Both technological innovations and cultural change are key to a sustainability transition
Bibliographic record
Abstract
Transitioning to sustainability will require technological innovations in the short term, but also cultural change to embrace traditional and Indigenous ideas of respect, responsibility, sufficiency, and reciprocity to reduce consumption in the long term.We are said to be living in the Anthropocene, a time when human activities are having as great an impact on the Earth system as other geological forces.According to the "Planetary Boundaries" framework, which uses the past 10,000 years (the Holocene) as a benchmark, human influence on the Earth system has greatly exceeded the "safe operating space" across multiple indicators, including climate change, biodiversity loss, and nutrient pollution [1].A critical message is that even if we solve the climate problem, the biodiversity and nutrient pollution challenges will remain.Taking biodiversity loss, its biggest cause is habitat loss for plants and animals because of land-use change [2].And the biggest cause of land-use change is agriculture [3].So the main leverage for addressing the biodiversity crisis is through modifying land use for agriculture.How do we do that?We essentially have 2 options.One is to limit the amount of land used for farming by intensifying agriculture.Technology has greatly increased the productivity of agriculture since the 1940s by increasing inputs (e.g., through use of irrigation and fertilizers) and through the development of new seed varieties [3].Corn yields in the United States of America, for example, increased 6-fold, from approximately 2 tonnes/ha during 1866 to 1940 to nearly 12 tonnes/ha in 2022.Similarly, the Green Revolution increased wheat and rice yields in Asia and Latin America since the 1960s [3].But input intensification itself can exacerbate biodiversity loss and also lead to the depletion of freshwater, soil degradation, nutrient pollution, and greenhouse gas emissions [3].Alternatively, we could adopt agroecological farming practices that tread more lightly on the land [4].Agroecological practices (e.g., intercropping and agroforestry) aim to incorporate ecological processes such as natural pest regulation and biological nitrogen fixation but are often dismissed for having lower yields.Organic agriculture (used here as a proxy for agroecology in the absence of broadscale assessments of the latter) has many environmental benefits per unit area; however, the environmental benefits per unit product are comparable to conventional farming due toAU : PleasecheckwhetherthechangesmadeinthesentenceOrganicagricultureðusedh its approximately 20% lower yields [5].In other words, for growing the same amount of food, organic farms are not discernibly better for the environment than
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".