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.

The research aims to map environmental protection strategies and the related control tools and to identify the links among companies with the largest number of employees and sites in Hungary. The research questions were answered using a questionnaire survey method. The authors used cluster analysis to classify the 205 company strategies into the identified strategy clusters: Leaders, Awakeners, and Laggards. Then, the examined 21 environmental management control tools in the sample were divided into four groups: strategic, administrative, methodological and economic. Economic and strategic methods were the most common in the sample. The authors used cross-tabulation analysis to examine whether there is a statistically proven relationship between belonging to environmental strategy clusters and specific control tools. The analysis showed significant but weak to moderate relationships. According to Cramer’s V and the contingency coefficient, the closest relationship between the tested environmental management control tools and membership in environmental strategy clusters is shown by evaluating investments, assessing the economic viability of environmental strategies, and running an environmental training program for employees. In case of the robust lambda indicator, a significant relationship was found by examining the economics of environmental strategies and identifying environmental success factors and eco-balances. It can be concluded that the companies under examination follow a set of environmental goals, which they have incorporated into their strategic objectives. They use the available environmental management control toolbox to develop their strategies and to monitor their implementation to varying degrees.

Olive production is threatened by a fungal pathogen, Armillaria mellea (Vahl. Fr.) P. Kumm.,causing decline in trees worldwide. Effectiveness of once and twice applications of fungicides hexaconazole, propicoconazole and thiophanate-methyl and application of biological agent (Trichoderma harzianum) to control A. mellea was studied at orchard scale during four years. T. harzianum inhibited the pathogen growth on agar media. This antagonistic fungus provided a 25% control efficiency of A. mellea on olive trees younger than 15 years which was the same as control efficiency of once application of hexaconazole. Control efficiencies as perfect as 100% were determined on younger (<15 years old) diseased olive trees treated with once applications of thiophanate-methyl and hexaconazole, and twice applications of thiophanate-methyl. Moreover, olive tree age was significantly effective on fungicidal control efficiency. Hence, this four-year research advanced our understanding of sustainable olive production in study region and other geographical areas with similar agro-ecological characteristics.

With the vigorous development of international trade and the in-depth advancement of economic globalization, China is facing the increasingly serious problem of invasive alien species, which poses a major threat to China’s ecological environment, economic development and human health. At present, although China has a comprehensive institutional norms in the prevention and control of invasion of alien species, but in the face of the challenge of invasion of alien species, China is still facing problems such as insufficient legal basis and imperfect specific legal system. Based on this understanding, this paper discusses the prevention and control of invasive alien species legal regulation, that although in recent years China has made certain achievements in the field of prevention and control of invasive alien species, but still faces a number of problems to be solved, should promote the relevant legislative amendments, and combined with the experience of developed countries to summarize the perfect.

This study explores the determinants of control loss in eating behaviors, employing decision tree regression analysis on a sample of 558 participants. Guided by Self-Determination Theory, the findings highlight amotivation (β = 0.48, p < 0.001) and external regulation (β = 0.36, p < 0.01) as primary predictors of control loss, with introjected regulation also playing a significant role (β = 0.24, p < 0.05). Consistent with Self-Determination Theory, the results emphasize the critical role of autonomous motivation and its deficits in shaping self-regulation. Physical characteristics, such as age and weight, exhibited limited predictive power (β = 0.12, p = 0.08). The decision tree model demonstrated reliability in explaining eating behavior patterns, achieving an R² value of 0.39, with a standard deviation of 0.11. These results underline the importance of addressing motivational deficits in designing interventions aimed at improving self-regulation and promoting healthier eating behaviors.