Using Google search data to inform global climate change adaptation policy


The well-being of human societies in many parts of the world is threatened by climate change. While climate change is global, impacts are local and regional, and vulnerability varies widely across communities, countries, and regions. Climate change awareness has been related to how willingly communities adapt to climate change; thus, identifying communities’ awareness could help to gain insights into communities’ willingness to adopt climate change policy. In this study, we use culturomics to analyze big data from Google™ search queries to group countries based on their awareness, potential willingness, and potential capacity to deal with climate change. We demonstrate that culturomics can be used to allocate countries along a typology gradient, ranging from high-risk and high awareness to low-risk and low awareness, to climate change. Furthermore, we identify a positive correlation between countries’ climate vulnerability and awareness of climate change. As the Paris Agreement establishes a global goal to “enhance adaptive capacity, strengthen resilience and reduce vulnerability to climate change,” identifying countries’ potential adaptive capacity to climate impacts is critical. Pairing culturomics insights with climate vulnerability is a novel approach to facilitate international climate change adaptation.

This is a preview of subscription content, log in to check access.

Fig. 1


  1. Adger WN, Barnett J, Brown K et al (2012) Cultural dimensions of climate change impacts and adaptation. Nat Clim Chang 3:112–117.

    Article  Google Scholar 

  2. Althor G, Watson JEM, Fuller RA (2016) Global mismatch between greenhouse gas emissions and the burden of climate change. Sci Rep 6:20281.

    Article  Google Scholar 

  3. Burler D (2013) When Google got flu wrong. Nature 494:5–6.

    Article  Google Scholar 

  4. Cavanagh P, Lang C, Li X et al (2016) Searching for the determinants of climate change interest. Geogr J 2014:1–8.

    Article  Google Scholar 

  5. Cinner JE, Adger WN, Allison EH et al (2018) Building adaptive capacity to climate change in tropical coastal communities. Nat Clim Chang 8.

    Article  Google Scholar 

  6. Correia RA, Jepson P, Malhado ACM, Ladle RJ (2017) Internet scientific name frequency as an indicator of cultural salience of biodiversity. 78:549–555.

    Article  Google Scholar 

  7. Haddad BM (2003) Property rights, ecosystem management, and John Locke’s labor theory of ownership. Ecol Econ 46:19–31.

    Article  Google Scholar 

  8. Hidasi-Neto J, Loyola R, Cianciaruso MV (2015) Global and local evolutionary and ecological distinctiveness of terrestrial mammals: identifying priorities across scales. Divers Distrib 21:548–559.

    Article  Google Scholar 

  9. Hijmans RJ, Cameron SE, Parra JL et al (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978.

    Article  Google Scholar 

  10. IPCC (2013) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. (eds Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V. Cambridge

  11. Knight KW (2017) Public awareness and perception of climate change: a quantitative cross-national study. Environ Sociol 2:101–113.

    Article  Google Scholar 

  12. Ladle RJ, Correia RA, Do Y et al (2016) Conservation culturomics. Front Ecol Environ 14:269–275.

    Article  Google Scholar 

  13. Lang C (2014) Do weather fluctuations cause people to seek information about climate change? Clim Chang 125:291–303.

    Article  Google Scholar 

  14. Lang C, Ryder JD (2016) The effect of tropical cyclones on climate change engagement. Clim Chang 135:625–638.

    Article  Google Scholar 

  15. Lassen NB, Madsen R, Vatrapu R (2014) Predicting iPhone sales from iPhone tweets. Proc IEEE 18th Int Enterp Distrib object Comput Conf 2014–Decem:81–90.

  16. Lee TM, Markowitz EM, Howe PD et al (2015) Predictors of public climate change awareness and risk perception around the world. Nat Clim Chang 5.

    Article  Google Scholar 

  17. Lineman M, Do Y, Kim JY, Joo G-J (2015) Talking about climate change and global warming. PLoS One 10:e0138996.

    Article  Google Scholar 

  18. Marshall NA, Park S, Howden SM et al (2013) Climate change awareness is associated with enhanced adaptive capacity. Agric Syst 117:30–34.

    Article  Google Scholar 

  19. Mills M, Mutafoglu K, Adams VM et al (2016) Perceived and projected flood risk and adaptation in coastal Southeast Queensland, Australia. Clim Change.

    Article  Google Scholar 

  20. Muccione V, Allen SK, Huggel C, Birkmann J (2017) Differentiating regions for adaptation financing: the role of global vulnerability and risk distributions. Wiley Interdiscip Rev Clim Chang 8(2).

    Google Scholar 

  21. Proulx R, Massicotte P, Pépino M (2014) Googling trends in conservation biology. Conserv Biol 28:44–51.

    Article  Google Scholar 

  22. R Core Team (2015) R: A language and environment for statistical computing.

  23. Ripberger JT (2011) Capturing curiosity: using internet search trends to measure public attentiveness. Policy Stud J 39:239–259.

    Article  Google Scholar 

  24. Sachs JD, Baillie JEM, Sutherland WJ et al (2009) Biodiversity conservation and the millennium development goals. Science 325:1502–1503.

    Article  Google Scholar 

  25. Samson J, Berteaux D, Mcgill BJ, Humphries MM (2011) Geographic disparities and moral hazards in the predicted impacts of climate change on human populations. Glob Ecol Biogeogr 20:532–544.

    Article  Google Scholar 

  26. Sisco MR, Bosetti V, Weber EU (2017) When do extreme weather events generate attention to climate change? Clim Chang 143:227–241.

    Article  Google Scholar 

  27. Stadelmann M, Persson Å, Ratajczak-Juszko I, Michaelowa A (2014) Equity and cost-effectiveness of multilateral adaptation finance: are they friends or foes? Int Environ Agreements Polit Law Econ 14:101–120.

    Article  Google Scholar 

  28. Stephens-davidowitz S (2014) The cost of racial animus on a black candidate: evidence using Google search dataF. J Public Econ 118:26–40.

    Article  Google Scholar 

  29. The World Bank. (2016) GNI per capita, PPP (current international $).

  30. UNFCCC (2015) Paris Agreement. Conf Parties its twenty-first Sess 21932:32. FCCC/CP/2015/L.9/Rev.1

  31. Wilde GR, Pope KL (2013) Worldwide trends in fishing interest indicated by internet search volume. Fish Manag Ecol 20:211–222.

    Article  Google Scholar 

  32. World Resources Institute (2014) World resources institute, climate analysis indicators tool: WRI’s climate data explorer. In: Available

Download references


CA is supported through an Australian Postgraduate Award and a top-up scholarship through the ARC Centre of Excellence for Environmental Decisions. NB is supported by ARC Grant DE150101552. The authors would like to thank Dr. Jason Samson for providing climate vulnerability data and Dr. Megan Evans, Blake Alexander Simmons, Felicia Jane Runting, Dr. Morena Mills, and Associate Professor Jonathan Rhodes for reviewing and providing useful comments during the drafting process of the manuscript.

Author information



Corresponding author

Correspondence to Carla L. Archibald.

Electronic supplementary material


(DOCX 6154 kb)


(XLXS 42 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Archibald, C.L., Butt, N. Using Google search data to inform global climate change adaptation policy. Climatic Change 150, 447–456 (2018).

Download citation