arXiv subjects

Juan C. Rocha

Publications and source records attributed to Juan C. Rocha.

6 recordsLinked to original sources

arXiv2025

Inequality traps detected in sustainable development goals data

The relationship between inequality and the biosphere has been hypothesized to mutual dependecies and feedbacks. If that is true, such feedbacks may give rise to inequality regimes and potential tipping points between them. Here we explore synergies and trade-offs between inequality and biosphere-related sustainable development goals. We used the openly available SDG datasets by the World Bank (WB) and United Nations (UN) and applied ordination methods to distill interactions between economic inequality and the environmental impact across countries. Our results confirm the existence of inequality regimes, and we find preliminary evidence that corruption may be a candidate driver of tipping between regimes.

physics.soc-ph↗

arXiv2025

Regime shifts and transformations in social-ecological systems: Advancing critical frontiers for safe and just futures

Current research challenges in sustainability science require us to consider nonlinear changes e.g. shifts that do not happen gradually but can be sudden and difficult to predict. Central questions are therefore how we can prevent harmful shifts, promote desirable ones, and better anticipate both. The regime shifts and transformations literature is well-equipped to address these questions. Yet, even though both research streams stem from the same intellectual roots, they have developed along different paths, with limited exchange between the two, missing opportunities for cross- fertilisation. We here review the definitions and history of both research streams to disentangle common grounds and differences. We propose avenues for future research and highlight how stronger integration of both research streams could support the development of more powerful approaches to help us navigate toward safe and just futures.

physics.soc-ph↗

arXiv2025

AI for a Planet Under Pressure

Artificial intelligence (AI) is already driving scientific breakthroughs in a variety of research fields, ranging from the life sciences to mathematics. This raises a critical question: can AI be applied both responsibly and effectively to address complex and interconnected sustainability challenges? This report is the result of a collaboration between the Stockholm resilience Centre (Stockholm University), the Potsdam Institute for Climate Impact Research (PIK), and Google DeepMind. Our work explores the potential and limitations of using AI as a research method to help tackle eight broad sustainability challenges. The results build on iterated expert dialogues and assessments, a systematic AI-supported literature overview including over 8,500 academic publications, and expert deep-dives into eight specific issue areas. The report also includes recommendations to sustainability scientists, research funders, the private sector, and philanthropies.

cs.CY↗

arXiv2025

Identifying companies and financial actors exposed to marine tipping points

Climate change and other anthropogenic pressures are likely to induce tipping points in marine ecosystems, potentially leading to declines in primary productivity and fisheries. Despite increasing attention to nature-related financial risks and opportunities within the ocean economy, the extent to which these tipping points could affect investors has remained largely unexplored. Here we used satellite data to track fishing vessels operating in areas prone to marine regime shifts, as identified by their loss of resilience and vulnerability to marine heatwaves, and uncovered their corporate beneficial owners and shareholders. Despite some data gaps, we identified key countries, companies, and shareholders exposed to tipping risk. We also outline the potential challenges and opportunities that these actors may face if marine ecosystems shift to less productive states.

cs.CE↗

arXiv2022

Networks of climate change: Connecting causes and consequences

Understanding the causes and consequences of, and devising countermeasures to, global warming is a profoundly complex problem. Network representations are sometimes the only way forward, and sometimes able to reduce the complexity of the original problem. Networks are both necessary and natural elements of climate science. Furthermore, networks form a mathematical foundation for a multitude of computational and analytical techniques. We are only beginning to see the benefits of this connection between the sciences of climate change and network science. In this review, we cover the wide spectrum of network applications in the climate-change literature -- what they represent, how they are analyzed, and what insights they bring. We also discuss network data, tools, and problems yet to be explored.

physics.soc-ph↗

arXiv2022

Ecosystems are showing symptoms of resilience loss

Ecosystems around the world are at risk of critical transitions due to increasing anthropogenic pressures and climate change. Yet it is unclear where the risks are higher or where in the world ecosystems are more vulnerable. Here I measure resilience of primary productivity proxies for marine and terrestrial ecosystems globally. Up to 29% of global terrestrial ecosystem, and 24% marine ones, show symptoms of resilience loss. These symptoms are shown in all biomes, but Arctic tundra and boreal forest are the most affected, as well as the Indian Ocean and Eastern Pacific. Although the results are likely an underestimation, they enable the identification of risk areas as well as the potential synchrony of some transitions, helping prioritize areas for management interventions and conservation.

q-bio.PE↗