Re-examining rates of lithium-ion battery technology improvement and cost decline

Lithium-ion technologies are increasingly employed to electrify transportation and provide stationary energy storage for electrical grids, and as such their development has garnered much attention. However, their deployment is still relatively limited, and their broader adoption will depend on their potential for cost reduction and performance improvement. Understanding this potential can inform critical climate change mitigation strategies, including public policies and technology development efforts. However, many existing models of past cost decline, which often serve as starting points for forecasting models, rely on limited data series and measures of technological progress. Here we systematically collect, harmonize, and combine various data series of price, market size, research and development, and performance of lithium-ion technologies. We then develop representative series for these measures and employ performance curve models to estimate improvement rates. We also develop a method to incorporate additional performance characteristics into these models, including energy density and specific energy performance metrics. When energy density is incorporated into the definition of service provided by a lithium-ion cell, estimated technological improvement rates increase considerably, suggesting that previously reported improvement rates might underestimate the rate of lithium-ion technologies' change. Moreover, our estimates suggest the degree to which lithium-ion technologies' price decline might have been limited by performance requirements other than cost per energy capacity. These rates also suggest that battery technologies developed for stationary applications, where restrictions on volume and mass are relaxed, might achieve faster cost declines, though engineering-based mechanistic cost modeling is required to further characterize this potential.Comment: 37 pages, 11 figure

Metals Production Requirements for Rapid Photovoltaics Deployment

If global photovoltaics (PV) deployment grows rapidly, the required input materials need to be supplied at an increasing rate. In this paper, we quantify the effect of PV deployment levels on the scale of metals production. For example, we find that if cadmium telluride {copper indium gallium diselenide} PV accounts for more than 3% {10%} of electricity generation by 2030, the required growth rates for the production of indium and tellurium would exceed historically-observed production growth rates for a large set of metals. In contrast, even if crystalline silicon PV supplies all electricity in 2030, the required silicon production growth rate would fall within the historical range. More generally, this paper highlights possible constraints to the rate of scaling up metals production for some PV technologies, and outlines an approach to assessing projected metals growth requirements against an ensemble of past growth rates from across the metals production sector. The framework developed in this paper may be useful for evaluating the scalability of a wide range of materials and devices, to inform technology development in the laboratory, as well as public and private research investment

Methane mitigation timelines to inform energy technology evaluation

Energy technologies emitting differing proportions of methane (CH[subscript 4]) and carbon dioxide (CO[subscript 2]) vary significantly in their relative climate impacts over time, due to the distinct atmospheric lifetimes and radiative efficiencies of the two gases. Standard technology comparisons using the global warming potential (GWP) with a fixed time horizon do not account for the timing of emissions in relation to climate policy goals. Here we develop a portfolio optimization model that incorporates changes in technology impacts based on the temporal proximity of emissions to a radiative forcing (RF) stabilization target. An optimal portfolio, maximizing allowed energy consumption while meeting the RF target, is obtained by year-wise minimization of the marginal RF impact in an intended stabilization year. The optimal portfolio calls for using certain higher-CH[subscript 4]-emitting technologies prior to an optimal switching year, followed by CH[subscript 4]-light technologies as the stabilization year approaches. We apply the model to evaluate transportation technology pairs and find that accounting for dynamic emissions impacts, in place of using the static GWP, can result in CH[subscript 4] mitigation timelines and technology transitions that allow for significantly greater energy consumption while meeting a climate policy target. The results can inform the forward-looking evaluation of energy technologies by engineers, private investors, and policy makers.MIT Energy InitiativeMassachusetts Institute of Technology. Charles E. Reed Faculty Initiative FundNew England University Transportation Center (DOT Grant DTRT12-G-UTC01)National Science Foundation (U.S.). Graduate Research Fellowship (Grant 1122374

Growth in metals production for rapid photovoltaics deployment

If global photovoltaics (PV) deployment grows rapidly, the required input materials need to be supplied at an increasing rate. We quantify the effect of PV deployment levels on the scale of annual metals production. If a thin-film PV technology accounts for 25% of electricity generation in 2030, the annual production of thin-film PV metals would need to grow at rates of 15-30% per year. These rates exceed those observed historically for a wide range of metals. In contrast, for the same level of crystalline silicon PV deployment, the required silicon production growth rate falls within the historical range.United States. Dept. of Energy (Grant DE-EE0006131

