The present paper draws on climate science and the philosophy of science in order to evaluate climate-model-based approaches to assessing climate projections. We analyze the difficulties that arise in such assessment and outline criteria of adequacy for approaches to it. In addition, we offer a critical overview of the approaches used in the IPCC working group one fourth report, including the confidence building, Bayesian and likelihood approaches. Finally, we consider approaches that do not feature in (...) the IPCC reports, including three approaches drawn from the philosophy of science. We find that all available approaches face substantial challenges, with IPCC approaches having as a primary source of difficulty their goal of providing probabilistic assessments. (shrink)

Climate model projections are used to inform policy decisions and constitute a major focus of climate research. Confidence in climate projections relies on the adequacy of climate models for those projections. The question of how to argue for the adequacy of models for climate projections has not gotten sufficient attention in the climate modelling community. The most common way to evaluate a climate model is to assess in (...) a quantitative way degrees of “model fit”; i.e., how well model results fit observation-based data (empirical accuracy) and agree with other models or model versions (robustness). However, such assessments are largely silent about what those degrees of fit imply for a model’s adequacy for projecting future climate. We provide a conceptual framework for discussing the evaluation of the adequacy of models for climate projections. Drawing on literature from philosophy of science and climate science, we discuss the potential and limits of inferences from model fit. We suggest that support of a model by background knowledge is an additional consideration that can be appealed to in arguments for a model’s adequacy for long-term projections, and that this should explicitly be spelled out. Empirical accuracy, robustness and support by background knowledge neither individually nor collectively constitute sufficient conditions in a strict sense for a model’s adequacy for long-term projections. However, they provide reasons that can be strengthened by additional information and thus contribute to a complex non-deductive argument for the adequacy of a climate model or a family of models for long-term climate projections. (shrink)

The present paper draws on climate science and the philosophy of science in order to evaluate climate-model-based approaches to assessing climate projections. We analyze the difficulties that arise in such assessment and outline criteria of adequacy for approaches to it. In addition, we offer a critical overview of the approaches used in the IPCC working group one fourth report, including the confidence building, Bayesian and likelihood approaches. Finally, we consider approaches that do not feature in (...) the IPCC reports, including three approaches drawn from the philosophy of science. We find that all available approaches face substantial challenges, with IPCC approaches having as a primary source of difficulty their goal of providing probabilistic assessments. (shrink)

When attendant to the agency of models and the general context in which they perform, climate models can be seen as instrumental policy tools that may be evaluated in terms of their adequacy for purpose. In contrast, when analysed independently of their real-world usage for informing decision-making, the tendency can be to prioritise their representative role rather than their instrumental role. This paper takes as a case study the development of the UK Climate Projections 2009 in relation (...) to its probabilistic treatment of uncertainties and the implications of this approach for adaptation decision-making. It is considered that the move towards ensemble-based probabilistic climate projections has the benefit of encouraging organisations to reshape their adaptation strategies and decisions towards a risk-based approach, where they are confronted definitively with climate modelling uncertainties and drawn towards a more nuanced understanding of how climate impacts could affect their operations. This is further illustrated through the example of the built environment sector, where it can be seen that the probabilistic approach may be of limited salience for the urban heat island in the absence of a corresponding effort towards a more place-based analysis of climate vulnerabilities. Therefore, further assessment of the adequacy-for-purpose of climate models might also consider the usability of climate projections at the urban scale. (shrink)

1. Introduction; Elisabeth A. Lloyd and Eric Winsberg.- Section 1: Confirmation and Evidence.- 2. The Scientific Consensus on Climate Change: How Do We Know We’re Not Wrong?; Naomi Oreskes.- 3. Satellite Data and Climate Models Redux.- 3a. Introduction to Chapter 3: Satellite Data and Climate Models; Elisabeth A. Lloyd.- Ch. 3b Fact Sheet to "Consistency of Modelled and Observed Temperature Trends in the Tropical Troposphere"; Benjamin D. Santer et al..- Ch. 3c Reprint of "Consistency of Modelled and (...) Observed Temperature Trends in the Tropical Troposphere"; Benjamin D. Santer et al..- 4. The Role of ’Complex’ Empiricism in the Debates about Satellite Data and Climate Models; Elisabeth A. Lloyd.- 5. Reconciling Climate Model/Data Discrepancies: The Case of the Trees That Didn’t Bark; Michael Mann.- 6. Downscaling of Climate Information; Linda O. Mearns et al..- Section 2: Uncertainties and Robustness.- 7. The Significance of Robust Model Projections; Wendy S. Parker.- 8. Building Trust, Removing Doubt? Robustness Analysis and Climate Modeling; Jay Odenbaugh.- Section 3: Climate Models as Guides to Policy.- 9. Climate Model Confirmation: From Philosophy to Predicting Climate in the Real World; Reto Knutti.- 10. Uncertainty in Climate Science and Climate Policy; Jonathan Rougier and Michel Crucifix.- 11. Communicating Uncertainty to Policy Makers: The Ineliminable Role of Values; Eric Winsberg.- 12. Modeling Climate Policies: A Critical Look at Integrated Assessment Models; Mathias Frisch.- 13. Modelling Mitigation and Adaptation Policies to Predict their Effectiveness: The Limits of Randomized Controlled Trials; Alexandre Marcellesi and Nancy D. Cartwright. (shrink)

