Accelerating Climate Innovation: A Mechanistic Approach and Lessons for Policymakers


Prof. Jessika E. Trancik and Dr. Micah S. Ziegler
Institute for Data, Systems, and Society
Massachusetts Institute of Technology

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Executive summary

Significant improvement and deployment of sustainable technologies will likely be needed over coming decades for society to meet its global targets for climate change mitigation. Achieving these targets will require rapid rates of technological progress, including the rates at which we, as a society, invent, develop, and adopt climate solution technologies.

Government policy can play a crucial role in enabling technological progress. Public policies have the potential to influence decisions made by stakeholders across the landscape of technological innovation, from start-ups, established corporations, and investors to academic institutions, nonprofit organizations, and the government itself. Policy tools for supporting technological change can be divided into two categories: “technology-push” policies that enhance the supply of technologies (e.g., government funding for research and development and demonstration projects) and “market-expansion” policies that can increase demand for new technologies (e.g., regulations, subsidies, and government procurement). Enhancing the effectiveness of these policies will be critical for making the best use of limited public funds and limited time to mitigate the worst impacts of climate change.

To improve the efficacy of policies, we want to know: which strategies can accelerate the rate of innovation for climate solutions? Which government policy tools are the most effective in different scenarios? Recent research on the drivers of technological improvement helps address these questions. In this report, we describe an approach advanced in the Trancik Lab at MIT to identify promising mechanisms of technological change that can be targeted by efforts to accelerate innovation.

In the Trancik Lab, one focus area is on understanding and quantitatively modeling the drivers of underlying progress for a range of technologies—what we term the “mechanisms of technological change. These mechanisms can refer to both specific, measurable changes in a technology, such as increased efficiency or lower input prices, or to more general improvement processes, including research and development and emergent phenomena such as economies of scale. Studying the mechanisms that drive technology improvements such as the exponential reductions in cost observed in recent decades for solar modules and lithium-ion batteries, helps provide insights into how policymakers, researchers, and the private sector can better target these mechanisms to accelerate future technological progress.

This mechanism-focused approach to studying innovation differs from previous efforts by relating each change to a feature of a technology or its manufacturing process to performance improvements, even when many changes occur simultaneously that interact with one another in determining costs. When this approach is used to study past changes in a technology, we can identify which mechanisms mattered, quantify their contributions, and answer important questions. For example, what percentage of a technology’s cost decline was caused by a decrease in the price of raw materials as opposed to the building of larger manufacturing plants? Or, how much of its improvement can be attributed to research and development as opposed to economies of scale? These insights then allow decision-makers to identify public policies or business practices that could help drive further improvement, within physical limits.

In this report we review the concepts and methods underlying this mechanism-focused approach to understanding innovation, and then demonstrate the application of the approach and the types of insights that can be derived through case studies of three energy technologies: solar photovoltaics, lithium-ion batteries, and nuclear fission power plants.

Key takeaways for policymakers from the research include:

