Purpose: Today’s challenges underscore the importance of energy across all segments of life. This scientific paper investigates the multifaceted relationship between energy efficiency, energy import reliance, population heating access, renewable energy integration, electricity production capacities, internet utilization, structural EU funds, and education/training within the framework of economic development. Methodology: Using data from selected European countries and employing self-organizing neural networks (SOM) and linear regression, this research explores how these interconnected factors influence the journey toward a sustainable and prosperous economic future. Results: The analysis revealed a strong connection between energy efficiency and numerous socioeconomic factors of modern times, with most of these connections being non-linear in nature. Conclusion: As countries work toward sustainable development goals, prioritizing energy efficiency can contribute to improved quality of life, economic growth, and environmental sustainability.

Background: Digital transformation in the sports industry has become increasingly crucial for sustainable development, yet comprehensive empirical evidence on policy effectiveness and risk management remains limited. Purpose: This study investigates the impact of policy support and risk factors on digital transformation in sports companies, examining heterogeneous effects across different firm characteristics and regional contexts. Methods: Using panel data from 168 sports companies listed on China's A-shares markets and the New Third Board from 2019 to 2023, this study employs multiple regression analyses, including baseline models, instrumental variables estimation, and robustness tests. The digital transformation level is measured through a composite index incorporating digital infrastructure, capability, and innovation dimensions. Results: The findings reveal that policy support significantly enhances digital transformation levels (coefficient = 0.238, p < 0.01), while financial risks demonstrate the strongest negative impact (−0.162, p < 0.01). Large firms and state-owned enterprises show stronger responses to policy support (0.312 and 0.278, respectively, p < 0.01). Regional development levels significantly moderate the effectiveness of policy implementation. Conclusions: The study provides empirical evidence for the differential effects of policy support and risk factors on digital transformation across various firm characteristics. The findings suggest the need for differentiated policy approaches considering firm size, ownership structure, and regional development levels. Implications: Policy makers should develop targeted support mechanisms addressing specific challenges faced by different types of firms, while considering regional disparities in digital transformation capabilities.

With the advent of the big data era, the amount of various types of data is growing exponentially. Technologies such as big data, cloud computing, and artificial intelligence have achieved unprecedented development speed, and countries, regions, and multiple fields have included big data technology in their key development strategies. Big data technology has been widely applied in various aspects of society and has achieved significant results. Using data to speak, analyze, manage, make decisions, and innovate has become the development direction of various fields in society. Taxation is the main form of China’s fiscal revenue, playing an important role in improving the national economic structure and regulating income distribution, and is the fundamental guarantee for promoting social development. Re examining the tax administration of tax authorities in the context of big data can achieve efficient and reasonable application of big data technology in tax administration, and better serve tax administration. Big data technology has the characteristics of scale, diversity, and speed. The effect of tax big data on tax collection and management is becoming increasingly prominent, gradually forming a new tax collection and management system driven by tax big data. The key research content of this article is how to organically combine big data technology with tax management, how to fully leverage the advantages of big data, and how to solve the problems of insufficient application of big data technology, lack of data security guarantee, and shortage of big data application talents in tax authorities when applying big data to tax management.

To achieve the energy transition and carbon neutrality targets, governments have implemented multiple policies to incentivize electricity suppliers to invest in renewable energy. Considering different government policies, we construct a renewable energy supply chain consisting of electricity suppliers and electricity retailers. We then explore the impact of four policies on electricity suppliers’ renewable energy investments, environmental impacts, and social welfare. We validated the results based on data from Wuxi, Jiangsu Province, China. The results show that government subsidy policies are more effective in promoting electricity suppliers to invest in renewable energy as consumer preferences increase, while no-government policies are the least effective. We also show that electricity suppliers are most profitable under the government subsidy policy and least profitable under the carbon cap-and-trade policy. Besides, our results indicate that social welfare is the worst under the carbon cap-and-trade policy. With the increase in carbon intensity and renewable energy quota, social welfare is the highest under the subsidy policy. However, the social welfare under the renewable energy portfolio standard is optimal when the renewable energy quota is low.

Urbanization process affects global socio-economic development. Originally tied to modernization and industrialization, current urbanization policy is focused on productivity, economic activities, and environmental sustainability. This study examines impact of urbanization in various regions of Kazakhstan, focusing on environmental, social, labor, industrial, and economic indicators. The study aims to assess how different indicators influence urbanization trends in Kazakhstan, particularly regarding environmental emissions and pollution. It delves into regional development patterns and identifies key contributing factors. The research methodology is based on classical economic theories of urbanization and modern interpretations emphasizing sustainability and socio-economic impacts and includes two stages. Shannon entropy measures diversity and uncertainty in urbanization indicators, while cluster analysis identifies regional patterns. Data from 2010 to 2022 for 17 regions forms the basis of analysis. Regions are categorized into groups based on urbanization levels leaders, challenged, stable, and outliers. This classification reveals disparities in urban development and its impacts. Findings stress the importance of integrating environmental and social considerations into urban planning and policies. Targeted interventions based on regional characteristics and urbanization levels are recommended to enhance sustainability and socio-economic outcomes. Tailored urban policies accommodating specific regional needs are crucial. Effective management and policy-making demand a nuanced understanding of these impacts, emphasizing region-specific strategies over a uniform approach.

A new method has been proposed to estimate top heat losses of vertical flat plate liquid/air collectors with double glazing. Empirical relations have been developed for the temperatures of glass covers, thus facilitating the calculation of individual heat transfer coefficients. The values of individual heat transfer coefficients therefore obtained can be used in the proposed analytical equation for the estimation of the top heat loss coefficient of the vertical collector with double glazing. The analytical equation has been developed for collector tilt angle of 60 to 90 degrees, plate temperature of 323 K to 423 K, absorber coating emittance of 0.1 to 0.95, air gap spacing of 20 mm to 50mm between the plate and inner glass cover, air gap spacing of 20 mm to 50mm between glass covers, wind heat transfer coefficient of 5 W/m2K to 30 W/m2K, and ambient temperature of 263K to 313K. The accuracy of the analytical equation has been validated for the said range of variables in comparison to numerical solutions, and the values of the top heat loss coefficient are found to be within 2.5 percent compared to numerical solutions.