5 results
Global polycrisis: the causal mechanisms of crisis entanglement
- Part of
- Michael Lawrence, Thomas Homer-Dixon, Scott Janzwood, Johan Rockstöm, Ortwin Renn, Jonathan F. Donges
-
- Journal:
- Global Sustainability / Volume 7 / 2024
- Published online by Cambridge University Press:
- 17 January 2024, e6
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
-
Multiple global crises – including the pandemic, climate change, and Russia's war on Ukraine – have recently linked together in ways that are significant in scope, devastating in effect, but poorly understood. A growing number of scholars and policymakers characterize the situation as a ‘polycrisis’. Yet this neologism remains poorly defined. We provide the concept with a substantive definition, highlight its value-added in comparison to related concepts, and develop a theoretical framework to explain the causal mechanisms currently entangling many of the world's crises. In this framework, a global crisis arises when one or more fast-moving trigger events combine with slow-moving stresses to push a global system out of its established equilibrium and into a volatile and harmful state of disequilibrium. We then identify three causal pathways – common stresses, domino effects, and inter-systemic feedbacks – that can connect multiple global systems to produce synchronized crises. Drawing on current examples, we show that the polycrisis concept is a valuable tool for understanding ongoing crises, generating actionable insights, and opening avenues for future research.
Non-technical summaryThe term ‘polycrisis’ appears with growing frequently to capture the interconnections between global crises, but the word lacks substantive content. In this article, we convert it from an empty buzzword into a conceptual framework and research program that enables us to better understand the causal linkages between contemporary crises. We draw upon the intersection of climate change, the covid-19 pandemic, and Russia's war in Ukraine to illustrate these causal interconnections and explore key features of the world's present polycrisis.
Technical summaryMultiple global crises – including the pandemic, climate change, and Russia's war on Ukraine – have recently linked together in ways that are significant in scope, devastating in effect, but poorly understood. A growing number of scholars and policymakers characterize the situation as a ‘polycrisis’. Yet this neologism remains poorly defined. We provide the concept with a substantive definition, highlight its value-added in comparison to related concepts, and develop a theoretical framework to explain the causal mechanisms currently entangling many of the world's crises. In this framework, a global crisis arises when one or more fast-moving trigger events combines with slow-moving stresses to push a global system out of its established equilibrium and into a volatile and harmful state of disequilibrium. We then identify three causal pathways – common stresses, domino effects, and inter-systemic feedbacks – that can connect multiple global systems to produce synchronized crises. Drawing on current examples, we show that the polycrisis concept is a valuable tool for understanding ongoing crises, generating actionable insights, and opening avenues for future research.
Social media summaryNo longer a mere buzzword, the ‘polycrisis’ concept highlights causal interactions among crises to help navigate a tumultuous future.
Ten new insights in climate science 2021: a horizon scan
- Maria A. Martin, Olga Alcaraz Sendra, Ana Bastos, Nico Bauer, Christoph Bertram, Thorsten Blenckner, Kathryn Bowen, Paulo M. Brando, Tanya Brodie Rudolph, Milena Büchs, Mercedes Bustamante, Deliang Chen, Helen Cleugh, Purnamita Dasgupta, Fatima Denton, Jonathan F. Donges, Felix Kwabena Donkor, Hongbo Duan, Carlos M. Duarte, Kristie L. Ebi, Clea M. Edwards, Anja Engel, Eleanor Fisher, Sabine Fuss, Juliana Gaertner, Andrew Gettelman, Cécile A.J. Girardin, Nicholas R. Golledge, Jessica F. Green, Michael R. Grose, Masahiro Hashizume, Sophie Hebden, Helmke Hepach, Marina Hirota, Huang-Hsiung Hsu, Satoshi Kojima, Sharachchandra Lele, Sylvia Lorek, Heike K. Lotze, H. Damon Matthews, Darren McCauley, Desta Mebratu, Nadine Mengis, Rachael H. Nolan, Erik Pihl, Stefan Rahmstorf, Aaron Redman, Colleen E. Reid, Johan Rockström, Joeri Rogelj, Marielle Saunois, Lizzie Sayer, Peter Schlosser, Giles B. Sioen, Joachim H. Spangenberg, Detlef Stammer, Thomas N.S. Sterner, Nicola Stevens, Kirsten Thonicke, Hanqin Tian, Ricarda Winkelmann, James Woodcock
-
- Journal:
- Global Sustainability / Volume 4 / 2021
- Published online by Cambridge University Press:
- 18 October 2021, e25
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
-
Non-technical summary
We summarize some of the past year's most important findings within climate change-related research. New research has improved our understanding about the remaining options to achieve the Paris Agreement goals, through overcoming political barriers to carbon pricing, taking into account non-CO2 factors, a well-designed implementation of demand-side and nature-based solutions, resilience building of ecosystems and the recognition that climate change mitigation costs can be justified by benefits to the health of humans and nature alone. We consider new insights about what to expect if we fail to include a new dimension of fire extremes and the prospect of cascading climate tipping elements.
