to emerge at the level of individual scientists, yet they still need to find their way to the level of scientific project management. In this paper we discuss the justifications and barriers to implement a higher degree of extended knowledge integration in applied science projects such as ACCENT. It is felt that the community of atmospheric chemists and physicists is mature for such an implementation and recommendations are given to help and make this happen.     

Urban sustainability science: prospects for innovations through a system’s perspective,relational and transformations’ approaches: This article belongs to Ambio’s 50th Anniversary Collection. Theme: Urbanization

In this perspective, we present how three initial landmark papers on urban sustainability research contributed to the larger sustainability science scholarship and paved the way for the continued development of urban sustainability research. Based on this, we propose three conceptual innovation pathways to trace the progression of urban sustainability science: First, urban sustainability from a system’s perspective, meaning that urban sustainability requires integrative solutions to work in the tripled social-ecological-technological system setting. Second, urban sustainability from a (people and place) relational perspective, meaning urban sustainability is a contested and dynamic social-ecological contract of cities. As a governance mission, urban sustainability requires evidence from research that can inform coordinated action to bridge people, places, meanings, visions and ecosystems. Third, urban sustainability from a transformative science perspective, meaning that for urban sustainability to be achieved and progressed, deep transformations are required in systems, relations, policies and governance approaches. Our proposal for the future of urban sustainability science centres on emphasizing the relevance and policy applicability of systems’ thinking, value and place thinking and transitions/transformations thinking as fundamental to how knowledge is co-produced by research science, policy and society and becomes actionable.     

Acid rain and air pollution: 50 years of progress in environmental science and policy

Because of its serious large-scale effects on ecosystems and its transboundary nature, acid rain received for a few decades at the end of the last century wide scientific and public interest, leading to coordinated policy actions in Europe and North America. Through these actions, in particular those under the UNECE Convention on Long-range Transboundary Air Pollution, air emissions were substantially reduced, and ecosystem impacts decreased. Widespread scientific research, long-term monitoring, and integrated assessment modelling formed the basis for the policy agreements. In this paper, which is based on an international symposium organised to commemorate 50 years of successful integration of air pollution research and policy, we briefly describe the scientific findings that provided the foundation for the policy development. We also discuss important characteristics of the science–policy interactions, such as the critical loads concept and the large-scale ecosystem field studies. Finally, acid rain and air pollution are set in the context of future societal developments and needs, e.g. the UN’s Sustainable Development Goals. We also highlight the need to maintain and develop supporting scientific infrastructures.

     

Opportunities for Increasing Resilience and Sustainability of Urban Social–Ecological Systems: Insights from the URBES and the Cities and Biodiversity Outlook Projects

Urban futures that are more resilient and sustainable require an integrated social–ecological system approach to urban policymaking, planning, management, and governance. In this article, we introduce the Urban Biodiversity and Ecosystem Services (URBES) and the Cities and Biodiversity Outlook (CBO) Projects as new social–ecological contributions to research and practice on emerging urban resilience and ecosystem services. We provide an overview of the projects and present global urbanization trends and their effects on ecosystems and biodiversity, as a context for new knowledge generated in the URBES case-study cities, including Berlin, New York, Rotterdam, Barcelona, and Stockholm. The cities represent contrasting urbanization trends and examples of emerging science–policy linkages for improving urban landscapes for human health and well-being. In addition, we highlight 10 key messages of the global CBO assessment as a knowledge platform for urban leaders to incorporate state-of-the-art science on URBES into decision-making for sustainable and resilient urban development.     

Policy and Science Implications of the Framing and Qualities of Uncertainty in Risks: Toxic and Beneficial Fish from the Baltic Sea

Policy and research issues in the framing and qualities of uncertainties in risks are analyzed, based on the assessments of dioxin-like compounds (DLCs) and other ingredients in Baltic Sea fish, a high-profile case of governance. Risks are framed broadly, to then focus on dioxins and beneficial fatty acids, fish consumption, human health, and science-management links. Hierarchies of uncertainty (data, model, decision rule, and epistemic) and ambiguity (of values) are used to identify issues of scientific and policy contestation and opportunities for resolving them. The associated complexity of risks is illustrated by risk–benefit analyses of fish consumption and by evaluations of guideline values, highlighting value contents and policy factors in presumably scientific decision criteria, and arguments used in multi-dimensional risk and benefit comparisons. These comparisons pose challenges to narrow assessments centered, for e.g., on toxicants or on food benefits, and to more many-sided and balanced risk communication and management. It is shown that structured and contextualized treatment of uncertainties and ambiguities in a reflexive approach can inform balances between wide and narrow focus, detail and generality, and evidence and precaution.     

