Funds from this grant support an effort by a new non-profit, Actuate, led by Arati Prabhakar and Wade Shen, to begin to develop, build, and test “DataSafes,” an end-to-end, multi-party, and privacy-preserving system for sharing, linking, modeling, and analyzing sensitive data. Using advanced mathematical techniques like differential privacy and fully homomorphic encryption, DataSafes will provide a platform where data that would be dangerous to share openly can be analyzed in provably safe, trustworthy, and productive ways. Potential use cases include bankers who want to detect fraud patterns across their industry without identifying their clients, doctors who want to quantify disease spread without identifying their patients, or economists who want to identify poverty causes without identifying tax filers. Grant funds will allow Prabhakar and Shen to begin developing the open-source protocols, prototypes, standards, and code libraries that constitute the foundational technological architecture of the platform. Grant expenditures will be overseen by the Hopewell Fund, a fiscal sponsor for Actuate.

Behavioral economics has been highly successful at cataloguing patterns of bias, inconsistency, and even irrationality in everyday human decision-making. One of the great remaining challenges in the field, however, is to present a coherent explanation for why such behaviors exist, to derive the empirical findings of behavioral economics from theoretical models of how our brains work. This grant funds a joint project by economist Michael Woodford of Columbia University and neuroscientist Rava Azeredo da Silveira of the University of Basel, to develop more realistic models of the role that memory limitations play in generating the patterns of economic behavior documented by behavioral psychologists. Woodford and da Silveira will build a set of theoretical models that incorporate the assumption that memory carries a cost. As agents move through time, they create internalized perceptions of the world. Recalling these internal perceptions with flawless precision, they hypothesize, is very costly, and thus as we move from one period to the next, the rational reluctance to pay this “memory cost” produces an increasingly noisy representation of past internal perceptions, resulting in the systematic deviations from optimality observed by behavioral economists. Grant funds will support the theoretical modeling and comparative analysis, as well as a postdoctoral fellow and graduate student who will work on the project.

This grant funds research by David Popp and Daniel Acuna of Syracuse University to examine the impact of federal funding of research in the energy sector. Popp and Acuna are particularly interested in understanding how researchers respond to changes in federal funding priorities.  When a federal agency announces a funding initiative in a new area of energy research, who ultimately performs that research? Perhaps established researchers working on other topics change directions and begin working on the new priority, speeding progress in the new area but moving away from the subject areas they abandoned. Or perhaps new, early career researchers without well-established research agendas enter the field in response to the availability of federal funding. Determining how federal funding impacts researcher interests and scholarly trajectories is an under-explored topic in energy systems innovation. To examine these questions, Popp and Acuna will deploy machine learning techniques that analyze a longitudinal publications dataset created from Elsevier’s Scopus and Thompson Reuter’s Web of Science archives.  By indexing changes in researcher publications to changes in federal funding, Popp and Acuna will be able to examine if and how researchers change the direction of their work in response to different kinds of funding calls from different federal agencies.

Understanding the lifecycle of energy technologies is important for policymakers interested in modeling and predicting the likely future path of technological development.  The situation is complicated because many novel technologies draw from other related fields, with such knowledge spillovers playing an important role in technological advancement.  Funds from this grant support work by a team led by Erin Baker and Anna Goldstein at the University of Massachusetts, Amherst  who are studying the pathways of two newly developed energy technologies: offshore wind energy and bioenergy with carbon capture and storage.  Through a careful analysis of patent records, the team will attempt to quantify where each technology is in its lifecycle, how much of the technology represents genuinely new innovation, and how much of their technological development is the application of more mature technologies from neighboring fields. For instance, in the case of newly viable offshore wind farms, in order to assess the potential for future growth it is important to understand how much the viability of these technologies depends on advances in wind tower or blade design taken from their land-based counterparts and what can be leearned from applying processes originally developed for offshore oil and gas rigs. The same goes for bioenergy with carbon capture and storage, which draws on drilling and related technologies developed for other applications. Since both wind and bioenergy will play an increasingly central role as the U.S. transitions to a low-carbon economy, the project promises to advance our understanding of the likely pace of innovation of these two crucial technologies in the energy sector.

