Environmental, social, and governance (ESG) principles have become essential for companies seeking to avoid greenwashing and promote genuine sustainability practices. This study aims to identify the practices, barriers, and strategies for adopting ESG as a competitive strategy. A systematic literature review was conducted, which included a bibliometric analysis of 496 documents. This generated four clusters, with content analysis applied to 20 documents presenting the highest PageRank per cluster, for a total of 80 documents. The clusters are (i) innovation and green performance, (ii) influence on corporate and financial performance, (iii) communication and transparency, and (iv) diverse governance and corporate sustainability. For every cluster, the practices, barriers, and strategies for adopting ESG principles were analyzed. In addition, a research agenda was developed with questions for future research and methodological suggestions. Finally, a framework with the main practices is presented so that companies and decision‐makers can adopt ESG as a competitive strategy.
Heloísa Serafim Kuakoski, F. H. Lermen, G. Lenzi et al.· Sustainable Development· 0 citations
Maritime collision risk assessment is essential for ensuring navigational safety under increasingly congested traffic conditions. In such contexts, autonomous vessels must evaluate risks efficiently and respond in real time, which remains challenging when using conventional methods. The widely used Dempster-Shafer (D-S) model, although effective in theory, suffers from high computational complexity and limited scalability when applied to multi-vessel encounters involving both Maritime Autonomous Surface Ships (MASS) and conventional vessels. To address this issue, a machine learning-based framework is proposed, in which an Extreme Gradient Boosting (XGBoost) model replaces the theoretical D-S model for Collision Risk Indicator (CRI) estimation. Automatic Identification System (AIS) data are used to construct realistic simulation scenarios, and the predicted CRI is continuously evaluated and integrated into an automatic collision avoidance algorithm. The proposed model achieves an R² of 95.36% during training and 90.21% in real-world simulation testing. In high-traffic scenarios involving more than 20 vessels, it demonstrates significantly faster processing speed than the D-S model. Safety analysis further shows that integrating CRI with the Velocity Obstacle (VO) algorithm reduces collision risk by 33.0% in autonomous-autonomous vessel encounters and 28.7% in autonomous-conventional vessel encounters. These results indicate that the proposed method supports efficient and scalable real-time collision risk management for autonomous maritime navigation.
Linna Li, Lingyu Zhang, Seyed Parsa Parvasi et al.· WMU Journal of Maritime Affa...· 0 citations
