Modeling Coupled Climate, Ecosystems, and Economic Systems

Brock, William A.; Xepapadeas, Anastasios

doi:10.2139/ssrn.2630974

Cited by 2 publications

(2 citation statements)

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“…Our uncertainty‐aversion formulation uses the entropic penalization 57 . The framework of uncertainty‐averse stochastic optimization has been formulated as a zero‐sum differential game between the decision‐maker as a minimizing player and an adverse maximizing player called nature 79 . This formulation has been widely employed in economics, 80,81 insurance, 82 and recently in environmental and ecological engineering 58,83 …”

Section: Mathematical Analysismentioning

confidence: 99%

A hybrid stochastic river environmental restoration modeling with discrete and costly observations

Yoshioka

Tsujimura

Hamagami

et al. 2020

Optim Control Appl Methods

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Summary A recent river environmental restoration problem is approached from a standpoint of stochastic control of hybrid regime‐switching diffusion processes with discrete and costly observations. This setting harmonizes with real problems because continuously obtaining environmental and ecological information is difficult, and is often costly. The main problem is to decide when and how much of the sediment should be supplied into a river environment to effectively suppress bloom of benthic algae. The interventions are allowed only at observation times. Finding the optimal river restoration policy ultimately reduces to solving an optimality equation in an unconventional form due to the observation cost and discrete observations. We show its unique solvability and present a connection with degenerate parabolic partial differential equations, with which we can construct an effective algorithm for its approximation. An uncertainty‐averse optimization problem is also considered as an advanced problem. Coefficients and parameter values are identified from experimental and observation results to numerically compute the optimal restoration policy of an existing river environment with and without uncertainty.

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Section: Mathematical Analysismentioning

confidence: 99%

A hybrid stochastic river environmental restoration modeling with discrete and costly observations

Yoshioka

Tsujimura

Hamagami

et al. 2020

Optim Control Appl Methods

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“…I focus on discussing the first five types of uncertainty and methods on making decisions in the face of these uncertainties (see Brock and Hansen (2018) for a more complete survey about ambiguity and misspecification). Moreover, I focus on reviewing discrete-time stochastic IAMs (see Brock and Xepapadeas (2018) for discussion of continuous-time IAMs).…”

Section: Introductionmentioning

confidence: 99%

The Role of Uncertainty and Risk in Climate Change Economics

Chari¹

2018

Preprint

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Integrated Assessment Models (IAMs) of the climate and economy aim to analyze the impact and efficacy of policies that aim to control climate change, such as carbon taxes and subsidies. A major characteristic of IAMs is that their geophysical sector determines the mean surface temperature increase over the preindustrial level, which in turn determines the damage function. Most of the existing IAMs are perfect-foresight forward-looking models, assuming that we know all of the future information. However, there are significant uncertainties in the climate and economic system, including parameter uncertainty, model uncertainty, climate tipping risks, economic risks, and ambiguity. For example, climate sensitivity, a well-known parameter that measures how much the equilibrium temperature will change if the atmospheric carbon concentration doubles, can range from one to ten in the literature. Climate damages are also uncertain: some researchers assume that climate damages are proportional to instantaneous output, while others assume that climate damages have a more persistent impact on economic growth. The spatial distribution of climate damages is also uncertain.Climate tipping risks represent (nearly) irreversible climate events that may lead to significant changes in the climate system, such as the Greenland ice sheet collapse, while the conditions, probability of tipping, duration, and associated damage are also uncertain. Technological progress in carbon capture and storage, adaptation, renewable energy, and energy efficiency are uncertain too. Future international cooperation and implementation of international agreements in controlling climate change may vary over time, possibly due to economic risks, natural disasters, or social conflict. In the face of these uncertainties, policymakers have to provide a decision that considers important factors such as risk aversion, inequality aversion, and sustainability of the economy and ecosystem. Solving this problem may require richer and more realistic models than standard IAMs, and advanced computational methods. The recent literature has shown that these uncertainties can be incorporated into IAMs and may change optimal climate policies significantly.(2017) argue that policymakers need a numerical value for the social cost of carbon (SCC) for policy evaluation and implementation, and producing a credible numerical value requires sophisticated computer models, i.e. IAMs. Brock and Hansen (2018) stress: "Defenses for policies that combat climate damage externalities induced by human activity need not require precise knowledge of the magnitude or timing of the potential adverse impacts. ...Waiting for precise knowledge of the eventual consequences of continued or expanded human induced CO2 emissions could make mitigation or adaptation extremely costly." Goulder (2020) calls for urgent and stronger policy action to address global climate change. This review focuses on recent work about the role of uncertainty in controlling climate change. Here I use a broad perspective...

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Modeling Coupled Climate, Ecosystems, and Economic Systems

Cited by 2 publications

References 54 publications

A hybrid stochastic river environmental restoration modeling with discrete and costly observations

A hybrid stochastic river environmental restoration modeling with discrete and costly observations

The Role of Uncertainty and Risk in Climate Change Economics

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