Pollution control with uncertain stock dynamics: When, and how, to be precautious

Athanassoglou, Stergios; Xepapadeas, Anastasios

doi:10.1016/j.jeem.2011.11.001

Cited by 98 publications

(63 citation statements)

References 36 publications

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“…In particular, these include applications of the smooth ambiguity model by Lange and Treich (2008), who provide comparative statics results of the role played by ambiguity in a simple two-period parametric model, and Millner et al (2013) and Lemoine and Traeger (2014), both of whom propose numerical models under ambiguity aversion. Other contributions include applications of the macroeconomic technique of robust control (Hansen and Sargent 2008) by Athanassoglou and Xepapadeas (2012), who consider an analytical pollution control problem, and by Rudik (2016), who applies the concept in an integrated assessment model (IAM) including learning.…”

Section: Introductionmentioning

confidence: 99%

Managing Catastrophic Climate Risks Under Model Uncertainty Aversion

2017

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W e propose a robust risk management approach to deal with the problem of catastrophic climate change that incorporates both risk and model uncertainty. Using an analytical model of abatement, we show how aversion to model uncertainty influences the optimal level of mitigation. We disentangle the role of preferences from the structure of model uncertainty, which we define by means of a simple measure of disagreement across models. With data from an expert elicitation about climate change catastrophes, we quantify the relative importance of these two effects and calibrate a numerical integrated assessment model of climate change. The results indicate that the structure of model uncertainty, and specifically how model disagreement varies in abatement, is the key driver of optimal abatement and that model uncertainty warrants a higher level of climate change mitigation.

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Section: Introductionmentioning

confidence: 99%

Managing Catastrophic Climate Risks Under Model Uncertainty Aversion

2017

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“…Note that our formulation of environmental quality and pollution is consistent with Brock and Taylor's (2005), where pollution is a by-product of productive activities meaning that when output is zero (infinite) pollution is zero (infinite) too. 4 Differently from Kijima et al (2011), where the environment is subject to completely exogenous shocks, in our framework the planner by choosing the share of emissions to abate endogenously determines the trend of the stochastic process affecting environmental quality. 5 Notice that in the u = 0 case, for sufficiently small levels of capital environmental quality might initially improve due to pollution absorption; however, as capital keeps growing it will decrease.…”

Section: The Modelmentioning

confidence: 99%

“…Existing works focus on the production side, looking at how uncertainty on the production process affects economic agents' decisions (Soretz, 2003;2004;, or show how uncertainty on the evolution of pollution may generate different development paths (Kijima et al, 2011;Privileggi and Marsiglio, 2013). To the best of our knowledge, apart from Athanassoglou and Xepapadeas (2012) and La Torre et al (2016) who analyze a pollution control problem under uncertainty without modeling the related macroeconomic effects, how uncertainty affects the trade off between environmental and economic performance in an economic growth setup has never been analyzed thus far.…”

Section: Introductionmentioning

confidence: 99%

Economic growth and abatement activities in a stochastic environment: a multi-objective approach

Marsiglio

Torre

2016

Ann Oper Res

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We analyze the effect of uncertainty on economic growth and environmental quality in a concave stochastic bicriteria dynamic problem, solved by means of scalarization techniques and the Hamilton-Jacobi-Bellman equation. Specifically, we focus on an endogenous growth model with purposive abatement activities, where environmental quality, subject to random shocks, is merely a source of utility and does not play any productive role. We show that even in the absence of direct (environmental) productivity effects, along the optimal path production results enhanced by an indirect productivity effect, which transforms environmental quality into the engine of growth. However, this does not prevent uncertainty to dampen economic and environmental performance, leading to a lower social welfare than in a deterministic framework. Forthcoming in Annals of Operations Research AbstractWe analyze the effect of uncertainty on economic growth and environmental quality in a concave stochastic bicriteria dynamic problem, solved by means of scalarization techniques and the HamiltonJacobi-Bellman equation. Specifically, we focus on an endogenous growth model with purposive abatement activities, where environmental quality, subject to random shocks, is merely a source of utility and does not play any productive role. We show that even in the absence of direct (environmental) productivity effects, along the optimal path production results enhanced by an indirect productivity effect, which transforms environmental quality into the engine of growth. However, this does not prevent uncertainty to dampen economic and environmental performance, leading to a lower social welfare than in a deterministic framework.

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“…On normative grounds it clashes with appealing axioms of rational choice, while from a descriptive standpoint it is often in conflict with observed patterns of behavior. 2 Given the complexity of many environmental phenomena, it is unsurprising that models of ambiguity aversion are increasingly being employed to address policy under conflicting expert opinion (Woodward and Bishop [47], Millner et al [30], Asano [3], Athanassoglou and Xepapadeas [4]). 3 1 See Gilboa and Marinacci [21] for a comprehensive recent survey.…”

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confidence: 99%

“…To fix ideas and prepare the ground for the paper's empirical exercise, let us suppose that a decision maker elicits the judgment of a set of experts on the effect of R&D investment on the future cost of a promising green technology. 4 Levels of R&D investment affect the decision maker's problem in two ways: (a) they alter experts' subjective probability distributions on the technology's future cost and (b) they are arguments of a utility function that measures the investment's cost-effectiveness as a function of R&D expenditure and the resulting technological improvement.…”

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confidence: 99%

Setting Environmental Policy When Experts Disagree

Athanassoglou¹,

Bosetti

2014

Environ Resource Econ

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How can a decision-maker assess the potential of environmental policies when a group of experts provides divergent estimates on their effectiveness? To address this question, we propose and analyze a variant of the well-studied α-maxmin model in decision theory. In our framework, and consistent to the paper's empirical focus on renewable-energy R&D investment, experts' subjective probability distributions are allowed to be action-dependent. In addition, the decision maker constrains the sets of priors to be considered via a parsimonious measure of their distance to a benchmark "average" distribution that grants equal weight to all experts. While our model is formally rooted in the decision-theoretic framework of Olszewski [37], it may also be viewed as a structured form of sensitivity analysis. We apply our framework to original data from a recent expert elicitation survey on solar energy. The analysis suggests that more aggressive investment in solar energy R&D is likely to yield significant dividends even, or rather especially, after taking expert ambiguity into account.

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Pollution control with uncertain stock dynamics: When, and how, to be precautious

Cited by 98 publications

References 36 publications

Managing Catastrophic Climate Risks Under Model Uncertainty Aversion

Managing Catastrophic Climate Risks Under Model Uncertainty Aversion

Economic growth and abatement activities in a stochastic environment: a multi-objective approach

Setting Environmental Policy When Experts Disagree

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