Climate modelling plays a crucial role for understanding and addressing the climate challenge, in terms of both mitigation and adaptation. It is therefore of central importance to understand to what extent climate models are adequate for relevant purposes, such as providing certain kinds of climate change projections in view of decision-making. In this perspective, the issue of the stability of climate models under small relevant perturbations in their structure seems particularly important. Within this framework, (...) a debate has emerged in the philosophy of science literature about the relevance for climate modelling of the mathematical notion of structural stability. This paper adresses several important foundational and epistemological questions that arise in this context, in particular about the the role of abstract mathematical considerations of a qualitative nature for concrete modelling projects with mainly quantitative purposes. (shrink)

The United Kingdom Climate Impacts Program’s UKCP09 project makes high-resolution forecasts of climate during the 21st century using state of the art global climate models. The aim of this paper is to introduce and analyze the methodology used and then urge some caution. Given the acknowledged systematic errors in all current climate models, treating model outputs as decision relevant probabilistic forecasts can be seriously misleading. This casts doubt on our ability, today, to make trustworthy, high-resolution (...) predictions out to the end of this century. (shrink)

Robustness with respect to climate models is often considered by philosophers of climate science to be a crucial issue in determining whether and to what extent the projections of the Earth’s future climate that models yield should be trusted. Parker (2011) and Lloyd (2009, 2015) have introduced influential accounts of robustness for climate models with seemingly conflicting conclusions. I argue that Parker and Lloyd are characterizing distinct notions of robustness and providing complementary insights. Confidence, if (...) warranted, need be by virtue of causally consistent climate models rather than by agreement upon projections by a diverse range of models. (shrink)

There is much debate on how social values should influence scientific research. However, the question of practical applicability of philosophers’ normative proposals has received less attention. Here, we test the attainability of Matthew J. Brown’s (2020) Moral Imagination ideal (MI ideal), which aims to help scientists to make warranted value-judgements through reflecting on goals, options, values, and stakeholders of research. Here, the tools of the MI ideal are applied to a climate modelling setting, where researchers are developing aerosol-cloud interaction (...) (ACI) parametrizations in an Earth System Model with the broader goal of improving climate sensitivity estimation. After the identification of minor obstacles to applying the MI ideal, we propose two ways to increase its applicability. First, its tools should be accompanied with more concrete guidance for identifying how social values enter more technical decisions in scientific research. Second, since research projects can have multiple goals, examining the alignment between broader societal aims of research and more technical goals should be part of the tools of the MI ideal. (shrink)

We give an overview of the practice of developing and using complex climate models, as seen from experiences in a major climate modelling center and through participation in the Coupled Model Intercomparison Project. We discuss the construction and calibration of models; their evaluation, especially through use of out-of-sample tests; and their exploitation in multi-model ensembles to identify biases and make predictions. We stress that adequacy or utility of climate models is best assessed via their skill (...) against more naïve predictions. The framework we use for making inferences about reality using simulations is naturally Bayesian, and has many points of contact with more familiar examples of scientific epistemology. While the use of complex simulations in science is a development that changes much in how science is done in practice, we argue that the concepts being applied fit very much into traditional practices of the scientific method, albeit those more often associated with laboratory work. (shrink)

Over the last several years, there has been an explosion of interest and attention devoted to the problem of Uncertainty Quantification (UQ) in climate science—that is, to giving quantitative estimates of the degree of uncertainty associated with the predictions of global and regional climate models. The technical challenges associated with this project are formidable, and so the statistical community has understandably devoted itself primarily to overcoming them. But even as these technical challenges are being met, a number of (...) persistent conceptual difficulties remain. So why is UQ so important in climate science? UQ, I would like to argue, is first and foremost a tool for communicating knowledge from experts to policy makers in a way that is meant to be free from the influence of social and ethical values. But the standard ways of using probabilities to separate ethical and social values from scientific practice cannot be applied in a great deal of climate modeling, because the roles of values in creating the models cannot be discerned after the fact—the models are too complex and the result of too much distributed epistemic labor. I argue, therefore, that typical approaches for handling ethical/social values in science do not work well here. (shrink)

Convergence of model projections is often considered by climate scientists to be an important objective in so far as it may indicate the robustness of the models’ core hypotheses. Consequently, the range of climate projections from a multi-model ensemble, called “model spread”, is often expected to reduce as climate research moves forward. However, the successive Assessment Reports of the Intergovernmental Panel on Climate Change indicate no reduction in model spread, whereas (...) it is indisputable that climate science has made improvements in its modelling. In this paper, after providing a detailed explanation of the situation, we describe an epistemological setting in which a steady model spread is not doomed to be seen as negative, and is indeed compatible with a desirable evolution of climate models taken individually. We further argue that, from the perspective of collective progress, as far as the improvement of the products of a multi-model ensemble is concerned, reduction of model spread is of lower priority than model independence. (shrink)

Projections of future climate change cannot rely on a single model. It has become common to rely on multiple simulations generated by Multi-Model Ensembles (MMEs), especially to quantify the uncertainty about what would constitute an adequate model structure. But, as Parker points out (2018), one of the remaining philosophically interesting questions is: “How can ensemble studies be designed so that they probe uncertainty in desired ways?” This paper offers two interpretations of what General Circulation Models (...) (GCMs) are and how MMEs made of GCMs should be designed. In the first interpretation, models are combinations of modules and parameterisations; an MME is obtained by “plugging and playing” with interchangeable modules and parameterisations. In the second interpretation, models are aggregations of expert judgements that result from a history of epistemic decisions made by scientists about the choice of representations; an MME is a sampling of expert judgements from modelling teams. We argue that, while the two interpretations involve distinct domains from philosophy of science and social epistemology, they both could be used in a complementary manner in order to explore ways of designing better MMEs. (shrink)

