Green project management competencies and sustainable development goals (SDGs): Empirical evidence
Bibliographic record
Abstract
This study assesses the green project management competencies of students in the College of Business at Prince Sattam bin Abdulaziz University. This study uses a structured questionnaire to evaluate ten competencies: teamwork, leadership, communication, conflict management, achievement motivation, cognitive skills, adaptability, self-control, negotiation, and social awareness. The Principal Component Analysis (PCA) and Cronbach Alpha results revealed that effective communication was the strongest competency, explaining 71.6% of the variance, followed by leadership (65.3%) and teamwork (60.6%). Self-control demonstrated the highest reliability (α = 0.966), emphasizing students’ ability to manage stress effectively. Conflict management explained 48.9% of the variance, while adaptability accounted for 57.4%, reflecting students’ resilience and flexibility. Cognitive skills explained 51.7%, highlighting critical thinking and problem-solving abilities. Negotiation explained 63.8%, emphasizing stakeholder collaboration, and social awareness accounted for 50.1%, reflecting cultural sensitivity and empathy. Achievement motivation explained 54.8%, underscoring students’ proactive and goal-oriented behaviors. These findings provide valuable insights into strengths and gaps in student competencies, offering a basis for targeted improvements. Green project management competencies are vital for advancing Sustainable Development Goals (SDGs). Green project management competencies are integral to advancing the Sustainable Development Goals (SDGs). Cognitive skills, teamwork, and communication enhance critical thinking and innovation, supporting SDG 4 (Quality Education). Leadership and adaptability drive sustainable initiatives, aligning with SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure). Additionally, teamwork and social awareness foster inclusive urban development (SDG 11), while self-control and resilience enable effective climate action (SDG 13). By aligning academic training with industry demands, this study supports the global sustainability agenda and Saudi Vision 2030. The study provides policymakers in Saudi Arabia with valuable insights into aligning educational strategies with industry demands, highlighting key gaps in green project management competencies. By addressing these gaps, policymakers can enhance curriculum design to develop sustainability-focused skills, advancing Saudi Vision 2030 goals for workforce readiness, economic growth, and global sustainability leadership.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".