Volume 190

This paper examines the challenges hindering rooftop solar (RTS) deployment in India and explores how a U.S.-India partnership can unlock its full potential. RTS, a decentralized and scalable renewable energy solution, is critical to India’s renewable energy goals, including its target of 40 GW of RTS capacity under the National Solar Mission by 2022 which remains unmet with only 10.9 GW installed as of 2023. Key barriers include high upfront costs, limited financing options, policy fragmentation, and resistance from distribution companies (DISCOMs). The paper identifies actionable solutions, leveraging U.S. expertise in innovative financing models, net metering policies, and grid modernization. Collaborative initiatives, such as USAID-backed loan guarantees and green bonds, could alleviate financing gaps, while technical assistance from the U.S. can support DISCOMs in integrating distributed solar. The paper also emphasizes the need for performance-based incentives, consumer awareness programs, and community solar models to accelerate adoption. A successful U.S.-India collaboration can address systemic challenges, enhance RTS scalability, and contribute to global climate goals. By leveraging shared expertise and resources, this partnership offers a pathway to strengthen India’s renewable energy transition while fostering bilateral cooperation in clean energy development.

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In today's rapidly developing digital era, social media has deeply integrated into the financial market and has multidimensional impacts on information asymmetry. This article delves into the role of social media in the dissemination of information in financial markets and explores its positive and negative effects on information asymmetry. Social media has expanded the channels for information dissemination, reduced the cost of information acquisition, correspondingly narrowed the information gap among investors, and enhanced market transparency. However, its massive amount of information and rapid and disorderly dissemination also bring about problems such as the proliferation of false information and information overload, exacerbating information asymmetry and causing market fluctuations and investor decision-making deviations. This article uses financial market data and case studies to analyze the impact of social media on information asymmetry in financial markets. Research has shown that while social media provides new opportunities to improve the information environment in financial markets, it also faces challenges. To effectively leverage the role of social media, it is necessary to strengthen information regulation, guide market participants to make reasonable use of social media, reduce information asymmetry, and promote the efficient and stable operation of financial markets.

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In contemporary China, a significant number of university graduates pursue careers that diverge from their original fields of study, reflecting a broader trend of academic and occupational mismatch. This review examines the principal factors influencing such career decision-making processes, with particular attention to the role of self-efficacy, alongside considerations of gender, environmental context, and cultural background. Among these variables, self-efficacy emerges as a particularly influential determinant, shaping individuals' confidence in their ability to meet professional and academic challenges. Within the Chinese context, self-efficacy has been extensively applied in domains such as management performance prediction, adolescent physical training, and educational interventions aimed at fostering both academic achievement and personal development. Empirical evidence suggests that positive experiences enhance self-efficacy, whereas repeated setbacks tend to diminish it. Strategies such as the accumulation of successful performance outcomes, integration into supportive social networks, access to reliable mentoring systems, and the maintenance of physical and psychological well-being have all been identified as effective means of strengthening self-efficacy. A deeper understanding of these mechanisms offers valuable insights for both educational policy and career guidance practices, with the potential to support students in navigating the transition from academic study to professional life.

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Under the wave of digital transformation, traditional accounting processes face challenges in audit risk control. Blockchain technology, with its unique advantages, offers a new approach to addressing this issue. Through case studies, this paper conducts an in-depth exploration of the application value of blockchain in accounting processes from the perspective of audit risk assessment. It provides a detailed analysis of the advantages and disadvantages. At the same time, by understanding the definitions, relationships, and differences between accounting and auditing, and by examining the traditional accounting process, it is found that there are audit risks in terms of information accuracy and completeness. By comparing and analyzing the distributed ledger feature of blockchain technology, it can be seen that the application of blockchain technology enhances the confidentiality of information. Research also shows that the application of blockchain technology in accounting processes can significantly enhance the credibility and authenticity of audit evidence, reduce audit inspection risks, and better assist auditors in identifying problems in accounting. The main research results of this paper are to explore the application value of blockchain technology in accounting processes from the perspective of audit risk assessment, thereby better addressing the drawbacks of traditional accounting processes.

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In the era of rapid digital transformation, artificial intelligence (AI) has emerged as a transformative force with broad applications across industries. However, its impact on corporate governance—particularly in Chinese enterprises—remains under-researched. This study addresses this gap by empirically examining how AI adoption influences corporate governance in Chinese A-share listed firms from 2010 to 2024, utilizing financial data from the CSMAR database. By measuring AI application through text-based proxy variables and assessing governance via agency costs, the research reveals that higher levels of AI adoption are significantly associated with lower agency costs. The study theoretically extends traditional governance determinants by integrating artificial intelligence, and, in practice, highlights AI’s role in reducing agency costs and enhancing governance efficiency. This work underscores the importance of technological innovation in shaping modern corporate governance and provides insights for firms and regulators navigating digital transformation. The research acknowledges unexplored mediating mechanisms, offering avenues for future studies on heterogeneous effects and operational pathways.

