Evaluating Low-Carbon Transportation Technologies When Demand Responds to Price.

The carbon intensity (CI) of travel is commonly used to evaluate transportation technologies. However, when travel demand is sensitive to price, CI alone does not fully capture the emissions impact of a technology. Here, we develop a metric to account for both CI and the demand response to price (DR) in technology evaluation, for use by distributed decision-makers in industry and government, who are becoming increasingly involved in climate change mitigation as the costs of lower-carbon technologies fall. We apply this adjusted carbon intensity (ACI) to evaluate ethanol-fueled, hybrid, and battery electric vehicles individually and against policy targets. We find that all of these technologies can be used to help meet a 2030 greenhouse gas emissions reduction target of up to 40% below 2005 levels and that decarbonized battery electric vehicles can meet a 2050 target of 80%, even when evaluated using the ACI instead of CI. Using the CI alone could lead to a substantial overshoot of emissions targets especially in markets with significant DR, including in rapidly growing economies with latent travel demand. The ACI can be used to adjust decarbonization transition plans to mitigate this risk. For example, in examining several transportation technologies, we find that accelerating low-carbon technology transitions by roughly 5-10 years would mitigate the risk associated with DR estimates. One particularly robust strategy is to remove carbon from fuels through faster decarbonization of electricity and vehicle electrification.

Net-zero emissions energy systems.

Some energy services and industrial processes-such as long-distance freight transport, air travel, highly reliable electricity, and steel and cement manufacturing-are particularly difficult to provide without adding carbon dioxide (CO2) to the atmosphere. Rapidly growing demand for these services, combined with long lead times for technology development and long lifetimes of energy infrastructure, make decarbonization of these services both essential and urgent. We examine barriers and opportunities associated with these difficult-to-decarbonize services and processes, including possible technological solutions and research and development priorities. A range of existing technologies could meet future demands for these services and processes without net addition of CO2 to the atmosphere, but their use may depend on a combination of cost reductions via research and innovation, as well as coordinated deployment and integration of operations across currently discrete energy industries.

Vehicle emissions of short-lived and long-lived climate forcers: trends and tradeoffs.

Evaluating technology options to mitigate the climate impacts of road transportation can be challenging, particularly when they involve a tradeoff between long-lived emissions (e.g., carbon dioxide) and short-lived emissions (e.g., methane or black carbon). Here we present trends in short- and long-lived emissions for light- and heavy-duty transport globally and in the U.S., EU, and China over the period 2000-2030, and we discuss past and future changes to vehicle technologies to reduce these emissions. We model the tradeoffs between short- and long-lived emission reductions across a range of technology options, life cycle emission intensities, and equivalency metrics. While short-lived vehicle emissions have decreased globally over the past two decades, significant reductions in CO2 will be required by mid-century to meet climate change mitigation targets. This is true regardless of the time horizon used to compare long- and short-lived emissions. The short-lived emission intensities of some low-CO2 technologies are higher than others, and thus their suitability for meeting climate targets depends sensitively on the evaluation time horizon. Other technologies offer low intensities of both short-lived emissions and CO2.

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.

Effectiveness of a segmental approach to climate policy.

Resistance to adopting a cap on greenhouse gas emissions internationally, and across various national contexts, has encouraged alternative climate change mitigation proposals. These proposals include separately targeting clean energy uptake and demand-side efficiency in individual end-use sectors, an approach to climate change mitigation which we characterize as segmental and technology-centered. A debate has ensued on the detailed implementation of these policies in particular national contexts, but less attention has been paid to the general factors determining the effectiveness of a segmental approach to emissions reduction. We address this topic by probing the interdependencies of segmental policies and their collective ability to control emissions. First, we show for the case of U.S. electricity how the set of suitable energy technologies depends on demand-side efficiency, and changes with the stringency of climate targets. Under a high-efficiency scenario, carbon-free technologies must supply 60-80% of U.S. electricity demand to meet an emissions reduction target of 80% below 1990 levels by midcentury. Second, we quantify the enhanced propensity to exceed any intended emissions target with this approach, even if goals are set on both the supply and demand side, due to the multiplicative accumulation of emissions error. For example, a 10% error in complying with separate policies on the demand and supply side would combine to result in a 20% error in emissions. Third, we discuss why despite these risks, the enhanced planning capability of a segmental approach may help counteract growing infrastructural inertia. The emissions reduction impediment due to infrastructural inertia is significant in the electricity sectors of each of the greatest emitters: China, the U.S., and Europe. Commonly cited climate targets are still within reach but, as we show, would require more than a 50% reduction in the carbon intensity of new power plants built in these regions over the next decade.

