This paper examines whether country-level macroeconomic, valuation, momentum, positioning, volatility, and financial-market signals can predict future monthly exchange rate returns and generate economic value in a long-short currency strategy. This paper constructs monthly returns for 46 currencies and matches them with country-level predictive signals. After stationarity tests and signal transformation, this paper then uses univariate regression models as an initial screening step and builds country-level multivariate forecasting regression models under two model selection approaches: a strict statistical selection method and a category-protected selection method. The selected signals are then used in an expanding-window forecasting framework to construct long-short portfolios. Results show substantial cross-country heterogeneity: no single signal or common signal set consistently predicts all currencies. However, the selected signals can generate meaningful portfolio economic value. Among all the specifications, the Top/Bottom 2 portfolio under the category-protected approach provides the best overall balance between return, volatility, and drawdown. It also outperforms the passive Equal-Weighted FX benchmark and has low correlation with the global equal-weighted local equity benchmark. Overall, the findings suggest that exchange rate predictability should be evaluated not only through regression statistics, but also through portfolio performance, risk control, and diversification value.

In this era where online payments are made more frequently with the help of modern technologies and infrastructure, a cashless society could be foreseen in a few decades. Whether its associated benefits overshadow the challenges has been heated discussed. This article analyzes particular fields based on foundations of current investigation, including crimes, social inequality, privacy issues, monetary policies and market regulatory. The evaluation of net social welfare that a cashless society gives is according to two dimensions: the order of severity and the difficulty of implementing effective solutions. As the investigation goes deeper, it becomes clear that associated issues such as cyber-crimes, financial exclusion and the complexity of redefining central banks' roles and new forms of monetary policy, could be eventually addressed in short run. Nevertheless, the long-run benefits of a cashless society are more long-lasting and imperative in various aspects, which overshadow the overall hazards.

Global governments and industrial enterprises have increasingly prioritized carbon mitigation and ecological conservation, prompting the rollout of diverse regulatory and fiscal tools to drive manufacturing decarbonization. This paper establishes a two-echelon low-carbon supply chain game framework to quantify how distinct policy interventions reshape manufacturers' clean technology investment and emission-cutting behaviors. Core analytical findings are as follows: When consumers exhibit strong green consumption preferences, the carbon cap-and-trade regime delivers maximized profitability for both upstream manufacturers and downstream retailers, alongside superior carbon reduction performance and minimized ecological harm. Nevertheless, fiscal subsidies targeting manufacturers primarily serve profit-maximizing motives rather than robust emission abatement, which implies trading mechanisms may underperform in curbing industrial carbon outputs. Social welfare dynamics hinge on the intensity of public low-carbon demand: emission trading outperforms alternatives once consumer green preference crosses a critical threshold, while manufacturer-specific fiscal incentives yield more favorable aggregate social welfare in most practical scenarios.

This study systematically examines the impact and mechanism of establishing artificial intelligence innovation development zones on regional entrepreneurial activity. Theoretically, the establishment of such zones significantly promotes local entrepreneurship through three pathways: stimulating digital economy vitality, driving offline experiential economic development, and enhancing regional R&D efficiency. These mechanisms expand market potential and reduce costs for entrepreneurial activities from both supply and demand sides, thereby increasing entrepreneurial dynamism. Empirically, this research uses panel data from 285 prefecture-level cities and above in China between 2009 and 2023, treating the designation of national new-generation artificial intelligence innovation development zones as a quasi-natural experiment. This study measures urban entrepreneurial activity as the number of registered enterprises per ten thousand residents, employing a multi-period difference-in-differences approach. Results show that the promotion effect of AI innovation zones on entrepreneurship exhibits significant regional heterogeneity—policy impacts are stronger in cities with underdeveloped factor markets and well-developed transportation infrastructure. At the city level, various foundational factors differentially affect entrepreneurial activity: higher per capita economic development, internet penetration, and education levels positively boost entrepreneurship, while excessive concentration of financial and fiscal resources exerts a certain inhibitory effect. Furthermore, the establishment of AI innovation zones not only enhances local entrepreneurial activity but also generates spillovers to other regions within the same province. Based on these findings, targeted policy recommendations are proposed to better leverage the demonstration and leadership role of these zones and sustainably unlock regional entrepreneurial vitality.

Manufacturing firms are being pushed to reduce energy use and carbon emissions while continuing to improve productivity and product quality. Against this background, this paper examines whether digital transformation helps firms achieve greener modes of production and management. Using A-share manufacturing companies listed on the Shanghai and Shenzhen stock exchanges during 2012–2024, the study tests the effect of digital transformation on corporate green transformation and explores the role of green investment. The results show that firms with higher digital transformation scores tend to record better green transformation performance. This relationship remains statistically significant after firm-level controls, firm fixed effects, and year fixed effects are included. The mechanism evidence is conditional rather than universal. Green investment does not serve as a complete transmission channel in the full manufacturing sample, but it has partial explanatory power in heavily polluting industries. In these sectors, digital transformation is more likely to be converted into energy-saving equipment renewal, environmental facility construction, and cleaner production projects. Further heterogeneity tests show larger estimated effects in heavily polluting and high-tech manufacturing industries, although the group differences are not statistically significant. The findings provide firm-level evidence on the digital–green linkage in manufacturing and suggest that policies should promote targeted digital–green integration, especially in pollution-intensive sectors.

