Erin Baker

Advanced solar R&D: Combining economic analysis with expert elicitations to inform climate policy

The relationship between R&D investments and technical change is inherently uncertain. In this paper we combine economics and decision analysis to incorporate the uncertainty of technical change into climate change policy analysis. We present the results of an expert elicitation on the prospects for technical change in advanced solar photovoltaics. We then use the results of the expert elicitations as inputs to the MiniCAM integrated assessment model, to derive probabilistic information about the impacts of R&D investments on the costs of emissions abatement.

Demand Subsidies Versus R&D: Comparing the Uncertain Impacts of Policy on a Pre-commercial Low-carbon Energy Technology

We combine an expert elicitation and a bottom-up manufacturing cost model to compare the effects of R&D and demand subsidies. We model their effects on the future costs of a low-carbon energy technology that is not currently commercially available, purely organic photovoltaics (PV). We find that: (1) successful R&D enables PV to achieve a cost target of 4c/kWh, (2) the cost of PV does not reach the target when only subsidies, and not R&D, are implemented, and (3) production-related effects on technological advanceNlearning-by-doing and economies of scaleNare not as critical to the long-term potential for cost reduction in organic PV than is the investment in and success of R&D. These results are insensitive to two levels of policy intensity, the level of a carbon price, the availability of storage technology, and uncertainty in the main parameters used in the model. However, a case can still be made for subsidies: comparisons of stochastic dominance show that subsidies provide a hedge against failure in the R&D program.

Opportunities for advances in climate change economics

Target carbons costs, policy designs, and developing countries There have been dramatic advances in understanding the physical science of climate change, facilitated by substantial and reliable research support. The social value of these advances depends on understanding their implications for society, an arena where research support has been more modest and research progress slower. Some advances have been made in understanding and formalizing climate-economy linkages, but knowledge gaps remain [e.g., as discussed in (1, 2)]. We outline three areas where we believe research progress on climate economics is both sorely needed, in light of policy relevance, and possible within the next few years given appropriate funding: (i) refining the social cost of carbon (SCC), (ii) improving understanding of the consequences of particular policies, and (iii) better understanding of the economic impacts and policy choices in developing economies.

Optimal Energy R&D Portfolio Investments in Response to a Carbon Tax

In this paper, we deal with a very timely issue-R&D strategies needed for compliance with a climate policy in an economically optimal way. We provide interesting insights into the composition of R&D portfolios across the main mitigation options for decision makers and policy makers. We address the optimal R&D investment response of a decision maker or an engineering manager-at the firm level with a portfolio of alternative technologies-to a rising carbon tax. Understanding the optimal allocation of investments in these technologies is crucial because like most economic resources, there is a limitation on the investment capabilities of a firm to undertake these innovative efforts. In addition, environmental R&D spending is irreversible and investment decisions made today have multiperiod consequences on the energy technologies landscape. Thus, we explore the reaction of a firms optimal investment in an energy R&D portfolio comprising four different technologies to increases in a future carbon tax. We find that investment allocation depends on the elasticity of substitution between fossil and nonfossil energy inputs, and the relative costs and efficacy of the R&D programs; and that overall investment tends to decrease in risk depending on firm flexibility and specifications.

Development of a Green Building Decision Support Tool: A Collaborative Process

In this paper, we discuss a collaborative process for developing a decision tool to support decisions around investment in green energy technologies. Our tool was developed specifically for the Hitchcock Center for the Environment, a local environmental education organization, and the development process began as an undergraduate student service learning project. Building on the student projects, we developed an Excel-based tool that allows users to select various combinations of technologies and instantly see the financial, environmental, and educational impacts of their choice. Given our initial parameters and the preferences of the Hitchcock Center staff, the optimal configuration included installing a biomass heating system and a composting toilet, but avoiding investment in other green technologies, yielding an annualized preference-adjusted cost of

Optimal Feed-in Tariff Schedules

5,252.58. Sensitivity analysis indicated that the optimal choice was not sensitive to environmental valuations, and only slightly sensitive to educational values. All participants in the process found the concept and practice of value elicitation to be useful and illuminating.

Increasing Risk and Increasing Informativeness: Equivalence Theorems

We analyze the design of optimal feed-in tariff schedules under production-based learning. We examine least cost policies in a simple two-period model that focuses on bringing down the levelized cost of renewable technologies to a predefined target under two well-known dynamics: learning-by-doing (LBD) and economies of scale (EOS). We show that, when the levelized cost reduction target is stringent, subsidies are required in both periods, regardless of the dynamics. However, when the target is moderate, the optimal policy is to subsidize only in one of the two periods: under the LBD dynamics, it is optimal to subsidize as early as possible, whereas under the EOS dynamics, it is optimal to subsidize as late as possible. Under the LBD dynamics the prevailing factor is the impact of early investment on cumulative experience, whereas under the EOS dynamics the prevailing factor is capital depreciation. The key takeaway is that, based on the underlying dynamics, the policy maker needs to adopt fundamentally different kinds of policies to promote renewable technologies.

Option Value and the Diffusion of Energy Efficient Products

When considering problems of sequential decision making under uncertainty, two of the most interesting questions are: How does the value of the optimal decision variable change with an increase in risk? How does the value of the optimal decision variable change with a more informative signal? In this paper, we show that if the payoff function is separable in the random variable, then one model can simultaneously answer both questions. This result holds for the reaction functions and equilibria of noncooperative games, as well as for single decision makers, with virtually no restrictions on the payoff functions. This is useful because otherwise it is very difficult to get at general results on the impact of learning. Furthermore, we clarify why the impacts of risk and a more informative signal are different when the payoff function is nonlinear in the random variable. It is because the directional impacts of informativeness are independent of risk attitude; the impacts of risk are not.

The Value of Better Information on Technology R&D Programs in Response to Climate Change

In a widely cited series of papers, Hassett and Metcalf argue that the slow diffusion of energy saving technology may be due to a high option value to waiting. While the authors clarify that this is relevant for yes/no decisions (such as whether to add insulation to a home), this argument has been widely cited even in investment decisions that involve a choice over multiple appliances or vehicles. In this note we consider how uncertainty and irreversibility would impact a consumer’s decision about when to buy which new product. We show that, a priori, applying an option value framework is as likely to lead to slow diffusion of inefficient products as to slow diffusion of efficient products. This casts some doubt on the idea that an option value framework is the primary driver of the slow diffusion of energy efficient technologies.

A control model of policy uncertainty and energy R&D investments

Expert elicitations are a promising method for determining how R&D investments are likely to have an impact on technological advance in climate change energy technologies. But, expert elicitations are time consuming and resource intensive. Thus, we investigate the value of the information gained in expert elicitations. More specifically, given baseline elicitations from one study, we estimate the expected value of better information (EVBI) from revisiting and improving these assessments. We find that the EVBI is very large in comparison with the cost of performing expert elicitations. We also find that EVBI is higher on technologies with larger budgets and with net values that are not too high or too low.