The world should fast track green energy. But not because of climate change.
A rapid transition to green energy is likely to save the world trillions of dollars compared to sticking with the current fossil fuel-based energy system, according to a new analysis.
The savings could amount to $12 trillion, and represent the cost of energy alone – not any benefits from curtailing climate change. “The green energy transition is going to save us money, and the faster we get on with it, the more we’ll save,” says study team member Rupert Way, a postdoctoral researcher at Oxford University in the UK.
Researchers have known for a while that conventional ways of modeling the energy transition consistently overestimate the costs of decarbonization and underestimate the rate at which green energy technology can be rolled out.
And when those pessimistic forecasts get folded into reports from the Intergovernmental Panel on Climate Change (IPCC) and other institutions, cost concerns become a barrier to climate action.
“The belief that the green energy transition will be expensive has been a major driver of the ineffective response to climate change for the past 40 years,” Way and his colleagues write in the journal Joule. Typically the conversation about decarbonization contrasts benefits like reduced air pollution and slowed climate change with the drawback of higher costs. “Our analysis suggests that such trade-offs are unlikely to exist: a greener, healthier, and safer global energy system is also likely to be cheaper,” the researchers write.
The crux of the matter is that key green energy technologies like solar and wind power are more like computers than they are like fossil fuels.
Fossil fuel prices are volatile in the short term, but stable in the long view: adjusted for inflation, fossil fuels cost pretty much what they did 140 years ago. But the cost of solar panels, wind power technology, and batteries has been dropping by about 10% per year over the last several decades. Meanwhile, the deployment of these technologies has also been increasing exponentially.
Many people are familiar with Moore’s law, which holds that the cost of certain technologies decreases exponentially over time, to explain the falling cost of electronics. The researchers used a similar principle known as Wright’s law – the cost of a technology drops exponentially as its use increases – in their analysis. Another way of thinking of this is as a learning curve where cost drops as experience increases.
The researchers used historical data on costs and deployment of more than 50 different energy technologies to forecast their future cost in three scenarios: a fast transition which would have the world eliminate fossil fuels by 2050, a slow transition that stretches the switch out to 2070, and no transition in which fossil fuels continue to dominate the energy system.
There is roughly an 80% chance that a fast transition to a green energy system will be cheaper than sticking with fossil fuels, the researchers report.
A fast transition to green energy will require massive investment in grid infrastructure, but is likely to deliver much cheaper energy that more than cancels out the infrastructure cost, they say. In 2050, the fast transition will require an investment in the electricity network to the tune of $670 billion per year, while sticking with fossil fuels requires just $530 billion per year in grid investment. But the total energy system cost is estimated at $6.3 trillion per year for the fossil fuel system and only $5.9 trillion per year for the fast transition.
Sober-sounding analysts often counsel a slow rollout of green energy to save on costs, but in fact a fast transition is likely to be cheaper than a slow one, the researchers found. “The faster we scale up these clean energy technologies, the faster their costs will continue to fall, and the easier it will be to displace expensive and polluting fossil fuels,” explains Way.
A 2022 IPCC report warned that decarbonizing the energy system in line with the goals of the Paris Agreement would result in a 1.3-2.7% loss of GDP worldwide. But the new analysis suggests that deep decarbonization is actually likely to raise global GDP.
The study doesn’t address exactly which policies will be needed to ensure the green energy transition is fast enough to be cheaper than the status quo. But Way says two strategies that could likely help are expanding and upgrading electricity grids worldwide, and supporting the development of green fuels over the next decade.
“Also, it’s worth remembering that the main results of our paper refer to just the transformation of the energy system,” he says. Once the costs of climate damages are factored in, the fast transition is likely to be tens or hundreds of trillions of dollars cheaper than the fossil fuel system. “There really appears to be very little downside to pursuing a rapid energy transition, and almost certainly a vast upside,” he says.
Source: Way R. et al. “Empirically grounded technology forecasts and the energy transition.” Joule 2022.
Image: ©Anthropocene Magazine.
