The involvement of major energy-exporting countries in geopolitical conflicts can easily lead to volatility in international energy markets and have an impact on the world economy. Based on a macroeconomic model and using counterfactual analysis and vector autoregression, the differential impacts of geopolitical conflicts on different economies through the volatility of the energy market was analyzed, and China's response measures based on the perspectives of energy security and national security was put forward. The results show that geopolitical conflicts have negative impacts on different economies through crude oil market volatility, with the European economy, which is more dependent on Russian energy, being affected to a greater extent. It is recommended to pay great attention to the risk of geopolitical conflicts, accelerate the formation of a diversified pattern of crude oil imports and energy consumption, stabilize investor expectations, improve the construction of the capital market and maintain the stability of the RMB exchange rate, so as to prevent the negative impacts that geopolitical conflicts may have on China's energy security.

As the most dynamic market entities, small and micro enterprises (SMEs) play a significant role in promoting China's economic development. Therefore, SMEs have always been the key support target of China's tax reduction policies and the focus of scholars' attention. The literature collection process was first introduced, with an analysis of literature characteristics and an exploration of the basic connotation of “SMEs” from multiple perspectives. Secondly, based on the dual perspectives of micro-enterprise operations and macroeconomic impacts, the research findings on the effectiveness of tax reduction policies for SMEs were comprehensively reviewed. After that, the main factors influencing the tax reduction effects for SMEs were analyzed from three aspects: the tax system, micro-enterprises and macroeconomic environment. Furthermore, the optimization path for tax reduction policies for SMEs was discussed. Finally, the current research deficiencies related to SMEs were analyzed, and four directions that future research in this field should focus on were pointed out. These include conducting in-depth research on the implementation effects and impact mechanisms of tax reduction policies for SMEs, thoroughly examining the linkage effects among tax reduction policies and between tax reduction policies and other macroeconomic policies, introducing more precise analytical tools to enhance the accuracy and effectiveness of tax reduction policy design, and conducting international comparative studies to provide references for the improvement of tax reduction policies in China.

The impact of artificial intelligence (AI) on the labor market, based on the Routine-Biased Technological Change paradigm, is widely acknowledged. However, existing job classification methods lack detail and accuracy. To address this limitation, the Chinese-BERT-wwm model was optimized to classify recruitment data from listed companies between 2013 and 2019 into routine and non-routine jobs, achieving a test set accuracy of accuracy of nearly 93%. Additionally, the GLM4 model was used to match job titles and descriptions to the "Chinese Occupational Classification (2022 Edition)" to identify digital occupations and analyze the impact of AI technology on labor demand structure. Empirical results show that higher AI technology levels significantly increase demand for non-routine jobs and reduce demand for routine jobs, with pronounced effects in non-state-owned enterprises, high-tech industries, and manufacturing. Further analysis reveals that the increased demand for non-routine jobs is primarily driven by growth in non-routine cognitive positions. Mechanism analysis shows that AI adoption increases non-routine job demand through productivity effects and the creation of new digital occupations, while reducing routine job demand through substitution effects. It expands the application of large language models in economic text analysis.

Talent governance is the cornerstone of local governance modernization. However, some regions excessively prioritize talent competition in reality, exacerbating the issue of governance fragmentation. A single-case study of talent governance practices in S Province was conducted from the theoretical perspectives of holistic governance and performance management to explore how local governments address governance fragmentation through the integration of policy tools and governance mechanisms, thereby responding effectively to the dual logic of talent governance: enhancing external competitiveness and optimizing internal resource allocation. The findings indicate that the talent governance system in S Province achieves structural coordination, functional integration and external collaboration through top-level design, while its implementation is energized by a performance evaluation mechanism centered on goal accountability. Grounded in the case analysis, the practical pathways and intrinsic mechanisms of the talent governance system in S Province are deeply analyzed, and a practical framework for local talent governance under the dual-logic perspective is developed, which provide some reference for advancing the modernization of local talent governance.

As the new round of technological revolution and industrial transformation deepens, the integration of the digital economy and manufacturing has become a key force in driving industrial upgrading. The theoretical framework known as the "techno-economic paradigm" refers to the economic patterns that emerge after technological innovation reshapes the macro and microeconomic structures and operational models. It reveals the evolutionary process through which the digital economy empowers the transformation and upgrading of manufacturing, spanning the stages of "technological system-economic structure-social institution." Although the focus of U. S. policies related to the digital economy may differ, they essentially adhere to the evolutionary logic of the "Techno-Economic Paradigm." These policies revolve around digital top-level design, digital technology development, digital talent training and cultivation, digital collaborative innovation, and the digital ecosystem. The ultimate goal is to drive the evolution of enterprises, industries, and economic systems, thereby achieving the digital transformation and upgrading of the manufacturing sector. In light of the current challenges faced by China's manufacturing industry, efforts to empower manufacturing transformation through the digital economy should focus on strengthening top-level design and policy frameworks, enhancing technological innovation and standards development, bolstering digital talent support, promoting collaborative innovation in all aspects, and building a hierarchy of manufacturing enterprises.

