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.

Under the increasingly urgent background of global economic transformation, upgrading, and innovation-driven high-quality development, exploring how innovation-driven policies facilitate the optimization and upgrading of urban industrial structures has become crucial for realizing Chinese-style modernization and advancing the development of new productive forces tailored to local conditions. The pilot policy of innovative cities in China was treated as a quasi-natural experiment. Panel data from 281 prefecture-level cities between 2006 and 2019 were utilized, and a multi-period difference-in-differences (DID) model was employed to evaluate the policy's impact on urban industrial restructuring. The results indicate that the innovative city pilot policy significantly promotes the sophistication of urban industrial structures, while its effect on industrial structure rationalization remains statistically insignificant. These findings are validated through a series of robustness tests. Heterogeneity analysis reveals more pronounced policy effects in cities with higher administrative levels, greater proportions of tertiary industry employment, development models not fully reliant on resource endowments, and those located within urban agglomeration economic belts. Mechanism analysis further demonstrates that the policy positively affects industrial structure sophistication through three channels: expanding the digital economy scale, optimizing resource allocation efficiency, and enhancing human capital supply. It empirically reveals the pathway through which innovation policies promote industrial structure optimization, providing both theoretical foundations and practical references for enhancing policy effectiveness and advancing high-quality regional economic development.

The cross-border flow restriction indices of 10 exporting and 48 importing countries and the export data of six emerging digital service industries from 2014 to 2021 were matched using the OECD-DSTRI database. The impact of bilateral data cross-border flow restrictions on digital service exports was empirically examined, and a mechanism test and heterogeneity analysis were developed from multiple perspectives. It is shown that bilateral data cross-border flow restrictions inhibit digital service exports, and that data cross-border flow restrictions in exporting countries have a greater inhibitory effect on digital service exports than in importing countries. Heterogeneity analysis reveals that data cross-border flow restrictions between developed countries and EU countries hinder digital services exports to a lesser extent, and that the specific impact of these measures varies according to the type of digital services industry. Mechanism tests show that cross-border data flow restrictions hinder digital services exports by increasing trade cost. Further extension of the analysis finds that digital infrastructure level enhancement and RTA digital trade agreement signing can reduce the extent to which data cross-border flow restrictions impede digital services exports. Further analysis finds that increased levels of digital infrastructure and the signing of RTA digital trade agreements can reduce the extent to which restrictions on the cross-border flow of data impede the export of digital services. The conclusions provide important empirical support for reducing cross-border data flow restrictions and thus empowering the opening up of digital services trade to the outside world to promote China's services foreign trade growth.

High-tech enterprises are important economic subjects of high-quality full employment, and their development cannot be separated from the support of S&T finance. Based on this, through the provincial panel data from 2010 to 2022, the way in which the development of S&T finance promotes the labor demand of high-tech enterprises was explored. It finds that the development of S&T finance has a significant positive driving effect on the labor demand of high-tech enterprises, with technological progress, extensive margin, and intensive margin as the effective conduction channels. At the same time, this effect presents significant heterogeneity characteristics, specifically in four terms high-skilled and low-skilled labor, regions with a higher degree of development of science and technology business incubators, regions with more active technology markets, and regions with intellectual property policy support. In addition, the positive moderating effect of S&T talent supply and entrepreneurship, and the negative moderating effect of government forfeitures are also found. Given this, the development of S&T finance should be promoted to facilitate technological innovation, improve entrepreneurship, help enterprises grow, and play the role of incentives of various policies and systems, to realize the financial contribution to high-quality employment.

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.

