A variety of mathematical models have been proposed to characterize and quantify the dependency of electricity supply technology costs on various drivers of technological change. The most prevalent ...model form, called a learning curve, or experience curve, is a log-linear equation relating the unit cost of a technology to its cumulative installed capacity or electricity generated. This one-factor model is also the most common method used to represent endogenous technical change in large-scale energy-economic models that inform energy planning and policy analysis. A characteristic parameter is the “learning rate,” defined as the fractional reduction in cost for each doubling of cumulative production or capacity. In this paper, a literature review of the learning rates reported for 11 power generation technologies employing an array of fossil fuels, nuclear, and renewable energy sources is presented. The review also includes multi-factor models proposed for some energy technologies, especially two-factor models relating cost to cumulative expenditures for research and development (R&D) as well as the cumulative installed capacity or electricity production of a technology. For all technologies studied, we found substantial variability (as much as an order of magnitude) in reported learning rates across different studies. Such variability is not readily explained by systematic differences in the time intervals, geographic regions, choice of independent variable, or other parameters of each study. This uncertainty in learning rates, together with other limitations of current learning curve formulations, suggests the need for much more careful and systematic examination of the influence of how different factors and assumptions affect policy-relevant outcomes related to the future choice and cost of electricity supply and other energy technologies.
•We review models explaining the cost of 11 electricity supply technologies.•The most prevalent model is a log-linear equation characterized by a learning rate.•Reported learning rates for each technology vary considerably across studies.•More detailed models are limited by data requirements and verification.•Policy-relevant influences of learning curve uncertainties require systematic study.
A look at the research and development of sodium-ion batteries is presented. The use of organic compounds as electrode materials is one of the topics discussed.
Many warn that the next stage of globalization--the offshoring of research and development to China and India--threatens the foundations of Western prosperity. But in The Venturesome Economy, ...acclaimed business and economics scholar Amar Bhidé shows how wrong the doomsayers are. Using extensive field studies on venture-capital-backed businesses to examine how technology really advances in modern economies, Bhidé explains why know-how developed abroad enhances--not diminishes--prosperity at home, and why trying to maintain the U.S. lead by subsidizing more research or training more scientists will do more harm than good.
Energy research and development (R&D) and environmental sustainability is often referred to as two interrelated trends, especially in the current context of the 4
th
industrial revolution. As a ...primary input of energy innovations, R&D in the energy sector constitutes a vital tool in addressing global environmental and energy challenges. In this frame, we observe the effects of disaggregated energy R&D on environmental pollution within the Environmental Kuznets Curve (EKC) framework in thirteen developed countries over the period 2003–2018. By employing the panel quantile regression technique, we find an inverted U-shaped nexus between economic growth and carbon emissions only in higher carbon-emitting countries, thus, confirming the EKC hypothesis. However, the U-shaped nexus is more predominant in lower carbon-emitting countries. As such, we demonstrate that there is not any single dynamic in the relationship between economic growth and pollution as reported in previous studies. Contrary to expectations, we find that energy efficiency research and development is more effective in curbing carbon emissions compared to fossil fuels and renewable energy research and development. The empirical results indicate also that only energy efficiency R&D mitigates significantly the CO
2
emissions from the 50
th
quantile up to 90
th
quantile, although the magnitude of the negative sign is more pronounced (in absolute term) at the highest quantile (90th). In this light, our findings would guide policymakers in the establishment of sustainable energy research and development schemes that will allow the preservation of equilibrium for the environment while also promoting energy innovations.
The study investigates the drivers of total factor productivity (TFP) growth, covering 99 European regions from 31 countries over the period 2000-13. It shows that human capital endowment had a ...positive effect upon TFP growth, particularly in advanced regions, but the effect from regions' own research and development (R&D) expenditures was largely absent. The effects of human capital and R&D on TFP growth varied with the productivity gap. Further, there was a threshold effect in convergence, where stronger TFP growth was associated with both a larger productivity gap and a higher initial level of productivity. Spatial spillover effects had a positive impact upon TFP growth.
•R&D costs of 106 new drugs were obtained from a survey of 10 biopharmaceutical firms.•Costs for compounds that were abandoned were linked to costs of approved compounds.•Pre-tax out-of-pocket per ...approval is $1395 million (2013 dollars).•Pre-tax capitalized per approval is $2558 million (2013 dollars).•Total capitalized costs were found to have increased at a real annual rate of 8.5%.•With post-approval R&D costs the estimate increases to $2870 million (2013 dollars).
The research and development costs of 106 randomly selected new drugs were obtained from a survey of 10 pharmaceutical firms. These data were used to estimate the average pre-tax cost of new drug and biologics development. The costs of compounds abandoned during testing were linked to the costs of compounds that obtained marketing approval. The estimated average out-of-pocket cost per approved new compound is $1395 million (2013 dollars). Capitalizing out-of-pocket costs to the point of marketing approval at a real discount rate of 10.5% yields a total pre-approval cost estimate of $2558 million (2013 dollars). When compared to the results of the previous study in this series, total capitalized costs were shown to have increased at an annual rate of 8.5% above general price inflation. Adding an estimate of post-approval R&D costs increases the cost estimate to $2870 million (2013 dollars).
We exploit staggered changes in state-level corporate tax rates to show that an increase in taxes reduces future innovation. A variety of tests, including those based on policy discontinuity at ...contiguous counties straddling borders of politically similar states, show that local economic conditions do not drive our results. The effect we document is consistent across the innovation spectrum: taxes affect not only patenting and R&D investment but also new product introductions, which we measure using textual analysis. Our empirical results are consistent with models that highlight the role of higher corporate taxes in reducing innovator incentives and discouraging risk-taking.
In today's digital age, many managers need to find new ways to manage collaborations where complementary partners co-create digital solutions. Collaborations with partners are at the center of ...digital projects, but managing these collaborations is challenging. This article objective is to investigate how research and development collaborations with scientific and business partners contribute to digital transformation. This has been achieved through an empirical investigation of three Brazilian manufacturers that are already on their journey toward digitalization. The research design involves multiple case studies and qualitative analysis, through data collection by interviews and questionnaires with participants from the tactical and strategic level followed by content analysis as well as nonparticipant observations. The findings indicate that the companies are at the early stage of building a digital ecosystem. However, they have already secured benefits from pursuing open innovation practices in the form of competitive advantage in operational processes. The results indicate that business success depends more on how (disruptive) technologies are developed and used by engaged people to add value to the enterprise, rather than simply adopting new technologies by themselves. Manufacturers need to act now to reshape their mindset, operational processes, and business models, respectively, enabling the understanding that: 1) organizational ecosystems are becoming more open and collaborative; 2) the value of data capture and analysis can be developed and used for data-driven learning, preventive and predictive capabilities, supporting decision-making; 3) customers' preferences need to be internalized to deliver customized experiences.
A standard real options model predicts a strong positive interaction effect between research and development (R&D) investment and product market competition. R&D-intensive firms tend to be riskier ...and earn higher expected returns than R&D-weak firms, particularly in competitive industries. Also, firms in competitive industries earn higher expected returns than firms in concentrated industries, especially among R&D-intensive firms. Intuitively, R&D projects are more likely to fail in the presence of more competition because rival firms could win the innovation race. Empirical evidence largely supports the model׳s predictions.
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