Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

Subjects

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

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.

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/.

References

  1. Renewable Capacity Statistics 2020 (IRENA, 2020); https://www.irena.org/publications/2020/Mar/Renewable-Capacity-Statistics-2020

  2. World Energy Outlook 2019 (IEA, 2019); https://www.iea.org/reports/world-energy-outlook-2019

  3. U.S. Wind Energy Manufacturing and Supply Chain: A Competitiveness Analysis (Global Wind Network, 2014); https://energy.gov/sites/prod/files/2014/09/f18/U.S.%20Wind%20Energy%20Manufacturing%20and%20Supply%20Chain%20Competitiveness%20Analysis_0.pdf

  4. Wiser, R. et al. Expert elicitation survey on future wind energy costs. Nat. Energy 1, 16135 (2016).

    Google Scholar 

  5. Wiser, R. et al. 2018 Wind Technologies Market Report 103 (US Department of Energy, Office of Energy Efficiency and Renewable, 2018).

  6. Industrial Strategy: Offshore Wind Sector Deal (HM Government, 2019); https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/790950/BEIS_Offshore_Wind_Single_Pages_web_optimised.pdf

  7. Hansen, U. E., Nygaard, I., Morris, M. & Robbins, G. The effects of local content requirements in auction schemes for renewable energy in developing countries: aA literature review. Renew. Sustain. Energy Rev. 127, 109843 (2020).

    Google Scholar 

  8. Schmidt, T. S., Schmid, N. & Sewerin, S. Policy goals, partisanship and paradigmatic change in energy policy — analyzing parliamentary discourse in Germany over 30 years. Clim. Policy 19, 771–786 (2019).

    Google Scholar 

  9. Lewis, J. I. The rise of renewable energy protectionism: emerging trade conflicts and implications for low carbon development. Glob. Environ. Polit. 14, 10–35 (2014).

    Google Scholar 

  10. Stokes, L. C. & Warshaw, C. Renewable energy policy design and framing influence public support in the United States. Nat. Energy 2, 17107 (2017).

    Google Scholar 

  11. Haakonsson, S. J. & Kirkegaard, J. K. Configuration of technology networks in the wind turbine industry. A comparative study of technology management models in European and Chinese lead firms. Int. J. Technol. Manag. 70, 281–299 (2016).

    Google Scholar 

  12. Binz, C. & Truffer, B. Global innovation systems—a conceptual framework for innovation dynamics in transnational contexts. Res. Policy 46, 1284–1298 (2017).

    Google Scholar 

  13. Pietrobelli, C. & Rabellotti, R. Global value chains meet innovation systems: are there learning opportunities for developing countries? World Dev. 39, 1261–1269 (2011).

    Google Scholar 

  14. Jurowetzki, R., Lema, R. & Lundvall, B.-Å. Combining innovation systems and global value chains for development: towards a research agenda. Eur. J. Dev. Res. 30, 364–388 (2018).

    Google Scholar 

  15. Lema, R. & Lema, A. Technology transfer? The rise of China and India in green technology sectors. Innov. Dev. 2, 23–44 (2012).

    Google Scholar 

  16. Lewis, J. I. Building a national wind turbine industry: experiences from China, India and South Korea. Int. J. Technol. Global. 5, 281–305 (2011).

    Google Scholar 

  17. Surana, K. & Anadon, L. D. Public policy and financial resource mobilization for wind energy in developing countries: a comparison of approaches and outcomes in China and India. Glob. Environ. Change 35, 340–359 (2015).

    Google Scholar 

  18. Binz, C., Gosens, J., Hansen, T. & Hansen, U. E. Toward technology-sensitive catching-up policies: insights from renewable energy in China. World Dev. 96, 418–437 (2017).

    Google Scholar 

  19. World Development Report 2020: Trading for Development in the Age of Global Value Chains (World Bank, 2020).

  20. Multinational Enterprises in the Global Economy—Heavily Debated but Hardly Measured (OECD, 2018); https://www.oecd.org/industry/ind/MNEs-in-the-global-economy-policy-note.pdf

  21. Antràs, P. Conceptual Aspects of Global Value Chains NBER Working Paper No. 26539 (NBER, 2019); https://doi.org/10.3386/w26539

  22. Wind Energy Industry Manufacturing Supplier Handbook (AWEA, 2011).

  23. Hobday, M. Product complexity, innovation and industrial organisation. Res. Policy 26, 689–710 (1998).

    Google Scholar 

  24. Huenteler, J., Schmidt, T. S., Ossenbrink, J. & Hoffmann, V. H. Technology life-cycles in the energy sector—technological characteristics and the role of deployment for innovation. Technol. Forecast. Soc. Change 104, 102–121 (2016).

