•Model: new ideas built by combining pre-existing components, with learning and fishing out.•More than 80 years of US patent data; 10,000 sets of three technological components (proxied by USPTO ...technology classifications).•Show each patent increases subsequent number of similar patents but decreases subsequent number of identical patents.•The learning effect dominates the fishing out effect.•Patent renewal data indicates results are not driven by changes in demand.
I develop a model of innovation where new technologies are combinations of pre-existing technological components. The model captures two opposing forces. The best ideas are used up (knowledge is exhaustible). However, as firms learn which technologies can be combined, new ideas become feasible (knowledge accumulates). I test the model with more than 80 years of US patent data. Technological components are proxied by 13,517 patent office technology classifications. These are reused and recycled in 10,000 distinct three-component sets. Consistent with a learning/fishing-out dynamic, I show patenting in one set of components is correlated with a subsequent increase in similar patents (sharing two of three components), but a subsequent decrease in identical patents (sharing all three components). I use patent renewal data to show my results are not driven by changes in demand for various technology bundles. My results suggest the positive impact of learning on subsequent patenting is larger than the negative impact of fishing out.
Mandates, which establish minimum use quotas for certain goods, are becoming increasingly popular policy tools to promote renewable energy use. In addition to mitigating the pollution externality of ...conventional energy, clean energy mandates have the goal of promoting research and development (R&D) investments in renewable energy technology. But how well do mandates perform as innovation incentives? To address this question, we develop a partial equilibrium model to examine the R&D incentives induced by a mandate, and compare this policy to two benchmark situations: laissez faire and a carbon tax. Innovation is stochastic and the model permits an endogenous number of multiple innovators. We present both analytical results and conclusions based on numerical simulations. We find that the optimal mandate is larger than it would be without the prospect of innovation, that neglecting the outlook for innovation significantly reduces welfare, and that the optimal mandate is more sensitive to assumptions about the innovation process than an optimal carbon tax. Furthermore, we find that mandates create relatively strong incentives for R&D investment in low quality innovations, but relatively weak incentives to invest in high-quality innovations. We also rank policies by expected welfare. An optimal carbon tax has higher expected welfare than an optimal mandate, and both have higher expected welfare than laissez faire. Moreover, in our endogenous innovation setting, a stronger result obtains: a simple carbon tax equal to the damage from pollution (unadjusted for the prospect of innovation) has higher expected welfare than an optimal mandate.
In combinatorial models of innovations, new technologies are built from combinations of pre-existing technological components. Researchers learn which components work well together by observing ...previously successful combinations and the pool of ideas can be 'fished out', i.e. exhausted, if it is not 'restocked' by the discovery of novel connections. We first show US patents have made increasingly less novel connections among technological constituents since the 1950s, and that the number of technological fields to which these connections are applicable has stopped growing since the 1980s. We then estimate the parameters of an ideas production function, and find parameter estimates consistent with technology fields being fished out if not continually restocked by the discovery of novel connections between technological components. We use the ideas production function to estimate the number of new patent applications induced by each patent granted between 1926 and 2001, and show this number has trended downward since the 1940s.
Biological innovations in agriculture did not enjoy protection by formal intellectual property rights (IPRs) for a long time, but the recent trend has been one of considerable broadening and ...strengthening of these rights. We document the nature and evolution of these IPRs and provide an assessment of their impacts on innovation. We integrate elements of the institutional history of plant IPRs with a discussion of the relevant economic theory and a review of applicable empirical evidence. Throughout this review, we highlight how the experience of biological innovation mirrors or differs from the broader literature on IPRs and innovation. We conclude with some considerations on the relationship between IPRs, market structure, and the pricing of proprietary inputs in agriculture.
While a rich body of literature has looked at greenhouse gas emissions in biogas production systems and the potential impacts of biogas production on food supply, broader issues relating to the ...economic, environmental and social pillars of sustainability need to be carefully considered. Drawing upon experiences from European countries, key outcomes associated with large-scale implementation of biogas and biomethane production are identified. Topics of particular interest include policy instruments, farm intensification, and supply chain risks. Conclusions are drawn by recommending policy directions for countries such as Ireland that are at earlier developmental stages for biogas and biomethane deployment.
