{"id":976377,"date":"2026-05-21T21:54:28","date_gmt":"2026-05-21T21:54:28","guid":{"rendered":"https:\/\/www.europesays.com\/uk\/976377\/"},"modified":"2026-05-21T21:54:28","modified_gmt":"2026-05-21T21:54:28","slug":"advancing-solar-and-wind-penetration-in-china-through-energy-complementarity","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/uk\/976377\/","title":{"rendered":"Advancing solar and wind penetration in China through energy complementarity"},"content":{"rendered":"
  • \n

    Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I. & Wernli, H. Balancing Europe\u2019s wind-power output through spatial deployment informed by weather regimes. Nat. Clim. Change 7<\/b>, 557\u2013562 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Lu, X. et al. Challenges faced by China compared with the US in developing wind power. Nat. Energy 1<\/b>, 16061 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Heide, D. et al. Seasonal optimal mix of wind and solar power in a future, highly renewable Europe. Renew. Energy 35<\/b>, 2483\u20132489 (2010).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Heide, D., Greiner, M., von Bremen, L. & Hoffmann, C. Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation. Renew. Energy 36<\/b>, 2515\u20132523 (2011).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Xiao, M., Wetzel, M., Pregger, T., Simon, S. & Scholz, Y. Modeling the supply of renewable electricity to metropolitan regions in China. Energies 13<\/b>, 3042 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Pedruzzi, R. et al. Review of mapping analysis and complementarity between solar and wind energy sources. Energy 283<\/b>, 129045 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Engeland, K. et al. Space-time variability of climate variables and intermittent renewable electricity production \u2013 a review. Renew. Sustain. Energy Rev. 79<\/b>, 600\u2013617 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Jurasz, J., Canales, F., Kies, A., Guezgouz, M. & Beluco, A. A review on the complementarity of renewable energy sources: concept, metrics, application and future research directions. Sol. Energy 195<\/b>, 703\u2013724 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Schindler, D., Behr, H. D. & Jung, C. On the spatiotemporal variability and potential of complementarity of wind and solar resources. Energy Convers. Manag. 218<\/b>, 113016 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Kapica, J., Canales, F. A. & Jurasz, J. Global atlas of solar and wind resources temporal complementarity. Energy Convers. Manag. 246<\/b>, 114692 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hajou, A., El Mghouchi, Y. & Chaoui, M. A new solar-wind complementarity index: an application to the climate of Morocco. Renew. Energy 227<\/b>, 120490 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Gao, Y. et al. The wind-solar hybrid energy could serve as a stable power source at multiple time scale in China mainland. Energy 305<\/b>, 132294 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Prasad, A. A., Taylor, R. A. & Kay, M. Assessment of solar and wind resource synergy in Australia. Appl. Energy 190<\/b>, 354\u2013367 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hu, J. et al. Reducing energy storage demand by spatial-temporal coordination of multienergy systems. Appl. Energy 329<\/b>, 120277 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Li, P., Lian, J., Ma, C. & Zhang, J. Complementarity and development potential assessment of offshore wind and solar resources in China seas. Energy Convers. Manag. 296<\/b>, 117705 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    de Souza Nascimento, M. M. et al. Offshore wind and solar complementarity in Brazil: a theoretical and technical potential assessment. Energy Convers. Manag. 270<\/b>, 116194 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Kruitwagen, L. et al. A global inventory of photovoltaic solar energy generating units. Nature 598<\/b>, 604\u2013610 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zhang, X., Xu, M., Wang, S., Huang, Y. & Xie, Z. Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine. Earth Syst. Sci. Data 14<\/b>, 3743\u20133755 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chen, Y., Zhou, J., Ge, Y. & Dong, J. Uncovering the rapid expansion of photovoltaic power plants in China from 2010 to 2022 using satellite data and deep learning. Remote Sens. Environ. 305<\/b>, 114100 (2024).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Xia, Z. et al. Mapping the rapid development of photovoltaic power stations in northwestern China using remote sensing. Energy Rep. 8<\/b>, 4117\u20134127 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Jiang, H. et al. Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery. Earth Syst. Sci. Data Discuss. 2021<\/b>, 1\u201317 (2021).<\/p>\n

