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Uncertainties, Values, and Climate Targets
Abstract
Using climate policy debates as a case study, I argue that a certain response to the argument from inductive risk, the hedging defense, runs afoul of a reasonable ethical principle: the no-passing-the-buck principle according to which scientists ought to offer their scientific judgment when such judgment is both possible and needed in public policy debates.
... Many philosophers, including those who defend the value-free ideal, respond to the underdetermination problem by claiming that scientists can use epistemic values, such as simplicity, explanatory power, and the like, to choose between competing hypotheses that fit the evidence equally well (Quine and Ullian 1974;Laudan 1984;Thagard 1988). Critics of the value-free ideal take the appeal to values one step further and argue that scientists are sometimes justified in using external values to choose between empirically equivalent hypotheses because epistemic values may fail to settle which hypothesis is better and there are often moral or social reasons not to remain neutral in such situations (Biddle 2013;Elliott 2011;Frisch 2020). ...
... For example, the Intergovernmental Panel on Climate Change (IPCC) (2014) has been evaluating six models of global climate dynamics for the last decade or so. Critics of the value-free ideal could argue that if all of these models fit the data equally well, the IPCC could recommend that policymakers use the model that best promotes public and environmental health, provided that the IPCC clarifies the factors that shape their reasoning and the limitations of their conclusions and findings (Elliott and Resnik 2014;Intemann 2015;Frisch 2020). The IPCC could recommend, for example, that the models that provide the lowest estimates of global warming by 2100 should not be used in policymaking because these models will not convince the public that the problem of climate change is serious, but also that the models that give the highest estimates should not be used because these might be perceived as alarmist. ...
... Another problem with Douglas' approach is that one might argue that there are some situations in which external values legitimately play a direct role in scientific judgment and decision-making (Biddle 2013;Elliott and McKaughan 2014). In the climate change case discussed above, for example, one could argue that climatologists may use external values, such as promoting public health or protecting the environment, when choosing among competing models of global climate that fit the data equally well (Intemann 2015;Frisch 2020). Steel (2015) also develops a functionalist approach. ...
In recent years, many philosophers of science have rejected the “value-free ideal” for science, arguing that non-epistemic values have a legitimate role to play in scientific inquiry. However, this philosophical position raises the question of how to distinguish between legitimate and illegitimate influences of values in science. In this paper, we argue that those seeking to address this “new” demarcation problem can benefit by drawing lessons from the “old” demarcation problem, in which philosophers tried to find a way of distinguishing between science and non-science. Many of those who worked on this problem ultimately found that efforts to provide necessary and sufficient conditions for defining science failed, and most concluded that the best solution to the problem was to characterize scientific hypotheses, theories, and research programs in terms of some common norms. We suggest that those seeking to distinguish between legitimate and illegitimate value influences on science would do well to adopt a similar approach. Rather than attempting to establish necessary and sufficient conditions for identifying appropriate value influences, it will be more fruitful to evaluate scientific activities based on their adherence to a set of epistemic and ethical norms that can be implemented in scientific practice by means of rules, conventions, policies, and procedures.
... In another line of response, Frisch (2020) shows that the decisions cannot be legitimately deferred or hedged based on a principle articulated by Elliott (2011): the no-passing-the-buck principle. Elliott rightly points out that it can be harmful for scientists to withhold judgment on matters when their technical judgment can help inform decision-makers. ...
... There is a kind of trade-off between informativeness and certainty, which is itself a kind of epistemic risk different from but relevant to inductive risks. 9 Hedging gets you certainty at the cost of informativeness, but science must be informative and policy-relevant in order to be consequential to decision-makers and thus to fulfill the social role that gives it social and political weight (Elliott, 2011;Steele, 2012;Steel, 2016;Brown, 2018a;Frisch, 2020; see Menon and Stegenga, 2023). It is not an either-or proposition (hedged or informative), but rather a trade-off that must be determined by relevant nonepistemic values. ...
There is a near consensus among philosophers of science whose research focuses on science and values that the ideal of value-free science is untenable, and that science not only is, but normatively must be, value-laden in some respect. The consensus is far from complete; with some regularity, defenses of the value-free ideal (VFI) as well as critiques of major arguments against the VFI surface in the literature. I review and respond to many of the recent defenses of the VFI and show that they generally fail to meet the mark. In the process, I articulate what the current burden of argument for a defense of the VFI ought to be, given the state of the literature.
