This study examines the pace and content of Climate Change Mitigation Technology (CCMT) inventions, focusing on the influence of government-funded research on patent characteristics. Utilizing data from the USPTO, we analyze the trends in CCMT patenting from 1988 to 2017 and reveal a significant increase in CCMT inventions. However, patents in hydrogen technology and Carbon Capture and Storage (CCS) are comparatively low, suggesting these fields are still in the early development stages. CCMT inventions rely heavily on government-funded research, particularly in CCS and hydrogen technology. CCMT inventions relying on government research are more complex and generate larger and more pervasive knowledge spillovers than their counterparts. However, they are less likely to be novel and tend to consolidate rather than destabilize existing technologies. Interestingly, the effect of government research reducing the likelihood of novelty is only observed in CCMT inventions and does not extend to utility patents. These findings highlight the role of government-funded research in facilitating high-quality CCMT inventions through knowledge spillovers. Our study underscores the importance of sustained and targeted public investment in CCMT R&D. 

Artificial Intelligence (AI) research is intrinsically innovative and serves as a source of innovation for research and development in a variety of domains. There is an assumption that AI can be considered “revolutionary science” rather than “normal science.” Using a dataset of nearly 300,000 AI publications, this paper examines the co-citation dynamics of AI research and investigates its trajectory from the perspective of knowledge creation as a combinatorial process. We found that while the number of AI publications grew significantly, they largely follows a normal science trajectory characterized by incremental and cumulative advancements. AI research that combines existing knowledge in highly conventional ways is a substantial driving force in AI and has the highest scientific impact. Radically new ideas are relatively rare. By offering insights into the co-citation dynamics of AI research, this work contributes to understanding its evolution and guiding future research directions.

Agglomeration is the tell-tale sign of cities and urbanization. Identifying and measuring agglomeration economies has been achieved by a variety of means and by various disciplines, including urban economics, quantitative geography, and regional science. Agglomeration is typically expressed as the non-linear dependence of many different urban quantities on city size, proxied by population. The identification and measurement of agglomeration effects is of course dependent on the choice of spatial units. Metropolitan areas (or their equivalent) have been the preferred spatial units for urban scaling modeling. The many issues surrounding the delineation of metropolitan areas have tended to obscure that urban scaling is principally about the measurable consequences of social and economic interactions embedded in physical space and facilitated by physical proximity and infrastructure. These generative processes obviously must exist in the spatial subcomponents of metropolitan areas. Using data for counties and urbanized areas in the United States, we show that the generative processes that give rise to scaling effects are not an artifact of metropolitan definitions and exist at smaller spatial scales.

Sustainable urban systems (SUS) science is a new science integrating work across established and emerging disciplines, using diverse methods, and addressing issues at local, regional, national, and global scales. Advancing SUS requires the next generation of scholars and practitioners to excel at synthesis across disciplines and possess the skills to innovate in the realms of research, policy, and stakeholder engagement. We outline key tenets of graduate education in SUS, informed by historical and global perspectives. The sketch is an invitation to discuss how graduates in SUS should be trained to engage with the challenges and opportunities presented by continuing urbanization.

The urban scaling framework views cities as integrated socioeconomic networks of interactions embedded in physical space. A crucial property of cities highlighted by this approach is that cities act to mix populations, a mixing both facilitated and constrained by physical infrastructure. Operationalizing a view of cities as settings for social interactions and population mixing—assembling a set of spatial units of analysis which contain the relevant social aspects of urban settlements—implies choices about the use of existing data, the assignation of data to locations, and the delineation of the boundaries of urban areas, all of which are far from trivial research decisions. Metropolitan areas have become the spatial unit of choice in urban economics and economic geography for investigating urban life as they are seen as encompassing the distinct phenomena of “urbanity” (proximity, density) and social interactions indirectly captured through a unified labor market. However, the population size and areal extent of metropolitan areas, as most often defined, render opaque the distinction between two salient types of urban population: those who work and those who reside within a metropolitan area. These two sets of individuals, among whom of course there is great overlap, putatively engage in different economic and social interactions which are in turn differently embedded in physical space. Availing ourselves of Swedish micro-level data for two distinct spatial units, tätorts (“dense localites”) and local labor markets, we can distinguish which types of populations and which types of spatial agglomerations are responsible for the observed scaling effects on productivity and physical infrastructure. We find that spatially contiguous labor markets are not enough to generate some of the most salient urban scaling phenomena. 