Technical summaryA synthesis is made of 10 topics within climate research, where there have been significant advances since January 2020. The insights are based on input from an international open call with broad disciplinary scope. Findings include: (1) the options to still keep global warming below 1.5 °C; (2) the impact of non-CO2 factors in global warming; (3) a new dimension of fire extremes forced by climate change; (4) the increasing pressure on interconnected climate tipping elements; (5) the dimensions of climate justice; (6) political challenges impeding the effectiveness of carbon pricing; (7) demand-side solutions as vehicles of climate mitigation; (8) the potentials and caveats of nature-based solutions; (9) how building resilience of marine ecosystems is possible; and (10) that the costs of climate change mitigation policies can be more than justified by the benefits to the health of humans and nature.
Social media summaryHow do we limit global warming to 1.5 °C and why is it crucial? See highlights of latest climate science.
Potential feedbacks between loss of biosphere integrity and climate change
- Steven J. Lade, Jon Norberg, John M. Anderies, Christian Beer, Sarah E. Cornell, Jonathan F. Donges, Ingo Fetzer, Thomas Gasser, Katherine Richardson, Johan Rockström, Will Steffen
-
- Journal:
- Global Sustainability / Volume 2 / 2019
- Published online by Cambridge University Press:
- 13 November 2019, e21
-
- Article
-
- You have access Access
- Open access
- HTML
- Export citation
-
Individual organisms on land and in the ocean sequester massive amounts of the carbon emitted into the atmosphere by humans. Yet the role of ecosystems as a whole in modulating this uptake of carbon is less clear. Here, we study several different mechanisms by which climate change and ecosystems could interact. We show that climate change could cause changes in ecosystems that reduce their capacity to take up carbon, further accelerating climate change. More research on – and better governance of – interactions between climate change and ecosystems is urgently required.
6 - Complex Network Techniques for Climatological Data Analysis
-
- By Reik V. Donner, Potsdam Institute for Climate Impact Research, Marc Wiedermann, Potsdam Institute for Climate Impact Research, Jonathan F. Donges, Potsdam Institute for Climate Impact Research
- Edited by Christian L. E. Franzke, Universität Hamburg, Terence J. O'Kane
-
- Book:
- Nonlinear and Stochastic Climate Dynamics
- Published online:
- 26 January 2017
- Print publication:
- 19 January 2017, pp 159-183
-
- Chapter
- Export citation
-
Summary
Abstract
Complex network theory provides a powerful toolbox for studying the structure of statistical interrelationships between multiple time series in various scientific disciplines. Complementing frequently used methods of eigenanalysis such as empirical orthogonal functions, climate networks allow to flexibly combine advanced nonlinear and informationtheoretical measures for quantifying interactions between climatological time series with manifold concepts and methods from complex network theory. This chapter summarizes the corresponding theoretical foundations as well as recent applications in the field of climate network analysis for different climatic observables, including the treatment of coupled climatological fields and heterogeneous spatial distributions of climate observations.
Introduction
Gaining information on climate variability using sophisticated methods of data analysis is one of the foremost tasks of statistical climatology. Inspired by classical methods from multivariate statistics, a large body of approaches has been utilized in past studies, including empirical orthogonal function (EOF) analysis, maximum covariance analysis (MCA) or canonical correlation analysis (CCA), to mention only some of the most prominent examples (von Storch and Zwiers, 2003). These purely statistical approaches have been successfully applied for studying a broad variety of climatological problems.
However, during recent decades concerns have been raised regarding the methodological limitations of the aforementioned approaches as well as the appropriate interpretation of the resulting findings. A first possible point of criticism is the implicit assumption of linearity of statistical interdependencies underlying methods like EOF analysis and MCA. In order to account for more general statistical relationships, nonlinear generalizations of these methods have been developed, relieving the requirement of linear independence between patterns to be addressed. Corresponding approaches include isometric feature mapping (Isomap, Tenenbaum et al. (2000); Gámez et al. (2004)), nonlinear (neural network-based) principal component analysis (Hsieh, 2004, 2009), and a variety of other techniques based on machine learning principal component analysis (Hsieh, 2004, 2009).
In addition to the linearity assumption, Monahan et al. (2009) identified several concerns regarding the interpretation of modes revealed by EOF analysis (which apply in a similar spirit also to other established techniques of statistical climatology).
List of contributors
-
- By Bjarne F. Alsbjoern, Caroline M. Apovian, Danny Collins, Roland N. Dickerson, Timothy Eden, Peter Faber, Andrew J. Ferguson, David C. Frankenfield, Dympna Gallagher, Maria Gabriella Gentile, Wilson I. Gonsalves, Andrew M. Hetreed, Michael H. Hooper, Jan O. Jansen, Aminah Jatoi, Ying Ji, Ilya Kagan, Andrew J. Kerwin, Dong Wook Kim, Andrew A. Klein, Alistair Lee, Shaul Lev, Peter K. Linden, Paul E. Marik, Robert Martindale, Peter McCanny, Paolo Merlani, Shay Nanthakumaran, Michael S. Nussbaum, Andreas Perren, Carla Prado, Jean-Charles Preiser, Minha Rajput-Ray, Sumantra Ray, Nils Siegenthaler, Mario Siervo, Jonathan A. Silversides, Pierre Singer, John A. Tayek, Euan Thomson, Krista L. Turner, Malissa Warren, Stephen T. Webb, Patricia Wiesen
- Edited by Peter Faber, Mario Siervo, University of Newcastle upon Tyne
-
- Book:
- Nutrition in Critical Care
- Published online:
- 05 April 2014
- Print publication:
- 06 March 2014, pp viii-xii
-
- Chapter
- Export citation