An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty

Analyses of global climate policy as a sequential decision under uncertainty have been severely restricted by dimensionality and computational burdens. Therefore, they have limited the number of decision stages, discrete actions, or number and type of uncertainties considered. In particular, other formulations have difficulty modeling endogenous or decision-dependent uncertainties, in which the shock at time t+1 depends on the decision made at time t. In this paper, we present a stochastic dynamic programming formulation of the Dynamic Integrated Model of Climate and the Economy (DICE), and the application of approximate dynamic programming techniques to numerically solve for the optimal policy under uncertain and decision-dependent technological change. We compare numerical results using two alternative value function approximation approaches, one parametric and one non-parametric. Using the framework of dynamic programming, we show that an additional benefit to near-term emissions reductions comes from a probabilistic lowering of the costs of emissions reductions in future stages, which increases the optimal level of near-term actions

Balancing solar PV deployment and RD&D: A comprehensive framework for managing innovation uncertainty in electricity technology investment planning

We present a new framework for studying the socially optimal level of generating capacity and public RD&D investments for the electric power sector under decision-dependent technical change uncertainty. We construct a bottom-up stochastic electricity generation capacity expansion model with uncertain endogenous RD&D-based technical change, focusing on solar PV RD&D investment planning for its current prominent role in sustainable energy and climate policy deliberations. We characterize the decision-dependent process of technical change uncertainty as unknown outcomes of RD&D investments that increase the likelihood of success with increasing amounts of RD&D, and calibrate to a novel expert elicitation dataset that accounts for this decision-dependence. The problem is framed as a multi-stage decision under uncertainty, where the decision maker learns and adapts to new information between decision periods. Specifically, our application considers four decision stages, with the decision-maker choosing investment levels for new capacity and solar PV RD&D, while learning about RD&D outcomes that can reduce solar PV costs between each stage. The problem is thus formulated to match the manner in which real-world decisions about RD&D investments in renewable energy are made, and avoids common assumptions of perfect foresight, or uncertainty but no learning, that are often used in practice. Numerical results show that when uncertainty and learning features are both included, the optimal solar PV RD&D investment strategy changes from solutions using other methods. Considering uncertainty and learning results in solar RD&D investment differences as high as 20 percent lower in the first-stage, and 300 percent higher in later stages. We also show that when uncertainty is considered without learning, the fraction of new solar PV capacity investments can be depressed. Overall, this paper shows that it is possible to unify several realistic features of the deployment and development problem for the electricity sector to meet sustainability goals into one framework

Integrating uncertainty into public energy research and development decisions

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 integration of technological uncertainty into energy policy: expert elicitations, integrated assessment models, and decision frameworks. Here we review efforts to incorporate all three approaches to facilitate public energy R&D decision-making under uncertainty. We highlight emerging insights that are robust across elicitations, models, and frameworks, relating to the allocation of public R&D investments, and identify gaps and challenges that remain

An Approximate Dynamic Programming Framework for Modeling Global Climate Policy under Decision-Dependent Uncertainty

for modeling global climate policy under decision-dependent uncertaint

An Approximate Dynamic Programming Framework for Modeling Global Climate Policy under Decision-Dependent Uncertainty

Analyses of global climate policy as a sequential decision under uncertainty have been severely restricted by dimensionality and computational burdens. Therefore, they have limited the number of decision stages, discrete actions, or number and type of uncertainties considered. In particular, other formulations have difficulty modeling endogenous or decision-dependent uncertainties, in which the shock at time t+1 depends on the decision made at time t. In this paper, we present a stochastic dynamic programming formulation of the Dynamic Integrated Model of Climate and the Economy (DICE), and the application of approximate dynamic programming techniques to numerically solve for the optimal policy under uncertain and decision-dependent technological change. We compare numerical results using two alternative value function approximation approaches, one parametric and one non-parametric. Using the framework of dynamic programming, we show that an additional benefit to near-term emissions reductions comes from a probabilistic lowering of the costs of emissions reductions in future stages, which increases the optimal level of near-term actions.

Many-objective robust decision making for managing an ecosystem with a deeply uncertain threshold response

Managing ecosystems with deeply uncertain threshold responses and multiple decision makers poses nontrivial decision analytical challenges. The problem is imbued with deep uncertainties because decision makers do not know or cannot converge on a single probability density function for each key parameter, a perfect model structure, or a single adequate objective. The existing literature on managing multistate ecosystems has generally followed a normative decision-making approach based on expected utility maximization (MEU). This approach has simple and intuitive axiomatic foundations, but faces at least two limitations. First, a prespecified utility function is often unable to capture the preferences of diverse decision makers. Second, decision makers’ preferences depart from MEU in the presence of deep uncertainty. Here, we introduce a framework that allows decision makers to pose multiple objectives, explore the trade-offs between potentially conflicting preferences of diverse decision makers, and to identify strategies that are robust to deep uncertainties. The framework, referred to as many-objective robust decision making (MORDM), employs multiobjective evolutionary search to identify trade-offs between strategies, re-evaluates their performance under deep uncertainty, and uses interactive visual analytics to support the selection of robust management strategies. We demonstrate MORDM on a stylized decision problem posed by the management of a lake in which surpassing a pollution threshold causes eutrophication. Our results illustrate how framing the lake problem in terms of MEU can fail to represent key trade-offs between phosphorus levels in the lake and expected economic benefits. Moreover, the MEU strategy deteriorates severely in performance for all objectives under deep uncertainties. Alternatively, the MORDM framework enables the discovery of strategies that balance multiple preferences and perform well under deep uncertainty. This decision analytic framework allows the decision makers to select strategies with a better understanding of their expected trade-offs (traditional uncertainty) as well as their robustness (deep uncertainty)

