Ten Simple Rules for Incorporating the UN Sustainable Development Goals (SDGs) into Environmental and Natural Science Courses
Bibliographic record
Abstract
In 2015, the United Nations (UN) established 17 Sustainable Development Goals (SDGs) to combat poverty, inequality, and climate change. Recently, integrating these goals into higher education curricula has emerged as essential for fostering positive environmental and civic engagement. Challenges persist, particularly the prioritization of social and economic concerns over eco-centered principles and ecopedagogy. Education must emphasize the politics of environmental issues, advocating sustainable practices that benefit both humans and nonhuman species. This article proposes ten simple rules for incorporating the SDGs into undergraduate courses in ecology, evolution, and environmental science. We conducted a literature review, analyzing 940 publications from 2014 to 2024 using ISI Web of Science and Google Scholar. We focused on fields like Green Sustainable Science and Technology and Environmental Education to identify the best practices for integrating SDGs. Our findings link pedagogical successes to the SDGs, facilitating effective educational strategies. For practical sustainability education, students must grasp the interplay between the environment and societal elements such as diversity, justice, and resilience. Connecting course topics to the SDGs offers a powerful framework for teaching undergraduates about complex environmental challenges. Our research highlights a novel approach to embedding SDGs in environmental education, promoting critical thinking and literacy across various settings.
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 | 0.051 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".