Philosophers of science have offered various accounts of climate model evaluation which have largely centered on model-fit assessment. However, despite the wide-spread prevalence of process-based evaluation in climate science practice, this sort of model evaluation has been undertheorized by philosophers of science. In this paper, I aim to expand this narrow philosophical view of climate model evaluation by providing a philosophical account of process evaluation that is rooted in a close examination of scientific (...) practice. I propose dynamical adequacy as a metric by which scientists test and evaluate models that represent and produce key regional climate processes and features. I argue that process-based evaluation confirms the adequacy of a regional climate model for simulating and predicting future changes of a specific regional climate feature. I offer a case study of how, in practice, scientists establish the reliability of model projections by assessing the dynamical adequacy of such models. I also show how process-based evaluation mitigates some well-known shortcomings of model-fit assessment and supports the adequacy and reliability of climate model projections against philosophical objections, like confirmational holism. Such adequacy is especially important for decision makers who need reliable regional model projections to guide their climate change mitigation and adaptation policies. (shrink)

Non-epistemic values pervade climate modelling, as is now well documented and widely discussed in the philosophy of climate science. Recently, Parker and Winsberg have drawn attention to what can be termed “epistemic inequality”: this is the risk that climate models might more accurately represent the future climates of the geographical regions prioritised by the values of the modellers. In this paper, we promote value management as a way of overcoming epistemic inequality. We argue that value management can (...) be seriously considered as soon as the value-free ideal and inductive risk arguments commonly used to frame the discussions of value influence in climate science are replaced by alternative social accounts of objectivity. We consider objectivity in Longino's sense as well as strong objectivity in Harding's sense to be relevant options here, because they offer concrete proposals that can guide scientific practice in evaluating and designing so-called multi-model ensembles and, in fine, improve their capacity to quantify and express uncertainty in climate projections. (shrink)

This chapter identifies conditions under which robust predictive modeling results have special epistemic significance—related to truth, confidence, and security—and considers whether those conditions are met in the context of climate modeling today. The findings are disappointing. When today’s climate models agree that an interesting hypothesis about future climate change is true, it cannot be inferred, via the arguments considered here anyway, that the hypothesis is likely to be true, nor that confidence in the hypothesis should be significantly (...) increased, nor that a claim to have evidence for the hypothesis is now more secure. In some other modeling contexts, the prospects for such arguments are brighter. (shrink)

Between the fourth and the recent fifth IPCC report, science as well as policy making have made great advances in dealing with uncertainties in global climate models. However, the uncertainties public decision making has to deal with go well beyond what is currently addressed by policy makers and climatologists alike. It is shown in this paper that within an anthropocentric framework, a whole hierarchy of models from various scientific disciplines is needed for political decisions as regards climate change. (...) Via what is sometimes referred to as ‘uncertainty cascade’ in the technical literature, the uncertainties of various models accumulate in the input to political decision making. This paper aims to chart the uncertainties in the multi-disciplinary enterprise of current climate modeling in broad strokes. It is shown that the uncertainty cascade makes it impossible to quantify uncertainties in the form of probability estimates. Moreover the paper highlights how global climate models fail to provide probability estimates. A better treatment of the uncertainties of climate predictions on the political level would require an overhaul of the current IPCC practice that separates the various scientific fields in various working groups. (shrink)

The United Kingdom Climate Impacts Programme’s UKCP09 project makes high-resolution projections of the climate out to 2100 by post-processing the outputs of a large-scale global climate model. The aim of this paper is to describe and analyse the methodology used and then urge some caution. Given the acknowledged systematic, shared errors of all current climate models, treating model outputs as decision-relevant projections can be significantly misleading. In extrapolatory situations, such as projections (...) of future climate change, there is little reason to expect that post-processing of model outputs can correct for the consequences of such errors. This casts doubt on our ability, today, to make trustworthy probabilistic projections at high resolution out to the end of the century. (shrink)

This is the second of three parts of an introduction to the philosophy of climate science. In this second part about modelling climate change, the topics of climate modelling, confirmation of climate models, the limits of climate projections, uncertainty and finally model ensembles will be discussed.

Climate change adaptation is largely a local matter, and adaptation planning can benefit from local climate change projections. Such projections are typically generated by accepting climate model outputs in a relatively uncritical way. We argue, based on the IPCC’s treatment of model outputs from the CMIP5 ensemble, that this approach is unwarranted and that subjective expert judgment should play a central role in the provision of local climate change projections intended to (...) support decision-making. (shrink)

Philosophers of science have paid close attention to climate simulations as means of projecting the severity and effects of climate change, but have neglected the full diversity of methods in climate science. This paper shows the philosophical richness of another method in climate science: the practice of using paleoclimate analogues to inform our climate projections. First, I argue that the use of paleoclimate analogues can offer important insights to philosophers of the historical sciences. Rather (...) than using the present as a guide to the past, as is common in the historical sciences, paleoclimate analogues involve using the past as a guide to the future. I thereby distinguish different methods in the historical sciences and argue that these distinctions bear on debates over whether the historical sciences can produce generalizations or predictions. Second, I suggest that paleoclimate analogues might actually be considered a type of climate model, and, as such, their use expands on common characterizations of models to include those that are full-scale, naturally occurring, and non-manipulable. (shrink)