Our research note focuses on whether policy changes in the transportation sector contribute to green innovations, measured as patents. We explore federal policies, specifically energy policies such as Energy Policy Act of 1992 or 2005, that provide incentives for the development of environmentally friendly technologies in transportation. Drawing on a combination of qualitative and quantitative data, we construct and test several novel policy measures while controlling for other factors that may influence green transportation innovations. We develop a unique legislative timeline with the use of key-informant interviews, literature searches, and legislative updates reviews. Only one policy variable (executive orders) appears to have a positive effect on the number of patents in green transportation innovation sphere. High capital expenditures for pollution abatement decrease innovation activity, while high operating expenditures increase innovation activity. The federal policies over the time period analyzed seem to create uncertainty rather than provide a clear incentive for innovation. The executive orders may affect innovation levels; further tests are needed with the changes in political leadership. The results of our research note signal that stronger and more consistent incentives at federal level may be necessary to realize desired policy outcomes.

Biofuels are a regular focus of public policy. The productivity of innovation in biofuel technologies is rarely addressed either in research or policy. Yet as innovation in any field grows complex and costly it can experience reductions in productivity and diminishing returns to investments. We examine here the productivity of investments in the technologies used to produce biofuels, using data from the U.S. Patent and Trademark Office. The results show that the productivity of innovation in biofuel technologies is declining. Continuation of this trend will in time force reductions in research investments in biofuel technologies. We discuss policy approaches to address declining returns to research investments.

An essential feature of a modern patenting system is a classification schema for organizing, indexing and coding the technical information contained in a patent. Patent classification systems make it possible for patent examiners and prospective inventors to search through existing patents in order to find information pertinent to evaluating a patent application’s purported novelty. Patent classification systems also support the construction of a taxonomy for the various sources of inventive novelty embodied in patented inventions. Until 2013 the U.S. Patent Office utilized the United States Patent Classification system and since then it has used the Cooperative Patent Classification (CPC) system; these two systems implement very different classification logics with the CPC aiming at greater granularity. Here we examine the extent to which the two patent classification systems generate similar historical narratives as to the sources of inventive novelty. Despite the differences in classification principles, common patterns are revealed regardless of which classification system is used to identify technologies. Invention is primarily a cumulative process where new inventions are developed from combining existing technologies. Refinements (the re-use of existing technologies) and combinations of previously existing technological functionalities predominate in the patent record, while inventions embodying previously unseen technologies are very rare. The rate at which inventions representing non-refinements have been introduced into the stock of inventions has kept pace with the generation of inventions representing refinements, thereby feeding the combinatorial process.

Urban economies are composed of diverse activities, embodied in labor occupations, which depend on one another to produce goods and services. Yet little is known about how the nature and intensity of these interdependences change as cities increase in population size and economic complexity. Understanding the relationship between occupational interdependencies and the number of occupations defining an urban economy is relevant because interdependence within a networked system has implications for system resilience and for how easily can the structure of the network be modified. Here, we represent the interdependencies among occupations in a city as a non-spatial information network, where the strengths of interdependence between pairs of occupations determine the strengths of the links in the network. Using those quantified link strengths we calculate a single metric of interdependence–or connectedness–which is equivalent to the density of a city’s weighted occupational network. We then examine urban systems in six industrialized countries, analyzing how the density of urban occupational networks changes with network size, measured as the number of unique occupations present in an urban workforce. We find that in all six countries, density, or economic interdependence, increases superlinearly with the number of distinct occupations. Because connections among occupations represent flows of information, we provide evidence that connectivity scales superlinearly with network size in information networks.

Near the end of World War II, President Franklin Roosevelt asked Vannevar Bush, director of the wartime Office of Scientific Research and Development, to prepare a report on the post-war role of government in promoting science. In his famous report, Bush wrote: “Advances in science will … bring higher standards of living, will lead to the prevention or cure of diseases, will promote conservation of our limited national resources, and will assure means of defence against aggression” (Bush, 1945: 10). This statement, so characteristic of our faith in science, became the basis for the emphasis on innovation that we know today. It is a system that has brought material prosperity in the industrialized countries and high levels of employment. Innovation has fostered the complexity of modern societies. Bush’s statement reflects what is called technological optimism, a faith in technology to solve problems.