Past,current and future Swedish freshwater monitoring from an authority perspective

The synthesis of the BONUS+ research is introduced. The HELCOM Baltic Sea Action Plan is examined as a case to illustrate the potentials and challenges in building the science–policymaking interface on a macroregional level. The projects address environmental challenges in the Baltic Sea as defined by the Baltic Sea Action Plan, or consider the environmental governance and decision making within the Baltic Sea context in general. Eutrophication, biodiversity, hazardous substances, maritime activities, and the environment governance are addressed, as are crosscutting issues, such as the impact of climate change, maritime spatial planning and impacts of future development on ecosystem services. The projects contributed to relevant policy developments: 37 consultations carried out at EU level, 49 modifications to policy documents and action plans, 153 suggestions for the efficacy of pertinent public policies and governance, and in 570 occasions, scientists working in BONUS+ projects served as members or observers in scientific and stakeholder committees.     

Investing in climate change adaptation and mitigation: A methodological review of real-options studies

Uncertain future payoffs and irreversible costs characterize investment in climate change adaptation and mitigation. Under these conditions, it is relevant to analyze investment decisions in a real options framework, as this approach takes into account the economic value associated with investment time flexibility. In this paper, we provide an overview of the literature adopting a real option approach to analyze investment in climate change adaptation and mitigation, and examine how the uncertain impacts of climate change on the condition of the human environment, risk preferences, and strategic interactions among decisions-makers have been modeled. We found that the complex nature of uncertainties associated with climate change is typically only partially taken into account and that the analysis is usually limited to decisions taken by individual risk neutral profit maximizers. Our findings call for further research to fill the identified gaps.Electronic supplementary materialThe online version of this article (10.1007/s13280-020-01342-8) contains supplementary material, which is available to authorized users.     

Reactivity-Based Hydrocarbon Controls: Scientific Issues and Potential Regulatory Applications

The California Air Resources Board and the South Coast Air Quality Management District hosted a conference on April 8-9, 1991 to examine the scientific issues associated with reactivity-based hydrocarbon controls, and to identify the obstacles to potential regulatory applications. Owing to residual uncertainties in the underlying science, and the complex emission measurement capabilities required for enforcement, a general consensus emerged on the need for further research before application of reactivity-based controls. A number of recommendations were made for research on the remaining scientific, enforcement, and policy issues, many of which have led to cooperative efforts initiated since the conference.     

Organizing For Risk Oriented Climate Alteration Research

The ubiquity in effects and complexity of modern environmental issues requires careful consideration of focused research to insure that answers to key policy oriented questions are obtained. Broad-based, unstructured research programs have proven to be inefficient instruments for characterizing risks from environmental stress. Both the expense and the importance of timeliness of information preclude a traditional “bottom-up” approach to research. Instead, a more “top-down” organized approach that links the natural and socioeconomic sciences has advantages to support environmental risk assessment and research prioritization of climate alteration. Early examples of analysis using an integrating framework for risk assessment focus on the need for research on human interactions and the environmental damage function in addition to the basic earth sciences. Nonlinearities in environmental impact of climate change, and uncertainties in the extent of growth of energy efficiencies, are seen to be key unknowns in the risk assessment of climate alternatives. To date, earth science research has not been structured well for environmental risk assessment of the climate change question. The U.S. national research program aimed at risk assessment of climate alteration is examined as an example. The examination suggests that the present conceptual plan falls short of an optimum structure derived from exploitation of an integrating framework, even though it is rich in scientific strength and diversity. To strengthen its public value and accessibility, the research program could account in its planning for prioritized needs defined by an integrating analysis for risk assessment and management.     

Towards a definition of climate science

The intrinsic difficulties in building realistic climate models and in providing complete, reliable and meaningful observational datasets, and the conceptual impossibility of testing theories against data imply that the usual Galilean scientific validation criteria do not apply to climate science. The different epistemology pertaining to climate science implies that its answers cannot be singular and deterministic; they must be plural and stated in probabilistic terms. Therefore, in order to extract meaningful estimates of future climate change from a model, it is necessary to explore the model's uncertainties. In terms of societal impacts of scientific knowledge, it is necessary to accept that any political choice in a matter involving complex systems is made under unavoidable conditions of uncertainty. Nevertheless, detailed probabilistic results in science can provide a baseline for a sensible process of decision-making.     

Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

The legacy from the 50 years of acid rain research,forming present and future research and monitoring of ecosystem impact: This article belongs to Ambio’s 50th Anniversary Collection. Theme: Acidification

Acid rain and acidification research are indeed a multidisciplinary field. This field evolved from the first attempts to mitigate acid freshwater in the 1920s, then linking acid rain to the acidification in late 1950s, to the broad project-concepts on cause and effect from the late 1960s. Three papers from 1974, 1976 and 1988 demonstrate a broad approach and comprise scientific areas from analytical chemistry, biochemistry, limnology, ecology, physiology and genetics. Few, if any, environmental problems have led to a public awareness, political decisions and binding limitations as the story of acid rain. Acid precipitation and acidification problems still exist, but at a lower pressure, and liming has been reduced accordingly. However, the biological responses in the process of recovery are slow and delayed. The need for basic science, multidisciplinary studies, long time series of high-quality data, is a legacy from the acid rain era, and must form the platform for all future environmental projects.     