Resources for the Future (RFF) plays a central role in producing independent energy and environmental economics research, largely due to the contributions of the many energy and environmental microeconomic simulation models the organization maintains. These workhorse models allow researchers to examine many aspects of energy systems, with models feeding into numerous academic publications, policy reports, and energy and environmental decision-making processes. This grant supports efforts by RFF to update and enhance the usefulness of its modeling platforms by producing additional modules, increasing granularity, and creating linkages between them.  The models to be augmented include the Dynamic Regional-General Equilibrium Model (known as DR-GEM); the Engineering, Economic, and Environmental Electricity Simulation Tool; and RFF platforms that model employment, the electricity market, and the vehicle market. Planned improvements include adding more detailed information on the interactions between different industrial sectors; better information about solar, wind, and other renewables; detailed data on how new and used car sales vary across states, and up-to-date projections about the likely phase out of U.S. coal plants. In addition, the RFF team will develop a new land-use carbon model that will look at the land-use implications and likelihood of land-use change associated with various energy development and policies, such as those related to biofuels, biomass with carbon capture and storage, and offset programs that may be used to compensate for hard-to-decarbonize emissions.

Various industrial processes are major contributors to greenhouse gas emissions, but without good evidence on how different industrial sectors contribute to emissions, policymakers are left without reliable data to help inform regulatory efforts. In particular, wastewater management facilities and agricultural waste processing sites generate methane and nitrous oxide, both powerful greenhouse gases, and also serve as the source of local air pollutants, such as ammonia. However, information about the magnitude of emissions from these sites, and how emissions differ across such sites in different regions, is poorly known. Without baseline information, it is difficult to design even basic greenhouse gas management strategies, like how to quantify emissions reductions from these facilities. This grant funds a project team led by Mark Zondlo at Princeton University and Francesca Hopkins at the University of California, Riverside to equip and deploy two mobile laboratories—technologically-outfitted cars and vans—that are designed to take precise emissions measurements at multiple scales around these sites. By partnering with non-governmental organizations, these mobile laboratories will monitor multiple wastewater management and agricultural waste processing sites on both the East Coast and West Coast. The compiled data will provide one of the best sources of evidence about the scale of emissions at these sites and have the potential to inform new modes of management for emissions produced by these industrial processes.

In addition to the pressing need to transition electricity generation to more widespread use of renewables, attention needs to be given to how these low-carbon systems might be integrated with other net-zero interventions. While wind and solar power deservedly receive much attention, not only is geothermal energy an important low-carbon source of electricity, but there are creative ways of integrating geothermal systems with other net-zero approaches to create a virtuous cycle of clean power generation and carbon sequestration. A team of Brian Ellis at the University of Michigan, Jeffrey Bielicki at The Ohio State University, and Jeremiah Johnson at North Carolina State University will address these challenges by examining the potential to pump carbon dioxide underground—after being captured from industrial processes such as coal-fired power generation—in order to enhance renewable geothermal power systems. How might these carbon dioxide streams get pumped into the subsurface? They are sent underground using excess electricity generated by wind or solar installations. These researchers will examine various dimensions of such systems that might allow the use of carbon-rich fluids to boost renewable energy production while simultaneously being sequestered underground. First, this team will investigate basic geological characteristics related to how differences in rock pore size and mineralogy may influence how these carbon-rich fluids flow in underground reservoirs. Second, they will look to aggregate this information about basin-scale geological characteristics to shed light on the viability of using such carbon-rich fluids to enhance geothermal energy production across power systems that have rather different power production and geographical characteristics. The results have the potential to inform how such net-zero systems might be optimized to help make renewables like geothermal power even more attractive as a twenty first century energy source.