Enterprise green innovation drives sustainable development and contributes to the realization of a ‘beautiful China’. It enhances resource utilization, reduces energy consumption, and achieves economic-environmental objectives through technological advancements. This paper examines the impact of the gender composition of a company’s CEO and CFO on green innovation by empirical research method using the data of the firms listed on Chinese capital market from 2015 to 2022. Our findings indicate that: (1) Male CEOs and CFOs are more likely to promote green innovation compared to their female counterparts; (2) Leadership teams comprising opposite-sex pairs tend to weaken the promotion of green innovation. These conclusions are consistent across state-owned enterprises and within the manufacturing sector. This study provides a novel perspective on enterprise green innovation, offering insights for companies regarding their green innovation strategies and for policymakers in shaping relevant policies.

Environmental Education (EE) programs are of crucial importance. EE are aimed at global citizenship to generate new knowledge and new, more participatory and conscious ways of acting in the environment. This study, therefore, wants to verify the effectiveness of a training intervention that is based on education on climate change issues and on the active participation of subjects in the dimension of the small psychological group. At the intervention 309 students took part, equally distributed by gender (52.1% males), 64.4% enrolled in primary school, 35.6% enrolled in lower secondary school. A quantitative protocol was administered to evaluate the effectiveness of the intervention. The study shows an increase in pro-environmental behaviors and their stability even after 15–30 days. The intervention seems to be effective in triggering pro-environmental behaviors and maintaining them in the following weeks. The results of this study highlight the need to develop environmental education pro-grams in schools to increase levels of knowledge and awareness on the issue of climate change.

This research analyses the effects of openness, telecommunications, and institutional nexus on economic growth in African countries using a panel model with data from 16 landlocked countries from 1996 to 2021 and employing the pooled mean group estimation technique that mitigates bias from country heterogeneity and discerning short-term and long-term equilibrium dynamics and two-step system-generalized method of moments (GMM) estimation for robustness check. The empirical findings indicate that openness exerts a significantly positive effect on economic growth in the models. This supports the neoclassical model, suggesting that being landlocked should not impede economic growth, but rather, growth should depend on opportunities available to each country. However, institutions and telecommunications show a mixed correlation with economic growth. These findings can guide landlocked developing countries in enhancing their exports and fostering skill acquisition to attract advanced technology. In conclusion, policymakers should improve macroeconomic policies, telecommunications infrastructure, and institutional structure to strengthen the sustainability of economic growth in African landlocked countries.

This study introduces an innovative approach to assessing seismic risks and urban vulnerabilities in Nador, a coastal city in northeastern Morocco at the convergence of the African and Eurasian tectonic plates. By integrating advanced spatial datasets, including Landsat 8–9 OLI imagery, Digital Elevation Models (DEM), and seismic intensity metrics, the research develops a robust urban vulnerability index model. This model incorporates urban land cover dynamics, topography, and seismic activity to identify high-risk zones. The application of Landsat 8–9 OLI data enables precise monitoring of urban expansion and environmental changes, while DEM analysis reveals critical topographical factors, such as slope instability, contributing to landslide susceptibility. Seismic intensity metrics further enhance the model by quantifying earthquake risk based on historical event frequency and magnitude. The calculation based on higher density in urban areas, allowing for a more accurate representation of seismic vulnerability in densely populated areas. The modeling of seismic intensity reveals that the most susceptible impact area is located in the southern part of Nador, where approximately 50% of the urban surface covering 1780.5 hectares is at significant risk of earthquake disaster due to vulnerable geological formations, such as unconsolidated sediments. While the findings provide valuable insights into urban vulnerabilities, some uncertainties remain, particularly due to the reliance on historical seismic data and the resolution of spatial datasets, which may limit the precision of risk estimations in less densely populated areas. Additionally, future urban expansion and environmental changes could alter vulnerability patterns, underscoring the need for continuous monitoring and model refinement. Nonetheless, this research offers actionable recommendations for local policymakers to enhance urban planning, enforce earthquake-resistant building codes, and establish early warning systems. The methodology also contributes to the global discourse on urban resilience in seismically active regions, offering a transferable framework for assessing vulnerability in other coastal cities with similar tectonic risks.

This study aims to examine the mediating role of institutional trust (IT) between perceived corruption and subjective well-being (SWB) using data from 1566 households in a developing country. It deploys ordinary least square (OLS) and an ordered logit model within the generalized structural equation model. Results show that individuals who perceived no corruption in a country report more IT and higher levels of SWB. Furthermore, the direct effects of good governance, perceived IT, and the absence of corruption on SWB is also positive. Moreover, satisfaction with hospital services also improves happiness and life satisfaction levels. This study improves and validates how corruption is assessed to support future measures that reduce its harmful effects. Moreover, the masses must have widespread awareness about the critical nature of corruption and IT relative to well-being. This study also highlights the need to develop strong institutions to improve trust and minimize corruption.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

The objective of this work was to analyze the effect of the use of ChatGPT in the teaching-learning process of scientific research in engineering. Artificial intelligence (AI) is a topic of great interest in higher education, as it combines hardware, software and programming languages to implement deep learning procedures. We focused on a specific course on scientific research in engineering, in which we measured the competencies, expressed in terms of the indicators, mastery, comprehension and synthesis capacity, in students who decided to use or not ChatGPT for the development and fulfillment of their activities. The data were processed through the statistical T-Student test and box-and-whisker plots were constructed. The results show that students’ reliance on ChatGPT limits their engagement in acquiring knowledge related to scientific research. This research presents evidence indicating that engineering science research students rely on ChatGPT to replace their academic work and consequently, they do not act dynamically in the teaching-learning process, assuming a static role.