Abstract
Technology is transforming societies worldwide. A major innovation is the emergence of robotics and autonomous systems (RAS), which have the potential to revolutionize cities for both people and nature. Nonetheless, the opportunities and challenges associated with RAS for urban ecosystems have yet to be considered systematically. Here, we report the findings of an online horizon scan involving 170 expert participants from 35 countries. We conclude that RAS are likely to transform land use, transport systems and human–nature interactions. The prioritized opportunities were primarily centred on the deployment of RAS for the monitoring and management of biodiversity and ecosystems. Fewer challenges were prioritized. Those that were emphasized concerns surrounding waste from unrecovered RAS, and the quality and interpretation of RAS-collected data. Although the future impacts of RAS for urban ecosystems are difficult to predict, examining potentially important developments early is essential if we are to avoid detrimental consequences but fully realize the benefits.
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Acknowledgements
We are grateful to all of the participants who took part in this study, and to J. Bentley for preparing the figures. This work was funded by the UK government’s Engineering and Physical Sciences Research Council (grant EP/N010523/1: ‘Balancing the Impact of City Infrastructure Engineering on Natural Systems using Robots’). Z.G.D. was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (consolidator grant no. 726104).
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Authors and Affiliations
Contributions
M.D. conceived the study. M.D., M.A.G., Z.G.D., S.G., J.C.F. and M.J.F. developed and tested the questionnaire and webinar materials. A.A., T.A., P.M.L.A., F.A., C.A., A.J.B., A. Barkwith, A. Berland, C.J.B., C.C.R.-B., L.B.B., D.C., R.C., T.C., S. Connop, S. Crossland, M.C.D., D.A.D., C.D., C.T.D., E.C.E., F.J.E., P.G., N.M.G., B.G., A.K.H., J.D.H., C.H., M.H., D.F.H., T.I., I.-C.I., D.K., T.K., I.K., S.J.L., S.B.L., I.M.-F., P. Manning, P. Massini, S.M., D.D.M., A.O., G.P.L., L.P.-U., K.P., G.P., T.J.P., K.E.P., R.A.R., U.R., S.G.P., H.R., J.P.S., S.d.S., S.S., C.E.S., A.S., T.S., R.P.H.S., C.D.S., M.C.S., T.V.d.V., S.J.V., P.H.W., C.-L.W., M.W., N.S.G.W., J.Y., K.Y. and K.P.Y. contributed data. M.A.G. collated and analysed these data. M.A.G., M.D. and Z.G.D. led writing the paper. A.A., T.A., P.M.L.A., F.A., C.A., A.J.B., A. Barkwith, A. Berland, C.J.B., C.C.R.-B., L.B.B., D.C., R.C., T.C., S. Connop, S. Crossland, M.C.D., D.A.D., C.D., C.T.D., E.C.E., F.J.E., N.M.G., B.G., A.K.H., J.D.H., C.H., M.H., D.F.H., T.I., I.-C.I., D.K., T.K., I.K., S.J.L., S.B.L., I.M.-F., P. Manning, P. Massini, S.M., D.D.M., A.O., G.P.L., L.P.-U., K.P., G.P., T.J.P., K.E.P., R.A.R., U.R., S.G.P., H.R., J.P.S., S.d.S., S.S., C.E.S., A.S., R.P.H.S., C.D.S., M.C.S., T.V.d.V., S.J.V., P.H.W., C.-L.W., M.W., N.S.G.W., J.Y., K.Y. and K.P.Y. contributed and agreed to the final version.
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Extended data
Extended Data Fig. 1 The Global North (green) and Global South (blue), with countries represented by participants in round one of the horizon scan indicated with darker shading.
Countries represented from the Global North were: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Israel, Italy, Netherlands, New Zealand, Poland, Portugal, Romania, Spain, Sweden, Switzerland, United Kingdom and United States of America. Countries represented from the Global South were: Argentina, Brazil, Chile, China, Colombia, Ethiopia, India, Malawi, Malaysia, Mexico, Nigeria, South Africa and Togo.
Extended Data Fig. 2 Opportunities associated with robotics and automated systems for urban biodiversity and ecosystems according to participants working in the research sector and other sectors.
a, Participants working in the research sector (n = 66). b, Participants working in other sectors (n = 32). The distribution of summed participant scores (range: -8 to +8) across four criteria (likelihood, impact, extent, novelty) for each of the 32 opportunities. Items are ordered according to percentage of participants in (a) who gave summed scores greater than zero. Percentage values indicate the proportion of participants giving negative, neutral and positive scores (left hand side, central and right hand side of the shaded bars respectively). The full wording agreed by the participants for each opportunity can be found in Supplementary Table 1: ‘mm’ is an abbreviation for ‘monitoring and management’; item number given in parenthesis is for cross referencing between figures and tables.
