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

Nature Energy volume 5, pages 811–821 (2020)Cite this article

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Abstract

Wind energy can contribute to national climate, energy and economic goals by expanding clean energy and supporting economies through new manufacturing industries. However, the mechanisms for achieving these interlinked goals are not well understood. Here we analyse the wind energy manufacturing global value chain, using a dataset on 389 component supplier firms (2006–2016) that work with 13 original equipment manufacturers. We assess how technology complexity, that is, the knowledge intensity and difficulty of manufacturing components, shapes the location of suppliers. For countries without existing wind industries, we find evidence of the emergence of suppliers for only low-complexity components (for example, towers and generators). For countries with existing wind industries, we find that suppliers’ evolution, that is, changes in their international supply relationships, is less likely for high-complexity components (for example, blades and gearboxes). Our findings show the importance of understanding technologies along with firms and countries within global value chains for achieving policy goals.

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Fig. 1: Diversity in number and geographic spread of suppliers by wind turbine component.
Fig. 2: Technology complexity estimates of wind turbine components.
Fig. 3: Change in international supplier–OEM relationships between 2006 and 2016 with increasing technology complexity.
Fig. 4: Relationship between the number of supplier countries of each component and the average complexity of the component.
Fig. 5: Coefficient plots showing the relationship between international evolution, technology complexity and other control variables.
Fig. 6: The highest complexity of wind turbine components in a country in a given year.

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Data availability

The database on the global manufacturing value chain developed for this study was built on third-party reports published by Navigant Consulting, with additional details obtained from Orbis, Amadeus, Bloomberg and Derwent World Patents Index. Restrictions apply to the availability of these third-party data and so the dataset is not publicly available. Data are however available upon reasonable request from the corresponding author. Supplier data (without the supplier company name) are available at https://github.com/kavsurana/tech-complexity-project/. The source data underlying Figs. 1–6 are provided as source data. The source data underlying Supplementary Figs. 1 and 2 are provided as Supplementary Data 1 and 2.

Code availability

The source and code to replicate the analysis are available at https://github.com/kavsurana/tech-complexity-project/.

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Acknowledgements

Funding for this research was provided by the US National Science Foundation under grant number 1829252; and the UK Economic and Social Research Council under grant number ES/S010688/1. M. George, L. He, A. Hammerstingl and F. Traimer helped with cleaning and verifying the dataset. D. Li and M. Vigil provided valuable feedback on the concepts behind this paper.

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Authors and Affiliations

  1. Center for Global Sustainability, School of Public Policy, University of Maryland, College Park, MD, USA

    Kavita Surana & Nathan Hultman

  2. Campus Straubing for Biotechnology and Sustainability and School of Management, Technical University of Munich, Straubing, Germany

    Claudia Doblinger

  3. Centre for Environment, Energy and Natural Resource Governance, Department of Land Economy, University of Cambridge, Cambridge, UK

    Laura Diaz Anadon

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Contributions

K.S, C.D. and L.D.A developed the research idea and concept. K.S. and C.D collected and analysed the data. K.S., C.D., L.D.A. and N.H. interpreted the results and conducted policy analysis. K.S. and C.D. wrote the manuscript. L.D.A. and N.H. edited the manuscript. K.S., L.D.A. and N.H. secured project funding.

Corresponding authors

Correspondence to Kavita Surana or Laura Diaz Anadon.

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Supplementary information

Supplementary Information

Supplementary Tables 1–6, Figs. 1 and 2, and refs. 1–7.

Reporting Summary

Supplementary Data 1

Source data for Supplementary Fig. 1. Comparison of technology complexity calculated from different approaches.

Supplementary Data 2

Source data for Supplementary Fig. 2. Distribution of wind component suppliers by country and the processed dataset to calculate results.

Source data

Source Data Fig. 1

Data on number of suppliers by country and by component, and the base dataset.

Source Data Fig. 2

Data points for the product complexity index using HS02 values.

Source Data Fig. 3

Source data on suppliers and the country of the OEM they supply to, the base dataset and the complexity dataset.

Source Data Fig. 4

Summary of the number of countries and firms for each component, the base dataset and the complexity dataset.

Source Data Fig. 5

Statistical model results and the processed dataset to calculate results.

Source Data Fig. 6

Summary of data on maximum complexity in a country, the base dataset and the complexity dataset.

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Surana, K., Doblinger, C., Anadon, L.D. et al. Effects of technology complexity on the emergence and evolution of wind industry manufacturing locations along global value chains. Nat Energy 5, 811–821 (2020). https://doi.org/10.1038/s41560-020-00685-6

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