Industrial automation technology, represented by industrial robots, has changed socioeconomic development. Using a general equilibrium framework and panel data on Chinese listed manufacturing firms from 2011 to 2019, this study divides firm innovation performance into innovation quantity and quality, incorporating industrial robots, resource misallocation, and firm innovation performance into a unified theoretical framework for empirical research. The results show that adopting industrial robots can effectively promote firms' innovation performance from both the dimensions of innovation quantity and quality, but there exists an inverted U-shaped relationship between industrial robot adoption and firm innovation performance. Industrial robot adoption can promote firm innovation performance by alleviating labor misallocation among firms. However, it can also hinder firm innovation performance by alleviating capital misallocation. Furthermore, the impact of industrial robots on firms' innovation performance exhibits significant heterogeneity. The positive innovation effect of industrial robots is relatively stronger in high-tech industries, capital-intensive, and collaborative innovation firms.

Nowdays emerging markets face immense challenges in creating large scale and sustainable jobs due to the fragmentation of global production. Understanding the impact of Global Value Chain (GVC) integration on domestic labour markets and how to implement effective industrial policy to protect local jobs from dislocation as technology develops will be instrumental in emerging markets creating a more competitive global economy. Therefore, this study fills this gap by investigating the relationship between GVC participation, position and GVC linkages impacts on total, skilled, unskilled, male and female employment in Indian manufacturing sector from 2009 to 2022. The results indicate that GVC participation and its forward linkages have a significant and positive impact on all employment types, demonstrating that a greater level of participation in downstream activities creates employment opportunities and helps facilitate the participation of workers. An inverse relationship was evident for both GVC position and backward linkages with employment across all categories. These relationships may also reflect potential labor displacement. In addition, it was noted that technology intensity moderated these relationships by changing the magnitude and direction of GVC-employment relationships based upon the level of technological adoption by manufacturers. Furthermore, the increase in industrial exports partially offsets the effect of GVC integration on employment levels. Therefore, export growth represents an important channel for transmitting the impact of GVC integration onto employment. To help support employment-intensive and inclusive growth in manufacturing in India and other developing countries, this research identifies several policy recommendations for promoting GVC integration as well as jobs creation.

ABSTRACT Collaborative governance is becoming a practical imperative. At the same time, resolving complex public affairs could offer opportunities to overcome bottlenecks, while Artificial Intelligence (AI) could help. This study draws on polycentric governance theory and multilayer network theory to explain why collaborative governance could occur and how it could be influenced by AI, respectively. Mathematical deductions from both micro and macro perspectives are employed to reinforce this theoretical viewpoint. Using Chinese cities as a case study, the impact of AI on collaborative governance has been tested in practice, supported by benchmark regression and subsequent tests for endogeneity and robustness. Mechanism tests demonstrate AI's functions by advancing digital government, lifting corporate transparency, and improving public knowledge. Moreover, econometric evidence from difference‐in‐differences, threshold regressions, and spatial econometric models robustly addresses concerns about AI. This study offers a quantifiable multi‐stakeholder governance framework and actionable policy insights for China and the Global South.

The escalating global demand for sustainable energy solutions underscores the urgent need to understand the drivers of renewable energy adoption, aligning closely with Sustainable Development Goal 7 (SDG 7). Hence, this study explores how outward foreign direct investment (FDI), green transition strategies, industrial activity, urbanization, and governance quality shape green energy penetration across five developed European economies between 2000 and 2022. Employing the CS-ARDL method, the results indicate that outward FDI, effective governance, and green transition initiatives enhance the renewable energy penetration. At the same time, rapid industrial growth and urban expansion tend to hinder it. Causality tests reveal that outward FDI, industrial growth, and urbanization have a one-way effect on renewable energy use. In contrast, governance quality and green transitions share a two-way relationship with renewable energy penetration. The findings highlight the critical role of institutional strength and international investment in promoting renewable energy. Policymakers are encouraged to prioritize strong governance frameworks and environmentally aligned FDI strategies, while integrating sustainability into urban and industrial planning to support a broader green energy shift.

Many countries around the world have introduced pension reforms to address the challenges posed by the growing elderly population and the need for sustainable public pensions. Besides, economic policy uncertainty and the potential shift of resources toward green growth raise additional concerns for the elderly well-being. Therefore, this study examines the impact of economic policy uncertainty and green growth on public pension spending in 16 OECD (Organization for Economic Co-operation and Development) economies from 1997 to 2023. We employed advanced panel data models that account for cross-sectional dependence and other potential issues associated with longitudinal data sets. The findings reveal that high economic policy uncertainty is associated with a decrease in public pension spending, whereas green growth is associated with an increase in public pension spending. Meanwhile, the employment rate, inflation rate, and social spending are found to play a mediating role in reducing the negative impact of economic policy uncertainty on public pension spending in OECD economies. Building on the findings of the study, we propose a 2-pronged policy framework that outlines strategies for safeguarding pensioners from short-run economic uncertainties while simultaneously proposing long-run interventions for their continuous support.