    CAS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Yu, J., Wang, Z., Majumdar, A. & Rajagopal, R. Deepsolar: a machine learning framework to efficiently construct a solar deployment database in the United States. Joule 2<\/b>, 2605\u20132617 (2018).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Ortiz, A. et al. An artificial intelligence dataset for solar energy locations in India. Sci. Data 9<\/b>, 497 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Malof, J. M., Bradbury, K., Collins, L. M. & Newell, R. G. Automatic detection of solar photovoltaic arrays in high resolution aerial imagery. Appl. Energy 183<\/b>, 229\u2013240 (2016).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zhang, Z. et al. Carbon mitigation potential afforded by rooftop photovoltaic in China. Nat. Commun. 14<\/b>, 2347 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Mallapaty, S. et al. How China could be carbon neutral by mid-century. Nature 586<\/b>, 482\u2013483 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Wang, J. et al. Inherent spatiotemporal uncertainty of renewable power in China. Nat. Commun. 14<\/b>, 5379 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    National Energy Administration, China. National Energy Administration releases 2024 national power industry statistics. National Energy Administration https:\/\/www.nea.gov.cn\/20250121\/097bfd7c1cd3498897639857d86d5dac\/c.html<\/a> (2025).<\/p>\n<\/li>\n

  • \n

    Yin, J., Molini, A. & Porporato, A. Impacts of solar intermittency on future photovoltaic reliability. Nat. Commun. 11<\/b>, 4781 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Howe, C. As China\u2019s renewable capacity soars, utilisation lags, data show. Reuters https:\/\/www.reuters.com\/sustainability\/climate-energy\/chinas-renewable-capacity-soars-utilisation-lags-data-show-2025-08-05\/<\/a> (2025).<\/p>\n<\/li>\n

  • \n

    Ren, G., Wan, J., Liu, J. & Yu, D. Spatial and temporal assessments of complementarity for renewable energy resources in China. Energy 177<\/b>, 262\u2013275 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Jani, H. K., Kachhwaha, S. S., Nagababu, G. & Das, A. Temporal and spatial simultaneity assessment of wind-solar energy resources in India by statistical analysis and machine learning clustering approach. Energy 248<\/b>, 123586 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Santos-Alamillos, F., Pozo-V\u00e1zquez, D., Ruiz-Arias, J. A., Von Bremen, L. & Tovar-Pescador, J. Combining wind farms with concentrating solar plants to provide stable renewable power. Renew. Energy 76<\/b>, 539\u2013550 (2015).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Xu, L., Wang, Z. & Liu, Y. The spatial and temporal variation features of wind-sun complementarity in China. Energy Convers. Manag. 154<\/b>, 138\u2013148 (2017).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Jerez, S. et al. An action-oriented approach to make the most of the wind and solar power complementarity. Earth\u2019s Future 11<\/b>, e2022EF003332 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Guo, Y. et al. Variation-based complementarity assessment between wind and solar resources in China. Energy Convers. Manag. 278<\/b>, 116726 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Johlas, H., Witherby, S. & Doyle, J. R. Storage requirements for high grid penetration of wind and solar power for the MISO region of North America: a case study. Renew. Energy 146<\/b>, 1315\u20131324 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Tong, D. et al. Geophysical constraints on the reliability of solar and wind power worldwide. Nat. Commun. 12<\/b>, 6146 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     CAS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n     PubMed Central<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Liu, L. et al. Optimizing wind\/solar combinations at finer scales to mitigate renewable energy variability in China. Renew. Sustain. Energy Rev. 132<\/b>, 110151 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Cao, K., Zhou, C., Church, R., Li, X. & Li, W. Revisiting spatial optimization in the era of geospatial big data and GeoAI. Int. J. Appl. Earth Obs. Geoinf. 129<\/b>, 103832 (2024).<\/p>\n


    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Li, C. & Song, L. Regional differences and spatial convergence of green development in China. Sustainability 14<\/b>, 8511 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Fang, W. et al. Assessment of wind and solar power potential and their temporal complementarity in China\u2019s northwestern provinces: insights from ERA5 reanalysis. Energies 16<\/b>, 7109 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146<\/b>, 1999\u20132049 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Sun, K., Xiao, B., Liu, D. & Wang, J. Deep high-resolution representation learning for human pose estimation. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 5693\u20135703 (IEEE, 2019).<\/p>\n<\/li>\n

