Credit scoring has become more data-driven as online lending platforms collect larger and more varied borrower records. Machine learning methods can model nonlinear patterns that traditional scorecards often miss, but their decisions are harder to explain in a regulated lending environment. This paper discusses how explainable artificial intelligence can be used to make credit scoring models more transparent, with a focus on SHAP and LIME. Using the Lending Club dataset and recent empirical evidence from credit-risk studies, the paper compares the predictive role of ensemble learning models with the interpretive roles of SHAP and LIME. The discussion shows that ensemble methods can provide strong discrimination across public credit datasets, while the usefulness of a model also depends on whether its outputs can be audited and communicated. SHAP is better suited to global feature analysis, model review, and risk-policy design. LIME is more useful when a single loan decision must be explained to staff or customers. Used together, the two methods offer a practical route to balance accuracy, transparency, and compliance in credit scoring.

A common way to judge infrastructure projects is by whether they finish on time and stay under budget. However, daily commuting experience often contradicts this simple measure. This paper examines the Chongqing rail transit system, which saves commuters an average of 11.3 minutes per trip compared to other modes. Based on surveys (N=342) and semi-structured interviews, the findings indicate that crowding, boredom, conflicts between tourists and regular commuters, and stress are not merely sociological side effects but hidden economic costs. Time savings lose value if commuters arrive at work mentally drained. The results show that daily commuters — those who rely most on the system — report the lowest satisfaction and the highest stress. The long-term success of a transit system therefore depends not only on speed and budget but also on whether it provides a tolerable experience for all users.

Against the backdrop of deepening digital transformation,many e-commerce platforms developed rapidly through the "Recommendation + Mall" model. Among them,Douyin, which has established this dual-business model through short video and live streaming, has become a leading player in the e-commerce sector. However, the platform continuously increases marketing investment for traffic acquisition, raising operating capital occupation and posing new challenges to its cash flow management. Therefore, this paper uses Douyin's dual business model of "Recommendation + Mall"as an example to explore the working capital management strategies of content-based e-commerce platforms in the context of digital transformation.The research methods include a literature review to examine theories related to working capital management. The research results indicate that Douyin's "Recommendation + Mall" model effectively improves the working capital turnover efficiency of the platform.However, this model over-reliance on marketing and promotion expenses increases capital occupation and brings greater pressure on corporate cash management. Therefore,the platform needs to optimize its working capital management system.

With the growing emphasis on sustainable development, environmental, social, and governance (ESG) performance is increasingly being recognised as one of the factors influencing company's financing decisions. Some of the earlier studies suggest that higher ESG ratings tend to lower a firm's financing costs. However, the existing literature has not examined the underlying mechanisms of ESG rating divergence thoroughly. This study reviews the literature on ESG ratings, financing costs, market competitiveness, and rating divergence, and explains how ESG ratings affect financing costs. The analysis indicates that higher ESG ratings enhance market competitiveness by strengthening innovation capability and corporate reputation, which brings about lower information asymmetry between the firm and outside parties, encourages trust in general,thereby decreasing the cost of both debt and equity financing. If ESG ratings divergence arises, uncertainty increases externally and the ESG signal loses credibility, which weakens the favourable effect of higher rating. Nevertheless, such divergence may also motivate some firms to improve information disclosure and increase innovation. This study extends the literature by proposing market competitiveness as a mediator and ESG rating divergence as a moderator, providing both theoretical and practical implications for corporations, investors, and regulators.

Taking China 's A-share listed companies from 2011 to 2023 as research samples, this paper conducts empirical analysis based on the two-way fixed effect model. The results show that financial technology has a significant promoting effect on corporate green innovation activities. Further mechanism test shows that Fintech mainly exerts its influence through two paths : first, alleviating the financing constraints faced by enterprises and improving the financial availability of green R & D activities ; second, promote the digital transformation of enterprises and enhance their ability in green technology R & D and application. The moderating effect analysis reveals that the good ESG performance of enterprises can positively regulate the promotion effect of FinTech, that is, the higher the ESG rating, the stronger the enabling effect of FinTech on green innovation. The research conclusions of this paper provide an empirical basis and policy enlightenment for improving the sustainable financial support system and guiding enterprises to accelerate the green and low-carbon transformation.