This paper examines the impact of green finance–digital supply chain synergy on international logistics trade efficiency in the Jing-Jin-Ji region and explores the underlying mechanisms. Using panel data for 13 cities from 2013 to 2024, this study constructs green finance and digital supply chain indices based on the entropy weight method and measures their synergy using a coupling coordination degree model. International logistics trade efficiency is estimated through a super-efficiency DEA model, and a fixed-effects model is employed for empirical analysis. The results show that green finance, digital supply chains and their synergistic development exhibit an overall upward trend despite regional disparities. Green finance–digital supply chain synergy significantly improves international logistics trade efficiency, with stronger effects observed in regions with higher levels of development. Further analysis reveals that resource allocation efficiency and digital financial inclusion serve as important transmission channels. These findings provide new evidence on the role of financial and digital synergies in enhancing logistics performance and offer policy implications for promoting coordinated regional development and high-quality trade growth.

This study investigates whether factor-model selection drives the mixed evidence on ESG pricing in China's A-share market, where "green discount" and "green premium" coexist. Taking the Liu–Stambaugh–Yuan (LSY) four-factor model as the benchmark pricing framework and combining Shangdao Ronglv ESG ratings with CSMAR data, it examines model dependence, market capitalization heterogeneity, policy asymmetry, and risk transmission. The results show that ESG pricing conclusions are strongly model-dependent: the estimated ESG premium reverses sign once the local LSY factors and firm fundamentals are controlled, and statistically significant evidence of a premium is concentrated among large-cap stocks. The 2016 green finance policy is associated with dimensionally asymmetric changes in corporate ESG performance, although pre-existing trends limit strict causal identification. The environmental and governance dimensions transmit through opposite stock-price-risk paths, a pattern consistent with a dimensional hedging interpretation. These results imply that credible ESG pricing evidence in the A-share market requires an empirical specification—and above all a pricing benchmark—suited to the Chinese market.

Whether digital governance can be converted into measurable disaster-mitigation performance is an important criterion for evaluating smart-city development. Taking China's national smart-city pilot program as a quasi-natural experiment, this study uses a balanced panel of 300 prefecture-level cities from 2009 to 2023 and applies a staggered difference-in-differences design to estimate its effect on urban disaster resilience. The results show that the pilot significantly reduces direct disaster losses as a share of GDP and also lowers disaster fatalities, the share of the population affected, and a composite disaster-damage index. The findings remain robust after accounting for meteorological conditions, region-specific annual shocks, and differential trends associated with pre-policy disaster risk. Event-study estimates, placebo tests, and alternative estimators for staggered treatment timing provide further support for the baseline conclusion. Channel analysis indicates that improved emergency-resource allocation and technological innovation are the clearest pathways, whereas monitoring, early warning, and interdepartmental data sharing are reflected mainly in broader improvements in governance capacity. The evidence suggests that smart-city infrastructure contributes to disaster resilience when digital systems are integrated into emergency procedures, resource coordination, and cross-departmental collaboration.

The expanding new energy vehicle industry requires manufacturers to finance R&D, capacity construction, supply chain integration and overseas operation simultaneously. This paper investigates BYD's diversified financing modes and evaluates their effects on enterprise growth, liquidity and risk control. It adopts the research method combining case study, literature review, financial statement analysis and structural comparison. It also analyzes internal financing, bank loans, bond financing, equity financing, trade credit and policy-based green finance, focusing on financial data from 2022 to 2025. The results demonstrate that operating cash flow and retained earnings remain the foundation of BYD's financing system, while external financing becomes more significant during the accelerated globalization stage. BYD's shift from self-financed development to a more balanced but more complex capital structure in 2025 is marked by delayed operating cash generation, significant private placement, and rapid loan growth. Diversification improves financing capacity and term flexibility, but also raises higher requirements for liquidity management, refinancing, exchange rate and governance.

Against the backdrop of rapid global growth in digital service trade and complex international dynamics, risks associated with digital service trade have emerged as critical factors affecting industrial security and trade stability. This study examines China's trade with 32 OECD member states, utilizing digital service trade data from the UNCTAD database (2010~2024). Employing complex network analysis and kernel density estimation, it constructs a digital service trade network between China and OECD countries to reveal trade network characteristics, track the evolution of trade risks, and construct an early warning table. Findings indicate that while the density of China's digital service trade network with OECD countries continues to rise, reflecting increasingly close trade ties, a pronounced core-periphery structure persists. Kernel density estimation reveals that China's digital service trade competitiveness with OECD countries exhibits a fluctuating evolutionary feature, with some countries exhibiting clusters of heightened risk. Early warning system based on risk metrics identifies Greece, Hungary and others as orange-level high-alert countries, providing precise targets for the prevention and control of digital service trade risks. The findings of this study provide empirical support and policy references for China to address digital service trade risks and optimize its trade layout.