Avoiding the Tragedy of the Horizon: Portfolio Design for Climate Change-Related Risk Management and the Low-Carbon Energy Transition

Bender, Jennifer; Bridges, Todd Arthur; Shah, Kushal; Weiner, Alison

doi:10.1007/978-3-030-55613-6_10

Cited by 2 publications

(1 citation statement)

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“…Short-term focus due to economic pressure [58] Reputational-risk-influenced decisions [27,28,31,59,60] Powerful-stakeholder-influenced decisions [26,61,62] Fear of revealing excessive information [63] Data-based challenges Data acquisition, treatment, and validation cost [64,65] Data normalization challenges [66,67] Inconsistent reporting by firms [1,67] Lack of historical data availability, missing data [66,67] Methodological challenges…”

Section: Behavioral Challengesmentioning

confidence: 99%

Navigating the Challenges of Environmental, Social, and Governance (ESG) Reporting: The Path to Broader Sustainable Development

Chopra,

Senadheera,

Dissanayake

et al. 2024

Sustainability

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The ascent of environmental, social, and governance (ESG) reporting has established itself as a global standard in financial markets, reflecting a paradigm shift toward corporate sustainability. Despite this, persistent concerns surround the quality of ESG reporting and its tangible impact on Sustainable Development (SD). To address the imperative transition toward a broader SD agenda within the ESG reporting framework, this study delves into contemporary issues and challenges associated with ESG reporting. It emphasizes the scarcity of interdisciplinary expertise across diversified fields, which is a crucial element for establishing robust reporting mechanisms capable of encompassing the multifaceted nature of sustainability. To address this, ESG reporting should extend beyond its company-centric focus, adapting traditional accounting systems to more effectively incorporate evolving ESG disclosure demands. This adjustment will facilitate a transparent portrayal of environmental and social impacts. The Social and Environmental Accounting (SEA) framework presents a structured approach to facilitate this transformation. This study underscores key SEA aspects that will shape future research, including enhancing data accuracy, standardizing sustainability metrics, evaluating the influence of ESG reporting on stakeholders, and refining disclosure formats.

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Section: Behavioral Challengesmentioning

confidence: 99%

Navigating the Challenges of Environmental, Social, and Governance (ESG) Reporting: The Path to Broader Sustainable Development

Chopra,

Senadheera,

Dissanayake

et al. 2024

Sustainability

View full text Add to dashboard Cite

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Value Creation in Sustainable Energy Transition Using Reinforcement Learning

Cheraghi,

Bratvold,

Muhammad

et al. 2024

SPE Europe Energy Conference and Exhibition

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The global challenge of climate change has prompted significant steps to reduce CO2 emissions, guided by international agreements like the Paris Agreement, which set targets for the transition from fossil fuels to renewable sources. Acting too slowly could result in future losses and reputational issues, while moving too swiftly could jeopardize shareholder value due to the marginal profitability or potential losses resulting from technology challenges and the immaturity of many renewable projects. To navigate this complex landscape wisely, energy companies will benefit from developing Sequential Decision Making (SDM) policies to maximize value creation from decision flexibility under uncertainties. However, achieving this goal is not trivial. This necessitates the use of decision-analysis and optimization methods that deliver robust decision insights without prohibitive computational costs or delays. In this research, we propose a multi-objective SDM framework to model the dynamic energy landscape up to 2050, allowing exploration of various decision strategies related to different portfolios for allocating funds across three sectors: hydrocarbon, renewables, and CO2 reduction investments. This approach aims to maximize value during the transformation while accounting for uncertainties in hydrocarbon and renewable energy productions, energy prices, and production costs. The framework targets three main objectives: maximizing profit, minimizing CO2 emission, and enhancing competitive advantage. Our research evaluates the benefits of using Reinforcement Learning (RL) to solve for the optimal investment decision policy for the SDM context. The sequential decisions of the energy company shape the virtual dynamic environment by influencing various variables. Subsequently, the environment responds with new variable states and immediate value feedback, reflecting the three defined objectives. Through repeated interactions, the RL algorithm navigates the extensive state space, learning the optimal decision policy amidst uncertainties.

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Avoiding the Tragedy of the Horizon: Portfolio Design for Climate Change-Related Risk Management and the Low-Carbon Energy Transition

Cited by 2 publications

References 0 publications

Navigating the Challenges of Environmental, Social, and Governance (ESG) Reporting: The Path to Broader Sustainable Development

Navigating the Challenges of Environmental, Social, and Governance (ESG) Reporting: The Path to Broader Sustainable Development

Value Creation in Sustainable Energy Transition Using Reinforcement Learning

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