Empirically grounded technology forecasts and the energy transition
Context & scale
Decisions about how and when to decarbonize the global energy system are highly influenced by estimates of the likely cost. Most energy-economy models have produced energy transition scenarios that overestimate costs due to underestimating renewable energy cost improvements and deployment rates. This paper generates probabilistic cost forecasts of energy technologies using a method that has been statistically validated on data for more than 50 technologies. Using this approach to estimate future energy system costs under three scenarios, we find that compared to contuinuing with a fossil fuel-based system, a rapid green energy transition is likely to result in trillions of net savings. Hence, even without accounting for climate damages or climate policy co-benefits, transitioning to a net-zero energy system by 2050 is likely to be economically beneficial. Updating models and expectations about transition costs could dramatically accelerate the decarbonization of global energy systems.
Future energy system costs will be determined by a combination of technologies that produce, store, and distribute energy. Their costs and deployment will change with time due to innovation, competition, public policy, concerns about climate change, and other factors. To provide some perspective on the likely future energy system, Figure 1 shows how the energy landscape has evolved over the last 140 years. Figure 1A shows the historical costs of the principal energy technologies, and Figure 1B gives their deployment; both of which are on a logarithmic scale. As we approach the present in Figure 1A, the diagram becomes more congested, making it clear that we are in a period of unprecedented energy diversity, with many technologies with global average costs around $100/MWh competing for dominance.
The long-term trends provide a clue as to how this competition may be resolved: The prices of fossil fuels such as coal, oil, and gas are volatile, but after adjusting for inflation, prices now are very similar to what they were 140 years ago, and there is no obvious long-range trend. In contrast, for several decades the costs of solar photovoltaics (PV), wind, and batteries have dropped (roughly) exponentially at a rate near 10% per year. The cost of solar PV has decreased by more than three orders of magnitude since its first commercial use in 1958.1
Figure 1B shows how the use of technologies in the global energy landscape has evolved since 1880, when coal passed traditional biomass. It documents the slow exponential rise in the production of oil and natural gas over a century and the rapid rise and plateauing of nuclear energy. But perhaps the most remarkable feature is the dramatic exponential rise in the deployment of solar PV, wind, batteries, and electrolyzers over the last decades as they transitioned from niche applications to mass markets. Their rate of increase is similar to that of nuclear energy in the 1970s, but unlike nuclear energy, they have all consistently experienced exponentially decreasing costs. The combination of exponentially decreasing costs and rapid exponentially increasing deployment is different from anything observed in any other energy technologies in the past, and positions these key green technologies to challenge the dominance of fossil fuels within a decade.
How likely is it that clean energy technology costs will continue to drop at similar rates in the future? Under what conditions will this happen, and what does this imply for the overall cost of the green energy transition? Is there a path forward that can get us to net-zero emissions cheaply and quickly? We address these questions here by applying empirically tested, state-of-the-art cost forecasting methods to energy technologies.
Historically, most energy-economy models have underestimated deployment rates for renewable energy technologies and overestimated their costs2, 3, 4, 5, 6, 7, which has led to calls for alternative approaches and more reliable technology forecasting methods 8, 9, 10, 11, 12, 13, 14, 15. Recent efforts have made progress in this direction16, 17, 18, 19, but they are largely deterministic in nature. The methods we use are probabilistic, allowing us to view energy pathways through the lens of placing bets on technologies. After all, powering modern economies requires betting on some technologies one way or another, be they clean technologies or more fossil fuels—the best we can do is make good bets. Which technologies should we bet on, and how likely are they to pay off? We focus on solar, wind, batteries, and electrolyzers, which we call here “key green technologies”, because they could play crucial roles in decarbonization and have strong progress trends that are well documented in publicly available datasets. We also consider the major incumbent energy technologies and compare our forecasts with projections made by influential energy-economy models. We investigate three different energy transition scenarios and discuss the implications for whole system costs and transition pathways.
Figure 1 provides a glimpse into the diverse nature of technological change as technologies rise and fall from dominance.20, 21, 22 It reflects how innovation and technological learning produce different outcomes for different technologies. The diversity of rates of technological improvement for energy technologies seen in Figure 1 is typical of technologies in general.23, 24, 25 Roughly speaking, technologies can be divided into two groups based on their rates of improvement. For the first group, comprising the vast majority of technologies, inflation-adjusted costs have remained roughly constant through time. Fossil fuels provide a good example: although there has been enormous progress in technologies for discovery and extraction, as easily accessible resources are depleted, it becomes necessary to extract less accessible resources, creating a “running-to-stand-still” dynamic in which prices have remained roughly constant for more than a century (this is true for all minerals26,27). Another example of a non-improving technology is carbon capture and storage (CCS); despite significant effort, over its 50-year commercial history for enhanced oil recovery, costs have not declined at all.28,29 There are even cases, such as nuclear power, where costs have increased. By contrast, for a select group of improving technologies, costs have dropped roughly exponentially while deployment has increased exponentially.23, 24, 25 Rates of improvement for technologies such as optical fibers and transistors are as high as 40%–50% per year. Solar PV, wind, and batteries have behaved similarly but with improvement rates closer to 10% (see Document S1 section “The heterogeneity and persistence of technological change”). This makes unit costs for these technologies predictable, even if the specific technological innovations that lead to lower costs are not predictable.
