An Appraisal of Nigeria’s Progress in Achieving the SDG-13 Climate Action Goal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The impacts of climate change on the planet are increasingly felt with projections suggesting even greater impacts in the immediate term and, as such, the need for concerted efforts directed at curbing them. Although Nigeria battles with huge development needs, and its economy is confronted with a rapidly deteriorating fiscal space and rising levels of debt, the country has shown commitment to achieving the United Nations 2015 Sustainable Development Goals (SDG) by 2030. This situation creates a gap between the SDG agenda and the workability of the goal. This paper appraises efforts made by Nigeria’s Federal Government to achieve the Climate Action Goal by 2030. The country’s level of implementation of the set SDG 13 was, first, evaluated using the Environmental Performance Index (EPI). Subsequently, the role of the Anthony Nyong Climate Centre of Excellence (ANCCE) towards achieving the SDG 13 was explored. Results from the 2018 EPI scorecard ranks Nigeria 100th (54.76%) out of 180 countries on 24 performance indicators across ten categories covering environmental health and ecosystem vitality. Similarly, results from SDG index and dashboard, places Nigeria at 42nd (47.07%) out of 56 African countries. Even though these results show that ‘challenges remain’ in achieving climate action, Nigeria is on track toward achieving the SDG agreement. Furthermore, the establishment of ANCCE has so far achieved building of partnerships with organizations and other universities locally and internationally, capacity building among academics and the establishment of a waste management project in a tertiary institution in the country. A bottom-up approach aimed at achieving Climate action through activities of similar centers that can provide insight into a country’s best practices and contribute in many ways to achieving the SDG 13 in Nigeria are suggested.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it