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.

  • Article
  • Published:

Scenicness assessment of onshore wind sites with geotagged photographs and impacts on approval and cost-efficiency

Nature Energy volume 6, pages 663–672 (2021)Cite this article

Subjects

Abstract

Cost-efficiency and public acceptance are competing objectives for onshore wind locations. The impact of ‘scenicness’ on these two objectives has been difficult to quantify for wind projects. We analyse the link between economic wind resources and beautiful landscapes with over 1.5 million ‘scenicness’ ratings of around 200,000 geotagged photographs from across Great Britain. We find evidence that planning applications for onshore wind are more likely to be rejected when proposed in more scenic areas. Compared to the technical potential of onshore wind of 1,700 TWh at a total cost of £280 billion, removing the 10% most scenic areas implies about 18% lower generation potential and 8–26% higher costs. We also consider connection distances to the nearest electricity network transformer, showing that the connection costs constitute up to half of the total costs. The results provide a quantitative framework for researchers and policymakers to consider the trade-offs between cost-efficiency and public acceptance for onshore wind.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Frequency distributions of scenicness values and number of votes for the wind polygons associated with planning applications.
Fig. 2: Transformers tagged in OpenStreetMap and urban and rural area classifications in Great Britain.
Fig. 3: Cumulative costs and electricity generation potentials of onshore wind in Great Britain.
Fig. 4: Cost–potential curves for four scenicness thresholds, 3.67, 4.67, 5.8 and 10, in Great Britain.
Fig. 5: Normalized marginal LCOEs and cumulative generation potential for scenicness quantiles (deciles).

Similar content being viewed by others

Data availability

The data employed in this paper can be accessed on Figshare at https://figshare.com/articles/dataset/Quantifying_the_trade-off_between_cost-efficiency_and_public_acceptance_for_onshore_wind/12998693. Source data can be accessed at the locations specified in the main text and the Methods.

References

  1. Department of Business Energy Industrial Strategy. Energy and Climate Change Public Attitude Tracker, Wave 25 https://www.gov.uk/government/statistics/energy-and-climate-change-public-attitudes-tracker-wave-25 (2018).

  2. YouGov, Renewable UK Survey http://d25d2506sfb94s.cloudfront.net/cumulus_uploads/document/3hx70b1nzc/RenewableUK_June18_GB_w.pdf (2018).

  3. Bell, D., Gray, T., Haggett, C. & Swaffield, J. Re-visiting the ‘social gap’: public opinions and relations of power in the local politics of wind energy. Environ. Politics 22, 115–135 (2013).

    Article  Google Scholar 

  4. Fast, S. et al. Lessons learned from Ontario wind energy disputes. Nat. Energy 1, 15028 (2016).

    Article  Google Scholar 

  5. Boudet, H. S. Public perceptions of and responses to new energy technologies. Nat. Energy 4, 446–455 (2019).

    Article  Google Scholar 

  6. Petrova, M. A. From NIMBY to acceptance: toward a novel framework—VESPA—for organizing and interpreting community concerns. Renew. Energy 86, 1280–1294 (2016).

    Article  Google Scholar 

  7. Molnarova, K. et al. Visual preferences for wind turbines: location, numbers and respondent characteristics. J. Appl. Energy 92, 269–278 (2012).

    Article  Google Scholar 

  8. Wolsink, M. Co-production in distributed generation: renewable energy and creating space for fitting infrastructure within landscapes. Landsc. Res. 43, 542–561 (2018).

    Article  Google Scholar 

  9. Betakova, V., Vojar, J. & Sklenicka, P. Wind turbines location: how many and how far? Appl. Energy 151, 23–31 (2015).

    Article  Google Scholar 

  10. Van der Horst, D. NIMBY or not? Exploring the relevance of location and the politics of voiced opinions in renewable energy siting controversies. Energy Policy 35, 2705–2714 (2007).