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The dynamic nature of the stock market, shaped by a complex interplay of economic and non-economic variables, presents both opportunities and challenges for investors seeking to optimize their returns. Prudent participation necessitates careful consideration of risk mitigation strategies, often involving the construction of diversified investment portfolios. In the pursuit of enhanced predictive capabilities, investors frequently turn to mathematical models to discern underlying patterns and anticipate future stock movements. However, the inherent complexity of financial markets means that various analytical approaches can yield divergent and potentially biased forecasts. Among the array of available methodologies, the Autoregressive Integrated Moving Average (ARIMA) model stands out as a widely utilized statistical tool for time series analysis and forecasting. Its appeal lies in its relative simplicity, relying exclusively on the historical behavior of the target variable itself, thereby obviating the need for external explanatory factors. Furthermore, under appropriate conditions and parameter selection, the ARIMA model has demonstrated the potential for achieving a considerable degree of predictive accuracy. This research endeavors to develop and implement an ARIMA model specifically tailored to forecasting the future stock trends of JPMorgan Chase & Co., a prominent player in the financial sector.

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This paper focuses on the acquisition between Microsoft and LinkedIn, offering an in depth and comprehensive analysis of its financial performance. Through methods like case study, event study with Cumulative Abnormal Return Rate (CAR) calculation, and comparative analysis, it closely examines the short-term market responses around the acquisition announcement. Specifically, it analyzes how the stock prices of both companies fluctuated during this period based on CAR analysis. Meanwhile, it also studies the long-term trends of crucial financial metrics such as revenue, profit, and asset utilization. The study reveals that the market’s reactions were a mix of positive and negative in the short term. However, over the long term, the acquisition has gradually generated positive synergies, significantly enhancing Microsoft’s competitiveness in relevant markets. This research enriches the theoretical understanding of corporate mergers and acquisitions (M&A) financial performance and provides practical and actionable guidance for future M&A activities in the technology industry. It can assist companies in better evaluating potential acquisition targets, formulating more effective integration strategies, and achieving more successful M&A outcomes.

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This paper investigates the impact of financial market volatility on supply chain management decisions, focusing on two major global retailers: Walmart and Amazon. As global markets become increasingly interconnected, financial fluctuations—driven by factors such as economic recessions, geopolitical conflicts, and speculative behaviors—pose significant challenges to businesses. These challenges are particularly evident in supply chain operations, where companies often face heightened uncertainty regarding costs and demand. This study explores how Walmart and Amazon adapt their procurement strategies, optimize inventory management, and enhance risk mitigation practices during periods of financial instability, such as the COVID-19 pandemic. The research employs qualitative case studies to understand how these companies respond to market disruptions and maintain operational continuity. Findings indicate that both companies have successfully leveraged their unique business models, such as Walmart’s multi-channel strategy and Amazon’s technological innovations, to navigate financial volatility. This paper highlights the importance of flexibility, adaptability, and technological integration in ensuring supply chain resilience amidst uncertain economic conditions. The study provides valuable insights for businesses aiming to strengthen their supply chain strategies in response to fluctuating market forces.

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Technology-driven social entrepreneurship, as an emerging paradigm that integrates technological innovation with social missions, offers innovative solutions to address the plight of the 700 million extremely poor people worldwide and the uneven distribution of educational opportunities. This paper reviews the literature in both Chinese and English from 2015 to 2025, revealing the core practical paths of digital technologies such as artificial intelligence and block-chain in optimizing resource allocation, for instance, enhancing efficiency and increasing the coverage of VR classrooms for educational inclusiveness. By constructing a three-dimensional collaborative framework of "technology-education-policy", this paper clarifies the three-stage evolution law that social enterprises need to go through: demand validation (0-1 year), model optimization (1-3 years), and scale diffusion (3-5 years). The study emphasizes that the mere instrumentalization of technology may exacerbate the digital divide, while the "technology + culture" model that integrates local knowledge has achieved remarkable results in minority areas. This paper provides a theoretical basis for policymakers to optimize regional technology sharing mechanisms and for educational institutions to design "technology + public welfare" interdisciplinary courses.

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Centralized accounting has been a prevalent paradigm for financial data management in contemporary organizations. This study examines the development, implementation, and effects of centralized accounting systems on financial transparency and operating efficiency. A detailed review of literature is augmented by empirical study using a mixed-methodology that brings quantitative insights from company financial reports and qualitative data based on industry studies. The principal analytical methods used include statistical tests and simulation models, utilizing financial information obtained from credible financial organizations and respected industry databases. Evidence shows that centralized accounting systems significantly improve data amalgamation, report accuracy and decisionmaking, while reducing redundancy in administrative tasks. However, complexities in amalgamation, resistance to changes in organizations, and major investment costs are also found to be impediments. It is concluded that strategic planning, coupled with effective implementation mechanisms are necessary for optimal utilization of centralized accounting practice benefits. Practical applications of this study seek to provide future guidance to financial management systems and enable the development of innovative methods in company accounting practices.

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