  • Research and development (R&D) and market-expansion policies each played essential and non-substitutable roles in spurring innovation in technologies. Our examination of solar PV modules demonstrated that both “technology-push” policies (i.e., public R&D funding) and market-expansion policies contributed significantly to the observed cost declines, and that the mechanisms they targeted differed considerably. Support for R&D led to cost reduction through improvements in conversion efficiency and reductions in the quantities of materials required per cell, among other low-level mechanisms. Meanwhile, market-expansion policies were essential for stimulating private R&D, as well as the growth in the sizes of manufacturing plants and bulk purchasing that reduced costs via economies of scale.
  • Government support was an important driver of cost decline. Some of the initial research and development, including invention and early improvement of solar photovoltaic technology, relied on government funding, including substantial R&D support from the U.S. Government. This government funding was crucial as some of the early materials science and physics research leading to these improvements would have been considered too risky to pursue for a private company. Similarly, government passage of market-expansion policies, for example in Germany and Japan, was instrumental in incentivizing the growth of solar companies in the private sector, and in turn both the private R&D and economies of scale that lead to substantial cost reductions.
  • Many other technologies are also likely to need both R&D funding and market-expansion policies. Policymakers involved in setting climate, energy, and industrial policies and those designing R&D budgets should coordinate to ensure that the potential benefits of both types of policies are captured, based on an assessment of innovation mechanisms. The balance of support for these different policy approaches might also need to be adjusted at different stages in the lifecycle of a technology, based on the most promising mechanisms for technological improvement given the features of the technology and its stage in development. Examples of technologies that could benefit from this approach, in addition to those described in this report, include other types of batteries (e.g., stationary, fast-charging, those based on abundant materials), electrolyzers, wind turbine components, fuel cells, electric vehicles (e.g., battery electric and hydrogen fuel cell vehicles), and even entire infrastructures such as those for producing hydrogen and charging electric vehicles.
  • R&D investment can be beneficial well past initial commercialization of a technology. Our research suggests that sustained, and not just early-stage, R&D support can be an important driver of cost reduction for clean energy technologies. Conventional wisdom in technology policymaking is that ‘‘science and technology–push’’ processes should precede ‘‘demand-pull’’ processes. Our results are consistent with this model, but also show that technology-push policies, such as government funding for basic and applied research and development, may remain important for certain technologies even long after demand-pull processes also begin to contribute to cost reduction and other technology improvement.
  • Technologies with low levels of “design complexity”, or high “modularity”, may be particularly well positioned to advance rapidly. In the case of lithium-ion batteries, R&D concurrently contributed to many low-level mechanisms of cost change, which highlights a feature of lithium-ion batteries that might help explain their rapid improvement: the diversity of materials and chemistry combinations that can be used in these devices. Our results are consistent with other research that suggests that technologies that allow some components to be improved without requiring changes elsewhere in a design can improve significantly more quickly than those with many dependencies between components.
  • For some energy technologies and infrastructures, carefully designed mechanistic cost change modeling and demonstration projects could contribute substantially to technological cost improvement. Some technologies are constructed mostly in the field rather than in manufacturing plants. Examples of such technologies include nuclear power plants, electricity transmission systems, and some proposed infrastructures for producing hydrogen gas. For those technologies, cost reducing innovations might be identified through a combination of 1) cost change modeling that connects technological features to resulting costs, as described in this report, and 2) building demonstration projects that can provide empirical data to refine modeling assumptions. Neglecting this approach can lead to unanticipated cost overruns, as has been observed in U.S. nuclear power plant construction.
  • Analysis of prospective public policies that aim to drive technological improvement should consider physical features of technologies and relevant infrastructures. The methodology advanced here shows how valuable it can be to begin with a model of the features of a technology or infrastructure that affect cost or another performance metric of interest. Even policies seemingly far removed from traditional R&D policies can potentially jumpstart significant innovation. However, it will be important to target the innovation mechanisms with the greatest potential impact, and we only know to target those mechanisms through first identifying them. This identification will require studies that clearly delineate how changes in technologies have, and could in the future, influence performance and cost. When researchers and technology developers seek funding to support their efforts, their proposals can be strengthened by analyses that consider the mechanisms of technological change and clearly, and where possible quantitatively, delineate how their technical proposals relate to the performance improvements or cost reductions they project. Similarly, government agencies could perform, or fund, independent analyses and expert elicitations, to both provide additional understanding and identify research directions that could effectively accelerate the development of clean energy technologies.
  • Policymakers and regulators should emphasize the need to collect and share empirical data on technology variables affecting costs and other aspects of technology performance, and how these variables change over time. The studies of technological change we describe and the insights this research provides require extensive collection of data that is often difficult to obtain. Policymakers could require firms and researchers receiving funding from various governmental initiatives to collect and share such data, and also specifically allocate funding for collecting data on technologies and how they change. These data include details on technologies’ components and other features, as well as on their manufacturing processes, and on technologies’ performance and cost, and how these change over time. These data enable investigation of the possible mechanisms of technological change. Relevant data can be collected from academic institutions, businesses, and government agencies, and then be made available to researchers. In addition to implementing data collection and sharing requirements for projects receiving government support, another promising opportunity to collect data on the deployment of energy technologies in the earlier stages of market maturity would be through the funding and design of demonstration projects and hubs. Data on component costs and specifications, and importantly how they change over time, could be collected through these projects and be made available to researchers.


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