This study presents a comprehensive bibliometric and topic modeling analysis of the academic literature on green and sustainable finance. Using 1372 peer-reviewed articles indexed in the Web of Science up to 2024, we identify key publication trends, influential authors, prominent journals, and thematic clusters shaping the field. The analysis reveals an exponential growth in publications since 2017 and highlights the dominance of journals such as Journal of Sustainable Finance & Investment and Sustainability. Text mining techniques, including TF-IDF and Latent Dirichlet Allocation (LDA), are applied to abstracts to extract the most relevant terms and classify articles into four latent topics. The findings suggest a growing focus on the impact of green finance on carbon emissions, energy efficiency, and firm performance, particularly in the context of China. This study offers valuable insights for researchers and policymakers by mapping the intellectual structure and identifying emerging research frontiers in the rapidly evolving field of green finance.

In this research we evaluate the impact that various types of private contributions to public schools can have on their performance. We start from the assumption that greater involvement in the social role of business is key to accelerating the achievement of development goals. Although there is abundant literature that measures different types of effects from private aid to education at an individual level, this study provides an aggregate measure of the effect of simultaneous interventions. We use a benchmarking approach on a sample of 269,117 Colombian students from 1,224 schools, of which 53% received some assistance. The findings may help to stimulate companies to participate in solving the problems of development, while calling attention to some social criteria that can improve the allocative efficiency of such resources. The results suggest that a consistent process of accountability is also required to quantify the real impact of these contributions.

Electricity tariffs in emerging economies are often influenced by macroeconomic volatility and regulatory design, affecting both affordability and system stability. Understanding these interactions is crucial for anticipating price fluctuations and ensuring sustainable energy policy. This paper examines the influence of macroeconomic conditions on electricity tariff dynamics in Colombia by integrating econometric and machine learning approaches. Using monthly data from 2009 to 2024 and a set of 153 macroeconomic indicators condensed via principal component analysis (PCA), we assess the predictive performance of vector autoregressive (VAR), SARIMAX, and XGBoost models, as well as a hybrid VAR–XGBoost specification. Impulse-response analysis reveals that tariff components exhibit limited sensitivity to macroeconomic shocks, underscoring the buffering role of regulation and sector-specific drivers. However, forecasting exercises demonstrate that accuracy is highly component-specific: SARIMAX performs best for transmission and restrictions, and VAR dominates for distribution and losses, while the hybrid model outperforms for generation and commercialization. These findings highlight that although macroeconomic pass-through into tariffs is weak, hybrid approaches that combine structural econometric dynamics with nonlinear learning can deliver tangible forecasting gains. The study contributes to the literature on electricity pricing in emerging economies and offers practical insights for regulators and policymakers concerned with tariff predictability and energy affordability.