Effects of technology complexity on the emergence and evolution of wind industry manufacturing locations along global value chains

Surana, Kavita; Doblinger, Claudia; Anadón, Laura Díaz; Hultman, Nathan

doi:10.1038/s41560-020-00685-6

Cited by 39 publications

(13 citation statements)

References 50 publications

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“…The results may extend to other low-carbon technology sectors, such as wind generating systems and electric vehicles, with caveats related to the supply chain integration and complexity of technological components. Wind generating systems, for example, have a very globally-integrated and specialized trade in intermediate components [18]; as a result, achieving "national markets" for the entire wind supply chain could lead to even larger disruptions in terms of costs and reduced learning.…”

Section: Discussionmentioning

confidence: 99%

Quantifying the cost savings of global solar photovoltaic supply chains

Helveston

Davidson

2022

Nature

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Achieving carbon neutrality requires deploying renewable energy at unprecedented speed and scale, yet countries sometimes implement policies that increase costs by restricting the free flow of capital, talent, and innovation in favor of localizing benefits such as economic growth, employment, and trade surpluses. Here we assess the cost savings from a globalized solar photovoltaic (PV) module supply chain. We develop a two-factor learning model using historical capacity, component, and input material price data of solar PV deployment in the U.S., Germany, and China. We estimate that the globalized PV module market has saved PV installers in the U.

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Section: Discussionmentioning

confidence: 99%

Quantifying the cost savings of global solar photovoltaic supply chains

Helveston

Davidson

2022

Nature

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“…In addition to the high initial investment costs, there is also an investment risk with wind or solar plants, for example, which can influence the cost structure with high marketing prices through risk premiums. Such and other feedback effects lead to complex system dynamics that need to be carefully examined [5]. One instrument to control the innovation dynamics are market premiums, which are set through tenders.…”

Section: Energy Technology and Sustainabilitymentioning

confidence: 99%

Renewable Energy and Sustainable Digitalisation: Challenges for Europe

Mainzer

2022

Chemistry-Didactics-Ecology-Metrology

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The key to a successful strategy for Europe is sustainable innovation. The current political, economic and military crisis should be used to accelerate the shift to sustainable innovations in order to exit the path dependency of fossil energies. Such as diversity of portfolios at stock markets, diversity of energy and digital technologies ensures resilient behaviour in an innovation portfolio to respond flexibly to the risks of the future and to recover overall in the event of selective setbacks. Sustainable technologies must not be based on a single solution, but rather the entire technological potential must be bundled in an innovation portfolio. A case study is presented in which energy-efficient digitalisation is linked to a sustainable circular economy. A democratic legal system is not only an expression of the human rights highlighted in the UN Charter, but also provides a certain and reliable legal framework for innovations and markets on which technological and economic competition takes place.

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“…One possible explanation for the fact that most model-based forecasts were lower than EE forecasts emerges from the literature on the role of technology modularity or complexity (52,78,79) as determinants of technology innovation trajectories. Largescale nuclear power plants and (to some extent) bioelectricity and CSP plants would fall into the category of less modular technologies compared to solar panels and batteries, for instance.…”

Section: Model-based and Elicitation Forecasts Of 2030 Energy Technologymentioning

confidence: 99%

Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition

Meng

Way

Verdolini

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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We conduct a systematic comparison of technology cost forecasts produced by expert elicitation methods and model-based methods. Our focus is on energy technologies due to their importance for energy and climate policy. We assess the performance of several forecasting methods by generating probabilistic technology cost forecasts rooted at various years in the past and then comparing these with observed costs in 2019. We do this for six technologies for which both observed and elicited data are available. The model-based methods use either deployment (Wright’s law) or time (Moore’s law) to forecast costs. We show that, overall, model-based forecasting methods outperformed elicitation methods. Their 2019 cost forecast ranges contained the observed values much more often than elicitations, and their forecast medians were closer to observed costs. However, all methods underestimated technological progress in almost all technologies, likely as a result of structural change across the energy sector due to widespread policies and social and market forces. We also produce forecasts of 2030 costs using the two types of methods for 10 energy technologies. We find that elicitations generally yield narrower uncertainty ranges than model-based methods. Model-based 2030 forecasts are lower for more modular technologies and higher for less modular ones. Future research should focus on further method development and validation to better reflect structural changes in the market and correlations across technologies.

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Effects of technology complexity on the emergence and evolution of wind industry manufacturing locations along global value chains

Cited by 39 publications

References 50 publications

Quantifying the cost savings of global solar photovoltaic supply chains

Quantifying the cost savings of global solar photovoltaic supply chains

Renewable Energy and Sustainable Digitalisation: Challenges for Europe

Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition

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