The paper describes the development of a model for representing the ethical climate of a business community. It describes the steps followed in identifying the model’s components and in validating the model’s structure through use of expert panels. The expert panel validation methodology has yielded a weighting scheme for use in the model’s eventual operationalization, whose derivation, together with the analysis performed on qualitative discoveries of the process, is described. The model’s development is part (...) of a larger research project that is intended to result in a validated instrument for measuring and comparing ethical climates of different business communities and for assessing longitudinal progress of individual business communities. The project’s ultimate goal is to provide an index measure that can also serve as a “report card” assessment tool at a level of analysis, which has not yet been addressed in the literature – that of the business community. (shrink)

Machine learning methods have recently created high expectations in the climate modelling context in view of addressing climate change, but they are often considered as non-physics-based ‘black boxes’ that may not provide any understanding. However, in many ways, understanding seems indispensable to appropriately evaluate climate models and to build confidence in climate projections. Relying on two case studies, we compare how machine learning and standard statistical techniques affect our ability to understand the climate system. (...) For that purpose, we put five evaluative criteria of understanding to work: intelligibility, representational accuracy, empirical accuracy, coherence with background knowledge, and assessment of the domain of validity. We argue that the two families of methods are part of the same continuum where these various criteria of understanding come in degrees, and that therefore machine learning methods do not necessarily constitute a radical departure from standard statistical tools, as far as understanding is concerned. (shrink)

Roman Frigg and others have developed a general epistemological argument designed to cast doubt on the capacity of a broad range of mathematical models to generate “decision relevant predictions.” In this article, we lay out the structure of their argument—an argument by analogy—with an eye to identifying points at which certain epistemically significant distinctions might limit the force of the analogy. Finally, some of these epistemically significant distinctions are introduced and defended as relevant to a great many of the predictive (...) mathematical modeling projects employed in contemporary climate science. (shrink)

Climatologists have recently introduced a distinction between projections as scenario-based model results on the one hand and predictions on the other hand. The interpretation and usage of both terms is, however, not univocal. It is stated that the ambiguities of the interpretations may cause problems in the communication of climate science within the scientific community and to the public realm. This paper suggests an account of scenarios as props in games of make-belive. With this account, we explain (...) the difference between projections that should be make-believed and other model results that should be believed. (shrink)

PurposeThis study is justified by the economic importance of information on greenhouse gases, as well as the interest in the question of governance structure after the adoption of the objectives of the 2030 Agenda. The problem is also explained by the lack of research that has investigated the relationship between the best governance structure that contributes to achieving sustainability goals, including climate actions (SDG13) and clean energy adoption (SDG7) as part of the 2030 Agenda.Design/methodology/approachThe level of disclosure is measured (...) on the basis of the carbon disclosure score calculated by the carbon disclosure project (CDP). The study sample consists of 387 US companies that voluntarily participated in the CDP survey from 2011 to 2018. The authors use panel data analysis based on multiple regression models.FindingsThe results confirm the influential role of board size, director independence, the presence of women on the board and the presence of an environmental committee on carbon disclosure. In terms of carbon disclosure, the results suggest that a better governance structure is likely to reduce carbon emissions and improve carbon performance practices. Similarly, the analyses show a different representation of the role of corporate governance in high-carbon sectors compared to low-carbon sectors.Research limitations/implicationsThis study has some limitations. First, the sample is only interested in US companies that responded to the CDP questionnaire during the period 2011–2018. Thus, the results cannot be generalized to countries with different governance structures. Second, the data from this study on carbon disclosure, specifically focuses on CDP reporting to determine the carbon disclosure score. In this sense, the findings on information disclosed do not necessarily address disclosures through other media, such as a company’s website or a press release.Originality/valueSustainability and commitment to the sustainable development goals (SDGs) are more likely to exist in companies that have good governance and, in particular, a better board. The research is inspired by the SDGs. The study aims to examine the relationship between carbon disclosure and corporate governance in the context of SDGs. Indeed, this research work contributes to achieving sustainability goals, including climate actions (SDG13) and clean energy adoption (SDG7). (shrink)

Climate change assessments rely upon scenarios of socioeconomic developments to conceptualize alternative outcomes for global greenhouse gas emissions. These are used in conjunction with climate models to make projections of future climate. Specifically, the estimations of greenhouse gas emissions based on socioeconomic scenarios constrain climate models in their outcomes of temperatures, precipitation, etc. Traditionally, the fundamental logic of the socioeconomic scenarios—that is, the logic that makes them plausible—is developed and prioritized using methods that are very (...) subjective. This introduces a fundamental challenge for climate change assessment: The veracity of projections of future climate currently rests on subjective ground. We elaborate on these subjective aspects of scenarios in climate change research. We then consider an alternative method for developing scenarios, a systems dynamics approach called ‘Cross-Impact Balance’ (CIB) analysis. We discuss notions of ‘objective’ and ‘objectivity’ as criteria for distinguishing appropriate scenario methods for climate change research. We distinguish seven distinct meanings of ‘objective,’ and demonstrate that CIB analysis is more objective than traditional subjective approaches. However, we also consider criticisms concerning which of the seven meanings of ‘objective’ are appropriate for scenario work. Finally, we arrive at conclusions regarding which meanings of ‘objective’ and ‘objectivity’ are relevant for climate change research. Because scientific assessments uncover knowledge relevant to the responses of a real, independently existing climate system, this requires scenario methodologies employed in such studies to also uphold the seven meanings of ‘objective’ and ‘objectivity.’. (shrink)

For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many ti...

