Since the dawn of the 21st century, major global economies have maintained prolonged periods of low interest rates to stimulate economic growth, establishing a stable low-interest-rate macroenvironment. However, in recent years, driven by factors such as elevated inflation and tightening monetary policies, the global interest rate landscape has embarked upon a structural transition from prolonged low rates towards higher rates. This shift has profoundly impacted the pricing logic of financial markets and investors' asset allocation frameworks. Traditional allocation strategies now face the risk of becoming obsolete, creating an urgent need for systematic research to provide theoretical and practical support. Focusing on the impact of evolving interest rate environments on investors' asset allocation decisions, this analysis examines asset performance across different interest rate cycles, with a specific emphasis on transition periods. By examining representative cases of high and low interest rates, it traces the historical performance of various asset classes during distinct interest rate phases and presents data analysis. Research findings indicate that bonds and growth equities demonstrate superior performance during low-interest-rate periods, while value equities, commodities, and cash-like assets yield more stable returns during high-interest-rate phases. Key signals identifying interest rate transition periods—such as unexpected inflation surges and central bank policy shifts—are identified. Risk management strategies proposed include diversified allocation, dynamic duration adjustment, and increased allocation to inflation-hedging assets, offering practical guidance for investors to navigate interest rate cycles and optimise asset allocation.

Nowadays, the new energy vehicle industry has emerged as a core engine to drive the growth of the green economy under the background of global energy transition and the "dual carbon" goals . As climate change becomes increasingly severe recently, reducing carbon emissions and developing clean energy have become a unanimous global consensus. Many countries have enacted policies to support the upgrading of industrial chains related to new energy vehicles. China, which boasts one of the largest NEV markets worldwide, is striving to achieve peak carbon emissions and carbon neutrality by innovating the batteries of new energy vehicles to enhance the competitiveness of its automotive sector. The study takes BYD as a case to conduct a holistic analysis of the importance and the developmental potential of the green bonds to substantiate that the green bond fosters the innovation of batteries in new energy vehicles, meaning that the case study method is employed throughout the research.

In the post-pandemic world economy, public debt has reached unprecedented levels. Fiscal sustainability and debt risk management have thus become central to macroeconomic stability. This paper reviews theoretical and empirical perspectives on fiscal sustainability and compares how advanced economies and emerging market economies differ in debt dynamics, risk structures, and policy responses. It discusses key indicators such as the debt-to-GDP ratio, fiscal deficit, and the interest-growth differential (r-g). The findings reveal that while advanced economies can sustain high debt levels due to institutional credibility and negative r-g, emerging markets remain vulnerable to external shocks and refinancing pressures. This comparative analysis underscores the necessity of context-specific, multi-dimensional frameworks for assessing fiscal health, moving beyond a one-size-fits-all approach based solely on debt thresholds. The paper concludes with suggestions for future research directions, including the integration of institutional quality and climate-related fiscal risks into sustainability analysis.

In recent years, blind boxes have quickly gained popularity among young consumers by offering the thrill of the unknown. This new consumption model has transformed blind boxes from mere commodities into outlets for consumers to vent their emotions, express themselves, and engage in social interactions. Consumers experience the thrill of opening the boxes to obtain unpredictable rewards, which triggers psychological anticipation and pleasure, thereby enhancing the appeal of blind boxes. However, some consumers become overly obsessed with obtaining the blind boxes they desire or pursuing rare editions, leading to excessive spending and causing personal financial stress as well as negative emotions such as anxiety and depression. The popularity of blind boxes is driven by complex psychological mechanisms. This study aims to explain these mechanisms from easily understandable aspects such as social comparison and self-identity, compulsive buying disorder, and operant conditioning. The study focuses on the psychological motivations behind blind box consumption, analyzes its potential harms, and provides certain solutions and strategies. The research finds that understanding the psychological motivations of blind box consumption can help prevent excessive spending behavior, enhance consumers' self-regulation abilities, and provide a scientific basis for social management and business practices.

This study examines the relationship between interest rates and household consumption in China from 1993 to 2023 within a Keynesian Consumption Framework. Using time-series OLS regressions, the study distinguishes between short-term and long-term interest rates to capture liquidity and intertemporal effects, while controlling for GDP growth rate, inflation, and ageing demographics. Separate models for rural and urban households are examined to assess regional heterogeneity. The results reveal a negative overall relationship between interest rates and household consumption, consistent with the Keynesian transmission mechanism. However, only long-term interest rates exhibit a statistically significant effect for rural households, where limited credit access amplifies sensitivity to borrowing costs. In contrast, urban consumption appears largely unaffected by monetary changes, reflecting stronger financial stability and wealth buffers. Among the control variables, the ageing population ratio exerts a robust and positive influence, absorbing the significance for interest rate variables. This suggests that demographic structure moderates the impact of interest rate fluctuations. The findings highlight that monetary policy alone is insufficient to stimulate household consumption in China. Strengthening financial inclusion, social safety systems, and demographic coordination is essential to enhance monetary transmission and support China further towards a more consumption-driven and regionally balanced economy.

