Rupert Way’s research while affiliated with University of Oxford and other places

Rapidly decarbonizing the global energy system is critical for addressing climate change, but concerns about costs have been a barrier to implementation. Most energy-economy models have historically underestimated deployment rates for renewable energy technologies and overestimated their costs. These issues have driven calls for alternative approaches and more reliable technology forecasting methods. Here, we use an approach based on probabilistic cost forecasting methods that have been statistically validated by backtesting on more than 50 technologies. We generate probabilistic cost forecasts for solar energy, wind energy, batteries, and electrolyzers, conditional on deployment. We use these methods to estimate future energy system costs and explore how technology cost uncertainty propagates through to system costs in three different scenarios. Compared to continuing with a fossil fuel-based system, a rapid green energy transition will likely result in overall net savings of many trillions of dollars—even without accounting for climate damages or co-benefits of climate policy.

Significance Forecasting is essential to design efforts to address climate change. We conduct a systematic comparison of probabilistic technology cost forecasts produced by expert elicitation and model-based methods. We assess their performance by generating probabilistic cost forecasts of energy technologies rooted at various years in the past and then comparing these with observed costs in 2019. Model-based methods outperformed expert elicitations both in terms of capturing 2019 observed values and producing forecast medians that were closer to the observed values. However, all methods underestimated technological progress in almost all technologies. We also produce 2030 cost forecasts and find that elicitations generally yield narrower uncertainty ranges than model-based methods and that model-based forecasts are lower for more modular technologies.

An analysis of historical cost trends of energy technologies shows that the decades long increase in the deployment of renewable energy technologies has consistently coincided with steep declines in their costs. For example, the cost of solar photovoltaics has declined by three orders of magnitude over the last 50 years. Similar trends are to be found with wind, energy storage, and electrolysers (hydrogen-based energy). Such declines are set to continue and will take several of these renewable technologies well below the cost base for current fossil fuel power generation. Most major climate mitigation models produced for the IPCC and the International Energy Agency have continually underestimated such trends despite these trends being quite consistent and predictable. By incorporating such trends into a simple, transparent energy system model we produce new climate mitigation scenarios that provide a contrasting perspective to those of the standard models. These new scenarios provide an opportunity to reassess the common narrative that a Paris-compliant emissions pathway will be expensive, will require reduced energy reliability or economic growth, and will need to rely on technologies that are currently expensive or unproven as scale. This research provides encouraging evidence for governments that are looking for greater ambition on decarbonising their economies while providing economic growth opportunities and affordable energy.

We must exploit socioeconomic tipping points and amplifiers

We consider how to optimally allocate investments in a portfolio of competing technologies using the standard mean-variance framework of portfolio theory. We assume that technologies follow the empirically observed relationship known as Wright's law, also called a “learning curve” or “experience curve”, which postulates that costs drop as cumulative production increases. This introduces a positive feedback between cost and investment that complicates the portfolio problem, leading to multiple local optima, and causing a trade-off between concentrating investments in one project to spur rapid progress vs. diversifying over many projects to hedge against failure. We study the two-technology case and characterize the optimal diversification in terms of progress rates, variability, initial costs, initial experience, risk aversion, discount rate and total demand. The efficient frontier framework is used to visualize technology portfolios and show how feedback results in nonlinear distortions of the feasible set. For the two-period case, in which learning and uncertainty interact with discounting, we compare different scenarios and find that the discount rate plays a critical role.

This report assesses the technology innovation implications of NDCs, technology portfolio choices, and international competitiveness in clean technologies. Chapter 1 consists of a quantitative analysis showing the export and innovative strength of countries in 14 low-carbon technologies. Most countries of the analyzed panel exhibit a specialization in at least one low-carbon technology. Chapter 2 estimates experience curves of energy technologies and finds that that it is likely that wind, solar and storage technologies will become much cheaper in the near future, and that this progress can be accelerated by increasing near-term investments. Fossil fuel and nuclear based technologies have only a low chance of significant future progress. Country case studies present past experiences with low-carbon technologies, future possibilities, and discuss different policy options. Using the example of wind energy in Brazil and South Africa, the results of chapter 3 suggest that a rightly designed climate policy together with Local Content Requirements (LCR) can indeed be a driving force for a strong local industry supporting decarbonization. Chapter 4 highlights that industrial and technological competitiveness are not also always related and identifies the main barriers in China to further innovation in its PV sector. Chapter 5 determines the technological potential and competitiveness of electric mobility technologies in Italy. Chapter 6 presents an analysis of a technology innovation system (TIS) of concentrated solar power (CSP) in South Africa and identifies certain technologies in which South Africa can create a comparative advantage. Chapter 7 finds positive prospects for wind energy in the Brazilian climate policy.

This paper considers how to optimally allocate investments in a portfolio of competing technologies. We introduce a simple model representing the underlying trade-off - between investing enough effort in any one project to spur rapid progress, and diversifying effort over many projects simultaneously to hedge against failure. We use stochastic experience curves to model the idea that investing more in a technology reduces its unit costs, and we use a mean-variance objective function to understand the effects of risk aversion. In contrast to portfolio theory for standard financial assets, the feedback from the experience curves results in multiple local optima of the objective function, so different optimal portfolios may exist simultaneously. We study the two-technology case and characterize the optimal diversification as a function of relative progress rates, variability, initial cost and experience, risk aversion and total demand. There are critical regions of the parameter space in which the globally optimal portfolio changes sharply from one local minimum to another, even though the underlying parameters change only marginally, so a good understanding of the parameter space is essential. We use the efficient frontier framework to visualize technology portfolios and show that the feedback leads to nonlinear distortions of the feasible set.

We consider how to optimally allocate investments in a portfolio of competing technologies using the standard mean-variance framework of portfolio theory. We assume that technologies follow the empirically observed relationship known as Wright's law, also called a "learning curve" or "experience curve", which postulates that costs drop as cumulative production increases. This introduces a positive feedback between cost and investment that complicates the portfolio problem, leading to multiple local optima, and causing a trade-off between concentrating investments in one project to spur rapid progress vs. diversifying over many projects to hedge against failure. We study the two-technology case and characterize the optimal diversification in terms of progress rates, variability, initial costs, initial experience, risk aversion, discount rate and total demand. The efficient frontier framework is used to visualize technology portfolios and show how feedback results in nonlinear distortions of the feasible set. For the two-period case, in which learning and uncertainty interact with discounting, we compare different scenarios and find that the discount rate plays a critical role.