Computer simulation modeling is an important part of contemporary scientific practice but has not yet received much attention from philosophers. The present project helps to fill this lacuna in the philosophical literature by addressing three questions that arise in the context of computer simulation of Earth's climate. Computer simulation experimentation commonly is viewed as a suspect methodology, in contrast to the trusted mainstay of material experimentation. Are the results of computer simulation experiments somehow deeply problematic in ways that the (...) results of material experiments are not? I argue against categorical skepticism toward the results of computer simulation experiments by revealing important parallels in the epistemologies of material and computer simulation experimentation. It has often been remarked that simple computer simulation models---but not complex ones---contribute substantially to our understanding of the atmosphere and climate system. Is this view of the relative contributions of simple and complex models tenable? I show that both simple and complex climate models can promote scientific understanding and argue that the apparent contribution of simple models depends upon whether a causal or deductive account of scientific understanding is adopted. When two incompatible scientific theories are under consideration, they typically are viewed as competitors, and we seek evidence that refutes at least one of the theories. In the study of climate change, however, logically incompatible computer simulation models are accepted as complementary resources for investigating future climate. How can we make sense of this use of incompatible models? I show that a collection of incompatible climate models persists in part because of difficulties faced in evaluating and comparing climate models. I then discuss the rationale for using these incompatible models together and argue that this climate model pluralism has both competitive and integrative components. (shrink)

This article contributes to recent work on justice in resilience-based projects for climate adaptation. At present, the model commonly used for guiding normative reflection in this domain is the tripartite model of justice, whereby justice is seen as comprising distributive, procedural and recognitional aspects. After discussing some conceptual problems and practical shortcomings of this model, we propose an alternative model with six forms of justice or kinds of justice demands: distributive, procedural, intergenerational, restorative and retributive (...) justice, and justice in system outcomes. We also illustrate some advantages of this model with respect to representative accounts of the tripartite model. (shrink)

Various geoengineering technologies that would deliberately alter the climate system have been proposed as a way to alleviate risks of global warming. Technologies that would shield incoming sunlight to cool the planet, so called solar radiation management, are particularly controversial. Considering insights from social studies of simulation modeling and research on expectations in science and technology, I argue that climate modeling has a central role in producing visions of SRM. I draw upon an empirical analysis of scientific research (...) on SRM to examine how a creative play with technological ideas becomes possible through climate modeling. This enables scientists to project and study environmental impacts of speculative SRM methods in virtual experiments and to develop and refine ideas for adjusting sunlight. Hence, while climate models are used to improve scientific understandings of climate system behavior and to anticipate possible environmental impacts of SRM, they also become inventive tools, allowing scientists to envision novel ways of climate control and optimization. Given the importance of simulation studies to knowledge production on SRM, I critically reflect on the challenges that arise when visions about an engineered climate future are first and foremost produced in climate simulations. (shrink)

The public debate on climate change is filled with moral claims. However, scientific knowledge about the role that morality, ethics, and values play in this issue is still scarce. Starting from this research gap, we focus on corporations as central decision makers in modern society and analyze how they respond to societal demands to take responsibility for climate change. While relevant literature on business ethics and climate change either places a high premium on morality or presents a (...) strong skeptical bias, our sociological model depicts morality as an indeterminate force: it can lead to both workable solutions or merely reinforce the status quo, depending on what different corporations make of it. We describe, on the one side, the diffusion of moral values in the media discourse on climate change and, on the other side, the specific responses of corporations. While the media discourse generates a pressure on corporations to act responsibly, their moral claims do not provide clear advice for action. As a result, morality becomes available to organizations as a medium that can be re-specified according to their internal dynamics. Corporations transform moral values into something compatible with their own structures through a variety of different responses: introducing formal ethical structures (e.g., codes of conduct), initiating value-oriented projects, or developing informal moral norms, and so on. In some occurrences, morality becomes a mere façade, while in others it serves as a decision-making criterion and deeply influences core activities in firms. (shrink)

In this article, we describe a project in which philosophy, in combination with methods drawn from mental modeling, was used to structure dialogue among stakeholders in a region-scale climate adaptation process. The case study we discuss synthesizes the Toolbox dialogue method, a philosophically grounded approach to enhancing communication and collaboration in complex research and practice, with a mental modeling approach rooted in risk analysis, assessment, and communication to structure conversations among non-academic stakeholders who have a common interest in planning (...) for a sustainable future. We begin by describing the background of this project, including details about climate resiliency efforts in West Michigan and the Toolbox dialogue method, which was extended in this project from academic research into community organization involving the West Michigan Climate Resiliency Framework Initiative. This extension involved application of several methods, which are the focus of the Methods section. We then present and discuss preliminary results that suggest the potential for philosophical dialogue to enhance mutual understanding in complex community initiatives that focus on sustainable responses to climate change. Overall, the article supplies a detailed, instructive example of how philosophy can support policy-relevant decision-making processes at the community level. (shrink)

Effective public policy to mitigate climate change footprints should build on data-driven analysis of firm-level strategies. This article’s conceptual approach augments the resource-based view of the firm and identifies investments in four firm-level resource domains to develop capabilities in climate change impact mitigation. The authors denote the resulting framework as the GISTe model, which frames their analysis and public policy recommendations. This research uses the 2008 Carbon Disclosure Project database, with high-quality information on firm-level climate change (...) strategies for 552 companies from North America and Europe. In contrast to the widely accepted myth that European firms are performing better than North American ones, the authors find a different result. Many firms, whether European or North American, do not just “talk” about climate change impact mitigation, but actually do “walk the talk.” European firms appear to be better than their North American counterparts in “walk I,” denoting attention to governance, information management, and systems. But when it comes down to “walk II,” meaning actual Technology-related investments, North American firms’ performance is equal or superior to that of the European companies. The authors formulate public policy recommendations to accelerate firm-level, sector-level, and cluster-level implementation of climate change strategies. (shrink)

