Project management for sustainability in times of uncertainty: A systematic review of economic policy uncertainty and corporate strategic responses
Bibliographic record
Abstract
Project management and corporate strategic responses can play active roles in Corporate Environmental and Sustainability Performance (CESP) in times of Economic Policy Uncertainty (EPU) as per Sustainable Development Goals (SDGs). The current literature is missing a review of such linkages. Thus, this review article contributes to the literature by critically evaluating the literature on the complex connection between EPU and CESP, focusing on the concepts of green innovation, Environmental, Social, and Governance (ESG) outcomes, and project management strategies. Following a comprehensive systematic review approach, this article selects 76 empirical studies across developed, underdeveloped, and emerging markets. The findings disclose that EPU was carrying a mixed effect with both a barrier and a potential driver of sustainability initiatives at the firm level. Thus, the increasing EPU reduces long-term green investments, disrupts energy transitions, increases emissions, and increases environmental risk exposure on the one hand. On the other hand, it also promotes project management, strategic adaptation, innovation, and Corporate Social Responsibility (CSR) among firms with strong governance structures and digital capabilities. Moreover, board diversity, CSR disclosure quality, institutional strength, media attention, and technological transformation played their moderating role in determining the effect of EPU on CESP. Furthermore, sectoral heterogeneity and regional asymmetries were also found to be responsible for determining the relationship between EPU and CESP. These findings would guide the policy implications for investors and corporate leaders aiming to enhance CESP under the volatile EPU.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".