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.

This study evaluates the influence of quality certificates on sustainable food production in Poland, considering economic, social, and environmental dimensions. Analyzing 25 different certificates, the research explores their criteria, procedures, and costs across various food product categories, including meat, fish, and plant-based products. The study provides a detailed review of certification processes, from initiation to audits and inspections. It identifies both commonalities and differences among certificates, each addressing unique aspects such as environmental impact, worker rights, and product origins. Despite the diversity in standards and procedures, the study underscores the need for standardized international criteria to improve transparency and meet consumer expectations, highlighting the significant role of quality certificates in advancing sustainable food production.

The holding of soccer events has an important impact on modern urban activities, which is conducive to the economic development, social harmony, cultural integration and regional integration of cities. However, massive energy is consumed during the event preparation and infrastructure construction, resulting in an increase in the city’s carbon emissions. For the sustainable development of cities, it is important to explore the theoretical mechanism and practical effectiveness of the relationship between soccer events and urban carbon emissions, and to adopt appropriate policy management measures to control carbon emissions of soccer events. With the development of green technology, digitalization, and public transportation, the preparation and management methods of soccer events are diversified, and the possibility of carbon reduction of the event is further increased. This paper selects 17 cities in China from 2011 to 2019 and explores the complex impact of soccer events on urban carbon emissions by using green technology innovation, digitalization level and public transportation as threshold variables. The results show that: (1) Hosting soccer events increases carbon emissions with an impact coefficient of 0.021; (2) There is a negative single-threshold effect of green innovation technology, digitalization level and public transportation on the impact of soccer events on carbon emissions, with the impact coefficients of soccer events decreasing by 0.008, 0.01 and 0.06, respectively, when the threshold variable crosses the threshold. These findings will enhance the attention of city managers to the management of carbon emissions from soccer events and provide guidance for reducing carbon emissions from soccer events through green technology innovation, digital means and optimization of public transportation.

In Urban development, diversity respect is needed to prioritize and balance the urban development design for sustainable eco-city development. As a result, this research aimed to investigate the causal factor pathways of social network factors influencing sustainable eco-city development in the northeastern region of Thailand through a quantitative research approach. With the aim to survey insightful information, the analysis unit was conducted at the individual level with three hundred and eighty-three (383) samplings in Khon Kaen and Udon Thani provinces, including univariate analysis and multivariate analysis, using path analysis and multiple linear regression. The study results indicated that two pathways of social network factors influencing sustainable eco-city development were indirect influence factors. The indirect influence factor consists of information exchange, benefits exchange in the network, and members’ role in the social network. Additionally, the study revealed that the pathway has influences through social network types and the economic and social dimensions of sustainable cities (R² = 0.330). Therefore, this study concluded that sustainable eco-city development should be implemented through community networks and economic and social network development for environmental development through social network types.