Vietnam's pursuit of sustainable development amid rising environmental challenges highlights the critical role of green innovation in balancing economic growth with environmental preservation. In this regard, this study explores the dual impact of green innovation as proxied by green patent activity on economic growth and environmental sustainability in Vietnam. Specifically, it investigates whether innovation-driven technological progress can simultaneously enhance economic growth and mitigate carbon emissions. The Bootstrap Autoregressive Distributed Lag (ARDL) results demonstrate that green patent exerts a positive long-run effect on economic growth while significantly reducing carbon emissions, reaffirming its catalytic role in promoting low-carbon development. Furthermore, the findings reveal that carbon emissions are positively associated with economic growth, suggesting that industrial expansion continues to exert environmental pressure. Additionally, financial development, foreign investment, trade openness, and population growth enhance economic growth, though their environmental impacts differ financial development and foreign investment mitigate emissions through technology transfer, whereas population, income growth, and trade openness worsen environmental quality. The negative growth of green total factor productivity (GTFP) and carbon-adjusted TFP (CTFP) suggests that Vietnam's eco-efficiency potential is underutilized. Finally, Granger causality tests indicate a unidirectional link from green patent to economic growth and a bidirectional relationship between economic growth and carbon emissions, highlighting the interplay between green patent, economic growth, and environmental pressure. This study contributes to the literature by providing one of the earliest empirical insights into the growth-environment nexus in Vietnam through the lens of green innovation and offering valuable policy implications for sustainable development.

Understanding the factors that drive tourism growth is essential for crafting effective economic policies. This study explores the factors influencing Vietnam's tourism growth from 1990 to 2020, focusing on economic growth, foreign direct investment in tourism, renewable energy consumption, and inflation. The ARDL bound test, ECM, and Granger causality test were used to analyze these relationships. The results indicate long-term associations between economic growth, FDI, inflation, renewable energy consumption, and tourism development. Economic growth positively impacts tourism growth, while renewable energy consumption negatively impacts it. FDI in tourism does not significantly contribute to overall tourism growth. Inflation also contributes to a decrease in tourism growth. Granger causality tests show unidirectional causality between tourism revenue and economic growth, FDI, inflation, and renewable energy use. This study identifies important determinants of tourism growth in Vietnam and provides policy suggestions. It also highlights directions for future research.

In the digital economy era, the development of new infrastructure, particularly network infrastructure, can foster enterprise performance growth, both directly and by promoting digital technology innovation. This study empirically examines network infrastructure's impact on enterprise performance using a combination of macro-level data from Chinese cities and micro-level data from Chinese listed enterprises. It explores the mediating role of digital technology innovation and investigates heterogeneity based on enterprise size and innovation type. The findings reveal that network infrastructure significantly enhances enterprise performance, a conclusion that remains robust even when using the 'Broadband China' policy as a quasi-natural experiment and the degree of urban topographic relief as an instrumental variable. Network infrastructure contributes to enterprise performance by enhancing both the quantity and quality of digital technology innovation. The impact of network infrastructure on enterprise performance is more pronounced for firms with non-independent innovation and for micro and small enterprises. These findings offer valuable insights for policymakers, urban planners, and business managers aiming to leverage network infrastructure for economic growth. Future research could explore how the impact of network infrastructure on firm performance varies across different political and economic contexts, industries, and between listed and non-listed companies.

The global political and economic landscape is rapidly evolving, with increasing geopolitical risks and climate-related fluctuations. This study analyzes the impact of geopolitical risk, economic and trade policy uncertainty, and disaggregated financial development on greenhouse gas (GHG) emissions in China from 1997-M1 to 2024-M12, using quantile-on-quantile regression and crossquantilogram methods. The empirical findings reveal that geopolitical risk has a mixed impact on emissions, depending on economic conditions. Also, economic policy uncertainty consistently raises emissions, similarly trade policy uncertainty is positively associated with emissions, especially at higher quantiles. Moreover, financial development generally increases emissions, with financial institutions amplifying this effect. However, financial market development reduces emissions at lower quantiles but increases them at higher ones, signifying a dual influences. To effectively tackle climate change, China should embrace a holistic policy approach that weaves together economic, financial, and geopolitical strategies. This includes adopting carbon pricing and green subsidies, steering investments through strong ESG standards, expanding access to climate finance, and deepening international cooperation to drive a just and inclusive low-carbon transition.