Government data openness will have a significant impact on promoting green economic innovation activities. Investigating the mechanism of how government data openness affects urban green innovation efficiency is of crucial significance for accurately understanding their relationship, promoting efficiency in facilitating green economic development. Using panel data from 286 prefecture-level cities from 2011 to 2021, a multi-period double-difference model was employed to explore the impact of government data openness on urban green innovation efficiency. Government data openness effectively enhances urban green innovation efficiency. The results show that open government data can effectively enhance the efficiency of urban green innovation, and the impact is mainly realized through three paths: promoting talent concentration, stimulating innovation and entrepreneurship, and optimizing the regulatory environment. Heterogeneity analysis shows that the effect of open government data on urban green innovation efficiency is stronger in cities in central and western China, with high public concern for environmental protection and high levels of network infrastructure. Extended research finds that open government data can realize the "quantitative and qualitative increase" of green innovation, and that improving the quality of open government data platforms can strengthen its green innovation effect. In addition, this effect is also verified at the micro-firm level. The article provides useful insights for promoting high-quality open government data and green innovation in cities, and provides empirical references for exploring policy design that meets the development requirements of new quality productivity.

Using A-share listed companies from 2009 to 2022 as research samples, an empirical examination was conducted to investigate the impact of digital transformation on organizational resilience in different environments with varying levels of uncertainty. The heterogeneity characteristics, the mediating mechanisms of organizational innovation, the moderating effects of environmental uncertainty, and the moderated mediation model were explored. The findings are as follows. Digital transformation has a significant positive impact on both the stability and flexibility dimensions of organizational resilience. Corporate innovation output partially mediates the effects of digital transformation on organizational resilience. Environmental uncertainty can moderate the baseline effects between digital transformation and the stability and flexibility of organizational resilience. Environmental uncertainty can moderate the first half of the mediating effects, indicating that the stronger the environmental uncertainty faced by the company, the more digital transformation can promote organizational innovation output. At the same time, environmental uncertainty can also moderate the second half of the mediating effects on the stability of organizational resilience. Thus, with the increase in organizational innovation output, the stability of organizational resilience will be greatly improved as the environmental uncertainty faced by the company becomes stronger.

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.

To accelerate the construction of data factor market, activate the 'vitality' of market development and optimize the 'order' of resource allocation. It is urgent to build a multi-level and multi-agent data factor ecosystem and its new mechanism of market incentive and effective supervision and collaborative governance, so as to effectively promote the circulation and utilization of data factors and the release of value, and give full play to the role of data as a new factor of production to empower the development of new quality and productivity. Based on this, the hierarchical structure of the data element market was analyzed, the economic theoretical logic and social welfare effects of data element value creation was explained, and a collaborative governance model for data element value creation with five main entities includin government, supply side, demand side, platform, and data merchant was constructed. Propose to build an ecosystem of "market led, government guided, and supply-demand linkage" throughout the entire process and chain of data element circulation and trading, with multiple linkage and co construction, in order to activate the development vitality of the multi subject and multi-level data element market and achieve high resource allocation efficiency of data resource elements. Explore the regulatory priorities and their shifts at different stages, and clarify the principles of synergy between market incentives and effective government regulation. Finally, propose implementation path suggestions for the process of data element valuation, in order to promote the high standard supply, efficient circulation, high-level application, and effective supervision of data elements, and achieve high-quality development of the data element market.

Co-innovation is considered to be of great significance in addressing the lack of innovation capacity of a single entity. Under the influence of information technology, data assets are found to facilitate resource integration, achievement sharing, and collaborative innovation among enterprises within the supply chain. They are regarded as the core driving force for promoting co-innovation among enterprises. Based on empirical evidence from A-share listed companies in China between 2010 and 2022, the role of data assets in the channels and paths of co-innovation among upstream and downstream collaborators was explored. The results show that data assets exert a significant positive influence on firms’ co-innovation. The enhancement of firms’ total factor productivity, the advancement of human capital structure, and the alleviation of financing constraints are identified as key transmission mechanisms through which data assets contribute to firms’ co-innovation. The promotional effect of data assets on co-innovation is found to be more pronounced in large-scale, high-tech, and manufacturing enterprises. The findings confirm that data assets can enable enterprises to enhance co-innovation and offer both theoretical references and practical foundations for the country to develop new driving forces related to data elements and advance enterprise co-innovation.