Enhancing Canada's sustainable development goals: Leveraging neutrosophic programming for agenda 2030
Bibliographic record
Abstract
The study employed neutrosophic programming to optimize Canada's efforts toward achieving the Sustainable Development Goals (SDGs) by 2030. It focused on maximizing GDP and employment while minimizing carbon emissions and electricity consumption. The findings indicated substantial progress in GDP and employment, with GDP projections aligning closely with ARIMA forecast values. However, the optimization results for reducing carbon emissions and electricity consumption were less favorable, as both exceeded the 2030 targets, though slightly below ARIMA forecasts. These outcomes underscore the ongoing challenge of balancing economic growth with environmental sustainability. Neutrosophic programming proved effective in managing uncertainties and imprecise data, particularly in addressing complex, sometimes conflicting objectives like those within the SDGs. Future strategies include advancing greener technologies in high-emission sectors, introducing policy measures such as incentives for renewable energy, stricter emissions regulations, subsidies for green technologies, and increasing investment in sustainable technology research and development. • To develop a neutrosophic programming framework to address uncertainties in Canada's progress toward achieving Agenda 2030 SDGs. • To optimize Canada's contributions towards achieving the SDGs by 2030, focusing on maximizing GDP and employment while minimizing carbon emissions and electricity consumption. • To identify and prioritize key SDG targets in Canada, with a focus on areas requiring immediate attention and resource allocation. • To offer recommendations for designing and implementing sustainable policies aligned with Canada’s Agenda 2030 goals.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".