    Article  Google Scholar 

  11. Schumacher, K. et al. Public acceptance of renewable energies and energy autonomy in different energy policy contexts: a comparative study in the French, German and Swiss Upper Rhine region. Energy Policy 126, 315–332 (2019).

    Article  Google Scholar 

  12. Sonnberger, M. & Ruddat, M. Local and socio-political acceptance of wind farms in Germany. Technol. Soc. 51, 56–65 (2017).

    Article  Google Scholar 

  13. Warren, C., Lumsden, C., O’Dowd, S. & Birnie, R. Green on green: public perceptions of wind power in Scotland and Ireland. J. Environ. Plan. Manag. 48, 853–875 (2005).

    Article  Google Scholar 

  14. Department for Communities and Local Government. Final Decision on Scout Moor Wind Farm Application https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/625856/17-07-06_FINAL_DL_Scout_Moor_Wind_Farm.pdf (2017).

  15. Seresinhe, C. I., Preis, T., MacKerron, G. & Moat, H. S. Happiness is greater in more scenic locations. Sci. Rep. 9, 4498 (2019).

    Article  Google Scholar 

  16. Bornstein, R. D. & Johnson, D. S. Urban-rural wind velocity differences. Atmos. Environ. 11, 597–604 (1977).

    Article  Google Scholar 

  17. Ueckerdt, F. et al. System LCOE: What are the Costs of Variable Renewables? https://doi.org/10.2139/ssrn.2200572 (2013).

  18. Price, J., Mainzer, K., Petrović, S., Zeyringer, M. & McKenna, R. The implications of landscape visual impact on future highly renewable power systems: a case study for Great Britain. IEEE Trans. Power Syst. https://doi.org/10.1109/TPWRS.2020.2992061 (2020).

  19. Dalla Longa, F. et al. Wind Potentials for EU and Neighbouring Countries: Input Datasets for the JRC-EU-TIMES Model JRC109698, EUR 29083 EN (Publications Office of the European Union, 2018); https://doi.org/10.2760/041705

  20. Zeyringer, M. et al. Designing low-carbon power systems for Great Britain in 2050 that are robust to the spatiotemporal and inter-annual variability of weather. Nat. Energy 3, 395–403 (2018).

    Article  Google Scholar 

  21. Ryberg, D. S., Tulemat, Z., Stolten, D. & Robinius, M. Uniformly constrained land eligibility for onshore European wind power. Renew. Energy 146, 921–931 (2020).

    Article  Google Scholar 

  22. Staffell, I. & Pfenninger, S. Using bias-corrected reanalysis to simulate current and future wind power output. Energy 114, 1224–1239 (2016).

    Article  Google Scholar 

  23. Bosch, J., Staffell, I. & Hawkes, A. Temporally-explicit and spatially-resolved global onshore wind energy potentials. Energy https://doi.org/10.1016/j.energy.2017.05.052 (2017).

  24. Höltinger, S. et al. Austria’s wind energy potential – a participatory modeling approach to assess socio-political and market acceptance. Energy Policy 98, 49–61 (2016).

    Article  Google Scholar 

  25. Jäger, T., McKenna, R. & Fichtner, W. The feasible onshore wind energy potential in Baden-Württemberg: a bottom-up methodology considering socio-economic constraints. Renew. Energy 96, 662–675 (2016).

    Article  Google Scholar 

  26. Harper, M. et al. Assessing socially acceptable locations for onshore wind energy using a GIS-MCDA approach. Int. J. Low-Carbon Technol. 14, 160–169 (2019).

    Article  Google Scholar 

  27. Jobert, A., Laborgne, P. & Mimler, S. Local acceptance of wind energy: factors of success identified in French and German case studies. Energy Policy 35, 2751–2760 (2007).

    Article  Google Scholar 

  28. Kontogianni, A., Tourkolias, C., Skourtos, M. & Damigos, D. Planning globally, protesting locally: patterns in community perceptions towards the installation of wind farms. Renew. Energy 66, 170–177 (2014).

