Evaluating relative benefits of different types of R&amp;D for clean energy technologies

Shayegh, Soheil; Sanchez, Daniel L.; Caldeira, Ken

doi:10.1016/j.enpol.2017.05.029

Cited by 43 publications

(37 citation statements)

References 31 publications

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“…Early stage technologies, with typically higher learning rates (Grubler et al 1999;McDonald & Schrattenholzer 2001;Shayegh et al 2017), are surrounded by more uncertainties around the evolution of costs and profits, increasing the risk of losses or of no profits of the investment.…”

Section: Discussionmentioning

confidence: 99%

“…The learning rates summarizes all the history of the installations and costs of the technologies. Their level typically correlates to the stage of development (or maturity) and uncertainty around technologies (Grubler et al, 1999;McDonald & Schrattenholzer 2001;Shayegh et al 2017). Appendix 1 shows the highly similar patterns of learning rates comparing with other well-established measures of technology risk from the literature (e.g.…”

Section: Datamentioning

confidence: 91%

“…Learning refers to the reduction in the cost of the technology as a result of the increase in the number of units produced and accumulation of experience (Shayegh et al, 2017). The effect of increased experience in cost reduction was originally demonstrated in empirical studies in manufacturing (e.g., Wright, 1936, Arrow, 1971).…”

Section: Technological Risksmentioning

confidence: 99%

“…These rates are indicator of the ongoing changes underway in the new technology, whose technical and financial attributes may alter as it evolves. More imature technologies, which are still in the early stage of development, tend to present higher learning rates (Grubler et al, 1999;McDonald and Schrattenholzer, 2001;Shayegh et al, 2017) and so a higher risk of technology failure and of changing market conditions. Therefore learning rates is a possible measure of the technology risk (see also Rubin et al, 2015).…”

Section: Technological Risksmentioning

confidence: 99%

“…coal, natural gas) or for new additional capacity. Even thought their growth has been subsidized by the governments, uncertainties remain on the capacity to learn and to reduce costs for the different technologies (e.g., wind, solar PV) in order to become competitive against the conventional energies (Yeh & Rubin 2012;Rubin et al 2015;Shayegh et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Section: Discussionmentioning

confidence: 99%

Section: Datamentioning

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Section: Technological Risksmentioning

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Section: Technological Risksmentioning

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Do crowdfunding returns reward risk? Evidences from clean-tech projects

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Technological innovation and environmental quality nexus in India: Does inward remittance matter?

Villanthenkodath

Mahalik

2020

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104

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This article examines the relationships between technological innovation and environmental quality in India relying on the availability of annual data from 1980 to 2018. Both inward remittances and economic growth are also considered as key determinants in CO2 emissions and technological innovation functions. The result from utilizing autoregressive distributed lag (ARDL) bounds testing cointegration approach indicates the existence of long‐run relationship between the series. The combined cointegration test is also used as a result robust checking and validating the long‐run relationship. While considering the carbon dioxide emissions function, the findings document that technological innovation and economic growth degrade environmental quality in India via promoting atmospheric emissions in the long‐run, whereas U‐shaped relationship between inward remittances and carbon dioxide emissions is established. Further considering the technological innovation function, findings clearly indicate that both carbon dioxide emissions and economic growth promote the new technological innovation in the long‐run and also fails to confirm the inverted U‐shaped relationship between remittances inflows and technological innovation. On the policy front, we suggest that technological innovation should be made eco‐friendly and heavy utilization of inward remittances on pollution‐driving households’ appliances need to be discouraged in India for mitigating both climate change and global warming in the long‐run.

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Assessing learning in low carbon technologies: Toward a more comprehensive approach

Lewis

Nemet

2021

WIREs Climate Change

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With decades of experience developing and deploying low carbon technologies around the world, much has been learned. We assess six categories that represent the diversity of methodological approaches that have been used to study low carbon learning: (1) learning curves; (2) expert elicitations; (3) patent analysis; (4) engineering-based decomposition; (5) policy intervention studies; and (6) case studies. Based on a review of low carbon learning studies in these six areas, we summarize what we know about low carbon learning, and what we have yet to fully understand, including the methodological strengths and limitations of key studies conducted to date. We find that a more comprehensive understanding of low carbon learning is necessary and timely given the massive scale and short time horizon of the low carbon transition, and that there are real benefits to employing a comprehensive approach using multiple methods. We find a need for better data sets, and for studies of a more diverse set of technologies, as well as of interactions among technologies. In addition, studies should be more explicit about local context, with a particular need for additional focus on emerging and developing countries. We identify key topics that warrant further research, including technology specific learning methods; spatial distinctions and the local and global linkages that influence learning; and an expanded study of the cultural, social, environmental, and political factors that influence learning. Finally, we recommend more nuance in the design of policies directed at accelerating low carbon learning. This article is categorized under:The Carbon Economy and Climate Mitigation > Future of Global Energy K E Y W O R D S climate change, innovation, learning, low carbon technologies, methods 1 | INTRODUCTION Climate change necessitates the transition to a low carbon economy, presenting both challenges and opportunities. One main challenge is the scale of energy investment needed (estimated at between 1.6 and 3.8 trillion USD annually between 2016 and 2050) (Climate Policy Initiative, 2019; Intergovernmental Panel on Climate Change [IPCC], 2018), along with the need to overcome technological lock in and path dependency, all in a relatively short time horizon given looming climate disasters. However, many opportunities lie in the commercialization of low carbon energy

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Evaluating relative benefits of different types of R&D for clean energy technologies

Cited by 43 publications

References 31 publications

Do crowdfunding returns reward risk? Evidences from clean-tech projects

Do crowdfunding returns reward risk? Evidences from clean-tech projects

Technological innovation and environmental quality nexus in India: Does inward remittance matter?

Assessing learning in low carbon technologies: Toward a more comprehensive approach

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