Environmental justice and the SDGs: from synergies to gaps and contradictions
Bibliographic record
Abstract
Abstract Through their synergies, trade-offs, and contradictions, the sustainable development goals (SDGs) have the potential to lead to environmental justices and injustices. Yet, environmental justice (EJ), and social justice more broadly, are not currently embedded within the language and spirit of the SDGs. We part from the premise that “many ‘environmental’ problems are, by their very nature, problems of justice” (Lele, Wiley Interdiscip Rev Water 4:e1224, 2017). We review progress in EJ frameworks in recent years, arguing for the need to move beyond a focus on the four principles of mainstream EJ (distribution, procedure, recognition, and capabilities) towards a more intersectional decolonial approach to environmental justice that recognises the indispensability of both humans and non-humans. EJ frameworks, and the SDGs should recognise power dynamics, complex interactions among injustices, and listens to the different ‘senses of justice’ and desires of theorists, activists, and other stakeholder from the Global South. We analyze how EJ frameworks are, or fail to be, incorporated in the SDGs with a focus on the food–water–health nexus (SDG2, 3, 6); climate-energy (SDG7, 13), conservation (SDG14, 15); and poverty and inequality (SDG1, 10). We call attention to the ‘elephant in the room’—the failure to go beyond GDP but instead include economic growth as a goal (SDG8). We argue that sustainable degrowth and intersectional decolonial environmental justices would create better conditions for the transformative changes needed to reach the broader aim of the SDGs: to leave no one behind.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.088 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".