This paper aims to systematically analyze the current state of plastic waste legal supervision in China and to propose a vision for future governance frameworks. In recent years, along with the vigorous rise of emerging industries such as the express delivery industry and takeaway services, the consumption of plastic products has increased sharply. This trend has triggered profound reflection and high vigilance on the issue of plastic waste supervision. This trend has triggered profound reflection and acute vigilance regarding the regulation of plastic waste. Although the Chinese government has initiated multiple regulatory measures and achieved certain outcomes, from a macroscopic perspective, the issue of plastic waste pollution remains grave, and the relevant legal and regulatory system presents a complex situation with limited enforcement efficacy. Hence, it is exceptionally urgent and significant to deeply explore and formulate legislative strategies aimed at alleviating and regulating plastic waste pollution. This paper is dedicated to systematically analyzing the current state of plastic waste legal supervision from both international and domestic dimensions, and meticulously outlining the regulatory framework for plastic waste governance in China. Through the application of legal norm research methods, this paper dissects the flaws and challenges existing in the current governance mechanisms and further conducts a comparative study of the successful practices in this field in developed countries like the United States, with the intention of drawing valuable experiences. On this basis, this paper not only offers a forward-looking outlook on China’s future legislative tendencies in plastic waste pollution but also innovatively proposes a series of new insights and recommendations. These explorations aim to provide a more solid theoretical foundation and practical guidance for the governance approach to plastic waste pollution in China, promote the improvement and enhancement of the enforcement effectiveness of environmental regulations, and thereby effectively confront the global challenge of plastic pollution.

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.

The paper proposes a methodology for the analysis and evaluation of the traffic scheme of Bulgarian cities. The authors combine spatial, network, and socio-economic analyses of cities with transport operators’ financial-economic evaluation, sociological studies of transport habits, and the possibilities of new information technologies for transport modeling (such as geographic information systems). The model proposes several approaches to optimize the municipality’s transport scheme. It results from a new need to improve urban traffic, the quality of transport services, and the integration of urban transport into the regional economy of Stara Zagora municipality. It presents a description, analysis, and outline of the opportunities for developing urban transport connectivity and mobility in Stara Zagora municipality. The research results show a deficit of transport connectivity between the different parts of the city, reflecting on the regional economy’s development and the efficiency of the environment and the population.