Abstract
Assessing global progress on human adaptation to climate change is an urgent priority. Although the literature on adaptation to climate change is rapidly expanding, little is known about the actual extent of implementation. We systematically screened >48,000 articles using machine learning methods and a global network of 126 researchers. Our synthesis of the resulting 1,682 articles presents a systematic and comprehensive global stocktake of implemented human adaptation to climate change. Documented adaptations were largely fragmented, local and incremental, with limited evidence of transformational adaptation and negligible evidence of risk reduction outcomes. We identify eight priorities for global adaptation research: assess the effectiveness of adaptation responses, enhance the understanding of limits to adaptation, enable individuals and civil society to adapt, include missing places, scholars and scholarship, understand private sector responses, improve methods for synthesizing different forms of evidence, assess the adaptation at different temperature thresholds, and improve the inclusion of timescale and the dynamics of responses.
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Data availability
Our a priori methodological protocol is registered (06-12-2019) and available via the OSF website142: https://osf.io/ps6xj. We prepared a series of detailed methods to accompany this paper via the Nature Protocol Exchange, which include: Part 1—Introduction and overview of methods (https://doi.org/10.21203/rs.3.pex-1240/v1)131, Part 2—Screening Protocol (https://doi.org/10.21203/rs.3.pex-1241/v1)132, and Part 3—Coding protocol (https://doi.org/10.21203/rs.3.pex-1242/v1)133. The data presented in this manuscript included survey extraction of information on adaptation from peer-reviewed articles.
Code availability
References to relevant coding are listed in Methods of this manuscript, and include a machine learning platform143 (https://doi.org/10.5281/zenodo.4121525) and reconciliation of codes144 (https://doi.org/10.5281/zenodo.4010763).
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Acknowledgements
We thank the following individuals for contributions to various stages of this initiative: C. Abbey, C. Alarconr, S. Arowolo, K. Christopher, R. Cremades, E. Cremin, K. Dave, S. Davis, D. Die, S. D’haen, S. Gruza, T. Harrison, D. Heinrich, F. I. Hoefsloot, M. Hothman, K. Hou, J. Kumar, R. Lama, A. Mahanti, C. McOmber, A. Mukjerji, N. Nnebe, M. North, C. Ofeogbu, H. Panchal, S. Pandey, A. Pasha, J. Pathak, P. Shrestha, D. Singini, A. Srinidhi, C. Thangata, V. Thimmaiah, A. Welles, K. Wroten, A. Yue and K. Zhu. We thank T. Leuchtefeld and the SysRev team for extensive technical and design support in partnering with this initiative. This work was supported by the following funding grants: Agence Française de Développement (A.K.M.), UK Government Foreign, Commonwealth & Development Office and the International Development Research Centre, Ottawa, Canada (109419–001, N.P.S.), Agence Nationale pour la Recherche (French National Research Agency, ANR-10-LABX-14-01, A.K.M.), Agence Nationale pour la Recherche (French National Research Agency, ANR-15-CE03-0003, A.K.M.), Studienstiftung des Deutsches Volkes (P.N.S.), UK Economic and Social Research Council (ESRC) Doctoral Training Partnership (ES/P000622/1, project reference 2098296, T.L.), National Science Foundation, Directorate for Geosciences (no. 1935961, E.A.G.), Social Science and Humanities Research Council (SSHRC) (756-2021-0037, E.K.G.), China Scholarship Council, Australian National University—Climate Change Institute Supplementary Scholarship (Y.S.), Ministerio de Ciencia, Innovación y Universidades (MICINN) (BIO-ARID PID2020-115770RB-I, J.S.), European Research Council (grant ERC-SyG-2013-610028, IMBALANCE-P, J. Petzold), Ministerio de Ciencia, Innovación y Universidades (MICINN) (ELEMENTALSHIFT PID2019-110521GB-100, J. Penuelas), UGC-JRF scholarship, University Grants Commission, Ministry of Human Resource Development, Government of India (3643/(SC)(NET-DEC. 2015, P.K.), National Institute for Health Research (NIHR) (using the UK’s Official Development Assistance (ODA) Funding) and Wellcome under the NIHR–Wellcome Partnership for Global Health Research (218743/Z/19/Z, C.Z.-C), The Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine (G.W.P.), International Development Research Centre Canada (L.S.S.C.), Agricultural Experiment Station Hatch projects (CA-R-A-6689-H and CA-D-LAW-2352-H, R.R.H.), German Ministry for Education and Research—ARIADNE project and IPCC-AR6-III-2 project (03SFK5J0 and 01LG1910A, J.M.), German Ministry for Education and Research—ARIADNE project (03SFK5J0, M.W.C.), NSF-CNH2-LRUI-ROA Grant, Equitable and Resilience Urban Socio-ecological Systems (no. 245531, I.A.-R.) and Portland State University Vision 2025 Grant (I.A.-R.).