This study employs a non-linear iteration methodology to establish a set of indicators, namely technological complexity and innovation complexity, respectively for evaluating patent quality and the cities' innovation competitiveness. Using Chinese invention patent application data spanning from 2003 to 2020 as a case study, we reassess the innovation competitiveness in Chinese prefecture-level cities and investigate the spatiotemporal evolution of city innovation from the perspectives of technological diversity and technological complexity. Results show: (1) During the study period, overall technological innovation in Chinese cities increased. However, notable heterogeneity was observed among cities in different urban clusters. (2) Technological diversity, indicating innovation breadth, proves less effective than innovation complexity, typically associated with innovation depth, when assessing the cities' innovation competitiveness, especially for advanced cities. (3) Increased technological innovation in Chinese cities was associated with greater technological diversity and, more importantly, technological complexity enhancement. Nevertheless, noteworthy heterogeneity in technological innovation trajectories existed among cities: Innovation-advanced cities progressively shifted their focus from technological diversity to technological fields characterised by higher complexity, demonstrating a 'narrow and deep' tendency. In contrast, innovation-lagging cities tended to focus on technological diversity in fields with lower technological complexity, that is, emphasising innovation breadth rather than depth.List of Abbreviations: TC: Technological Complexity, TD: Technological Diversity, IC: Innovation Complexity, RPA: Relative Patent Advantage

Tourism is a key driver of regional development, yet a comprehensive framework to assess its multi-dimensional impacts is lacking. This study examines tourism's economic, social, and environmental impacts on regional development using panel data from 63 provinces (2014–2024). Employing Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multigroup Analysis (MGA), the study aims to analyze tourism's impacts on regional development, explore strategic management for balanced and sustainable development, and investigate regional differences in tourism-driven development across six regions in Vietnam. Findings reveal that tourism significantly influences economic growth, infrastructure, social well-being, and environmental sustainability. Furthermore, the MGA result shows significant variations in tourism's impact on regional development across different groups, with path coefficient differences confirming these disparities and statistically significant effects. Policy implications stress strategic planning, eco-friendly policies, and regional cooperation to maximize positive impacts and mitigate adverse effects.

Stock price informativeness (SPI) is a key concept measuring market efficiency. Fostering innovation within enterprises can enhance SPI. We posit that this amplification in emerging markets is partly due to executive ownership and insider trading. Using a rational expectation framework, we define SPI as the Kolmogorov-Smirnov distance between expected and actual stock price distributions. We conduct benchmark and mediation effects regression analyses using OLS method with data from China, along with instrumental variable regression, and validate our findings using data from Thailand and Indonesia. We also perform grouping regression analyses on Chinese companies funded by developed economies. Our findings indicate that enterprise innovation boosts SPI, with executive ownership partially mediating this effect. However, this mechanism is insignificant in enterprises funded by developed countries listed in China. Thus, the impact of enterprise innovation on SPI varies, with executive ownership playing a key role in emerging economies. Improving investor protection, particularly for medium and small investors, is critical to reducing agency costs, enhancing market efficiency, and fostering social welfare.

In the face of escalating environmental challenges, green innovation has emerged as a pivotal strategy for reconciling economic growth with ecological sustainability, particularly in rapidly industrializing regions such as Southeast Asia. This study investigates the role of green innovation in mitigating carbon dioxide (CO2) emissions across ten ASEAN countries from 2000 to 2023. Employing the STIRPAT framework and a Panel Autoregressive Distributed Lag (ARDL) model with Pooled Mean Group (PMG) estimation, the analysis captures both short-run dynamics and long-run equilibrium relationships. Green innovation is examined both as an independent variable and a moderator in the relationship between economic growth and environmental degradation. The findings reveal that green innovation significantly reduces CO2 emissions, while economic growth, energy consumption, industrialization, and trade openness exacerbate environmental impact. Robustness checks using alternative proxies-renewable energy consumption and green foreign direct investment-affirm the consistency of these results. The study offers region-specific policy insights, emphasizing the need for innovation-led strategies, clean energy transitions, and sustainable trade frameworks to achieve long-term environmental goals. By integrating theoretical and empirical perspectives, this research underscores the transformative potential of green innovation as a cornerstone of sustainable development in ASEAN.

Exploring the intrinsic relationship between artificial intelligence (AI) and the adjustment of human capital structure is crucial for stabilizing employment and improving livelihoods. Using a sample of A-share listed enterprises from 2007 to 2022, this study empirically investigates the impact of corporate AI innovation on the structure of human capital. The findings reveal that improvements in corporate AI innovation significantly promote the upgrading and optimization of human capital structures. Specifically, enhanced AI innovation leads to technological advancement, reduces the proportion of low-educated labour, and facilitates the structural upgrading of human capital. The positive effect of AI on human capital structure is more pronounced in state-owned enterprises, firms with high R&D intensity, those located in provinces with fewer universities, and those based in new first-tier cities. These findings provide valuable insights for policymakers in designing and adjusting talent employment strategies and policies supporting technological enterprises.