Role of Low Carbon Energy Technologies in Near Term Energy Policy

In the first part of this thesis, we use a multi-model framework to examine a set of possible future energy scenarios resulting from R&D portfolios of Solar, Nuclear, Carbon Capture and Storage (CCS), Bio-Fuels, Bio-Electricity and Batteries for electric transportation. We show that CCS significantly complements Bio-Electricity, while most of the other energy technology pairs are substitutes. From the probabilistic analysis of future energy scenarios we observe that portfolios with CCS tend to stochastically dominate those without CCS; portfolios with only renewables tend to be stochastically dominated by others; and that there are clear decreasing marginal returns to scale. We also find that, with higher damage risk, there is more incentive for technical advancement in CCS and less incentive for development of Solar energy technology. In the second part of this thesis, we examine the optimal R&D portfolio changes at the different R&D budget levels and how risk in climate damages affects the optimal R&D portfolio. We find that the optimal portfolio is generally not robust to risk, and the optimal investments in the energy technologies vary with risk in climate damages; however R&D investments in certain energy technologies, such as Nuclear, are robust under the different risk cases. We note that while CCS plays a significant role in the optimal portfolio when there is no risk in climate damages, it plays an even more significant role in the higher climate damage risk cases. We also find that R&D investment in the Biofuels energy technology increases significantly with increase in climate damage risk, while Solar, Batteries for Electric Transportation and Bio-Electricity technologies go out of favor with increases in climate damage risk. We also propose a methodology for obtaining solutions to subset portfolio problems, based on the characteristics of the individual technologies. We prove that the subset portfolio problem is optimal if the individual technology does not interact with any of the other technologies, we confirm this in our empirical portfolio problem. In the third part of this thesis, we conduct an illustrative global sensitivity analysis on a large scale integrated assessment model with a view to determining the primary drivers of uncertainty in the model and examining the effect of structural uncertainty on the model. We compare our results to a previous paper which conducted a one factor at a time sensitivity analysis and find that both sensitivity methods provide the same result which is different from findings from the previous paper. We find that model interactions are present even in our very limited illustrative analysis. We also conduct most of the steps needed for a full global sensitivity analysis of the model and highlight the challenges in conducting this analysis on the GCAM model. We show that there exist a need for global sensitivity analysis for accurate determination of the principal drivers of uncertainty in integrated models

Electricity generation and emissions reduction decisions under uncertainty : a general equilibrium analysis

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 169-183).The electric power sector, which accounts for approximately 40% of U.S. carbon dioxide emissions, will be a critical component of any policy the U.S. government pursues to confront climate change. In the context of uncertainty in future policy limiting emissions and future technology costs, society faces the following question: What should the electricity mix we build in the next decade look like? We can continue to focus on conventional generation or invest in low-carbon technologies. There is no obvious answer without explicitly considering the risks created by uncertainty. This research investigates socially optimal near-term electricity investment decisions under uncertainty in future policy and technology costs. It employs a novel framework that models decision-making under uncertainty with learning in an economy-wide setting that can measure social welfare impacts. Specifically, a computable general equilibrium (CGE) model is formulated as a two-stage stochastic dynamic program focused on decisions in the electric power sector. The new model is applied to investigate a number of factors affecting optimal near-term electricity investments: (1) policy uncertainty, (2) expansion rate limits on low-carbon generation, (3) low-carbon technology cost uncertainty, (4) technological learning (i.e., near-term investment lowers the expected future technology cost), and (5) the inclusion of a safety valve in future policy which allows the emissions cap to be exceeded, but at a cost. In modeling decision-making under uncertainty, an optimal electricity investment hedging strategy is identified. Given the experimental design, the optimal hedging strategy reduces the expected policy costs by over 50% compared to a strategy derived using the expected value for the uncertain parameter; and by 12-400% compared to strategies developed under a perfect foresight or myopic framework. This research also shows that uncertainty has a cost, beyond the cost of meeting a policy. In the experimental design used here, uncertainty in the future policy increases the expected cost of policy by over 45%. If political consensus can be reached and the climate science uncertainties resolved, setting clear, long-term policies can minimize expected policy costs. In addition, this work contributes to the learning-by-doing literature by presenting a stochastic formulation of technological learning in which near-term investments in a technology affect the probability distribution of the future cost of that technology. Results using this formulation demonstrate that learning rates lower than those found in the literature can lead to significant additional near-term investment in low-carbon technology in order to lower the expected future cost of the technology in case a stringent policy is adopted.Ultimately, this dissertation demonstrates that near-term investments in low-carbon technologies should be greater than what would be justified to meet near-term goals alone. Near-term low-carbon investments can lower the expected cost of future policy by developing a less carbon-intensive electricity mix, spreading the burden of emissions reductions over time, helping to overcome technology expansion rate constraints, and reducing the expected future cost of low-carbon technologies-all of which provide future flexibility in meeting a policy. The additional near-term cost of low-carbon investments is justified by the future flexibility that such investments create. The value of this flexibility is only explicitly considered in the context of decision-making under uncertainty.by Jennifer Faye Morris.Ph.D