    Google Scholar 

  25. Garud, R. & Karnoe, P. Bricolage versus breakthrough: distributed and embedded agency in technology entrepreneurship. Res. Policy 32, 277–300 (2003).

    Google Scholar 

  26. OECD SME and Entrepreneurship Outlook 2019 (OECD, 2019).

  27. Supply Chain Assessment —Wind Energy 2006–2014 (Navigant Research, 2014).

  28. Hausmann, R. et al. The Atlas of Economic Complexity (MIT Press, 2013).

  29. McNerney, J., Farmer, J. D., Redner, S. & Trancik, J. E. Role of design complexity in technology improvement. Proc. Natl Acad. Sci. USA 108, 9008–9013 (2011).

    Google Scholar 

  30. Novak, S. & Eppinger, S. D. Sourcing by design: product complexity and the supply chain. Manag. Sci. 47, 189–204 (2001).

    Google Scholar 

  31. Broekel, T. Using structural diversity to measure the complexity of technologies. PLoS ONE 14, e0216856 (2019).

    Google Scholar 

  32. Mealy, P., Farmer, J. D. & Teytelboym, A. Interpreting economic complexity. Sci. Adv. 5, eaau1705 (2019).

    Google Scholar 

  33. Balland, P.-A. & Rigby, D. The geography of complex knowledge. Econ. Geogr. 93, 1–23 (2017).

    Google Scholar 

  34. Fleming, L. & Sorenson, O. Technology as a complex adaptive system: evidence from patent data. Res. Policy 30, 1019–1039 (2001).

    Google Scholar 

  35. Huenteler, J., Ossenbrink, J., Schmidt, T. S. & Hoffmann, V. H. How a product’s design hierarchy shapes the evolution of technological knowledge—evidence from patent-citation networks in wind power. Res. Policy 45, 1195–1217 (2016).

    Google Scholar 

  36. Observatory of Economic Complexity—Product Complexity Rankings (MIT Media Lab, 2011); https://atlas.media.mit.edu/en/rankings/product/hs07/?year_range=2011-2016

  37. Paisemi, F. EU Energy Technology Trade: Import and Export (Publications Office of the European Union, 2017); https://setis.ec.europa.eu/sites/default/files/reports/eu_energy_technology_trade.pdf

  38. Wind, I. HS Codes and the Renewable Energy Sector. Research and Analysis (International Centre for Trade and Sustainable Development, 2008).

  39. Asheim, B. T. & Coenen, L. Knowledge bases and regional innovation systems: comparing Nordic clusters. Res. Policy 34, 1173–1190 (2005).

    Google Scholar 

  40. Lewis, J. I. & Wiser, R. H. Fostering a renewable energy technology industry: an international comparison of wind industry policy support mechanisms. Energy Policy 35, 1844–1857 (2007).

    Google Scholar 

  41. Qiu, Y. & Anadon, L. D. The price of wind power in China during its expansion: technology adoption, learning-by-doing, economies of scale, and manufacturing localization. Energy Econ. 34, 772–785 (2012).

    Google Scholar 

  42. Awate, S., Larsen, M. M. & Mudambi, R. Accessing vs sourcing knowledge: a comparative study of R&D internationalization between emerging and advanced economy firms. J. Int. Bus. Stud. 46, 63–86 (2015).

    Google Scholar 

  43. Nieto, M. J. & Rodríguez, A. Offshoring of R&D: looking abroad to improve innovation performance. J. Int. Bus. Stud. 42, 345–361 (2011).

    Google Scholar 

  44. Dunning, J. H. & Lundan, S. M. The internationalization of corporate R&D: a review of the evidence and some policy implications for home countries. Rev. Policy Res. 26, 13–33 (2009).

    Google Scholar 

  45. Doblinger, C., Dowling, M. & Helm, R. An institutional perspective of public policy and network effects in the renewable energy industry: enablers or disablers of entrepreneurial behaviour and innovation? Entrep. Reg. Dev. 28, 126–156 (2016).