... Steel (2016) points out that "even well confirmed scientific theories fall far short of the certainty of truisms such as 'coal burns'" (703). If scientific advice must be just as well-confirmed as such truisms, "scientists will have precious little informative advice to give" (707; see also John 2015b, Magnus 2018, Frisch 2020. Pamuk (2021) argues that there is a trade-off between the usefulness and (value) neutrality of expert advice: the more "neutral" a scientific claim, the less useful it is likely to be for policymakers. ...
Scientific hedges are communicative devices used to qualify and weaken scientific claims. Gregor Betz (2013) has argued – unconvincingly, we think – that hedging can rescue the value-free ideal for science. Nevertheless, Betz is onto something when he suggests there are political principles that recommend scientists hedge public-facing claims. In this paper, we recast this suggestion using the notion of public justification. We formulate and reject a Rawlsian argument that locates the justification for hedging in its ability to forge consensus. On our alternative proposal, hedging is often justified because it renders scientific claims as publicly accessible reasons.
... In other words, what drives uncertainty domestication in a model or modelling approach need not be informed by the decision situation per se, but rather by the constraints and affordances of the model and what is convenient given some set of interests, values, and priorities. A second point is that since the modelling and decision-making tasks are distributed among different actors with different roles there is always the potential for divergences, gaps, and conflicts among these actors with respect to their interests, values, and priorities (c.f., [32]). The modeller may prioritize stabilizing their model, which may involve disregarding some sources of uncertainty, whereas the policymaker may be guided by other considerations, such as for instance been accountable for preventing certain outcomes, which may lead to other choices being made with respect to which sources of uncertainty to focus on. ...
The COVID-19 pandemic illustrated many of the challenges with using science to guide planning and policymaking. One such challenge has to do with how to manage, represent and communicate uncertainties in epidemiological models. This is considerably complicated, we argue, by the fact that the models themselves are often instrumental in structuring the involved uncertainties. In this paper we explore how models ‘domesticate’ uncertainties and what this implies for science-for-policy. We analyse three examples of uncertainty domestication in models of COVID-19 and argue that we need to pay more attention to how uncertainties are domesticated in models used for policy support, and the many ways in which uncertainties are domesticated within particular models can fail to fit with the needs and demands of policymakers and planners.
... Climate scientists are only able to provide input on which climate targets to pursue if they are willing to stick their necks out and make more or less reasonable judgements beyond what they can be certain about (43). There is also the risk that suspending judgement-like when awaiting scientific consensus-can have significant ethical consequences and implications for public policy. ...
This review examines the alleged crisis of trust in environmental science and its impact on public opinion, policy decisions in the context of democratic governance, and the interaction between science and society. In an interdisciplinary manner, the review focuses on the following themes: the trustworthiness of environmental science, empirical studies of levels of trust and trust formation; social media, environmental science, and disinformation; trust in environmental governance and democracy; and co-production of knowledge and the production of trust in knowledge. The review explores both the normative issue of trustworthiness and empirical studies on how to build trust. The review does not provide any simple answers to whether trust in science is generally in decline or whether we are returning to a less enlightened era in public life with decreased appreciation of knowledge and truth. The findings are more nuanced, showing signs of both distrust and trust in environmental science.
Expected final online publication date for the Annual Review of Environment and Resources, Volume 47 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Earth system science (ESS) and modelling have given rise to a new conceptual framework in the recent decades, which goes much beyond climate science. Indeed, Earth system science and modelling have the ambition “to build a unified understanding of the Earth”, involving not only the physical Earth system components (atmosphere, cryosphere, land, ocean, lithosphere) but also all the relevant human and social processes interacting with them. This unified understanding that ESS aims to achieve raises a number of epistemological issues about interdisciplinarity. We argue that the interdisciplinary relations in ESS between natural and social / human sciences are best characterized in terms of what is called ‘scientific imperialism’ in the literature and we show that this imperialistic feature has some detrimental epistemic and non-epistemic effects, notably when addressing the issue of values in ESS. This paper considers in particular the core ESS concepts of Anthropocene, planetary boundaries and tipping points in the light of the philosophy of science discussions on interdisciplinarity and values. We show that acknowledging the interconnections between interdisciplinarity and values suggests ways for ESS to move forward in view of addressing the climate and environmental challenges.
