Evaluating the Role of Unit Size in Learning-by-Doing of Energy Technologies

Sweerts, Bart; Detz, Remko J.; Zwaan, Bob van der

doi:10.1016/j.joule.2020.03.010

Cited by 26 publications

(17 citation statements)

References 12 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also see technology‐specific learning factors across different technology characteristics. For example, small‐scale technologies—those with small unit size that are amenable to scale through aggregation—show faster learning than large scale technologies (Sweerts et al, 2020; Wilson et al, 2020). Sometimes known as granular technologies, they learn faster and become adopted faster, in part due to lower risk, more iterations and consequent opportunities for improvement, and fit a much broader variety of adoption contexts.…”

Section: The Need For a More Comprehensive Approachmentioning

confidence: 99%

“…However, the author points out gaps in the literature that impact these estimates, including the fact that actual electricity generation is not always included in learning rates, commodity price changes can distort actual costs, limited data for technologies with minimal deployment such as CSP, and a lack of studies focusing on energy storage technologies (Samadi, 2018). A review of 41 technologies found a range of À2% to 39% (median 14%) and noted higher learning rates for smaller scale technologies (Sweerts et al, 2020).…”

Section: Learning Curvesmentioning

confidence: 99%

See 1 more Smart Citation

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

Lewis

Nemet

2021

WIREs Climate Change

View full text Add to dashboard Cite

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

show abstract

Section: The Need For a More Comprehensive Approachmentioning

confidence: 99%

Section: Learning Curvesmentioning

confidence: 99%

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

Lewis

Nemet

2021

WIREs Climate Change

View full text Add to dashboard Cite

show abstract

“…In the long run, smaller unit sizes for energy technologies have been shown to increase the rate of learning-by-doing. 11 If construction risks decrease, the cost of capital generally declines. Smaller units may also reduce stress on supply chains, which was a cost headwind for past large-scale buildouts of nuclear.…”

Section: Microreactor Economicsmentioning

confidence: 99%

Can Distributed Nuclear Power Address Energy Resilience and Energy Poverty?

Gilbert

Bazilian

2020

Joule

View full text Add to dashboard Cite

“…In practice, however, there are myriad factors that affect costs beyond learning-by-doing; prominent examples include technology R&D, economies of scale, unit size, and exogenous influences such as materials input prices and exchange rate fluctuations. Studies have sometimes used two- or multi-factor learning curves (or other approaches) to account for these additional drivers ( Junginger and Louwen, 2020 ; Elia et al., 2020 ; Yu et al., 2011 ; Kavlak et al., 2018 ; Odam and de Vries, 2020 ; Elia et al., 2021 ; Zhou and Gu, 2019 ; Zheng and Kammen, 2014 ; Nemet, 2006 ; Sweerts, Detz, and van der Zwaan, 2020 ; Lilliestam et al., 2020 ). As well, there is a recognition that learning rates may not be constant ( Van Buskirk et al., 2014 ; Wei et al., 2017 ): industries may exhibit temporary periods of accelerated or decelerated learning based on changes in industrial structure, the emergence or relief of resource constraints, and sporadic improvements in specific components of a technology ( Junginger and Louwen, 2020 ; Wei et al., 2017 ; Ferioli, Schoots, and van der Zwaan, 2009 ; Yeh and Rubin, 2012 ).…”

Section: Introductionmentioning

confidence: 99%