    Google Scholar 

  46. Cox Pahnke, E., McDonald, R., Wang, D. & Hallen, B. Exposed: venture capital, competitor ties, and entrepreneurial innovation. Acad. Manag. J. 58, 1334–1360 (2014).

    Google Scholar 

  47. Doblinger, C., Surana, K. & Anadon, L. D. Governments as partners: the role of alliances in U.S. cleantech startup innovation. Res. Policy 48, 1458–1475 (2019).

    Google Scholar 

  48. Schilling, M. A. & Phelps, C. C. Interfirm collaboration networks: the impact of large-scale network structure on firm innovation. Manag. Sci. 53, 1113–1126 (2007).

    MATH  Google Scholar 

  49. Gereffi, G., Humphrey, J. & Sturgeon, T. The governance of global value chains. Rev. Int. Polit. Econ. 12, 78–104 (2005).

    Google Scholar 

  50. Sturgeon, T., Van Biesebroeck, J. & Gereffi, G. Value chains, networks and clusters: reframing the global automotive industry. J. Econ. Geogr. 8, 297–321 (2008).

    Google Scholar 

  51. Schmidt, T. S. & Huenteler, J. Anticipating industry localization effects of clean technology deployment policies in developing countries. Glob. Environ. Change 38, 8–20 (2016).

    Google Scholar 

  52. Brauch, H. G., Spring, Ú. O., Grin, J. & Scheffran, J. Handbook on Sustainability Transition and Sustainable Peace Vol. 10 (Springer, 2016).

  53. Haakonsson, S., Kirkegaard, J. K. & Lema, R. The decomposition of innovation in Europe and China’s catch-up in wind power technology: the role of KIBS. Eur. Plan. Stud. https://doi.org/10.1080/09654313.2020.1712329 (2020).

  54. Utility Scale Wind Towers from Canada, Indonesia, Korea, and Vietnam Investigation Nos. 701-TA-627-629 and 731-TA-1458-1461 (Preliminary) (USITC, 2019); https://www.usitc.gov/publications/701_731/pub4952.pdf

  55. Wilson, C. et al. Granular technologies to accelerate decarbonization. Science 368, 36–39 (2020).

    Google Scholar 

  56. Future of Wind: Deployment, Investment, Technology, Grid Integration and Socio-economic Aspects (IRENA, 2019); https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Oct/IRENA_Future_of_wind_2019.pdf

  57. Feinerer, I., Hornik, K. & Meyer, D. Text mining infrastructure in R. J. Stat. Softw. 25, 1–54 (2008).

    Google Scholar 

  58. Grün, B. & Hornik, K. topicmodels: an R package for fitting topic models. J. Stat. Softw. 40, 1–30 (2011).

    Google Scholar 

  59. Chan, G. The Commercialization of Publicly Funded Science: How Licensing Federal Laboratory Inventions Affects Knowledge Spillovers. Doctoral thesis, Harvard Univ. (2015); http://isites.harvard.edu/fs/docs/icb.topic1459278.files/CHAN-Gabriel_11-21-14_JMP%20-%20National%20Lab%20Patent%20Licensing.pdf

  60. Kim, G., Park, S. & Jang, D. in Soft Computing in Big Data Processing (eds. Lee, K. M. et al.) 71–80 (Springer International, 2014).

  61. Barrat, A., Barthélemy, M., Pastor-Satorras, R. & Vespignani, A. The architecture of complex weighted networks. Proc. Natl Acad. Sci. USA 101, 3747–3752 (2004).

    MATH  Google Scholar 

  62. Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJ. Complex Syst. 1695, 1–9 (2006).

    Google Scholar 

  63. Taglioni, D. & Winkler, D. Making Global Value Chains Work for Development (The World Bank, 2016); https://doi.org/10.1596/978-1-4648-0157-0

  64. Pedersen, T. L. ggforce: accelerating ‘ggplot2’. R package version 0.1 2 (2019).

  65. Hlavac, M. Stargazer: well-formatted regression and summary statistics tables. R package version 5 (2015).

Download references

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.

Author information

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

Authors
  1. Kavita Surana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Claudia Doblinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura Diaz Anadon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nathan Hultman
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41560-020-00685-6

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing