Mapping Essential Life Support Areas to Achieve the Sustainable Development Goals
Bibliographic record
Abstract
The United Nations 2030 Agenda for Sustainable Development (Agenda 2030) is a guiding star for countries, establishing a common vision for human and planetary well-being. However, approximately half of the Sustainable Development Goal (SDG) targets are severely or moderately off track for achievement by 2030, in part because decision-making around the SDGs is often undertaken by just a few governmental ministries. While the Agenda 2030 declares that the SDGs are “integrated and indivisible”, goals related to the environment often take a back seat to economic goals during national implementation. The UNDP led project ‘Mapping Nature for People and Planet’ demonstrates how countries can apply integrated spatial planning to facilitate inclusive decision-making for policy targets around the SDGs, the Kunming-Montreal Global Biodiversity Framework, the UN Framework Convention on Climate Change and other global conventions and frameworks. The project supports countries in developing a singular map of Essential Life Support Areas (ELSAs) that shows pathways for action to achieve multiple targets at once, including those at the nexus of nature, climate and sustainable development. At the base of the map are the country’s most pressing policy targets and current spatial data layers, hand-selected by national experts. This policy brief captures insights from this project to help policymakers use integrated spatial planning to support the achievement of SDGs, with a focus on those that are the most dependent on nature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".