•The timeline of biogas and biomethane development is presented with various drivers.•Emerging issues are identified regarding large-scale implementation.•Topics include policy instruments, farm intensification, supply chain risks.•Learnings are discussed for countries seeking to stimulate biogas development.
Innovation is essential for sustaining growth and economic development in a world that faces population increase, natural resource depletion, and environmental challenges. Incentives play a critical ...role in innovation because the required research and development activities are costly, and the resulting knowledge has the attributes of a public good. This paper discusses the economics of institutions and policies meant to provide incentives for research and innovation, and focuses on intellectual property rights, specifically patents, contracted research (for example grants), and innovation prizes. The main economic implications of these institutions are discussed, with particular attention paid to open questions and recent contributions.
While there is a rich body of literature on the wide range of barriers to investment in renewable energy technologies, little guidance exists on how to integrate the barriers into energy systems ...modelling. The present study develops a prototype to represent barriers such as risk perceptions and inertia in energy systems analysis. Employing a techno-economic model that describes the deployment of renewable heating technologies in Ireland, our results show the methodological importance of the representation of heat-users’ decision making process in energy systems analysis. Implications in promoting renewable heat, market development and regulation are discussed.
•We propose means to incorporate financial and non-financial barriers in energy system models.•We examine the methodological importance using a model of renewable heat.•We demonstrate how the representation of barriers can add nuance to policy design.
Serious concerns about the way research is organized collectively are increasingly being raised. They include the escalating costs of research and lower research productivity, low public trust in ...researchers to report the truth, lack of diversity, poor community engagement, ethical concerns over research practices, and irreproducibility. Open science (OS) collaborations comprise of a subset of open practices including open access publication, open data sharing and the absence of restrictive intellectual property rights with which institutions, firms, governments and communities are experimenting in order to overcome these concerns. We gathered two groups of international representatives from a large variety of stakeholders to construct a toolkit to guide and facilitate data collection about OS and non-OS collaborations. Ultimately, the toolkit will be used to assess and study the impact of OS collaborations on research and innovation. The toolkit contains the following four elements: 1) an annual report form of quantitative data to be completed by OS partnership administrators; 2) a series of semi-structured interview guides of stakeholders; 3) a survey form of participants in OS collaborations; and 4) a set of other quantitative measures best collected by other organizations, such as research foundations and governmental or intergovernmental agencies. We opened our toolkit to community comment and input. We present the resulting toolkit for use by government and philanthropic grantors, institutions, researchers and community organizations with the aim of measuring the implementation and impact of OS partnership across these organizations. We invite these and other stakeholders to not only measure, but to share the resulting data so that social scientists and policy makers can analyse the data across projects.
Serious concerns about the way research is organized collectively are increasingly being raised. They include the escalating costs of research and lower research productivity, low public trust in ...researchers to report the truth, lack of diversity, poor community engagement, ethical concerns over research practices, and irreproducibility. Open science (OS) collaborations comprise of a set of practices including open access publication, open data sharing and the absence of restrictive intellectual property rights with which institutions, firms, governments and communities are experimenting in order to overcome these concerns. We gathered two groups of international representatives from a large variety of stakeholders to construct a toolkit to guide and facilitate data collection about OS and non-OS collaborations. Ultimately, the toolkit will be used to assess and study the impact of OS collaborations on research and innovation. The toolkit contains the following four elements: 1) an annual report form of quantitative data to be completed by OS partnership administrators; 2) a series of semi-structured interview guides of stakeholders; 3) a survey form of participants in OS collaborations; and 4) a set of other quantitative measures best collected by other organizations, such as research foundations and governmental or intergovernmental agencies. We opened our toolkit to community comment and input. We present the resulting toolkit for use by government and philanthropic grantors, institutions, researchers and community organizations with the aim of measuring the implementation and impact of OS partnership across these organizations. We invite these and other stakeholders to not only measure, but to share the resulting data so that social scientists and policy makers can analyse the data across projects.
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