DS01 - Gestion sobre des ressources et adaptation au changement climatique

Integrating Deep Uncertainty in Climate Change Modelling – INDUCED

Deep uncertainty and climate change economics

Uncertainty is pervasive. If this assertion is true for most decision problems, it is of particular importance for the economic policy related to climate change. In this case indeed, decisions to be made have global, long-lasting and potentially irreversible consequences. The environmental challenge faced by humanity concerning global climate change therefore illustrates particularly well the importance of considering uncertainty when making a decision.

New ways of incorporating preferences people have with respect to deep uncertainty in the decision-making processes related to climate policy.

INDUCED proposes to address three challenges: (i) reviewing theoretically the properties of the alternative models that have been proposed to deal with deep uncertainty, identifying their strengths and weaknesses, and their applicability in the specific context of climate change; (ii) studying (using an experimental approach) the rationality of the alternative approaches and the intensity of deep uncertainty aversion, that could be directly used in the process of decision making in the face of deep uncertainty; and (iii) investigating whether a contextual framework affects decision-makers.<br /><br />The project builds on two fundamental observations, which constitute its main intellectual drivers: <br />Observation 1: Choosing among different climate policies is essentially an exercise in risk management that has to be performed in a situation of deep uncertainty. It requires a decision-making approach that is robust, in the sense that it does reasonably well across a wide range of distributions (or models), it is less sensitive to initial assumptions, it is valid for a wide range of futures, and it keeps options open.<br /><br />Observation 2: While it is increasingly recognized that individuals usually manifest aversion towards deep uncertainty, the extent to which deep uncertainty aversion exists and its prescriptive status are still an open question. In particular, very little has been said in the literature concerning the degree of deep uncertainty aversion or, how it compares to the degree of risk aversion which is used in applied economic models. If one recognizes that alternative decision models may have better explanatory power, and are potentially able to provide better predictions and guidelines in situations of deep uncertainty, it becomes essential to have a more precise idea of what are the underlying properties of these alternative models. In particular, it is important to know the values of the parameters that should be used to make predictions and design optimal policies.

Two different types of approaches are followed within the project. The first approach consists in investigating theoretical considerations, while the second approach consists in studying behavioural characteristics of the individuals using experimental methods. This means that we conduct experiments (in both laboratory settings and in the field) to collect data in order to test the validity of alternative decision theories and to estimate the parameters that are necessary for applications in the context of climate change economics.

Investigating the ambiguity preferences of a unique sample of real-life policy- makers at the Paris UN climate conference (COP21), we find that policymakers are generally ambiguity averse. Using a simple design, we are moreover able to show that these preferences are not necessarily due to an irrational behavior, but rather to intrinsic preferences over unknown probabilities. Exploring the heterogeneity within our sample, we also show that the country of origin and the degree of quantitative sophistication affect policymakers’ attitudes towards compound risk, but not towards ambiguity.

One project would be to push further the investigation initiated in Bosetti et al. (2017) and to analyze how policymakers, and in particular climate negotiators, react to the deep uncertainty that characterizes climate change.

Berger, L. and V. Bosetti (2019). Are policymakers ambiguity averse? The Economic Journal (accepted for publication)

Uncertainty is pervasive. If this assertion is true for most decision problems, it is of particular importance for the economic policy related to climate change. In this case indeed, decisions to be made have global, long-lasting and potentially irreversible consequences. The environmental challenge faced by humanity concerning global climate change therefore illustrates particularly well the importance of considering uncertainty when making a decision. Decisions concerning climate change have to be made in the presence of uncertainty concerning both the science of climate (due to the extreme complexity of the climate system) and some basic socio-economic and technology drivers (due to our inability to perfectly capture the way our socio-economic system would respond, mitigate and adapt to climate change). However, while it is now fully recognized that the presence of these uncertainties represents an essential datum of the climate change issue the way they are treated and integrated in the models we use to make predictions, or to design public policies remains unsatisfactory.

The objective of the project is to propose new ways of incorporating preferences people have with respect to deep uncertainty in the decision making processes related to climate policy. INDUCED proposes to achieve this objective by addressing three challenges: (i) reviewing theoretically the properties of the alternative models that have been proposed to deal with deep uncertainty, identifying their strengths and weaknesses, and their applicability in the specific context of climate change; (ii) studying the rationality of the alternative approaches and the intensity of deep uncertainty aversion that could be directly used in the process of decision making in the face of deep uncertainty; and (iii) investigating whether a contextual framework affects decision-makers.
The project builds on two fundamental observations, which constitute its main intellectual drivers:
• First observation: Choosing among different climate policies is essentially an exercise in risk management that has to be performed in a situation of deep uncertainty. It requires a decision making approach that is robust, in the sense that it does reasonably well across a wide range of distributions (or models), it is less sensitive to initial assumptions, it is valid for a wide range of futures, and it keeps options open.
• Second observation: While it is increasingly recognized that individuals usually manifest aversion towards deep uncertainty, the extent to which deep uncertainty aversion exists and its prescriptive status is still an open question. In particular, very little has been said in the literature concerning the degree of deep uncertainty aversion or, how it compares to the degree of risk aversion, which is used in applied economic models. If one recognizes that alternative decision models may have better explanatory power, and are potentially able to provide better predictions and guidelines in situations of deep uncertainty, it becomes essential to have a more precise idea of what are the underlying properties of these alternative models. In particular, it is important to know the values of the parameters that should be used to make predictions and design optimal policies.

The project aims at contributing to the debate on the way to address uncertainty in the context of climate change. This is of particular importance since the decisions to be made potentially have important consequences on the socio-economic environment and are associated with events that have never been encountered before.

Project coordination

Loic Berger (Lille - Economie et Management)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

LEM Lille - Economie et Management

Help of the ANR 248,400 euros
Beginning and duration of the scientific project: December 2017 - 48 Months

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