  • \n

    Wang, J. et al. Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43<\/b>, 3349\u20133364 (2021).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n     PubMed<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Zhang, H., Wang, Y., Dayoub, F. & Sunderhauf, N. VarifocalNet: an IoU-aware dense object detector. In Proc. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 8514\u20138523 (IEEE, 2021).<\/p>\n<\/li>\n

  • \n

    Perez, R., Ineichen, P., Seals, R., Michalsky, J. & Stewart, R. Modeling daylight availability and irradiance components from direct and global irradiance. Sol. Energy 44<\/b>, 271\u2013289 (1990).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Reda, I. & Andreas, A. Solar position algorithm for solar radiation applications. Sol. Energy 76<\/b>, 577\u2013589 (2004).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Holmgren, W. F., Hansen, C. W. & Mikofski, M. A. pvlib python: a Python package for modeling solar energy systems. J. Open Source Softw. 3<\/b>, 884 (2018).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Chen, S. et al. The potential of photovoltaics to power the belt and road initiative. Joule 3<\/b>, 1895\u20131912 (2019).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hu, W., Scholz, Y., Yeligeti, M., Bremen, L. & Deng, Y. Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences. Environ. Res. Lett. 18<\/b>, 094007 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Gruber, K., Regner, P., Wehrle, S., Zeyringer, M. & Schmidt, J. Towards global validation of wind power simulations: a multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the Global Wind Atlas. Energy 238<\/b>, 121520 (2022).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Goldwind. Goldwind sustainability report. Goldwind https:\/\/web.archive.org\/web\/20190819172242\/http:\/\/goldwindglobal.com\/images\/about\/duty\/report\/2017.pdf<\/a> (2017).<\/p>\n<\/li>\n

  • \n

    Feng, J., Feng, L., Wang, J. & King, C. W. Evaluation of the onshore wind energy potential in mainland China\u2014based on GIS modeling and EROI analysis. Resour. Conserv. Recycl. 152<\/b>, 104484 (2020).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Rinne, E., Holttinen, H., Kiviluoma, J. & Rissanen, S. Effects of turbine technology and land use on wind power resource potential. Nat. Energy 3<\/b>, 494\u2013500 (2018).<\/p>\n

    Article<\/a>\u00a0
    \n     ADS<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    National Energy Administration, China. Typical load profiles of provincial power grid. National Energy Administration https:\/\/www.ndrc.gov.cn\/xwdt\/tzgg\/202012\/P020201202546044875868.pdf<\/a> (2020).<\/p>\n<\/li>\n

  • \n

    Jiang, H. et al. High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles. Appl. Energy 348<\/b>, 121553 (2023).<\/p>\n

    Article<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Edmonds, J. Paths, trees, and flowers. Can. J. Math. 17<\/b>, 449\u2013467 (1965).<\/p>\n

    Article<\/a>\u00a0
    \n     MathSciNet<\/a>\u00a0
    \n
    \n Google Scholar<\/a>\u00a0\n <\/p>\n<\/li>\n

  • \n

    Hu, Y. & Liu, Y. China Solar\u2013wind complementarity analysis dataset and code. Zenodo https:\/\/doi.org\/10.5281\/zenodo.19079543<\/a> (2026).<\/p>\n<\/li>\n","protected":false},"excerpt":{"rendered":"Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I. & Wernli, H. Balancing Europe\u2019s wind-power output through spatial…\n","protected":false},"author":2,"featured_media":976378,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_share_on_mastodon":"0"},"categories":[3843],"tags":[18219,728,3965,3966,70,3695,16,15],"class_list":["post-976377","post","type-post","status-publish","format-standard","has-post-thumbnail","category-environment","tag-energy-efficiency","tag-environment","tag-humanities-and-social-sciences","tag-multidisciplinary","tag-science","tag-sustainability","tag-uk","tag-united-kingdom"],"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@uk\/116614795391517752","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/976377","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/comments?post=976377"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/976377\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media\/976378"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media?parent=976377"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/categories?post=976377"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/tags?post=976377"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}