The carbon footprint, which measures greenhouse gas emissions, is a good environmental indicator for choosing the best sustainable mode of transportation. The available emission factors depend heavily on the calculation methodology and are hardly comparable. The minimum and maximum scenarios are one way of making the results comparable. The best sustainable passenger transport modes between Rijeka and Split were investigated and compared by calculating the minimum and maximum available emission factors. The study aims to select the best sustainable mode of transport on the chosen route and to support the decision-making process regarding the electrification of the Lika railroad, which partially connects the two cities. In the minimum scenario, ferry transport without vehicles was the best choice when the transportation time factor was not relevant, and electric rail transport when it was. In the maximum scenario, the electric train and the ferry with vehicles were equally good choices. Road transportation between cities was not competitive at all. The comparison of the carbon footprint based on minimum and maximum scenarios gives a clear insight into the ratio of greenhouse gas emissions from vehicles in passenger transport. It supports the electrification of the Lika railroad as the best sustainable transport solution on the route studied.

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.

Research that discusses the impact of implementing Green Human Resource Management and environmentally friendly behavior, especially in sustainable tourism, is limited. It becomes crucial to understand how implementing good green human resource management practices in tourism sector organizations. To achieve the objectives of this research, a qualitative approach was used where the data and information collected were obtained through direct observation and interviews with tourism informants. The findings show the importance of environmentally friendly behavior as the implementation of green human resource management is able to improve tourism management. The uniqueness of this research is developing a model of human resource readiness in implementing environmentally friendly behavior towards sustainable tourism. This resource readiness will be reflected in the GHRM model in supporting sustainable tourism. The results of this research offer a model of sustainable Green Tourism which includes antecedents, implementation and results achieved. These antecedents come from internal and external (environmental ethics and management commitment) managers which will result in good GHRM implementation. This model will be the basis for implementing sustainable tourism in human resource management practices based on literature reviews and also tourism management practices.

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.

Village Finance System (SISKEUDES) is a village financial reporting application policy. The application of the SISKEUDES is as a form of accountability to be accessible and known by the community. However, communication problems, resources, knowledge and limited internet networks in many regions still cause problems in reporting process. The research used a qualitative descriptive method by conducting in-depth interviews and document analysis of Mamala Negeri SISKEUDES. The policy implementation model according to George Edward III was used as an analysis tool. This research was designed to be carried out for 5 (five) months to explore various data from various information regarding this research problem. The research findings are that the provision of facilities and infrastructure for Mamala Negeri supporting human resources is still limited, making it difficult to apply the SISKEUDES 2.0 application. Besides, the village also needs more systematic transaction planning, which allows each transaction to be recorded completely both planning and realization.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.