As a strategic emerging technology, artificial intelligence (AI) plays a significant role in guiding future societal transformation and has an empowering effect to help enterprises realize disruptive innovation. However, there is still a lack of in-depth analysis of the key elements and mechanisms of AI empowering enterprises to realize disruptive innovation in academia. The impact path of AI-empowered disruptive innovation for enterprises remains unknown. The exploratory multi-case study method based on Grounded Theory was applied to construct a theoretical model of AI empowering enterprises to realize disruptive innovation, and the fuzzy-set qualitative comparative analysis method was employed to explore the complex causal mechanism of AI empowering disruptive innovation in enterprises. The results highlight that five key factors for achieving AI-driven disruptive innovation in enterprises are service ecology, cooperation network, technological transition, context-depth-excavation, and organizational structure innovation. Among the five factors, technological transition is a necessary condition for AI to empower disruptive innovation in enterprises. There are three types and four paths of AI empowering disruptive innovation in enterprises including“technology-service ecology type”, “technology-scene-structural innovation type” and “technology-cooperation network type”. The results provide decision-making references for enterprises on selecting an AI empowerment path to drive disruptive innovation based on their unique circumstances.

The upgrading of human capital is an important guarantee for the realization of high-quality development of enterprises and a realistic requirement in the period of comprehensive green transformation of the economy. Based on the data of employee skill structure of Chinese A-share listed companies from 2010 to 2022, the data of enterprise green transformation were obtained through text analysis to explore the impact of green transformation on human capital upgrading of enterprises and its impact channels. The results show that green transformation has a significant promotion effect on enterprise human capital upgrading, and this conclusion still holds after a series of robustness tests, such as replacing variables, considering other policy shocks, and instrumental variable tests. The mechanism analysis shows that green transformation can alleviate enterprise liquidity constraints, promote enterprise capital deepening, increase enterprise R&D investment, trigger the creative destruction of different skills, and then promote enterprise human capital upgrading. Additionally, the human capital upgrading effect of green transformation is more obvious in state-owned enterprises, enterprises with high quality of internal control, non-technology-intensive enterprises, and enterprises in eastern regions. The results not only provide thoughts on the relationship between green transformation and human capital upgrading of enterprises, but also offer insights for the realization of the dual goals of economic and environmental benefits in the stage of high-quality development.

Digital platforms have both technical and market attributes, but how they influence business model innovation in digital platform companies and the underlying mechanisms of new value creation remain unclear. A longitudinal single-case study is adopted to focus on the development practice process of Kingdee’s digital platform. Based on the business model innovation theory, the value creation mechanism of non-native digital platform enterprises was explored from two dimensions of technology and market. By dividing the development of digital platforms into construction, growth, and expansion stages, the case analysis reveals that the value creation mechanisms in these stages were characterized by market-driven and technology-supported lock-in business model innovation releasing channel value, technology-driven and market-following complementary business model innovation creating complementary value, and dual-driven technology and market complementary and novel business model innovation deepening ecosystem value. Overall, the research conclusions are deemed to enrich and expand the studies on business model innovation and value creation of digital platform enterprises, and provide references for traditional software firms in their transformation into digital platform ones.

Influenced by digital technology's impact on rural economic and social development, live streaming has emerged as a novel driver for rural revitalization and farmer income growth. Using data from a survey of 924 rural households in Sichuan Province, 2SLS and LIML methods were applied to analyze the effects of live streaming on household income. Empirical results indicate that rural live streaming significantly increases farmer income. Further examination of income composition reveals that live streaming enhances wage, property, and operational income through employment creation, entrepreneurship opportunities, and land transfer facilitation. Differential effects are observed across content types and participant categories. Specifically, e-commerce livestreams demonstrate strong income-positive outcomes, while cultural-entertainment livestreams show limited effects. Regarding participant types, internet celebrities amplify income impacts in both e-commerce and cultural-entertainment formats. Government officials significantly enhance income effects in e-commerce livestreams, whereas ordinary farmers rely more on personal resources and community networks. Income gains from livestreaming increase with village-level adoption scale. Policy recommendations include strengthening rural e-commerce infrastructure, promoting integration with local industries, implementing differentiated support for participant categories, and replicating successful livestreaming models.

Meta-analysis, as an essential research tool, has been widely applied in various scientific fields such as medicine, management, education, and psychology. With the continuous development of statistical techniques, traditional meta-analysis has gradually derived a large number of advanced research methods. To help researchers and practitioners promptly capture and comprehend the current state of meta-analysis, it aims to comprehensively explore the principles, applications, and latest developments of meta-analysis. First, the fundamental principles, historical development and operational procedures of traditional meta-analysis was extensively examined. Secondly, advanced research methods derived from traditional meta-analysis was deeply discussed, including Multilevel meta-analysis, meta-analytic structural equation modeling, second-order meta-analysis, causal based meta-analysis and replication research. Finally, the significance and limitations of meta-analysis in scientific research was discussed. In sum, it provides researchers with a comprehensive overview of the meta-analysis, helps researchers identify its development trends and potential problems, and further aims at improving the method to adapt to changing research needs.