Based on digital technology, the digital economy has typical characteristics of high efficiency, greenness, intelligence, innovation, strong penetration and so on, which can promote the sporting goods manufacturing industry (SGMI) to realize the goal of green development. This study selects panel data from 30 provinces in China over the period of 2011 to 2022. And the green total factor productivity of the sporting goods manufacturing industry (SGTFP) is used to reflect the green development of SGMI. The level of digital economy development (DIG) and the SGTFP are measured by using the entropy method and the Super-SBM model with undesirable outputs. Based on the method of coupling coordination degree model, the coordinated development degree of DIG and SGTFP is analyzed first. Then, by making use of the fixed effect model, intermediary effect model and spatial Durbin model, the influence of DIG on the green development of SGMI and its mechanism are empirically studied. The results show that DIG, SGTFP and the degree of their coupling and coordination are generally on the rise. The benchmark regression results show that the coefficient of DIG on SGTFP is 0.213; that is, the digital economy can significantly promote the improvement of green development in SGMI. According to the analysis of the spatial Durbin model, the impact of the digital economy on SGTFP has a certain spatial spillover, that is, the development of digital economy in the region will have a certain promoting effect on the green development of SGMI in the surrounding region. The intermediary effect model analyzes the influence mechanism and finds that the digital economy mainly boosts SGTFP through green innovation technology and energy consumption structure.

African countries have shown interest in developing the legal framework for electronic payment as part of digital law. The article aims to analyze the role of the legal framework for electronic payment in the field of digital economy. It relies on a legal methodology through analyzing legal texts related to electronic payment. It also relies on the comparative and descriptive approaches whenever there is a scientific necessity. The article concluded that the legal framework plays an important role in the field of digital economy. This framework appears in the general rules of civil and commercial laws or through the laws of money and credit. Other laws also play a complementary role, such as criminal law and personal data protection laws.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

The increase in world carbon emissions is always in line with national economic growth programs, which create negative environmental externalities. To understand the effectiveness of related factors in mitigating CO₂ emissions, this study investigates the intricate relationship among macro-pillars such as economic growth, foreign investment, trade and finance, energy, and renewable energy with CO₂ emissions of the high gross domestic product economies in East Asia Pacific, such as China, Japan, Korea, Australia and Indonesia (EAP-5). Through the application of the Vector Error Correction Model (VECM), this research reveals the long-term equilibrium and short-term dynamics between CO₂ emissions and selected factors from 1991 to 2020. The long-term cointegration vector test results show that economic growth and foreign investment contribute to carbon reduction. Meanwhile, the short-term Granger causality test shows that economic growth has a two-way causality towards carbon emissions, while energy consumption and renewable energy consumption have a one-way causality towards carbon emissions. In contrast, the variables trade, foreign direct investment, and domestic credit to the private sector do not have two-way causality towards CO₂ emissions. The findings reveal that economic growth and foreign investment play significant roles in carbon reduction, which are observed in long-term causality relationships, while energy consumption and renewable energy are notable factors. Thus, the study offers implications for mitigating environmental concerns on national economic growth agendas by scrutinizing and examining the efficacy of related factors.