A complex systems framework for the sustainability doughnut
Bibliographic record
Abstract
Abstract Achieving sustainability is challenging as an environmental and socio‐economic objective, and as a complex concept whose multiple components and their interactions need to be considered. We develop a statistical model to investigate relationships among and between the planetary boundaries and social foundations of the sustainability ‘doughnut’ model. We find over 35 direct and indirect, positive and negative, influences of varying magnitude among seven boundaries (biodiversity loss, climate change, ocean acidification, land use, nitrogen and phosphorus cycles, atmospheric aerosol loading and freshwater use) and eleven foundations (energy, income, health, education, food, water, gender equality, resilience, jobs, voice and social equity). We observe that biodiversity loss is driven by other planetary boundaries (land‐use change and freshwater use), but also a social foundation (jobs, measured as vulnerable employment). The planetary boundaries of freshwater use and land use are also related: freshwater use is higher in urban centres than in rural areas. The planetary boundary of climate change is also related to land use (the extent of agricultural lands), and the social foundation of income per capita (greater income, higher carbon dioxide emissions). We also find that several social foundations are themselves interrelated. For example gender equality (measured as female participation in the work force) is mainly predicted by vulnerable employment. Also, food deficit increases with poverty level, but is alleviated by access to clean water. Education (literacy rate) and social equity (social insurance) can both lift people out of poverty. These inter‐relations suggest that both synergies and trade‐offs exist between and within boundaries and foundations. We provide a new conceptual framework that moves us away from the doughnut approach towards one that can begin to address the complex interactions that sustainability scientists and policy makers face when trying to maintain multiple social foundations while not compromising any of the planetary boundaries. We illustrate several hypothesis‐based relationships here, to suggest that everything is not related to everything else. It is possible to work out significant pathways within this complex system, which is necessary to implement policies. A free Plain Language Summary can be found within the Supporting Information of this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".