The value-free ideal in science has been criticised as both unattainable and undesirable. We argue that it can be defended as a practical principle guiding scientific research even if the unattainability and undesirability of a value-free end-state are granted. If a goal is unattainable, then one can separate the desirability of accomplishing the goal from the desirability of pursuing it. We articulate a novel value-free ideal, which holds that scientists should act as if science should be value-free, and we argue that even if a purely value-free science is undesirable, this value-free ideal is desirable to pursue.
Expert judgment can be seen throughout climate science and even more prominently when discussing climate tipping points. To provide an accurate characterization of expert judgment we begin by evaluating the existing literature on expertise as it relates to climate science as a whole, before then focusing the literature review on the role of expert judgment in the unique context of climate tipping points. From this we turn our attention to the structured expert elicitation protocols specifically developed for producing expert judgments about tipping points. We highlight that expert elicitation is not only used for the quantification of uncertainty in this context, but also for the very identification and characterization of tipping points and their interactions, making expert judgment in itself a genuine scientific output. The central role of expert judgment in this domain raises several epistemic issues that require careful attention. Among other topics, we discuss the relationship between expert judgment and modeling, as well as the nonepistemic values that are involved in the production of expert judgments, highlighting how the elicitation protocols can be used to manage these values. In the perspective of climate change, clarifying the epistemic foundations of expert judgment in this context can help to navigate the epistemic situation between self‐defeating alarmism and blind dismissal, thus contributing to a better understanding of the challenges related to climate (and Earth system) tipping points.
This article is categorized under: Climate, History, Society, Culture > Ideas and Knowledge
The Social Status of Climate Change Knowledge > Climate Science and Decision Making
This article develops a constructive criticism of methodological conventionalism. Methodological conventionalism asserts that standards of inductive risk ought to be justified in virtue of their ability to facilitate coordination in a research community. On that view, industry bias occurs when conventional methodological standards are violated to foster industry preferences. The underlying account of scientific conventionality, however, is insufficient for theoretical and practical reasons. Conventions may be justified in virtue of their coordinative functions, but often qualify for posterior empirical criticism as research advances. Accordingly, industry bias does not only threaten existing conventions but may impede their empirically warranted improvement if they align with industry preferences. My empiricist account of standards of inductive risk avoids such a problem by asserting that conventional justification can be pragmatically warranted but has, in principle, only a provisional status. Methodological conventions, therefore, should not only be defended from preference-based infringements of their coordinative function but ought to be subjected to empirical criticism.
This chapter discusses the philosophical viability of Ottmar Edenhofer and Martin Kowarsch's proposed pragmatic-enlightened model of science advising, as well as the practical application of their proposed model to the case of climate science advising. Edenhofer and Kowarsch's model makes central use of a cartographic metaphor-one in which scientists and policymakers craft and consider different scientific routes to various value-laden ethical, political, and social destinations. But the argument from inductive risk poses a significant challenge to the viability of the metaphor, and hence, to the workability of the model. The chapter opens with a discussion of the challenge that inductive risk poses for the pragmatic-enlightened model, and closes with a discussion of how stakeholder engagement must be reconceived in order to meet that challenge.
Although epistemic values have become widely accepted as part of scientific reasoning, non-epistemic values have been largely relegated to the "external" parts of science (the selection of hypotheses, restrictions on methodologies, and the use of scientific technologies). I argue that because of inductive risk, or the risk of error, non-epistemic values are required in science wherever non-epistemic consequences of error should be considered. I use examples from dioxin studies to illustrate how non-epistemic consequences of error can and should be considered in the internal stages of science: choice of methodology, characterization of data, and interpretation of results.
Article 2 of the United Nations Framework Convention on Climate Change [United Nations (1992) http://unfccc.int/resource/docs/convkp/conveng.pdf. Accessed February 9, 2009] commits signatory nations to stabilizing greenhouse gas concentrations in the atmosphere at a level that "would prevent dangerous anthropogenic interference (DAI) with the climate system." In an effort to provide some insight into impacts of climate change that might be considered DAI, authors of the Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change (IPCC) identified 5 "reasons for concern" (RFCs). Relationships between various impacts reflected in each RFC and increases in global mean temperature (GMT) were portrayed in what has come to be called the "burning embers diagram." In presenting the "embers" in the TAR, IPCC authors did not assess whether any single RFC was more important than any other; nor did they conclude what level of impacts or what atmospheric concentrations of greenhouse gases would constitute DAI, a value judgment that would be policy prescriptive. Here, we describe revisions of the sensitivities of the RFCs to increases in GMT and a more thorough understanding of the concept of vulnerability that has evolved over the past 8 years. This is based on our expert judgment about new findings in the growing literature since the publication of the TAR in 2001, including literature that was assessed in the IPCC Fourth Assessment Report (AR4), as well as additional research published since AR4. Compared with results reported in the TAR, smaller increases in GMT are now estimated to lead to significant or substantial consequences in the framework of the 5 "reasons for concern."