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The research was conceived, designed and led by a Coordination Team, which included L.B.-F., A.L., A.R.S., AP.F., M.W.C., N.R.H., J.C.M. and K.J.M. L.B.-F. led the overall project, M.W.C. led the machine learning, A.P.F. led the screening team, A.L. led the coding team, A.R.S. led the synthesis team and K.J.M. led the expert elicitation team. N.R.H. provided overall leadership and expertise in the systematic review methods. Expertise in machine learning and data science was provided by J.C.M. The following individuals comprised the Advisory Team, which reviewed, revised and refined the research design and protocols, aided the codebook development and conceptualization of the research, supported the team recruitment and guided alignment of the work with the IPCC timelines and methods: E. Totin, N.P.S., D. Deryng, D.R., M.v.A., C.H.T., A.T., L.C.S., C.S., M. New, M.D.M., J.C.M., A.K.M., S.L., T. Lissner, S.H., M.H., E.A.G., M.G., J.D.F., S.E., E.C.d.P., K.J.B., R.B. and R.B.K. The Screening Team screened all the documents for inclusion, and included I.V.C., G. Sotnik, M. Nielsen and A.P.F. The Coding Team conducted all the data extraction, and included: M.A., M.A.R.S., M.W., D. Doshi, T. Leiter, C.M., J.I.M.-S., G.W.-P., P.A.-A., I.A., N.C., W.K., C.G., V.I.C., K.J., E.K.G., A.S., G. Scarpa, E. Totin, K.D., N.C.H., C.J.K., P.K., B.P., N.P.S., E. Theokritof, D. Deryng, C.Z.-C., N.U., A.C.S., V.K., A.R.S., Y.S., L.Z., Z.Z., J.X., P.A.W., I.V.C., N.v.M., L.L.T.-H., H.T., S. Thakur, S. Templeman, K.D.S., M.Z.S., R.S., J.S., E.A.S., L.S.S.C., R.R.-D., C.R., P.P., J. Petzold, J. Penuelas, J.P.A., J.B.P.M., S.O., P.N.S., G.N.A., C.A.M., J. Mullenite, A.M., G.M., A.M.N., M.L.-S., O.L., S.F.K., M.J., E.T.J., L.T.M.H., A. Harden, R.R.H., G.H., T.H., A. Hill, E.A.G., L.G., A.G., A.F., A.D.F., C.A.F.E., E.D., S.C., T.C., D.C., K.E.B., I.B., R.B.K., S.L.B., E.B., S.E.A., I.A.-R., C.A., W.A., T.A. and T.Z.A. The Synthesis Team conducted and led the synthesis of the results: A.R.S., T. Leiter, C.M., N.C., W.K., C.G., V.I.C., E.K.G., A.S., G. Scarpa, E. Totin, K.D., N.C.H., C.J.K., P.K., B.P., N.P.S., E. Theokritof, D. Deryng and D.R. The expert elicitation team comprised K.J., K.J.M., E. Totin and J.N. L.B.-F. led manuscript writing. All the team members contributed to manuscript development and revisions, and approved the final manuscript.
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Extended data
Extended Data Fig. 1
Flowchart of GAMI database creation of papers published between 2013-2019 on actions undertaken by people in response to climate change or environmental conditions, events and processes that were attributed or theorized to be linked, at least in part, to climate change.
Extended Data Fig. 2
Summary of inclusion and exclusion criteria used for screening.
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Berrang-Ford, L., Siders, A.R., Lesnikowski, A. et al. A systematic global stocktake of evidence on human adaptation to climate change. Nat. Clim. Chang. 11, 989–1000 (2021). https://doi.org/10.1038/s41558-021-01170-y
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DOI: https://doi.org/10.1038/s41558-021-01170-y