AI-driven green marketing (artificial-intelligence-empowered green marketing) is changing how firms present green products and how consumers assess them. Its influence on green purchase intention, however, does not depend on recommendation efficiency alone. Consumers also consider whether environmental information is credible and how their data are used. This paper uses a literature-based theoretical analysis to examine the relationships among AI-driven green marketing, green trust, privacy concerns and consumers' green purchase intention, focusing on information verifiability, disclosure of AI use, personalized recommendations and consumer control. The analysis shows that AI-based recommendations can reduce search and comparison costs and improve the match between environmental attributes and consumer needs. These benefits are less likely to affect purchase decisions when green claims are vague or difficult to verify. Green trust is therefore important because consumers must first regard environmental information as authentic and credible. Opaque data collection, unclear recommendation logic and algorithmic classification based on limited behavioral traces may also increase privacy concerns and weaken acceptance of green products. Enterprises should balance recommendation efficiency, information authenticity and consumers' data rights. Green claims should be supported by identifiable evidence, the role of AI in information sorting or presentation should be explained, and consumers should be able to modify preferences, disable personalized recommendations and delete historical data. These practices may strengthen green trust and reduce privacy concerns in consumers' evaluation and purchase of green products.

In the Anthropocene, human social activities have replaced natural processes as the core force dominating the evolution of Earth's ecosystems. Compound ecological imbalance caused by energy development and transition has become a major manifestation of regional human-land contradiction. From an interdisciplinary perspective integrating human geography and environmental science, this paper focuses on the characteristics of energy-induced ecological imbalances, human-driven influencing mechanisms and optimal regulation paths. It sorts out the theoretical framework of human-energy-land coupling, summarizes two types of ecological imbalance, namely cumulative pollution risks from fossil energy development and spatial disturbance from new energy construction, and reveals the human-driven mechanisms of energy-ecological problems from the dimensions of socioeconomics and land space, and constructs a multi-dimensional comprehensive regulation system focusing on zonal control, spatial matching and cross-regional coordination. Adaptive strategies for coordinated regional development in line with energy transition are proposed. The study aims to alleviate the spatial mismatch and functional imbalance between energy development and ecological protection, providing theoretical support and practical reference for regional low-carbon transition and coordinated human-land governance in the Anthropocene.

This paper examines how Ultimate's grassroots presence and platform visibility might support sustainable development in China. It develops a possible framework for understanding Chinese Ultimate's next development stage. Ultimate already appears in many high schools, universities, clubs, and social-media contexts in urban China, but the management problem remains weak institutional absorption. Attention and informal participation are not systematically connected to pathways, athlete narratives, events, data systems, and commercial products. This paper uses a comparative case study with embedded practitioner background from Ultimate promotion, drawing on skateboarding, sport climbing, snooker, professional road cycling, basketball, and diving. It suggests that commercialization requires commercial market depth, including durable participation, spectatorship, commercial relationships, and identity attachment. The analysis therefore treats commercialization as repeated relationships among participants, audiences, organizations, and media rather than as exposure alone. Self-officiation and Spirit of the Game, which are distinctive features of Ultimate, are also treated as a governance tension for formalization.

Under the dual carbon targets, green finance faces growing demand as a key channel for financing and low-carbon transformation, while Artificial Intelligence (AI) technologies are growing quickly with financial services. This gives AI opportunities to strengthen green finance operations. This paper systematically studies the empowerment mechanism of artificial intelligence in the development of green finance. From the three dimensions of information empowerment, risk empowerment and regulatory empowerment, it reveals the adaptability of AI technology and green finance and its role, and puts forward the practical path of AI to empower green finance in the future. Through the combination of case analysis and quantitative and qualitative methods, this study draws three main findings: AI can integrate dispersed environmental information to provide investors with a clearer decision-making basis; AI model evaluates environmental risks and credit risks more accurately than traditional tools; automated monitoring can find greening faster and at a lower cost. Behavior. At the same time, the study points out the technology, policies and market conditions needed to maintain these results, which provides a practical reference for making green finance more transparent, accurate and reliable with the help of AI.

The Great Barrier Reef plays a vital role in protecting marine biodiversity, mitigating climate change and sustaining coastal economies. However, the consistent rise in global temperature and changes in seawater acidity have led to frequent coral bleaching events in recent years. This phenomenon has seriously threatened the stability of ecological balance so that it has become one of the most concerned environmental issues. This essay examines the core impacts of climate change and ocean acidification on marine ecosystems, taking the Great Barrier Reef's coral bleaching crisis as a typical case to analyze feasible solutions. This paper mainly uses literature review and case study methods, focusing on scientific research and environmental reports. The conclusion suggests that climate change and ocean acidification do have a huge impact on the Great Barrier Reef, including both physical and socio-economic impacts on the marine ecosystem. This emphasizes the importance of proper governance strategies.