    Article  Google Scholar 

  29. Renewable Energy Planning Database (Department of Business Energy Industrial Strategy, accessed 20 February 2019); https://www.gov.uk/government/publications/renewable-energy-planning-database-monthlyextract

  30. Harper, M. et al. Identifying key influences for planning acceptance of onshore wind turbines. In 30th International Conference on Efficiency, Cost, Optimisation, Simulation and Environmental Impact of Energy Systems (San Diego, July 2017).

  31. Roddis, P. et al. The role of community acceptance in planning outcomes for onshore wind and solar farms: an energy justice analysis. Appl. Energy https://doi.org/10.1016/j.apenergy.2018.05.087 (2018).

  32. McKenna, R., Hollnaicher, S., Ostmann v. d. Leye, P. & Fichtner, W. Cost-potentials for large onshore wind turbines in Europe. Energy 83, 217–229 (2015).

    Article  Google Scholar 

  33. Ryberg, D. et al. The future of European onshore wind energy potential: detailed distribution and simulation of advanced turbine designs. Energy https://doi.org/10.1016/j.energy.2019.06.052 (2019).

  34. Rinne, E. et al. Effects of turbine technology and land use on wind power resource potential. Nat. Energy 3, 494–500 (2018).

    Article  Google Scholar 

  35. McKenna, R. et al. Reviewing methods and assumptions for high-resolution large-scale onshore wind energy potential assessments. Preprint at https://arxiv.org/pdf/2103.09781 (2021).

  36. Seresinhe, C. I., Preis, T. & Moat, H. S. Using deep learning to quantify the beauty of outdoor places. R. Soc. Open Sci. 4, 170170 (2017).

    Article  MathSciNet  Google Scholar 

  37. Hevia-Koch, P. & Jacobsen, H. K. Comparing offshore and onshore wind development considering acceptance costs. Energy Policy 125, 9–19 (2019).

    Article  Google Scholar 

  38. Seresinhe, C. I., Preis, T. & Moat, H. S. Quantifying the impact of scenic environments on health. Sci. Rep. 5, 16899 (2015).

    Article  Google Scholar 

  39. Walker, G. et al. Trust and community: exploring the meanings, contexts and dynamics of community renewable energy. Energy Policy 38, 2655–2663 (2010).

    Article  Google Scholar 

  40. Walker, G. What are the barriers and incentives for community-owned means of energy production and use? Energy Policy 36, 4401–4405 (2008).

    Article  Google Scholar 

  41. Brown, K. et al. Empathy, place and identity interactions for sustainability. Glob. Environ. Change 56, 11–17 (2019).

    Article  Google Scholar 

  42. Devine-Wright, P. & Batel, S. My neighbourhood, my country or my planet? The influence of multiple place attachments and climate change concern on social acceptance of energy infrastructure. Glob. Environ. Change 47, 110–120 (2017).

    Article  Google Scholar 

  43. Palmer, J. F. & Hoffmann, R. E. Rating reliability and representation validity in scenic landscape assessments. Landsc. Urban Plan. 54, 149–161 (2001).

    Article  Google Scholar 

  44. Palmer, J. & Sullivan, R. Visual prominence as perceived in photographs and in-situ. J. Digital Landsc. Archit. 5-2020, 286–294 (2020).

    Google Scholar 

  45. Stamps, A. E. Use of photographs to simulate environments: a meta-analysis. Percept. Mot. Skills 713, 907–913 (1990).

    Article  Google Scholar 

  46. Suskevics, M. et al. Regional variation in public acceptance of wind energy development in Europe: what are the roles of planning procedures and participation? Land Use Policy 81, 311–323 (2019).

    Article  Google Scholar 

  47. Stadler, B., Purves, Ross S. & Tomko, M. Exploring the relationship between land cover and subjective evaluation of scenic beauty through user generated content. In 25th International Cartographic Conference (Paris, July 2011).