An evaluation of the role of risk-based decision-making in a former manufactured gas plant site remediation

Environmental remediation decisions are driven by the need to minimize human health and ecological risks posed by environmental releases. The Risk Assessment Guidance for Superfund Sites enunciates the principles of exposure and risk assessment that are to be used for reaching remediation decisions for sites under Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA). Experience with remediation management under CERCLA has led to recognition of some crucial infirmities in the processes for managing remediation: cleanup management policies are ad hoc in character, mandates and practices are strongly conservative, and contaminant risk management occurs in an artificially narrow context. The purpose of this case study is to show how a policy of risk-based decision-making was used to avoid customary pitfalls in site remediation. This case study describes the risk-based decision-making process in a remedial action program at a former manufactured gas plant site that successfully achieved timely and effective cleanup. The remediation process operated outside the confines of the CERCLA process under an administrative consent order between the utility and the New Jersey Department of Environmental Protection. A residential use end state was negotiated as part of this agreement. The attendant uncertainties, complications, and unexpected contingencies were overcome by using the likely exposures associated with the desired end state to structure all of the remediation management decisions and by collecting site-specific information from the very outset to obtain a detailed and realistic characterization of human health risks that needed to be mitigated. The lessons from this case study are generalizable to more complicated remediation cases, when supported by correspondingly sophisticated technical approaches.     

Air Quality Modeling and Decisions for Ozone Reduction Strategies

Abstract

Despite the widespread application of photochemical air quality models (AQMs) in U.S. state implementation planning (SIP) for attainment of the ambient ozone standard, documentation for the reliability of projections has remained highly subjective. An “idealized” evaluation framework is proposed that provides a means for assessing reliability. Applied to 18 cases of regulatory modeling in the early 1990s in North America, a comparative review of these applications is reported. The intercomparisons suggest that more than two thirds of these AQM applications suffered from having inadequate air quality and meteorological databases. Emissions representations often were unreliable; uncertainties were too high. More than two thirds of the performance evaluation efforts were judged to be substandard compared with idealized goals. Meteorological conditions chosen according regulatory guidelines were limited to one or two cases and tended to be similar, thus limiting the extent to which public policy makers could be confident that the emission controls adopted would yield attainment for a broad range of adverse atmospheric conditions. More than half of the studies reviewed did not give sufficient attention to addressing the potential for compensating errors. Corroborative analyses were conducted in only one of the 18 studies reviewed. Insufficient attention was given to the estimation of model and/or input database errors, uncertainties, or variability in all of the cases examined. However, recent SIP and policy‐related regional modeling provides evidence of substantial improvements in the underlying science and available modeling systems used for regulatory decision making. Nevertheless, the availability of suitable databases to support increasingly sophisticated modeling continues to be a concern for many locations. Thus, AQM results may still be subject to significant uncertainties. The evaluative process used here provides a framework for modelers and public policy makers to assess the adequacy of contemporary and future modeling work.     

Integrating information for better environmental decisions

As more is learned about the complex nature and extent of environmental impacts from progressive human disturbance, scientists, policy analysts, decision makers, educators, and communicators are increasingly joining forces to develop strategies for preserving and protecting the environment. The Eco-Informa Foundation is an educational scientific organization dedicated to promoting the collaborative development and sharing of scientific information. The Foundation participated in a recent international conference on environmental informatics through a special symposium on integrating information for better environmental decisions. Presentations focused on four general themes: (1) remote sensing and data interpretation, including through new knowledge management tools; (2) risk assessment and communication, including for radioactively contaminated facilities, introduced biological hazards, and food safety; (3) community involvement in cleanup projects; and (4) environmental education. The general context for related issues, methods and applications, and results and recommendations from those discussions are highlighted here.     

Sulfur Control In Coal Fired Power Plants: A Probabilistic Approach to Policy Analysis

The optimum level of sulfur pollution control for a coal fired power plant is the point where the sum of societal costs, due to pollution, and control costs is minimized. This basic microeconomic concept has been of limited practical value due to considerable uncertainty in estimating both costs. A probabilistic approach is used to characterize these uncertainties quantitatively for a hypothetical 1000 Mw_e plant located near Pittsburgh, Pennsylvania. Only mortality effects within a distance of 80 km of the plant have been included. The results allow explicit consideration of attitude toward risk and appropriate level of investment to prevent deaths. Limitations of the findings are discussed. Implications are described for policy based on alternative sets of values and assumptions.