A selection of the potential impacts of climate change – on agriculture,forestry, unmanaged ecosystems, sea level rise, human mortality, energyconsumption, and water resources – are estimated and valued in monetaryterms. Estimates are derived from globally comprehensive, internallyconsistent studies using GCM based scenarios. An underestimate of theuncertainty is given. New impact studies can be included following themeta-analytical methods described here. A 1 °C increase in the globalmean surface air temperature would have, on balance, a positive effect onthe OECD, China, and the Middle East, and a negative effect on othercountries. Confidence intervals of regionally aggregated impacts, however,include both positive and negative impacts for all regions. Global estimatesdepend on the aggregation rule. Using a simple sum, world impact of a1 °C warming would be a positive 2% of GDP, with a standarddeviation of 1%. Using globally averaged values, world impact would be anegative 3% (standard deviation: 1%). Using equity weighting, worldimpact would amount to 0% (standard deviation: 1%). Copyright Kluwer Academic Publishers 2002
This chapter examines two approaches to climate policy: expected utility calculations and a precautionary approach. The former provides the framework for attempts to calculate the social cost of carbon (SCC). The latter approach has provided the guiding principle for the United Nations Conference of Parties from the 1992 Rio Declaration to the Paris Agreement. The chapter argues that the deep uncertainties concerning the climate system and climate damages make the exercise of trying to calculate a well-supported value for the SCC impossible. Moreover, cost-benefit analyses are blind to important moral dimensions of the climate problem. Yet it is an open question to what extent an alternative, precautionary approach can result in specific policy recommendations such as the temperature targets of the Paris agreement.
The Trump administration is in the process of undoing what were the two central planks of President Obama’s climate policy: First, Trump has called for a review of how the social cost of carbon is calculated in used in analyses of regulatory rule making and, second, Trump has announced that the United States is withdrawing from the Paris Agreement. In this paper I examine some of the conservative critics’ objections to the first plank: calculations of the social cost of carbon in climate cost benefit analyses. I argue that while some of these criticisms are justified, the criticisms end up strengthening arguments for the importance of the second plank: the urgent need for an ambitious climate policy, in accord with the Paris Agreement, as precaution against exposing others to the risk of catastrophic harms.
Philosophers of science have recently debated whether second-order uncertainty in climate change forecasts demonstrates the applicability of the argument from inductive risk (AIR) to this case. This paper defends a generalized, normative, and structural interpretation of AIR to address challenges raised in this literature. The interpretation of AIR proposed is generalized by including the possibility that scientists may suspend judgment rather than accept or reject a hypothesis. In addition, it distinguishes between descriptive and normative versions of AIR, and provides reasons for preferring the latter. Finally, it emphasizes advantages of applying AIR at a structural rather than individual level.
The Paris Agreement duly reflects the latest scientific understanding of systemic global warming risks. Limiting the anthropogenic temperature anomaly to 1.5–2 °C is possible, yet requires transformational change across the board of modernity.
Scholars in philosophy, law, economics and other fields have widely debated how science, environmental precaution, and economic interests should be balanced in urgent contemporary problems, such as climate change. One controversial focus of these discussions is the precautionary principle, according to which scientific uncertainty should not be a reason for delay in the face of serious threats to the environment or health. While the precautionary principle has been very influential, no generally accepted definition of it exists and critics charge that it is incoherent or hopelessly vague. This book presents and defends an interpretation of the precautionary principle from the perspective of philosophy of science, looking particularly at how it connects to decisions, scientific procedures, and evidence. Through careful analysis of numerous case studies, it shows how this interpretation leads to important insights on scientific uncertainty, intergenerational justice, and the relationship between values and policy-relevant science.
This book examines a variety of ways in which societal values can be more effectively integrated into the numerous judgments associated with policy-relevant environmental research. It provides a detailed analysis of contemporary research on the hypothesis of hormesis (i.e., beneficial biological effects caused by low-dose exposure to pollutants that are ordinarily toxic). On the basis of this analysis, as well as shorter case studies of endocrine disruption and multiple chemical sensitivity, the book proposes three lessons. First, university conflict-of-interest policies are significantly limited in their ability to prevent interest groups from "hijacking" academic research on sensitive policy-relevant topics. Therefore, alternative strategies are needed to preserve science that serves the public interest. Second, concerned citizens and policy makers need to more carefully determine the most appropriate forms of broadly based deliberation to be employed in response to specific areas of sensitive, policy-relevant research. Third, the principle of informed consent, which currently provides guidance in the medical field, suggests a promising ethical framework that can help environmental scientists who disseminate debated findings to the public. Throughout, the book highlights many of the crucial ethical and societal questions surrounding research on the hormesis hypothesis. It thereby constitutes the first book-length manuscript to provide guidance to the concerned citizens, industry groups, scientists, and policy makers who are exploring ways to respond to this phenomenon.
Scientists describe the scale of the risks from unmanaged climate change as potentially immense. However, the scientific models, because they omit key factors that are hard to capture precisely, appear to substantially underestimate these risks. Many economic models add further gross underassessment of risk because the assumptions built into the economic modeling on growth, damages and risks, come close to assuming directly that the impacts and costs will be modest and close to excluding the possibility of catastrophic outcomes. A new generation of models is needed in all three of climate science, impact and economics with a still stronger focus on lives and livelihoods, including the risks of large-scale migration and conflicts.
Philosophers continue to debate both the actual and the ideal roles of values in science. Recently, Eric Winsberg has offered a novel, model-based challenge to those who argue that the internal workings of science can and should be kept free from the influence of social values. He contends that model-based assignments of probability to hypotheses about future climate change are unavoidably influenced by social values. I raise two objections to Winsberg’s argument, neither of which can wholly undermine its conclusion but each of which suggests that his argument exaggerates the influence of social values on estimates of uncertainty in climate prediction. I then show how a more traditional challenge to the value-free ideal seems tailor-made for the climate context.
Climate change presents us with a problem of intergenerational justice. While any costs associated with climate change mitigation measures will have to be borne by the world’s present generation, the main beneficiaries of mitigation measures will be future generations. This raises the question to what extent present generations have a responsibility to shoulder these costs. One influential approach for addressing this question is to appeal to neo-classical economic cost–benefit analyses and so-called economy-climate “integrated assessment models” to determine what course of action a principle of intergenerational welfare maximization would require of us. I critically examine a range of problems for this approach. First, integrated assessment models face a problem of underdetermination and induction: They are very sensitive to a number of highly conjectural assumptions about economic responses to a temperature and climate regime, for which we have no empirical evidence. Second, they involve several simplifying assumptions which cannot be justified empirically. And third, some of the assumptions underlying the construction of economic models are intrinsically normative assumptions that reflect value judgments of the modeler. I conclude that, while integrated assessment models may play a useful role as “toy models,” their use as tools for policy optimization is highly problematic.
The ideal of value free science states that the justification of scientific findings should not be based on non-epistemic (e.g. moral or political) values. It has been criticized on the grounds that scientists have to employ moral judgements in managing inductive risks. The paper seeks to defuse this methodological critique. Allegedly value-laden decisions can be systematically avoided, it argues, by making uncertainties explicit and articulating findings carefully. Such careful uncertainty articulation, understood as a methodological strategy, is exemplified by the current practice of the Intergovernmental Panel on Climate Change (IPCC).
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[T]he Precautionary Principle still has neither a commonly accepted definition nor a set of criteria to guide its implementation. 'There is,' Freestone... cogently observes, 'a certain paradox in the widespread and rapid adoption of the Precautionary Principle:' While it is applauded as a 'good thing,' no one is quite sure about what it really means or how it might be implemented. Advocates foresee precaution developing into 'the fundamental principle of environmental protection policy at [all] scales.'... Sceptics, however, claim its popularity derives from its vagueness; that it fails to bind anyone to anything or resolve any of the deep dilemmas that characterize modern environmental policy making.
When interests and preferences of researchers or their sponsors cause bias in experimental design, data interpretation or dissemination of research results, we normally think of it as an epistemic shortcoming. But as a result of the debate on science and values, the idea that all ‘extra-scientific’ influences on research could be singled out and separated from pure science is now widely believed to be an illusion. I argue that nonetheless, there are cases in which research is rightfully regarded as epistemologically deficient due to the influence of preferences on its outcomes. I present examples from biomedical research and offer an analysis in terms of social epistemology.
Impacts of 1.5°C Global Warming on Natural and Human Systems
- O Hoegh-Guldberg