  48. Roth, M. et al. Landscape as an area as perceived by people: empirically-based nationwide modelling of scenic landscape quality in germany. J. Digital Landsc. Archit. 3, 129–137 (2018).

    Google Scholar 

  49. Bertsch, V., Hall, M., Weinhardt, C. & Fichtner, W. Public acceptance and preferences related to renewable energy and grid expansion policy: empirical insights for Germany. Energy 114, 465–477 (2016).

    Article  Google Scholar 

  50. Firestone, J. & Kirk, H. A strong relative preference for wind turbines in the United States among those who live near them. Nat. Energy 4, 311–320 (2019).

    Article  Google Scholar 

  51. Wolsink, M. Wind power implementation: the nature of public attitudes: equity and fairness instead of ‘backyard motives’. Renew. Sustain. Energy Rev. 11, 1188–1207 (2007).

    Article  Google Scholar 

  52. Kortsch, T., Hildebrand, J. & Schweizer-Ries, P. Acceptance of biomass plants – results of a longitudinal study in the bioenergy-region Altmark. Renew. Energy 83, 690–697 (2015).

    Article  Google Scholar 

  53. Leibenath, M. & Lintz, G. Governance of energy landscapes between pathways, people and politics. Landsc. Res. 43, 471–475 (2018).

    Article  Google Scholar 

  54. Drechsler, M. et al. Efficient and equitable spatial allocation of renewable power plants at the country scale. Nat. Energy 2, 17124 (2017).

    Article  Google Scholar 

  55. UK Protected Area Datasets (Joint Nature Conservation Committee, accessed 10 March 2019); http://jncc.defra.gov.uk/ProtectedSites/SACselection/gis_data/terms_conditions.asp

  56. National Parks (August 2016) Full Extent Boundaries in Great Britain (ONS, accessed 10 March 2019); https://geoportal.statistics.gov.uk/datasets/countries-december-2017-full-extent-boundaries-in-great-britain?geometry=-44.206%2C51.102%2C39.422%2C59.783

  57. McFadden, D. in Frontiers in Econometrics (ed. Zarembka, P.) 105–142 (Academic Press, 2013).

  58. OpenStreetMap (OSM, accessed 16 March 2020); http://download.geofabrik.de/europe/great-britain.html

  59. ukcp09: Gridded Datasets of Monthly values – Mean Wind Speed (Knots) (Met Office, accessed 29 March 2020); https://data.gov.uk/dataset/44312870-4575-43cd-9ed8-9d8acc90a5b0/ukcp09-gridded-datasets-of-monthly-values-mean-wind-speed-knots

  60. Digest Of UK Energy Statistics (DUKES): Renewable Sources Of Energy (Department of Business Energy Industrial Strategy, accessed 14 March 2020); https://www.gov.uk/government/statistics/renewable-sources-of-energy-chapter-6-digest-of-united-kingdom-energy-statistics-dukes

  61. Wüstenhagen, R., Wolsink, M. & Bürer, M. J. Social acceptance of renewable energy innovation: an introduction to the concept. Energy Policy 35, 2683–2691 (2007).

    Article  Google Scholar 

  62. Lucke, D. Akzeptanz. Legitimität in der Abstimmungsgesellschaft (VS Verlag für Sozialwissenschaften, 1995); https://doi.org/10.1007/978-3-663-09234-6

  63. Schweizer-Ries, P. Energy sustainable communities: environmental psychological investigations. Energy Policy 36, 4126–4135 (2008).

    Article  Google Scholar 

  64. Rau, I., Schweizer-Ries, P. & Hildebrand, J. 2012. in Vulnerability, Risks, and Complexity: Impacts of Global Change on Human Habitats Vol. 3 (eds Kabisch, S. et al.) 177–190 (Hogrefe, 2012).

