Navigating India’s path to sustainable development goals: optimization and forecasting approaches
Bibliographic record
Abstract
Abstract The Sustainable Development Goals (SDGs) outlined in Agenda 2030 provide a global framework for achieving sustainable and inclusive growth. This study examines India’s progress toward these goals and proposes innovative solutions using forecasting and optimization modeling. The research focuses on balancing economic development—primarily measured through GDP growth—sustainability, and employment, which are central to India’s sustainable development challenges. To achieve this, we adopt a lexicographic goal programming framework, structuring the decision-making process into four hierarchical levels. The most critical goal is prioritized first, ensuring that decisions are made sequentially from the highest to the lowest priority. This approach allows for a structured evaluation of India’s development across key sectors such as agriculture, mining, trade, and construction. Beyond assessment, the study offers practical, data-driven solutions to accelerate SDG progress. A numerical example is presented to demonstrate the applicability of the proposed methodology, and the results are compared with fuzzy goal programming to validate the effectiveness of the approach. By integrating a structured decision-making framework with optimization techniques, this research provides context-aware strategies to align India’s economic, environmental, and social objectives. The findings contribute to informed policymaking, offering actionable insights to drive a more equitable, prosperous, and sustainable future by 2030.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".