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Promoting social information disclosure can develop sustainable banking. This paper aims to evaluate the social sustainability of banks by constructing a new interval-based composite indicator using the Thomson Reuters database. In this paper, to measure the social sustainability of European banks, we propose an approach to construct interval-based composite indicators that usefully extends the construction of the composite indicator and allows us to measure the uncertainty generated by the choices made in the construction of the composite indicator. The methodological approach is based on Monte Carlo simulation and allows us to improve the information provided by the composite indicators. Thus, we measure the value of the social indicator and its subcomponents and the uncertainty of the value due to the different possible weightings. The results show that the best international ESG practices in European banks are found in French and British banks, especially in Italian ones. Finally, we analyze innovative perspectives and propose policy recommendations to support sustainable banking ecosystems in light of the increasing attention to ESG indicator disclosure and its correspondence to reality. • The best practices are found in French and U.K. banks. • Skandinavian banks face non-excellent results. • The work is a first attempt to elaborate on a specific social disclosure index. • Results are a valuable baseline to understand the actions to take. [ABSTRACT FROM AUTHOR]

This paper aims to introduce an artificial intelligence approach to enhance short-term daily streamflow forecasts through the integration of machine learning (ML) techniques with meta -heuristic (MH) algorithms. To maximize performance, a comprehensive analysis involving the autocorrelation and partial autocorrelation functions of the observed streamflow was conducted to derive ten distinct input combinations, from which the most suitable set was selected for investigation. Subsequently, six distinct ML methods, namely, decision trees, support vector regression, long short-term memory, gated recurrent unit, multi-layer perceptron, and radial basis function (as the optimal input combination), were leveraged to predict one-day-ahead streamflow. The most promising outcomes from these six explored methods were synergistically amalgamated to enhance predictive precision. This amalgamation was then optimized by utilizing parameter values hyper-tuned by four different MH algorithms. The algorithms included the shuffled frog leaping algorithm, particle swarm optimization, ant colony optimization (ACO), and gray wolf optimization (GWO). In culmination, a case study centered around the "Wick" river in the UK was conducted to rigorously evaluate the performance of the proposed method, utilizing a range of statistical evaluation indices. The results underscored the efficacy of the proposed combined method in substantially refining streamflow predictions, exhibiting an impressive level of accuracy. Additionally, the outcomes emphasized that ACO and GWO stand out as the optimal algorithms for calibrating the parameters of the amalgamated model. For instance, the R2 values attained by the combined models optimized through the ACO and GWO algorithms were 0.69422 and 0.69444, respectively. These values notably surpass those observed in the initial models, substantiating the superiority of the applied optimization approaches. [ABSTRACT FROM AUTHOR]