Because the behavior of these two groups of technologies is so different, they require different cost forecasting models. Fossil fuels such as oil and gas are tradable commodities, and according to efficient markets theory, their prices should follow a random walk.30 This provides a useful approximation for roughly a decade, but over longer spans of time they display mean reversion.31,32 This makes autoregressive models a natural choice, and we use them to forecast oil, coal, and gas prices (see Experimental procedures and Document S1 sections “AR(1) process,” “Oil,” “Coal,” and “Gas”).
For the select group of technologies that are improving, improvement rates are remarkably consistent.33 For these technologies there are two dominant methods for quantitatively forecasting costs based on historical data. The first is a generalized form of Moore’s law, which says that costs drop exponentially as a function of time (i.e., at a fixed percentage per year).23,24,34The second is Wright’s law, which predicts that costs drop as a power law of cumulative production.35 This relationship is also called an experience curveor learning curve, and cumulative production is also called experience. (For a discussion of challenges and caveats concerning Wright’s law, see Document S1 section “Wright’s law caveats.”) Multifactor models have been proposed using additional input variables, such as patenting activity and research and development (R&D) expenditures, but data are limited and they require additional parameters. This can lead to overfitting, resulting in poor out-of-sample forecasts25 (see Document S1 section “Bias-variance trade-off”). Multifactor models have so far not been properly tested, and we do not use them here.
Successful technologies tend to follow an “S-curve” for deployment, starting with a long phase of exponential growth in production that eventually tapers off due to market saturation.22 Under Wright’s law, during the exponential growth phase costs drop exponentially in time, as they do for Moore’s law, but when production growth eventually slows, cost improvement also slows. Improving technologies often spend many decades in the exponential growth phase, making it hard to distinguish between Moore’s law and Wright’s law. Forecasts using the two models have similar accuracy in backtesting experiments.25
This brings up the important question of responsiveness to investment. Under Moore’s law, costs are assumed to change exogenously over time, independent of policy and investment. Under Wright’s law, costs depend on experience. Although experience does not directly cause costs to drop, it is correlated with other factors that do, such as level of effort and R&D, and has the essential advantage of being relatively easy to measure.36,37 For comparison, the historical time series displayed in Figure 1 are plotted as experience curves in Figure S17. The same heterogeneity of improvement rates seen in Figure 1 is evident for Wright’s law—the fact that fossil fuel prices have not dropped historically means that experience had no net effect—in stark contrast to key green technologies. In this paper, we focus on Wright’s law because it satisfies the basic intuition that exerting greater effort induces greater effects. (We repeated all our modeling experiments using Moore’s law and found that the qualitative conclusions are similar; see Document S1 section “Moore’s law results.” For a more thorough discussion of causality, see Document S1 section “Discussion on questions of causality.”)
Wright’s law has usually been used to generate point forecasts, meaning that the forecast is a deterministic function of experience, with no estimate of the error of the forecast. Early attempts at introducing error bars did not provide a priori functional forms, which made the data requirements for out-of-sample testing prohibitive.25,38 More recently, a priori error estimates were derived that predict forecasting accuracy as a function of historical improvement rates and volatility, and the number of data points available for forecasting.33,39 Based on comprehensive backtesting, this method was shown to generate reliable probabilistic estimates of future costs. This was done by selecting reference dates in the past and then, using only the data available at the time, making forecasts over all time horizons up to 20 years into the future with respect to each reference date. Using historical data for 50 different technologies, based on roughly 6,000 forecasts, the empirically observed forecast accuracy closely matched the a priori derived estimates on all time horizons up to 20 years ahead.33,39 Our main contribution in this paper is to systematically apply this method—which we call the stochastic experience curve or stochastic Wright’s law—to the energy transition.
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