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 study explores the interactions between inflation and stock market. We carried out a bibliometric analysis with R package to highlight the worldwide research trends in the field, covering the period of three crises (financial, health crisis and war of Ukraine). Next, using monthly data for the period from 1 March 2020 to 31 August 2023 and based on a vector autoregressive model, impulse response and variance decomposition are performed to explore the dynamic relationships between inflation and Greek stock market. The results reveal the existence of high volatility in Athens’ stock market during COVID-19 pandemic, owning to a shock of the inflation. Regarding the period of Ukrainian war, the study verified the Fama’s hypothesis that there is a negative relationship between inflation and stock returns. The findings have significant implications for investors and policy makers.

One functional class is described in terms of one-sided modulus of continuity and the modulus of positive (negative) variation on which there
is a uniform convergence of the truncated cardinal Whittaker functions.

The whole world is in a fuel crisis nearly approaching exhaustion, with climate change knocking at our doorsteps. In the fight against global warming, one of the principle components that demands technocratic attention is Transportation, not just as a significant contributor to atmospheric emissions but from a much broader perspective of environmental sustainability. 

From the traditional technocratic aspect of transport planning, our epiphany comes in the form of Land Use integrated sustainable transport policy in which Singapore has been a pioneer, and has led the way for both developed and developing nations in terms of mobility management. We intend to investigate Singapore’s Transport policy timeline delving into the past, present and future, with a case by case analysis for varying dimensions in the present scenario through selective benchmarking against contemporary cities like Hong Kong, London and New York. The discussions will include themes of modal split, land use policy, vehicular ownership, emission policy, parking policy, safety and road traffic management to name a few. A visualization of Singapore’s future in transportation particularly from the perspective of automated vehicles in conjunction with last mile solutions is also detailed.