Using past episodes of climate change as a source of evidence to inform our projections about contemporary climate change requires establishing the extent to which episodes in the deep past are analogous to the current crisis. However, many scientists claim that contemporary rates of climate change (e.g., rates of carbon emissions or temperature change) are unprecedented, including compared to episodes in the deep past. If so, this would limit the utility of paleoclimate analogues. In this paper, (...) I show how a data adjustment procedure called “temporal scaling,” which must be applied to both contemporary and past rate data, complicates the claim that contemporary rates are truly unprecedented. On top of giving actionable recommendations to scientists, this paper advances the philosophical literature concerning the use of models that are known to be somewhat disanalogous to their target systems. (shrink)

Phenologists track the seasonal behavior of plants and animals in response to climatic change. During the second half of the twentieth century, phenologists developed a large-scale project to monitor the flowering time of the common lilac across the United States. By the 1960s, this approach offered a potential plant-based indicator of anthropogenic climate change, a biological signal amidst the emerging narrative of increasing levels of atmospheric carbon dioxide. As a tangible representation of changes in climate—warmer temperatures lead to (...) earlier blooming—phenology proved highly legible to scientists, politicians, and laypeople. Yet, as phenology gained broader repute in the 1960s, both in agricultural stations and as a component program of the International Biological Program, it struggled to align itself epistemically with the regnant disciplinary assumptions of mid-century ecology. Operating in the hinterlands between laboratory and field, biology and meteorology, ecological theory and agronomy practice, phenologists challenged prevailing notions of the model organism and what it meant to study biology in the field. Rebranding the discipline as a component of ecosystem modeling, scientists successfully brought phenology within the purview of mainstream ecology. In so doing, however, they obscured its climate-relevant meteorological character and stymied the development of a biological narrative of climate change. (shrink)

What are the prospects for using population policy as tool to reduce carbon emissions? In this paper, we review evidence from population science, in order to inform debates in population ethics that, so far, have largely taken place within the academic philosophy literature. In particular, we ask whether fertility policy is likely to have a large effect on carbon emissions, and therefore on temperature change. Our answer is no. Prospects for a policy of fertility-reduction-as-climate-mitigation are limited by population momentum, (...) a demographic factor that limits possible variation in the size of the population, even if fertility rates change very quickly. In particular, a hypothetical policy that instantaneously changed fertility and mortality rates to replacement levels would nevertheless result in a population of over 9 billion people in 2060. We use a leading climate-economy model to project the consequence of such a hypothetical policy for climate change. As a standalone mitigation policy, such a hypothetical change in the size of the future population-much too large to be implementable by any foreseeable government programme-would reduce peak temperature change only to 6.4°C, relative to 7.1°C under the most likely population path. Therefore, fertility reduction is unlikely to be an adequate core approach to climate mitigation. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:A Political Theology of Climate Change by Michael S. Northcott, and: Restored to Earth: Christianity, Environmental Ethics, and Ecological Restoration by Gretel Van WierenKevin J. O’BrienA Political Theology of Climate Change Michael S. Northcott grand rapids, mi: eerdmans, 2013. 335 pp. $30.00Restored to Earth: Christianity, Environmental Ethics, and Ecological Restoration Gretel Van Wieren washington, dc: georgetown university press, 2013. 208 pp. $29.95These two excellent books, A (...) Political Theology of Climate Change by Michael S. Northcott and Restored to Earth by Gretel Van Wieren, offer distinct directions for Christian environmental ethics: Northcott develops a rigorous analysis of climate change as a failure of enlightenment rationality and politics, while Van Wieren argues that those who care about the earth can connect with it through the practice of ecological restoration. Northcott theologically defines a complex global problem; Van Wieren theologically engages a community-based solution. Each book advances Christian environmental ethics in important ways; considered together, they have much to teach about the content and methods of the field.Northcott’s A Political Theology of Climate Change argues that climate change is a “cosmopolitical crisis” and that Christians should embrace a more ecological and theological politics in response. Theologians and ethicists have long argued that environmental degradation has cosmological roots: The belief that human beings are separate from nature makes global-scale degradation and pollution acceptable. Northcott adds a nuanced political analysis, noting that modern politics is based on a denial “that the weather is political, or that politics influences the climate” (46). Enlightenment thinkers who laid the groundwork for contemporary political structures insisted that storms and floods were not divine responses to human behavior, so modern nation-states are premised on an ideological separation from nature. Climate change reveals the failure of such thinking, so the facts of climate change pose an inherently political challenge.Thus, failures to act on climate change are political failures. Mainstream politics cannot consider that economic growth is unsustainable because it is premised on ignorance about nature. Nations cannot collaborate to solve the problem because they have no common moral agreement “about the ends and goals of human life” (245). Any response to climate change that simply extends [End Page 198] the enlightenment project—by ascribing “rights” to the natural world, or by trusting existing secular political structures—is doomed to failure. Instead, climate change calls for a different cosmology and a different politics.Northcott argues that both can be found in the Christian tradition. Natural law thinking respects the relationship between human beings and external moral standards “set into the structure of the cosmos,” demanding careful accounting for the atmospheric consequences of human action (246). Virtue ethics teaches the ideal of politics as a “community of friendship and love,” nurturing practices of participation in the good life, lived with less consumption and less destructive energy (200).Northcott hopes that small-scale communities inspired by Christian cosmology and Christian politics will transition away from destructive practices and thereby demonstrate a new way of life to the world. His book concludes by reasserting that because climate change is a political problem, it is fundamentally a theological problem, calling human communities to “re-create the historic and customary connections between nature and culture, land and life, love for neighbour and nature which are central to the Jewish and Christian messianism of empire-challenging love” (316).In contrast to Northcott’s focus on political and philosophical failures, Gretel Van Wieren’s Restored to Earth seeks a “more positive, solution-oriented” approach to Christian ethics (viii). The book is about reconnecting humans with the natural world through ecological restoration, “the science and art of repairing ecosystems that have been damaged by human activities” (2), and it argues that restoration models moral life by creating physical, intellectual, emotional, communal, and spiritual connections with the natural world.Van Wieren begins with an interdisciplinary definition of ecological restoration and argues that views on the practice depend upon varied definitions of nature. She then moves to a nuanced account of restoration ecology as a spiritual practice (chapter 3) of community formation (chapter 4). Evocatively recounting a controlled prairie burn in Illinois on Maundy Thursday, Van Wieren... (shrink)

