Our study investigates the relationship between firm profitability, board characteristics, and the quality of sustainability disclosures, while examining the moderating effects of financial leverage and external audit assurance. A key focus is the distinction between Big 4 and non-Big 4 audit firms. Using data from Malaysia’s top 100 publicly listed organizations from 2018 to 2020, we analyze sustainability reports based on the Global Reporting Initiative (GRI) standards. Unexpectedly, our results indicate a negative association between firm profitability and board characteristics, challenging traditional assumptions. We find that non-Big 4 audit firms significantly enhance sustainability disclosure quality, contradicting the widely held belief in the superiority of Big 4 firms. Our finding introduces the “Big 4 dilemma” in the Malaysian context and calls for a reassessment of audit firm selection practices. Our study offers new perspectives on the strategic role of board composition and audit firm selection in advancing sustainability disclosures, urging Malaysian organizations to evaluate audit firms on criteria beyond the global prestige of Big 4 firms to improve sustainability reporting.

The transportation sector in India, which is a vital engine for economic growth, is progressively facing challenges related to climate change. Increased temperature, extreme weather conditions, and rising seas threaten physical infrastructure, service delivery, and the economy. This research examines efforts towards improving the climate resilience of India’s transport sector through policy interventions. Strategies encompass broadening the focus to cover the integration of sustainability, innovative technology deployment, and adaptive infrastructure planning. Multi-sectoral measures are proposed to guarantee longevity, equity and environmental protection. National transport infrastructure will be secured, people will be enabled to move sustainably, and India will take its position in the world economy as a climate-resilient country. Long-term resource management and promoting inclusive governance are critical to agri-transportation systems that can withstand the changing climate.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

This research aims to develop a Synergy Learning Model in the context of science learning.  This research was conducted at Islamic Junior High School, Madrasah Tsanawiyah Negeri 2 Medan, involving 64 students of Grade 7 as the research subject. The method used in this research refers to the development research approach (R&D). In collecting the data, the research employed test and non-test techniques. The results prove that the Synergy learning model developed is effective in improving student learning outcomes. This is evident through the t-test statistical test where the t-count of 4.26 is higher than the t-table of 1.99. In addition, the level of practicality with a score of 3.39 is categorized as practical. This learning model emphasizes the learning process that supports the development of science skills and develops students' competencies in planning, collaborating, and critically reflecting. The findings of this study contribute to pedagogical practices and literature in the field of science learning.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

While the notion of the smart city has grown in popularity, the backlash against smart urban infrastructure in the context of changing state-public relations has seldom been examined. This article draws on the case of Hong Kong’s smart lampposts to analyse the emergence of networked dissent against smart urban infrastructure during a period of unrest. Deriving insights from critical data studies, dissentworks theory, and relevant work on networked activism, the article illustrates how a smart urban infrastructure was turned into both a source and a target of popular dissent through digital mediation and politicisation. Drawing on an interpretive analysis of qualitative data collected from multiple digital platforms, the analysis explicates the citizen curation of socio-technic counter-imaginaries that constituted a consent of dissent in the digital realm, and the creation and diffusion of networked action repertoires in response to a changing political opportunity structure. In addition to explicating the words and deeds employed in this networked dissent, this article also discusses the technopolitical repercussions of this dissent for the city’s later attempts at data-based urban governance, which have unfolded at the intersections of urban techno-politics and local contentious politics. Moving beyond the common focus on neoliberal governmentality and its limits, this article reveals the underexplored pitfalls of smart urban infrastructure vis-à-vis the shifting socio-political landscape of Hong Kong, particularly in the digital age.