Investigating and modelling tipping dynamics in social metabolism - Conceptual considerations on resilience and malleability

Investigating and modelling tipping dynamics in social metabolism Conceptual considerations on resilience and malleability Helmut Haberl International Industrial Ecology Day 2025, session “Industrial Ecology approaches used in the emerging field of Positive Tipping Points”, Nov 21, 2025 (online) This research was funded in whole or in part by the Austrian Science Fund (FWF) [10.55776/EFP5] Nonlinear systems & tipping points •Tipping point = unstable system state: small perturbation result in fundamentally different outcomes •At a tipping point, a system reorganizes itself (in contrast to gradual change), often irreversibly, when it is drawn into a basin of attraction •Examples from climate science: runaway climate change, e.g. following thawing of permafrost or breakdown of C-rich ecosystems (Amazon basin) •Are there tipping elements in social systems? •Positive tipping points, e.g. transformation to sustainable social metabolism •Adverse tipping points, e.g. supply-chain disruptions, flip fowards autocracy 3 Blocked shipping routes Warfare Impacts of the climate crisis Multiple crises jeopardize resource supply Geopolitical tensions Pandemics → shocks & disturbances → nonlinear dynamics (tipping phenomena) → challenges established physical and institutional structures This research was funded in whole or in part by the Austrian Science Fund (FWF) [10.55776/EFP5] •Disruptive events hit highly complex global supply networks and may result in a wide range of tipping phenomena •They may contribute to changes in resource use as well as wellbeing and distribution •They alter power relations and option spaces of actors and institutions aiming to work towards sustainability Assessing network effects in social metabolism REMASS | Resilience and Malleability of Social Metabolism | doi: 10.55776/EFP5 | https://remass.boku.ac.at/ This research was funded in whole or in part by the Austrian Science Fund (FWF) [10.55776/EFP5] Methods used in SMR •Descriptive: Accounting for socio-metabolic stocks and flows. Data triangulation used for closing data gaps. •Linear: Stock-flow models in dynamic MFA trace inflows, outflows and accumulation of stocks but lack representation of feedbacks •Static/structural: Environmentally-extended Multi-regional Input-Output analysis links inputs and pressures to final consumption of products •Diagnostic: Evaluations of biophysical feasibility of supply-demand options (‚option spaces‘ Despite recent progress, current methods of socio-metabolic research (SMR) can‘t deal with nonlinearities Methods needed to deal with tipping points •Analyze non-linear dynamics: Tipping points are non-linearities in trajectories of complex systems → Models need to be able to represent such phenomena •Represent complex networked systems characterized by interactions between many elements connected by positive and negative feedback loops •In SMR, one would need to integrate social science (actors, institutions, power relations, etc.) with sociometabolic data & models (huge granularity needed) REMASS | Resilience and Malleability of Social Metabolism | doi: 10.55776/EFP5 | https://remass.boku.ac.at/ This research was funded in whole or in part by the Austrian Science Fund (FWF) [10.55776/EFP5] Aim: Harness big data to create a new ‘atomistic’ understanding of the economy Methods: Complex networked systems models, data-driven 1:1 agent-based models Outcomes: •Quantification of resilience •Linking national supply chains on firm-level to material flows and provisioning systems Analyzing national supply chain networks REMASS | Resilience and Malleability of Social Metabolism | doi: 10.55776/EFP5 | https://remass.boku.ac.at/ This research was funded in whole or in part by the Austrian Science Fund (FWF) [10.55776/EFP5] •Supply chains are non-trivial and highly dynamic •Understanding dynamics of social metabolism at tipping points needs models dealing with reconfigurations of supply chains and their unexpected outcomes •Reconfigurations of supply chains can lead to unexpected outcomes (see toy model) •This requires high-resolution data representing networks of individual economic agents, e.g. firms or households •Complex networked system models based on VAT data showing interactions of all firms in a country Tackling unexpected outcomes in complex networked systems REMASS | Resilience and Malleability of Social Metabolism | doi: 10.55776/EFP5 | https://remass.boku.ac.at/ This research was funded in whole or in part by the Austrian Science Fund (FWF) [10.55776/EFP5] Provisioning systems are key for transforming biophysical inputs into social outcomes REMASS | Resilience and Malleability of Social Metabolism | doi: 10.55776/EFP5 | https://remass.boku.ac.at/ Ongoing: systematic review & conceptual operationization This research was funded in whole or in part by the Austrian Science Fund (FWF) [10.55776/EFP5] Leverage points: the role of material stocks Haas et al 2026. Ecological Economics, 239, 108759 https://doi.org/10.1016/j.ecolecon.2025.108759 REMASS | Resilience and Malleability of Social Metabolism | doi: 10.55776/EFP5 | https://remass.boku.ac.at/ •Socioeconomic material stocks are a key driver of unsustainable resource use •Their key role was not sufficiently recognized in previous versions of the leverage point framework •Socioeconomic material stocks offer entry points for transformation strategies •Example: design of settlements – avoid urban sprawl