The impact of uncertainty on optimal emission policies

Botta, Nicola; Jansson, Patrik; Ionescu, Cezar

doi:10.5194/esd-9-525-2018

Cited by 6 publications

(7 citation statements)

References 10 publications

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“…This accounts for the fact that, in practice, decisions are not always implemented, be this because global coordination is necessarily imperfect, because global players tend to be in competition and legislations tend to have large inertia or perhaps because some other global challenge (a pandemic or an economic downturn) has taken center stage. As demonstrated in [32], limited effectiveness has a significant impact on optimal GHG emissions policies. Thus, it would be inappropriate to assume that decisions are always implemented with certainty.…”

Section: A Stylized Decision Processmentioning

confidence: 99%

“…Another caveat is about the notion of stylized decision process itself. We have introduced this notion in [32] and we will discuss it in more detail in section 4.3. The notion is closely related with that of storyline put forward in [10] but there are also important differences.…”

Section: Clarifications Caveats and Related Workmentioning

confidence: 99%

“…Reference mathematical introductions to SDP are given in sections 1.2 and 2.1 of [17] and [16], respectively. For an application of the theory to GHG emissions problems, see [32].…”

Section: The Theory In a Nutshellmentioning

confidence: 99%

“…Here, we only provide a concise, simplified overview. Motivation and details can be found in [41], [15] and [32]. For an introduction to the mathematical theory of optimal control, we recommend [16] and [17].…”

Section: Problem Solution Componentsmentioning

confidence: 99%

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Responsibility Under Uncertainty: Which Climate Decisions Matter Most?

Botta

Brede

Crucifix

et al. 2021

Preprint

Self Cite

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We propose a new method for estimating how much decisions under monadic uncertainty matter. The method is generic and suitable for measuring responsibility in finite horizon sequential decision processes. It fulfills “fairness” requirements and three natural conditions for responsibility measures: agency, avoidance and causal relevance. We apply the method to study how much decisions matter in a stylized greenhouse gas emissions process in which a decision maker repeatedly faces two options: start a “green” transition to a decarbonized society or further delay such a transition. We account for the fact that climate decisions are rarely implemented with certainty and that their consequences on the climate and on the global economy are uncertain. We discover that a “moral” approach towards decision making – doing the right thing even though the probability of success becomes increasingly small – is rational over a wide range of uncertainties.

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Section: A Stylized Decision Processmentioning

confidence: 99%

Section: Clarifications Caveats and Related Workmentioning

confidence: 99%

“…Reference mathematical introductions to SDP are given in sections 1.2 and 2.1 of [17] and [16], respectively. For an application of the theory to GHG emissions problems, see [32].…”

Section: The Theory In a Nutshellmentioning

confidence: 99%

Section: Problem Solution Componentsmentioning

confidence: 99%

See 2 more Smart Citations

Responsibility Under Uncertainty: Which Climate Decisions Matter Most?

Botta

Brede

Crucifix

et al. 2021

Preprint

Self Cite

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show abstract

“…In these systems, OCT tries to find an optimal choice for some control variable by optimizing a specific objective function 17 . Applied to Earth system models, the focus has been set on the design of climate regulators and their impact on climate modification 18,19 . Viability theory (VT) as a subfield of OCT can be stated as an example.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning in World-Earth system models to discover sustainable management strategies

Strnad

Barfuß

Donges

et al. 2020

Preprint

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Increasingly complex, non-linear World-Earth system models are used for describing the dynamics of the biophysical Earth system and the socio-economic and socio-cultural World of human societies and their interactions. Identifying pathways towards a sustainable future in these models for informing policy makers and the wider public, e.g. pathways leading to a robust mitigation of dangerous anthropogenic climate change, is a challenging and widely investigated task in the field of climate research and broader Earth system science. This problem is particularly difficult when constraints on avoiding transgressions of planetary boundaries and social foundations need to be taken into account. In this work, we propose to combine recently developed machine learning techniques, namely deep reinforcement learning (DRL), with classical analysis of trajectories in the World-Earth system. Based on the concept of the agent-environment interface, we develop an agent that is generally able to act and learn in variable manageable environment models of the Earth system. We demonstrate the potential of our framework by applying DRL algorithms to two stylized World-Earth system models. Conceptually, we explore thereby the feasibility of finding novel global governance policies leading into a safe and just operating space constrained by certain planetary and socio-economic boundaries. The artificially intelligent agent learns that the timing of a specific mix of taxing carbon emissions and subsidies on renewables is of crucial relevance for finding World-Earth system trajectories that are sustainable on the long term.We propose a framework for using deep reinforcement learning (DRL) as an approach to extend the field of Earth system analysis by a new method. We build our framework upon the agentenvironment interface concept. The agent can apply management options to models of the Earth system as the environment of interest and learn by rewards provided by the environment. We train our agent with a deep Q-neural network extended by current state-of-the-art algorithms. We find that the agent is able to learn novel, previously undiscovered policies that navigate the system into sustainable regions in two exemplary conceptual models of the World-Earth system.

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Responsibility Under Uncertainty: Which Climate Decisions Matter Most?

Botta

Brede

Crucifix

et al. 2023

Environ Model Assess

View full text Add to dashboard Cite

We propose a new method for estimating how much decisions under monadic uncertainty matter. The method is generic and suitable for measuring responsibility in finite horizon sequential decision processes. It fulfills “fairness” requirements and three natural conditions for responsibility measures: agency, avoidance and causal relevance. We apply the method to study how much decisions matter in a stylized greenhouse gas emissions process in which a decision maker repeatedly faces two options: start a “green” transition to a decarbonized society or further delay such a transition. We account for the fact that climate decisions are rarely implemented with certainty and that their consequences on the climate and on the global economy are uncertain. We discover that a “moral” approach towards decision making — doing the right thing even though the probability of success becomes increasingly small — is rational over a wide range of uncertainties.

show abstract

The impact of uncertainty on optimal emission policies

Cited by 6 publications

References 10 publications

Responsibility Under Uncertainty: Which Climate Decisions Matter Most?

Responsibility Under Uncertainty: Which Climate Decisions Matter Most?

Deep reinforcement learning in World-Earth system models to discover sustainable management strategies

Responsibility Under Uncertainty: Which Climate Decisions Matter Most?

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