Large scale scenario analysis of future low carbon energy options

Olaleye, Olaitan; Baker, Erin

doi:10.1016/j.eneco.2015.02.006

Cited by 13 publications

(3 citation statements)

References 49 publications

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“…Ricci et al 31 and Iyer et al 32 each perform a simple sensitivity analysis to bound possible future costs and efficiency of a single technology (carbon capture and storage (CCS) and nuclear, respectively) and its economic implications. Barron 34 perform a large scale, multi-parameter scenario analysis to identify technologies that are complements or substitutes at a societal level.…”

Section: Review Article Nature Energymentioning

confidence: 99%

Integrating uncertainty into public energy research and development decisions

2017

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P redicting the future is extremely difficult 1 , yet it is nonsensical to ignore the knowledge and understanding we can acquire from experts when making decisions. It is well understood that using experts, and using them wisely, is a key component of evidence-based policymaking 2 .In the energy sector, policymakers are faced with a number of near-term decisions, such as balancing technology R&D, performance standards, and subsidies; or designing energy technology R&D portfolios. Designing these policies involves managing the significant uncertainty that exists about how technologies will evolve with and without different policies 3 . In the wake of the December 2015 Paris Agreement, in which countries agreed to put in place national action plans to reduce greenhouse-gas emissions, and in the context of tight government budgets around the world, designing robust and cost-effective energy innovation policies in the face of technological uncertainty has become more pressing 4 . The question of how to design portfolios of R&D investments across a range of energy technologies has received much attention, most recently after the launch of the Mission Innovation and Breakthrough Energy Coalition pledges to increase public energy R&D funding and follow-on private investments in energy technologies. The R&D decision literature typically approaches this problem in two stages: first considering how R&D investments affect future technology costs 5 , then considering how future technology costs affect energy and climate policies (for example, EMF28; ref. 6).One method for informing policy decisions is to perform expert elicitations (a structured process for eliciting subjective probability distributions from subject-matter experts 7 ). Over the past 10 years, a number of researchers have performed expert elicitations to characterize the uncertainty around future cost and performance of various energy technologies, often subject to assumptions about public R&D investments. This information, however, is just one of the pieces of analysis needed to inform the design of optimal energy R&D Integrating uncertainty into public energy research and development decisions Laura Díaz Anadón 1,2 † *, Erin Baker 3 † and Valentina Bosetti 4,5 † Public energy research and development (R&D) is recognized as a key policy tool for transforming the world's energy system in a cost-effective way. However, managing the uncertainty surrounding technological change is a critical challenge for designing robust and cost-effective energy policies. The design of such policies is particularly important if countries are going to both meet the ambitious greenhouse-gas emissions reductions goals set by the Paris Agreement and achieve the required harmonization with the broader set of objectives dictated by the Sustainable Development Goals. The complexity of informing energy technology policy requires, and is producing, a growing collaboration between different academic disciplines and practitioners. Three analytical components have emerged to support the ...

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Section: Review Article Nature Energymentioning

confidence: 99%

Integrating uncertainty into public energy research and development decisions

2017

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“…Often, three values are explored-low, baseline and high-where the baseline values are considered most likely, along with "plausible" lower and upper values. Scenario analysis is used in energy system modeling to study the effect of different parameter values on future energy technology portfolios [19] and climate policies [20]. An alternative approach to parametric uncertainty involves using probabilistic distributions to characterize uncertainties in input parameters and then applying Monte Carlo analysis to find the distribution of the modeling outputs [21].…”

Section: Research Gapmentioning

confidence: 99%

Adoption Model Choice Affects the Optimal Subsidy for Residential Solar

Tibebu,

Hittinger,

Miao

et al. 2024

Energies

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Understanding the adoption patterns of clean energy is crucial for designing government subsidies that promote the use of these technologies. Existing work has examined a variety of adoption models to explain and predict how economic factors and other technology and demographic attributes influence adoption, helping to understand the cost-effectiveness of government policies. This study explores the impact of adoption modeling choices on optimal subsidy design within a single techno–economic framework for residential solar PV technology. We applied identical datasets to multiple adoption models and evaluated which model forms appear feasible and how using different choices affects policy decisions. We consider three existing functional forms for rooftop solar adoption: an error function, a mixed log-linear regression, and a logit demand function. The explanatory variables used are a combination of net present value (NPV), socio-demographic, and prior adoption. We compare how the choice of model form and explanatory variables affect optimal subsidy choices. Among the feasible model forms, there exist justified subsidies for residential solar, though the detailed schedule varies. Optimal subsidy schedules are highly dependent on the social cost of carbon and the learning rate. A learning rate of 10% and a social carbon cost of USD 50/ton suggest an optimal subsidy starting at USD 46/kW, while the initial subsidy is 10× higher (USD 540/kW) with a learning rate of 15% and social carbon cost of USD 70/ton. This work illustrates the importance of understanding the true drivers of adoption when developing clean energy policies.

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“…Bretschger and Zhang [14] report that for the Swiss economy the welfare loss due to carbon policy increased from 1.21% to 1.58% (a 30% increase in the cost of a carbon policy) if nuclear energy was phased out. Olaleye and Baker [15] examined the impact of technological advancement in energy technologies and determined that "Nuclear and CCS have the most impact on abatement costs, with CCS mostly important at high levels of abatement.". Iyer [16] examined small modular reactors and model results suggested that they have significant potential to reduce carbon emissions and improve energy security.…”

Section: The Cost Of Nuclear Drives the Cost Of Carbon Mitigationmentioning

confidence: 99%