Economic Management & Global Business Studies

This paper delves into the distinctions between China's and the United States' tax systems, underscoring the pressing requirement for tax reform in China. It scrutinizes the potential of these reforms to propel the evolution of social civilization, emphasizing equity, efficiency, and the digital metamorphosis of tax governance.

Introduction: The tax systems of China and the United States are divergent, mirroring their economic frameworks and policy priorities. The U.S. tax system prioritizes direct taxes, focusing on individual taxation and income redistribution. Conversely, China's system, with its emphasis on indirect taxes, is predominantly geared towards fiscal revenue collection from businesses. This comparative analysis unveils the imperative for China's tax system to adapt in accordance with global benchmarks and the burgeoning digital economy, and imagines the future of taxation in the coming eras

This essay examines the claim that the Australian Constitution is too difficult to amend by comparing it with the German Constitution (Basic Law). The Australian Constitution, enacted in 1901, requires a double majority in both national and state referenda, making the amendment process complex and challenging. In contrast, Germany's Basic Law allows amendments through a two-thirds majority in both houses of Parliament, presenting a more flexible and efficient model. By evaluating both constitutional frameworks, the essay argues that while the Australian process ensures broad support for changes, it also limits the ability to adapt to evolving societal needs. On the other hand, Germany's amendment procedure offers a more responsive and dynamic approach, allowing for constitutional changes that reflect modern developments. The comparison underscores the potential benefits of a more adaptable amendment process, suggesting that the rigidity of the Australian Constitution may impede necessary reforms.

This paper delves into the distinctions between China's and the United States' tax systems, underscoring the pressing requirement for tax reform in China. It scrutinizes the potential of these reforms to propel the evolution of social civilization, emphasizing equity, efficiency, and the digital metamorphosis of tax governance.

Introduction: The tax systems of China and the United States are divergent, mirroring their economic frameworks and policy priorities. The U.S. tax system prioritizes direct taxes, focusing on individual taxation and income redistribution. Conversely, China's system, with its emphasis on indirect taxes, is predominantly geared towards fiscal revenue collection from businesses. This comparative analysis unveils the imperative for China's tax system to adapt in accordance with global benchmarks and the burgeoning digital economy, and imagines the future of taxation in the coming eras

In the rapidly evolving e-commerce landscape, predicting consumer purchasing behavior is crucial for optimizing marketing strategies and improving sales. Traditional machine learning models often deliver high accuracy but lack interpretability, limiting their real-world application. This paper introduces the TriFusion Ensemble Model, a novel framework that not only combines Random Forest, Support Vector Machine (SVM), and Logistic Regression but also uniquely incorporates concepts from physical systems. In this approach, each base model is treated as a force contributing to the overall prediction, while the meta-learner acts as a balancing mechanism, minimizing classification error akin to reducing the system’s energy. This novel borrowing from physical systems is groundbreaking, as it reframes the model’s optimization process, enhancing both predictive accuracy and transparency. By structuring model interactions through energy minimization, the TriFusion Ensemble addresses the black-box nature of advanced machine learning models. Evaluated using AUC-ROC and SHAP-based feature importance analysis, the model shows moderate performance improvements over individual models while enhancing feature transparency. This innovative approach can extend beyond e-commerce to fields like healthcare and finance, offering a new paradigm for deploying complex machine learning models with both accountability and practical applicability, grounded in physical system principles.

A new integrative theoretical framework has theoretically identified and discussed AI's vital driving, mediating, and moderating role in enhancing a brand's value and eventually contributing toward brand equity sustainability. Some critical theories underlying the understanding of AI's impact on consumer-brand interactions are TAM, BPT, and CCT. The findings have explained how AI can be utilised to personalise consumers' experiences, add value to brand communication through multimodal content, and build emotional connections between consumers and brands. This paper also explores how AI facilitates eco-innovation and cultural adaptation to enhance the sustainability of brands' practices. Therefore, the study concludes that AI is an operational efficiency enabler and a strategic one in developing resilient, future-ready brands. Implications for research and practice are discussed, along with recommendations on solving the ethical and privacy challenges facing AI-driven branding strategies.