  65. Akaike, H. in Second International Symposium on Information Theory (ed. Petrov, B. N. and Csaki, F.) 267–281 (Akademiai Kiado, 1973).

  66. Shapefiles for the UK Boundaries (Office for National Statistics, 2019); https://data.gov.uk/dataset/37edc0ad-ffff-47c9-a01c-cb8d6123ec79/nuts-level-1-january-2018-ultra-generalised-clipped-boundaries-in-the-united-kingdom

  67. Digest Of UK Energy Statistics (DUKES): Electricity (Department of Business Energy Industrial Strategy, accessed 14 March 2020); https://www.gov.uk/government/statistics/electricity-chapter-5-digest-of-united-kingdom-energy-statistics-dukes

Download references

Acknowledgements

We gratefully acknowledge the contributions of D. Schlund, who carried out some of the wind analysis whilst a Student Assistant at KIT, as well as C. Moutard, on whose Master’s Thesis at DTU this article builds (Assessing the ‘acceptable’ onshore wind potential in the UK, 2019, https://findit.dtu.dk/en/catalog/2451029061). M. D’Andrea, K. Paidis and T. Jaenicke supported the preparation of early versions of the manuscript whilst Student Assistants at DTU. I.M. gratefully acknowledges financial support from Kraks Fond, Copenhagen (kraksfond@kraksfond.dk). T.P. and H.S.M. are grateful for support from The Alan Turing Institute under the EPSRC grant EP/N510129/1 (including awards TU/B/000006 and TU/B/000008).

Author information

Authors and Affiliations

  1. Energy Systems Analysis, DTU Management, Technical University of Denmark, Kongens Lyngby, Denmark

    R. McKenna & S. Petrović

  2. School of Engineering, University of Aberdeen, Aberdeen, UK

    R. McKenna

  3. Chair of Energy Economics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    J. M. Weinand

  4. Department of Economics, Copenhagen Business School, Frederiksberg, Denmark

    I. Mulalic

  5. Department of Spatial Economics, VU Amsterdam, Amsterdam, The Netherlands

    I. Mulalic

  6. Center for Global Cooperation, The Danish Energy Agency, Copenhagen, Denmark

    S. Petrović

  7. greenventory GmbH, Freiburg im Breisgau, Germany

    K. Mainzer

  8. Data Science Lab, Behavioural Science, Warwick Business School, University of Warwick, Coventry, UK

    T. Preis & H. S. Moat

  9. The Alan Turing Institute, London, UK

    T. Preis & H. S. Moat

Authors
  1. R. McKenna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. M. Weinand
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I. Mulalic
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Petrović
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. K. Mainzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. T. Preis
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. H. S. Moat
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.M. conceived and designed the research. R.M., J.M.W., I.M., S.P. and K.M. carried out the analysis. R.M., J.M.W., I.M., S.P., K.M., T.P. and H.S.M. contributed to analysis design and interpretation. T.P. and H.S.M. provided the scenicness data. R.M. led the preparation and revision of the manuscript. R.M., J.W., I.M., S.P., K.M., T.P. and H.S.M. drafted text and edited the manuscript. R.M. provided institutional and material support for the research.

Corresponding author

Correspondence to R. McKenna.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Energy thanks James Palmer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Information

Supplementary Information, Table 1 and Figs. 1 and 2.

Source data

Source Data Fig. 1

Source data for Fig. 1.

Source Data Fig. 2

Source data for Fig. 2.

Source Data Fig. 3

Source data for Fig. 3.

Source Data Fig. 4

Source data for Fig. 4.

Source Data Fig. 5

Source data for Fig. 5.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

McKenna, R., Weinand, J.M., Mulalic, I. et al. Scenicness assessment of onshore wind sites with geotagged photographs and impacts on approval and cost-efficiency. Nat Energy 6, 663–672 (2021). https://doi.org/10.1038/s41560-021-00842-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41560-021-00842-5

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