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.

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.

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.