Personal Vehicles Evaluated against Climate Change Mitigation Targets

Meeting global climate change mitigation goals will likely require that transportation-related greenhouse gas emissions begin to decline within the next two decades and then continue to fall. A variety of vehicle technologies and fuels are commercially available to consumers today that can reduce the emissions of the transportation sector. Yet what are the best options, and do any suffice to meet climate policy targets? Here, we examine the costs and carbon intensities of 125 light-duty vehicle models on the U.S. market today and evaluate these models against U.S. emission-reduction targets for 2030, 2040, and 2050 that are compatible with the goal of limiting mean global temperature rise to 2 °C above preindustrial levels. Our results show that consumers are not required to pay more for a low-carbon-emitting vehicle. Across the diverse set of vehicle models and powertrain technologies examined, a clean vehicle is usually a low-cost vehicle. Although the average carbon intensity of vehicles sold in 2014 exceeds the climate target for 2030 by more than 50%, we find that most hybrid and battery electric vehicles available today meet this target. By 2050, only electric vehicles supplied with almost completely carbon-free electric power are expected to meet climate-policy targets

Statistical Basis for Predicting Technological Progress

Forecasting technological progress is of great interest to engineers, policy makers, and private investors. Several models have been proposed for predicting technological improvement, but how well do these models perform? An early hypothesis made by Theodore Wright in 1936 is that cost decreases as a power law of cumulative production. An alternative hypothesis is Moore's law, which can be generalized to say that technologies improve exponentially with time. Other alternatives were proposed by Goddard, Sinclair et al., and Nordhaus. These hypotheses have not previously been rigorously tested. Using a new database on the cost and production of 62 different technologies, which is the most expansive of its kind, we test the ability of six different postulated laws to predict future costs. Our approach involves hindcasting and developing a statistical model to rank the performance of the postulated laws. Wright's law produces the best forecasts, but Moore's law is not far behind. We discover a previously unobserved regularity that production tends to increase exponentially. A combination of an exponential decrease in cost and an exponential increase in production would make Moore's law and Wright's law indistinguishable, as originally pointed out by Sahal. We show for the first time that these regularities are observed in data to such a degree that the performance of these two laws is nearly tied. Our results show that technological progress is forecastable, with the square root of the logarithmic error growing linearly with the forecasting horizon at a typical rate of 2.5% per year. These results have implications for theories of technological change, and assessments of candidate technologies and policies for climate change mitigation

Timelines for mitigating the methane impacts of using natural gas for carbon dioxide abatement

© 2019 The Author(s). Published by IOP Publishing Ltd. Reducing carbon dioxide (CO2) emissions through a reliance on natural gas can create a hidden commitment to methane (CH4) leakage mitigation. While the quantity of CH4 leakage from natural gas has been studied extensively, the magnitude and timing of the CH4 mitigation required to meet climate policy goals is less well understood. Here we address this topic by examining the case of US electricity under a range of baseline natural gas leakage rate estimates and emissions equivalency metrics for converting CH4 to CO2-equivalent emissions. We find that CH4 emissions from the power sector would need to be reduced by 30%-90% from today's levels by 2030 in order to meet a CO2-equivalent climate policy target while continuing to rely on natural gas. These CH4 emissions reductions are greater than the required CO2 reductions under the same policy. Alternatively, expanding carbon-free sources more rapidly could meet the 2030 target without reductions in natural gas leakage rates. The results provide insight on an important policy choice in regions and sectors using natural gas, between emphasizing a natural gas supply chain clean-up effort or an accelerated transition toward carbon-free energy sources

Consequences of equivalency metric design for energy transitions and climate change

Abstract Assessments of the climate impacts of energy technologies and other emissions sources can depend strongly on the equivalency metric used to compare short- and long-lived greenhouse gas emissions. However, the consequences of metric design choices are not fully understood, and in practice, a single metric, the global warming potential (GWP), is used almost universally. Many metrics have been proposed and evaluated in recent decades, but questions still remain about which ones perform better and why. Here, we develop new insights on how the design of equivalency metrics can impact the outcomes of climate policies. We distill the equivalency metric problem into a few key design choices that determine the metric values and shapes seen across a wide range of different proposed metrics. We examine outcomes under a hypothetical 1.5 or 2 ∘C policy target and discuss extensions to other policies. Across policy contexts, the choice of time parameters is particularly important. Metrics that emphasize the immediate impacts of short-lived gases such as methane can reduce rates of climate change but may require more rapid technology changes. Differences in outcomes across metrics are more pronounced when fossil fuels, with or without carbon capture and storage, play a larger role in energy transitions. By identifying a small set of consequential design decisions, these insights can help make metric choices and energy transitions more deliberate and effective at mitigating climate change