Determinants of the pace of global innovation in energy technologies.

Understanding the factors driving innovation in energy technologies is of critical importance to mitigating climate change and addressing other energy-related global challenges. Low levels of innovation, measured in terms of energy patent filings, were noted in the 1980s and 90s as an issue of concern and were attributed to limited investment in public and private research and development (R&D). Here we build a comprehensive global database of energy patents covering the period 1970-2009, which is unique in its temporal and geographical scope. Analysis of the data reveals a recent, marked departure from historical trends. A sharp increase in rates of patenting has occurred over the last decade, particularly in renewable technologies, despite continued low levels of R&D funding. To solve the puzzle of fast innovation despite modest R&D increases, we develop a model that explains the nonlinear response observed in the empirical data of technological innovation to various types of investment. The model reveals a regular relationship between patents, R&D funding, and growing markets across technologies, and accurately predicts patenting rates at different stages of technological maturity and market development. We show quantitatively how growing markets have formed a vital complement to public R&D in driving innovative activity. These two forms of investment have each leveraged the effect of the other in driving patenting trends over long periods of time.

Energy technologies evaluated against climate targets using a cost and carbon trade-off curve.

Over the next few decades, severe cuts in emissions from energy will be required to meet global climate-change mitigation goals. These emission reductions imply a major shift toward low-carbon energy technologies, and the economic cost and technical feasibility of mitigation are therefore highly dependent upon the future performance of energy technologies. However, existing models do not readily translate into quantitative targets against which we can judge the dynamic performance of technologies. Here, we present a simple, new model for evaluating energy-supply technologies and their improvement trajectories against climate-change mitigation goals. We define a target for technology performance in terms of the carbon intensity of energy, consistent with emission reduction goals, and show how the target depends upon energy demand levels. Because the cost of energy determines the level of adoption, we then compare supply technologies to one another and to this target based on their position on a cost and carbon trade-off curve and how the position changes over time. Applying the model to U.S. electricity, we show that the target for carbon intensity will approach zero by midcentury for commonly cited emission reduction goals, even under a high demand-side efficiency scenario. For Chinese electricity, the carbon intensity target is relaxed and less certain because of lesser emission reductions and greater variability in energy demand projections. Examining a century-long database on changes in the cost-carbon space, we find that the magnitude of changes in cost and carbon intensity that are required to meet future performance targets is not unprecedented, providing some evidence that these targets are within engineering reach. The cost and carbon trade-off curve can be used to evaluate the dynamic performance of existing and new technologies against climate-change mitigation goals.

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 the same. 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.

Role of design complexity in technology improvement.

We study a simple model for the evolution of the cost (or more generally the performance) of a technology or production process. The technology can be decomposed into n components, each of which interacts with a cluster of d - 1 other components. Innovation occurs through a series of trial-and-error events, each of which consists of randomly changing the cost of each component in a cluster, and accepting the changes only if the total cost of the cluster is lowered. We show that the relationship between the cost of the whole technology and the number of innovation attempts is asymptotically a power law, matching the functional form often observed for empirical data. The exponent α of the power law depends on the intrinsic difficulty of finding better components, and on what we term the design complexity: the more complex the design, the slower the rate of improvement. Letting d as defined above be the connectivity, in the special case in which the connectivity is constant, the design complexity is simply the connectivity. When the connectivity varies, bottlenecks can arise in which a few components limit progress. In this case the design complexity depends on the details of the design. The number of bottlenecks also determines whether progress is steady, or whether there are periods of stasis punctuated by occasional large changes. Our model connects the engineering properties of a design to historical studies of technology improvement.

Transparent and catalytic carbon nanotube films.

We report on the synthesis of thin, transparent, and highly catalytic carbon nanotube films. Nanotubes catalyze the reduction of triiodide, a reaction that is important for the dye-sensitized solar cell, with a charge-transfer resistance as measured by electrochemical impedance spectroscopy that decreases with increasing film thickness. Moreover, the catalytic activity can be significantly enhanced by exposing the nanotubes to ozone in order to introduce defects. Ozone-treated, defective nanotube films could serve as catalytic, transparent, and conducting electrodes for the dye-sensitized solar cell. Other possible applications include batteries, fuel cells, and electroanalytical devices.