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      Digitalization and the Anthropocene

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          Abstract

          Great claims have been made about the benefits of dematerialization in a digital service economy. However, digitalization has historically increased environmental impacts at local and planetary scales, affecting labor markets, resource use, governance, and power relationships. Here we study the past, present, and future of digitalization through the lens of three interdependent elements of the Anthropocene: ( a) planetary boundaries and stability, ( b) equity within and between countries, and ( c) human agency and governance, mediated via ( i) increasing resource efficiency, ( ii) accelerating consumption and scale effects, ( iii) expanding political and economic control, and ( iv) deteriorating social cohesion. While direct environmental impacts matter, the indirect and systemic effects of digitalization are more profoundly reshaping the relationship between humans, technosphere and planet. We develop three scenarios: planetary instability, green but inhumane, and deliberate for the good. We conclude with identifying leverage points that shift human–digital–Earth interactions toward sustainability.

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          Most cited references176

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          Deep learning and process understanding for data-driven Earth system science

          Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.
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            The trajectory of the Anthropocene: The Great Acceleration

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              Is Open Access

              The role of artificial intelligence in achieving the Sustainable Development Goals

              The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards.

                Author and article information

                Journal
                Annual Review of Environment and Resources
                Annu. Rev. Environ. Resour.
                Annual Reviews
                1543-5938
                1545-2050
                October 17 2022
                October 17 2022
                : 47
                : 1
                : 479-509
                Affiliations
                [1 ]Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany;
                [2 ]Sustainability Economics of Human Settlements, Technical University Berlin, Berlin, Germany
                [3 ]Department of Economics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
                [4 ]Fenner School of Environment and Society, Australian National University, Canberra, Australia
                [5 ]Center for International Security and Cooperation, Stanford University, Stanford, California, USA
                [6 ]ICLEI—Local Governments for Sustainability, Berlin, Germany
                [7 ]Hertie School, Berlin, Germany
                [8 ]Scientific and Technological Research Council of Turkey, Ankara, Turkey
                [9 ]Institute for Advanced Sustainability Studies, Potsdam, Germany
                [10 ]Microsoft, Seattle, Washington, USA
                [11 ]Environmental Law Institute, Washington, DC, USA
                [12 ]Max Planck Institute for the History of Science, Berlin, Germany
                [13 ]School of Computer Science, McGill University, Montreal, Canada
                [14 ]Mila—Quebec AI Institute, Montreal, Canada
                [15 ]Haus der Kulturen der Welt, Berlin, Germany
                [16 ]Sociology Department, University of Toronto, Toronto, Canada
                [17 ]Department of Law, University of Brescia, Brescia, Italy
                [18 ]RFF-CMCC European Institute on Economics and the Environment, Euro-Mediterranean Center on Climate Change, Milan, Italy
                [19 ]Environmental Change Institute, University of Oxford, Oxford, United Kingdom
                Article
                10.1146/annurev-environ-120920-100056
                8a2dc149-3842-44a7-9c36-282482b06963
                © 2022

                http://creativecommons.org/licenses/by/4.0/

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