A novel illustration of real options analysis to address the problem of probabilities under deep uncertainty and changing climate risk

Stroombergen, Adolf; Lawrence, Judy

doi:10.1016/j.crm.2022.100458

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…Due attention would then need to be paid to any assumed probabilities, while the justification of these probabilities would be difficult for deep uncertainties. A potentially promising method to address this has been proposed by Stroombergen and Lawrence (2022).…”

Section: Frontiers In Energy Researchmentioning

confidence: 99%

A robust investment decision to deploy bioenergy carbon capture and storage—exploring the case of Stockholm Exergi

Stenström,

Khatiwada,

Levihn

et al. 2024

Front. Energy Res.

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The upscaling of novel carbon dioxide removal, such as bioenergy carbon capture and storage (BECCS), to gigatonne scales is an urgent priority if global warming is to be limited to well below 2 °C. But political, economic, social, technological, environmental and regulatory uncertainty permeates BECCS projects and deters investors. To address this, we explore options to improve the robustness of BECCS deployment strategies in the face of multi-dimensional uncertainties. We apply Dynamic Adaptive Planning (DAP) through expert interviews and Robust Decision Making (RDM) through exploratory modelling, two decision making under deep uncertainty methods, to the case of Stockholm Exergi, an early mover aiming to deploy BECCS at a combined heat and power plant in the capital of Sweden. The main contributions of the research are to 1) illustrate how a quantification of robustness against uncertainty can support an investment decision to deploy BECCS 2) comprehensively cover uncertain vulnerabilities and opportunities of deploying BECCS, and 3) identify critical scenarios and adaptations to manage these uncertainties. The main conclusions are: investing in BECCS is relatively robust if assessing performance across many scenarios and if comparing the worst-cases of either investing, or not doing so. Not investing could miss out on up to € 3.8 billion in terms of net present value. The critical uncertainties of BECCS can be managed by strengthening biomass sustainability strategies and by gaining support for negative emission trading regulation on carbon markets, e.g., voluntary or Paris Agreement Article 6. Even in vulnerable scenarios of average electricity prices above 82 €/MWh, if trading regulation is implemented before 2030 and if negative emission prices exceed 151 €/CO2, investing in BECCS performs better than not doing so in 96% of cases. We suggest that facility-level parameters and cost-reductions are of little importance for BECCS investments and upscaling. It is regulatory certainty of operating revenues, e.g., through negative emission markets, that needs to be provided by policymakers.

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Section: Frontiers In Energy Researchmentioning

confidence: 99%

A robust investment decision to deploy bioenergy carbon capture and storage—exploring the case of Stockholm Exergi

Stenström,

Khatiwada,

Levihn

et al. 2024

Front. Energy Res.

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“…The fusion could be used to investigate economic impacts, such as impairment of housing markets due to inundation (Rodziewicz et al, 2022). The fusion could determine the timing of "cut-off probabilities", beyond which a proposed investment decision becomes undesirable (Stroombergen & Lawrence, 2022). The fusion could be transformed from a static projection to a learning scenario that can inform real options analysis (Völz & Hinkel, 2023a, 2023b.…”

Section: A Flexible Tool For Climate Scientistsmentioning

confidence: 99%

Fusion of Probabilistic Projections of Sea-Level Rise

Grandey,

Dauwels,

Koh

et al. 2024

Preprint

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Alternative projections of sea-level rise from ice sheet mass loss differ markedly. The differences drive ambiguity in probabilistic projections of sea level. Can the alternative projections be combined to produce a single probabilistic projection that quantifies a best estimate of the scientific uncertainty? To produce this best estimate, we propose a fusion of alternative projections. The fusion combines the complementary strengths of the ‘medium confidence’ and the ‘low confidence’ projections. Two assumptions undergird the fusion. First, we assume that the ‘medium confidence’ projections reliably sample the most-likely central possibilities. Second, we assume that the p-box outer bound – which includes the ‘low confidence’ projections – provides a more reliable estimate of unlikely tail possibilities driven by poorly-understood processes. Accordingly, the fusion is weighted towards the ‘medium confidence’ projections near the median and towards the p-box outer bound near the tails. We demonstrate our proposed fusion by combining alternative projections of sea-level change at the end of the 21st century. The fusion provides probabilistic answers: according to the fusion, the probability of global mean sea-level change exceeding 1.0 m by 2100 is 6% under a low-emissions scenario and 31% under a high-emissions scenario. The fusion also informs summary statements based on credible intervals: under a high-emissions scenario, for example, we are 90% certain that global mean sea level will rise by 0.5–2.2 m during the 21st century. By quantifying a best estimate of scientific uncertainty, the fusion caters to diverse users.

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“…Multi-criteria analysis is commonly used when impacts cannot be converted to a common metric [13]. Real options analyses, on the other hand, take into account time flexibility and irreversible costs [14], but are still usually based on an underlying assumption that values can be quantified and optimised. A modified application of real options analysis has been developed that avoids these limitations in situations of deep uncertainty associated with changing climate that stress tests options rather than optimises them [15].…”

Section: Introductionmentioning

confidence: 99%

Simulating the Impacts of an Applied Dynamic Adaptive Pathways Plan Using an Agent-Based Model: A Tauranga City, New Zealand, Case Study

Allison

Stephens

Blackett

et al. 2023

JMSE

Self Cite

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Climate change and relative sea-level rise (RSLR) will increasingly expose coastal cities to coastal flooding, erosion, pluvial and fluvial flooding, episodic storm-tide flooding and eventually, permanent inundation. Tools are needed to support adaptive management approaches that allow society to adapt incrementally by making decisions now without creating path dependency and compromising decision-making options in the future. We developed an agent-based model that integrates climate-related physical hazard drivers and socio-economic drivers. We used it to explore how adaptive actions might be sequentially triggered within a low-elevation coastal city in New Zealand, in response to various climate change and socio-economic scenarios. We found that different adaptive actions are triggered at about the same RSLR level regardless of shared socio-economic pathway/representative concentration pathway scenario. The timing of actions within each pathway is dictated mainly by the rate of RSLR and the timing and severity of storm events. For the representative study site, the model suggests that the limits for soft and hard protection will occur around 30 cm RSLR, fully-pumped water systems are viable to around 35 cm RSLR and infrastructure upgrades and policy mechanisms are feasible until between 40 cm and 75 cm RSLR. After 75 cm RSLR, active retreat is the only remaining adaptation pathway.

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A novel illustration of real options analysis to address the problem of probabilities under deep uncertainty and changing climate risk

Cited by 7 publications

References 25 publications

A robust investment decision to deploy bioenergy carbon capture and storage—exploring the case of Stockholm Exergi

A robust investment decision to deploy bioenergy carbon capture and storage—exploring the case of Stockholm Exergi

Fusion of Probabilistic Projections of Sea-Level Rise

Simulating the Impacts of an Applied Dynamic Adaptive Pathways Plan Using an Agent-Based Model: A Tauranga City, New Zealand, Case Study

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