With the rapid development of AI technology, the application in the field of auditing has become an important means to improve the efficiency and quality of auditing. AI technology has significantly improved the efficiency and quality of audit work by automating data analysis, risk assessment, and audit processes. However, as technology evolves, so do challenges such as data privacy, security, algorithmic transparency, and lack of talent. In order to address these issues, it is recommended to strengthen data protection, improve the accuracy and transparency of algorithms, formulate unified AI audit standards, and focus on cultivating audit talents with AI skills. In the future, the audit industry needs to actively manage and control risks while enjoying the convenience brought by AI, ensure the quality and credibility of audit work, and promote the development of audit services in a more efficient and intelligent direction.

The construction of the Chengdu-Chongqing Economic Circle holds a pivotal strategic position in China's regional development landscape. Attention is a scarce resource, the optimization of government decision-making hinges critically on effective attention allocation. This study examines 238 policy documents issued by the Sichuan Provincial Government and Chongqing Municipal Government pertaining to the Twin-City Economic Circle. Leveraging grounded theory and content analysis, we utilize Nvivo12 to process and analyze these texts, aiming to delineate the overall distribution and specific characteristics of the governments' attention allocation. Our findings reveal disparities in attention allocation across different timeframes and domains, along with issues such as imbalances in policy tool structures, attention allocation structures, and the precision of attention allocation. Consequently, we recommend that the Sichuan Provincial Government and Chongqing Municipal Government enhance the utilization of demand-oriented policy tools to refine their policy tool frameworks, prioritize non-urgent policy issues to optimize attention allocation, and refine the targets of attention allocation to enhance the precision of policy arrangements.

As a typical social enterprise, MYBank has gone through a tumultuous development process. Compared with the traditional banking model of "deposit+loan+intermediary business", MYBank adheres to the development orientation of "inclusive finance, serving small and micro businesses, and serving agriculture, rural areas, and farmers", create a new model of "310" Internet loans, and strive to become a real "preferred bank for small and micro businesses". Under the fierce competition in the financial industry, MYBank is facing numerous challenges. In terms of business scale, technical issues such as the limited identification range of the "Great Sparrow" have become the primary challenge. It's both an opportunity and a challenge, pressure and motivation. In the midst of unprecedented changes, MYBank still needs to further enhance their technological innovation capabilities, adheres to an open platform approach, and provides meticulous financial services to small and micro operators.

In order to solve the location routing problem (LRP) by means of  energy saving and environmental protection, we propose a LRP optimization model with bi-objectives of minimizing the carbon emissions and the distribution costs. To address the shortcomings of traditional heuristics in solving large-scale LRPs with poor generality and low efficiency, an improved fast non-dominated ranking genetic algorithm (NSGA-II) is proposed and applied to the LRP optimization. In order to improve the convergence and optimization ability of the algorithm, an improved method of adaptive crossover operator and adaptive population size adjustment is introduced on the basis of the original method. With the benchmark test example solved, the algorithm is able to design an accurate, efficient and intelligent scheduling scheme for solving the established LRP model. Compared with the traditional heuristic algorithm in terms of the overall quality of the solution and the convergence efficiency of a single solution, the feasibility and effectiveness of the proposed method are verified.

P2P (Peer to Peer) online lending is an emerging Internet finance mode that gathers small-amount fund lending to fund demander. This paper draws on the existing credit risk assessment research, combines rationality, science and other principles, according to the characteristics of the famous online loan platform, collects borrower information and combines computer technology to design a borrower credit risk assessment system. In this paper, we make improvements based on the famous LightGBM algorithm (Light Gradient Boosting Machine). Firstly, In the process of data input, the improved Convolutional Neural Network CNN model is adopted to extract features from the data. Specifically, the Global Average Pooling(GAP) layer is adopted to replace the full connection layer to improve the Convolutional Neural Network. This paper first proposes a P2P online loan default prediction model based on GCNN-LightgBM. The model integrates the advantages of the improved Convolutional Neural Network and LightGBM model, and realizes the efficient prediction of network loan default. Then, in order to improve the accuracy of P2P online loan borrower default prediction, this paper proposes a new model based on LightGBM and Bagging (LGB-BAG). LGB-BAG uses LightGBM as the base learner. With the help of LightGBM, which can effectively reduce the deviation of the model, and Bagging, which can reduce the variance of the model, the volatility of the prediction is further reduced (F1 variance), so that the LGB-BAG model has smaller deviation and variance, and the prediction effect is further improved. In our ablation experiment, the proposed model (GCNN-LGB-BAG) obtained an AUC of 0.86 and an accuracy of 0.97, both of which outperformed all benchmark models. This paper uses actual data to identify the loan risks of P2P online lending platforms, aiming to provide investment reference for investors and methodological support for relevant online lending regulators.