Climate models don’t give us probabilistic forecasts. To interpret their results, alternatively, as serious possibilities seems problematic inasmuch as climate models rely on contrary-to-fact assumptions: why should we consider their implications as possible if their assumptions are known to be false? The paper explores a way to address this possibilistic challenge. It introduces the concepts of a perfect and of an imperfect credible world, and discusses whether climate models can be interpreted as imperfect credible worlds. That would (...) allow one to use models for possibilistic prediction and salvage widespread scientific practice. (shrink)

Epidemiological models of the transmission of SARS-CoV-2 played an important role in guiding the decisions of policy-makers during the pandemic. Such models provide output projections, in the form of time -series of infections, hospitalisations, and deaths, under various different parameter and scenario assumptions. In this paper I caution against handling these outputs uncritically: raw model-outputs should not be presented as direct projections in contexts where modelling results are required to support policy -decisions. I argue that model (...) uncertainty should be handled and communicated transparently. Drawing on methods used by climate scientists in the fifth IPCC report I suggest that this can be done by: attaching confidence judgements to projections based on model results; being transparent about how multi-model ensembles are supposed to deal with such uncertainty; and using expert judgement to ‘translate’ model-outputs into projections about the actual world. In a slogan: tell me what you think (and why), not (just) what your models say. I then diffuse the worry that this approach infects model-based policy advice with some undesirably subjective elements, and explore how my discussion fares if one thinks the role of a scientific advisor is to prompt action, rather than communicate information. (shrink)

The central question of this thesis is how one can learn about particular targets by using models of those targets. A widespread assumption is that models have to be representative models in order to foster knowledge about targets. Thus the thesis begins by examining the concept of representation from an epistemic point of view and supports an account of representation that does not distinguish between representation simpliciter and adequate representation. Representation, understood in the sense of a representative model, is (...) regarded as a success term. That is, a representative model is one relatum in a relation of adequate representation (Chapter 2). When a representative model represents a target, it allows users of this model to learn something about the target. It is argued that a representative model has this epistemic function because it shares relevant features with the target. This presupposes a similarity view of representation. Similarity views of representation face serious objections, which will be rebutted (Chapter 3). One way of spelling out a similarity view of representation is to defend an indirect view of representation. In this thesis, which does not argue for an indirect view, it is assumed that the indirect view is a good option, if not the best, for articulating the similarity view. It is demonstrated how such an indirect view can be expanded to account for cases of technological modeling. A case study in bioengineering is used to show that the indirect view of representation must acknowledge a distinction between two directions of fit in relations between vehicles and targets. In this context the notion of design is interpreted as a relation between a vehicle and a target, thereby connecting ideas from philosophy of science with ideas from philosophy of technology (Chapter 4). Fictionalist accounts of models are intended to tackle the issue of the ontology of models. In this thesis, however, two prominent fictionalist accounts are discussed from an epistemological point of view in light of the central question regarding how one can learn about targets by using models. This question is addressed from the standpoint of Waltonian fictionalism. The result of the discussion is that the two discussed Waltonian fictionalist accounts cannot sufficiently answer the question. These accounts are criticized for their inability to deliver a satisfactory epistemology of representation (Chapter 5). Although Waltonian fictionalism is criticized, the present thesis also shows that the foundational theory of Waltonian fictionalism, the theory of make-believe can nevertheless be used to account for the distinction between projections and predictions that is made by the Intergovernmental Panel on Climate Change (Chapter 6). (shrink)

We argue that concerns about double-counting—using the same evidence both to calibrate or tune climate models and also to confirm or verify that the models are adequate—deserve more careful scrutiny in climate modelling circles. It is widely held that double-counting is bad and that separate data must be used for calibration and confirmation. We show that this is far from obviously true, and that climate scientists may be confusing their targets. Our analysis turns on a Bayesian/relative-likelihood approach (...) to incremental confirmation. According to this approach, double-counting is entirely proper. We go on to discuss plausible difficulties with calibrating climate models, and we distinguish more and less ambitious notions of confirmation. Strong claims of confirmation may not, in many cases, be warranted, but it would be a mistake to regard double-counting as the culprit. 1 Introduction2 Remarks about Models and Adequacy-for-Purpose3 Evidence for Calibration Can Also Yield Comparative Confirmation3.1 Double-counting I3.2 Double-counting II4 Climate Science Examples: Comparative Confirmation in Practice4.1 Confirmation due to better and worse best fits4.2 Confirmation due to more and less plausible forcings values5 Old Evidence6 Doubts about the Relevance of Past Data7 Non-comparative Confirmation and Catch-Alls8 Climate Science Example: Non-comparative Confirmation and Catch-Alls in Practice9 Concluding Remarks. (shrink)