In recent years, China has attached great importance to leveraging digital technologies to enhance tax governance, and “data-driven tax administration” has become a key pathway for improving the effectiveness of tax governance. In 2021, the General Office of the CPC Central Committee and the General Office of the State Council issued the Opinions on Further Deepening the Reform of Tax Collection and Administration, which emphasize the transition from “invoice-based tax control” to “data-driven, category-specific and targeted supervision,” and specifically call for strengthening intelligent tax big-data analytics. Under this data-driven governance framework, tax authorities have gradually accumulated massive volumes of tax-related data. However, the shortage of high-quality and actionable data resources, the lack of top-level design and unified planning for algorithm development, and the limited supply of professionals proficient in both information analysis and algorithmic modeling have constrained the transformation of data assets into governance capacity. Drawing on the theory of precision governance, this paper constructs a theoretical framework of “data-driven mechanisms—service optimisation—risk prevention and control—collaborative co-governance.” Through case analysis, we examine the policy text alongside the day-to-day obstacles that arise when agencies and firms try to use data at scale, and we track how these issues play out in operational settings. Our reading of the cases suggests that effective data governance is difficult to sustain without two basics working together: a shared business-semantic layer and routines that let departments coordinate in real time. Service quality improves when data can move lawfully and predictably across systems; in that setting, firms meet compliance obligations more easily and tax agencies gain clearer, faster signals. Risk control works best when models follow industry logic and can be explained to non-specialists, and when the surrounding incentives make it worthwhile for staff to use those tools. The three cases—Qingyuan’s “Tax Eagle-Eye System,” the NARI Group’s direct tax–enterprise connectivity, and Jiangxi’s “Intelligent Control Scenarios”—operate at different administrative and organizational tiers yet point to the same pressure points: data quality, incentive design, and the protection of taxpayer rights. Taken together, they offer a tractable sequence of steps for improving tax administration and, more modestly, add practice-based detail to ongoing efforts in national governance modernization.

This paper focuses on the integration relationship between rural revitalization and agricultural circular economy, sorts out the theoretical basis, analyzes the empowerment mechanism of rural revitalization from four dimensions: policy, talent, market, and governance, and explores the coordinated development path by comparing the current situation at home and abroad and taking Anji County as a case. The research shows that the rural revitalization strategy provides institutional guarantee and resource support for agricultural circular economy by building a multi-dimensional support system; agricultural circular economy effectively solves the problems of resource waste and environmental pollution, realizing the unity of economic, ecological and social benefits; Sino-foreign circular agriculture has commonalities in goals and drivers, but significant differences in driving methods, technical maturity and subject participation; foreign experience can provide reference for domestic market-oriented transformation; the practice of "policy coordination + mechanism innovation + technology empowerment" in Anji County has verified the feasibility of large-scale development of circular agriculture. This study constructs a "theory-mechanism-case" analysis framework, clarifies the coupling logic between the two, and provides theoretical support and practical reference for various regions to promote the in-depth integration of the two.

This paper develops a state-dependent continuous-time Markov chain (CTMC) framework for modelling credit rating migration and default risk. Traditional discrete-time and time-homogeneous approaches often fail to represent the continuous deterioration of credit quality or produce unstable transition estimates. Likewise, naïve implementations used in regulatory applications may generate unrealistic forecasts and ignore regime shifts in economic conditions. These limitations motivate the need for transition rates that adapt to observable macroeconomic indicators or latent risk factors. To address these issues, this paper proposes a state-dependent CTMC generator that integrates constrained calibration, Bayesian inference for discretely observed transitions, and entropy-regularized inverse modelling to stabilize generator estimation. Using both empirical and simulation studies across multiple rating systems and economic environments, this study shows that the proposed model improves transition-rate accuracy, produces more realistic forward-looking default probabilities, and responds more sensitively to changes in economic conditions than classical Markov-based benchmarks. The results indicate that state-dependent CTMCs provide a robust framework for credit risk measurement, stress testing, and scenario analysis, offering enhanced flexibility for modern risk-management applications.

Virtual currency has become one of the most sought-after alternative assets in the past decade with bitcoin being a leading example. value leapt from its starting price of $0.0025 to increase by more than 40 million times that amount, creating one of the greatest rises in value in the entire history of finance. In the past few years, many academic studies show that even though Bitcoin runs independently from traditional finance, but still there is a high correlation between Bitcoin and stock market. In particular, following the introduction of Bitcoin options back in 2017, Bitcoin now appears more predictive of stock return movements than before. Research by Afees A. Salisu and his coworkers display that a solitary Bitcoin price prediction model using an optimized predictive regression framework notably surpasses older ones. but don’t say how long this goes on Therefore this research will go to try and determine the time frame when Bitcoin is better at predicting the future of the stock market as opposed to stock options. Also, we’ll use machine learning techniques to train machine learning models to predict the movements of the stock market and see if they work.

This study investigates the determinants of Airbnb rental prices by analyzing a dataset of 54,117 listings. Focusing exclusively on non-geographic attributes — such as structural features, booking policies, and review scores —the research applies multiple machine learning models—linear regression, decision trees, and XGBoost—to predict log-transformed prices. Data preprocessing involved outlier treatment, encoding, and stratified train-test splitting. The results show that XGBoost achieved the best performance, with the lowest RMSE and the highest R², highlighting the importance of cancellation policies and structural characteristics in determining prices. The findings have practical implications for hosts and platforms, enabling them to optimize pricing strategies and improve occupancy rates.