Extended Data Fig. 3 Challenges associated with robotics and automated systems for urban biodiversity and ecosystems for participants working in the research sector and other sectors.
a, Participants working in the research sector (n = 66). b, Participants working in other sectors (n = 32). The distribution of summed participant scores (range: -8 to +8) across four criteria (likelihood, impact, extent, novelty) for each of the 38 challenges. Items are ordered according to percentage of participants in (a) who gave summed scores greater than zero. Percentage values indicate the proportion of participants giving negative, neutral and positive scores (left hand side, central and right hand side of the shaded bars respectively). The full wording agreed by the participants for each challenge can be found in Supplementary Table 1: ‘mm’ is an abbreviation for ‘monitoring and management’; item number given in parenthesis is for cross referencing between figures and tables.
Extended Data Fig. 4 Opportunities associated with robotics and automated systems for urban biodiversity and ecosystems according to participants based in the Global North and Global South.
a, Participants based in the Global North (n = 87). b, Participants based in the Global South (n = 11). The distribution of summed participant scores (range: -8 to +8) across four criteria (likelihood, impact, extent, novelty) for each of the 32 opportunities. Items are ordered according to percentage of participants in (a) who gave summed scores greater than zero. Percentage values indicate the proportion of participants giving negative, neutral and positive scores (left hand side, central and right hand side of the shaded bars respectively). The full wording agreed by the participants for each opportunity can be found in Supplementary Table 1: ‘mm’ is an abbreviation for ‘monitoring and management’; item number given in parenthesis is for cross referencing between figures and tables.
Extended Data Fig. 5 Challenges associated with robotics and automated systems for urban biodiversity and ecosystems according to participants based in the Global North and Global South.
a, Participants based in the Global North (n = 87). b, Participants based in the Global South (n = 11). The distribution of summed participant scores (range: -8 to +8) across four criteria (likelihood, impact, extent, novelty) for each of the 38 challenges. Items are ordered according to percentage of participants in (a) who gave summed scores greater than zero. Percentage values indicate the proportion of participants giving negative, neutral and positive scores (left hand side, central and right hand side of the shaded bars respectively). Boxes and * indicate a significant difference between the proportions of participants in (a) and (b) scoring the item greater than zero. The full wording agreed by the participants for each challenge can be found in Supplementary Table 1: ‘mm’ is an abbreviation for ‘monitoring and management’; item number given in parenthesis is for cross referencing between figures and tables.
Extended Data Fig. 6 Opportunities associated with robotics and automated systems for urban biodiversity and ecosystems according to participants with environmental expertise and those with non-environmental expertise.
a, Participants with environmental expertise (n = 65). b, Participants with non-environmental expertise (n = 33). The distribution of summed participant scores (range: -8 to +8) across four criteria (likelihood, impact, extent, novelty) for each of the 32 opportunities. Items are ordered according to percentage of participants in (a) who gave summed scores greater than zero. Percentage values indicate the proportion of participants giving negative, neutral and positive scores (left hand side, central and right hand side of the shaded bars respectively). Boxes and * indicate a significant difference between the proportions of participants in (a) and (b) scoring the item greater than zero. The full wording agreed by the participants for each opportunity can be found in Supplementary Table 1: ‘mm’ is an abbreviation for ‘monitoring and management’; item number given in parenthesis is for cross referencing between figures and tables.
Extended Data Fig. 7 Challenges associated with robotics and automated systems for urban biodiversity and ecosystems according to participants with environmental expertise and those with non-environmental expertise.
a, Participants with environmental expertise (n = 65). b, Participants with non-environmental expertise (n = 33). The distribution of summed participant scores (range: -8 to +8) across four criteria (likelihood, impact, extent, novelty) for each of the 38 challenges. Items are ordered according to percentage of participants in (a) who gave summed scores greater than zero. Percentage values indicate the proportion of participants giving negative, neutral and positive scores (left hand side, central and right hand side of the shaded bars respectively). Boxes and * indicate a significant difference between the proportions of participants in (a) and (b) scoring the item greater than zero. The full wording agreed by the participants for each challenge can be found in Supplementary Table 1: ‘mm’ is an abbreviation for ‘monitoring and management’; item number given in parenthesis is for cross referencing between figures and tables.
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Goddard, M.A., Davies, Z.G., Guenat, S. et al. A global horizon scan of the future impacts of robotics and autonomous systems on urban ecosystems. Nat Ecol Evol 5, 219–230 (2021). https://doi.org/10.1038/s41559-020-01358-z
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DOI: https://doi.org/10.1038/s41559-020-01358-z
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