This article examines initial-condition dependence and initial-condition uncertainty for climate projections and predictions. The first contribution is to provide a clear conceptual characterization of predictions and projections. Concerning initial-condition dependence, projections are often described as experiments that do not depend on initial conditions. Although prominent, this claim has not been scrutinized much and can be interpreted differently. If interpreted as the claim that projections are not based on estimates of the actual initial conditions of the (...) world or that what makes projections true are conditions in the world, this claim is true. However, it can also be interpreted as the claim that simulations used to obtain projections are independent of initial-condition ensembles. This article argues that evidence does not support this claim. Concerning initial-condition uncertainty, three kinds of initial-condition uncertainty are identified. The first is the uncertainty associated with the spread of the ensemble simulations. The second arises because the theoretical initial ensemble cannot be used in calculations and has to be approximated by finitely many initial states. The third uncertainty arises because it is unclear how long the model should be run to obtain potential initial conditions at pre-industrial times. Overall, the discussion shows that initial-condition dependence and uncertainty in climate science are more complex and important issues than usually acknowledged. 1Introduction2Projections and Predictions 2.1Predictions2.2Projections3Initial-Condition Dependence 3.1Projections 3.1.1Dynamical conditions justifying independence of initial conditions?3.1.2What projections can and cannot provide3.2Predictions4Initial-Condition Uncertainty 4.1Projections4.2Predictions5ConclusionAppendix. (shrink)

Recent years have seen a notable increase in the production of scientific expertise by large multidisciplinary groups. The issue we address is how reports may be written by such groups in spite of their size and of formidable obstacles: complexity of subject matter, uncertainty, and scientific disagreement. Our focus is on the International Panel on Climate Change, unquestionably the best-known case of such collective scientific expertise. What we show is that the organization of work within the IPCC aims to (...) make it possible to produce documents that are indeed expert reports. To do so, we first put forward the epistemic norms that apply to expert reports in general, that is, the properties that reports should have in order to be useful and to help decision-making. Section 2 claims that these properties are: intelligibility, relevance and accuracy. Based on this analysis, section 3 points to the difficulties of having IPCC reports indeed satisfying these norms. We then show how the organization of work within the IPCC aims at and to a large extent secures intelligibility, relevance and accuracy, with the result that IPCC reports can be relied on for decision-making. Section 4 focuses on the fundamentals of IPCC’s work organization--that is, division of labour within the IPCC--while section 5 investigates three frameworks that were introduced over the course of the functioning of the IPCC: the reviewing procedure of IPCC reports, the language that IPCC authors use to express uncertainty and the Coupled Model Intercomparison Project. Concluding remarks are offered in section 6. (shrink)

We provide an overview of a transdisciplinary project about sustainable forest management under climate change. Our project is a partnership with members of the Menominee Nation, a Tribal Nation located in northern Wisconsin, United States. We use immersive virtual experiences, translated from ecosystem model outcomes, to elicit human values about future forest conditions under alternative scenarios. Our project combines expertise across the sciences and humanities as well as across cultures and knowledge systems. Our management structure, governance, and leadership (...) behaviors have both fostered and constrained our work and must be continuously responsive to changing group dynamics. Our project presents opportunities for substantial contributions to society, including insights and knowledge about complementary ways of knowing, skills training, and professional development, and opportunities for reflexive learning about effective transdisciplinary, translational, and transformative scientific processes. (shrink)

This article identifies conditions under which robust predictive modeling results have special epistemic significance---related to truth, confidence, and security---and considers whether those conditions hold in the context of present-day climate modeling. The findings are disappointing. When today’s climate models agree that an interesting hypothesis about future climate change is true, it cannot be inferred---via the arguments considered here anyway---that the hypothesis is likely to be true or that scientists’ confidence in the hypothesis should be significantly increased or (...) that a claim to have evidence for the hypothesis is now more secure. (shrink)

This article serves as the introduction to the Annual Review special section entitled ‘Bernard Stiegler and the Internation Project: Computational Practices and Circumscribed Futures’. As such, it introduces the collective undertaking of the Internation Project in relation to Stiegler’s long career as a thinker, educator and community organizer. The introduction pursues a number of themes addressed in the section’s contributions, including pharmacological logic, transindividuation, computational practices, bifurcation and negentropy (means of slowing entropic processes at individual and collective levels). All of (...) these themes pertain to the climate crises the world collectively faces and posit means by which futures can be conceived in less detrimental and destructive economic, social, technological and intellectual ways. The Internation Collective as represented and furthered in this special section responds to the demands of climate crises through a macroeconomic model designed to combat entropy at various scales, from the bio-chemical to the biosphere. (shrink)

We argue that concerns about double-counting -- using the same evidence both to calibrate or tune climate models and also to confirm or verify that the models are adequate --deserve more careful scrutiny in climate modelling circles. It is widely held that double-counting is bad and that separate data must be used for calibration and confirmation. We show that this is far from obviously true, and that climate scientists may be confusing their targets. Our analysis turns on (...) a Bayesian/relative-likelihood approach to incremental confirmation. According to this approach, double-counting is entirely proper. We go on to discuss plausible difficulties with calibrating climate models, and we distinguish more and less ambitious notions of confirmation. Strong claims of confirmation may not, in many cases, be warranted, but it would be a mistake to regard double-counting as the culprit. (shrink)