This study constructs and empirically validates a Creative Activity Chain (CCA) structure model tailored for innovation in sustainable infrastructure development. In today’s competitive environment, fostering innovation is crucial for maintaining the relevance and effectiveness of infrastructure projects. The research underscores that a significant portion of a project’s long-term value is established during its initial concept and planning stages, highlighting the critical role of creativity in infrastructure development. The CCA model is developed through theoretical frameworks and empirical data, encompassing three key dimensions: creative subject chain, creative action chain, and creative operation chain. The model’s validity is tested with data from five large infrastructure development firms in China, involving 768 R&D staff as respondents. Rigorous statistical methods, including exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM), and regression analysis, confirm the model’s robustness. The findings reveal significant positive correlations between the creative activity chain’s dimensions and the successful development of sustainable infrastructure projects. Additionally, the study examines the mediating effect of link strength within the creative activity chain, demonstrating its substantial impact on project outcomes. Implications for management include promoting diverse creative teams, systematic process management, and leveraging varied operational tools to enhance creativity in infrastructure development. This research contributes to the literature by introducing an integrated model for managing creative activities in sustainable infrastructure development, offering practical insights for improving innovation processes.

This paper addresses the main logistics challenges in used car maritime traffic from Europe to West Africa. Thus, the methodology (quantitative and qualitative) analyses data from the International Organization of Motor Vehicle Manufacturers (OICA), from 2015 to 2023 of government and port authorities to show the importance of used car market for mobility and socioeconomic activities. This is supplemented by surveys based on direct observation in the field, questionnaires and interviews involving in Europe 55 stakeholders and 127 in Africa. The results demonstrate that cars used and their parts, but not wrecks, are essential for motorization in West Africa. A pre-export process needs to be set up to ensure that exported vehicles are parked in better condition to meet the required common environmental standards for sustainable mobility.

This study explores the impact of environmental degradation on public debt in the largest Southeast Asian (ASEAN-5) countries. Prior research has not examined environmental degradation as a possible determinant of public debt in the ASEAN region. As such, the primary objective is to examine key determinants of public debt, notably economic growth, trade openness, investment, and environmental degradation. Utilizing the Fully Modified Ordinary Least Squares (FMOLS) method and data from 1996 to 2021, the study reveals a negative correlation between investment and public debt. Conversely, a positive relationship exists between economic growth, environmental degradation, and public debt levels. These findings hold significant implications for policymakers seeking to craft effective economic and environmental strategies to ensure sustainable development in the ASEAN-5 region. Stronger economic growth can drive up public debt. Importantly, the study highlights the importance of tailored approaches, considering each country’s unique fiscal and developmental characteristics. Applying the Two-Gap Model enhances the understanding of these complex dynamics in shaping public debt and its relationship with environmental factors.

The use of public transport is one of the concepts of sustainable transport. However, people prefer to use private vehicles, which causes various problems, one of which is the high carbon emissions produced. This research aims to encourage programs to use passenger public transportation through a carbon tax. The method in this research is descriptive quantitative with primary data and secondary data. Secondary data was developed in the research by collecting literature study sources on the concept of sustainable transportation development as well as primary data carried out by analyzing calculations regarding the implementation of the carbon tax. There are several proposals that can significantly accelerate the achievement of goals, namely a collaborative approach through collaboration between local government agencies, a policy of progressively implementing a carbon tax as a coercive policy and supported by a program to provide supporting facilities for public transportation. Decision making in this research was carried out by looking at the percentage increase in public transportation use based on the application of a carbon tax or carbon tax.

The research aims to examine the determinants influencing the business commitment toward sustainable goals in Vietnam. To employ a quantitative research approach, we surveyed 208 business leaders in Vietnam to assess their perceptions and actions regarding sustainable goals. We explored the impact of internal enterprise characteristics and external facilitating factors on different dimensions of sustainable goals by using logistic regression models. This paper’s findings reveal that enterprise attributes, corporate leadership traits, and external factors significantly influence sustainable goal engagement. Notably, corporate leaders emerge as pivotal factors, particularly in their willingness to embrace risks and uncertainties. Moreover, this paper’s analysis identifies external factors with limited efficacy in fostering sustainable business operations. These insights hold significant implications for governmental institutions in Vietnam, offering valuable guidance for updating and refining policies.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.