This study investigates how digital economy policies influence firms' breakthrough and incremental innovations, with particular attention to the underlying mechanism of human capital upgrading. The analysis yields three key findings: (1) digital economy policies significantly promote enterprises' breakthrough innovations while suppressing their incremental innovations; (2) these policies significantly promote breakthrough innovation in non-state-owned enterprises (non-SOEs), high-tech and large-scale enterprises, but suppress it in small and medium-sized enterprises (SMEs), and reduce incremental innovation in state-owned enterprises (SOEs) and high-tech enterprises. (3) digital economy polices affect breakthrough and incremental innovation by promoting the accumulation of high-quality and high-skilled labour. This study contributes to the existing literature by shifting the analytical focus from regional digital economy development and enterprise-level digital transformation to the regional policy level. It reveals the heterogeneous effects of digital economy policies on breakthrough and incremental innovation within a unified framework based on patent citation data. Furthermore, it offers a theoretical explanation for these effects through the mechanism of human capital upgrading. Based on these findings, this study proposes policy recommendations aimed at enhancing digital economy policy leadership, formulating differentiated digital economy policies, balancing breakthrough and incremental innovation, and leveraging digital economy policies to promote the upgrading of human capital.

The relationship between tourism and economic development in the Association of Southeast Asian Nations (ASEAN) countries has long been debated, particularly concerning the tourism-led growth and economy-driven tourism growth hypotheses. This study investigates these competing perspectives, analyzing how tourism and economic growth influence each other across 11 ASEAN nations using panel causality tests and structural equation modeling. Findings reveal that economic growth promotes tourism development in all eleven ASEAN states. However, in larger economies, tourism substantially impacts economic growth, while economic development has a lesser influence on tourism. In less-developed ASEAN countries, neither hypothesis shows definitive evidence. The study highlights tourism’s significant role in driving economic growth, particularly in more developed nations, and encourages sustainable tourism practices in less-developed economies. This research contributes to the ongoing discussion on tourism’s dual role in shaping economic outcomes within the ASEAN region, offering insights for policymakers and tourism industry stakeholders.

This study examines the relationship between science and technology insurance (S&T insurance) and regional innovation from a macro innovation input-output perspective. Based on provincial panel data from 2010-2019, the impact of S&T insurance on regional innovation is examined using a dynamic panel regression model. The results indicate that S&T insurance has a significant promotion effect on innovation inputs with a lagged effect but a negative effect on innovation outputs. Further, there is regional and subject heterogeneity in the role of S&T insurance. S&T insurance has a significant effect on innovation input in the central and eastern regions, but not in the western regions; S&T insurance has a significant effect on innovation in enterprises, but not on non-enterprise organisations. This study also examines whether there is a spillover effect of S&T insurance on regional innovation. The results show a spillover effect on innovation inputs but no spillover effect on innovation outputs. Finally, the authors suggest promoting the development of the S&T insurance market to improve regional innovation capacity.

China's high-speed rail (HSR) construction provides an ideal ‘quasi-natural experiment’ in which regional issues are investigated. This study explores how HSR affects innovation efficiency and explores the mediating effect of urban form. Based on panel data comprising 280 cities in China from 2006 to 2016, this study uses a time-varying difference-in-differences method to analyze whether HSR affects regional innovation, and it explores the mediating effect of urban form. The results show the following. (1) HSR significantly promotes regional innovation. The parallel trend test shows that the treatment group and the control group have the same trend before HSR construction. (2) Knowledge spillovers are important manifestations through which HSR promotes regional innovation. HSR can significantly improve the level of total factor productivity and human capital. (3) Urban form mediates the impact of HSR on regional innovation significantly, which varies across the different indexes of different urban forms.

The patent transfer (PT) has been an essential means of technological catch-up and control for countries and regions. The development and diffusion of PT is influenced by space and network. Analyzing China's patent transfer network (PTN) from 2001 to 2020 through spatial and complex network analysis, we found that the PTN became denser with significant Matthew effect, hierarchical and small-world properties. Beijing, Shanghai, and Shenzhen remained as national hubs, while new hubs like Suzhou, Guangzhou, Foshan, and Hangzhou, acting as intermediates, emerged. Network communities become more homogenised and geographically clustered due to the resonance effect. There was a shift from disperse hubs to spatial agglomerations, with the construction of long-distance shortcuts breaking spatial constraints on intercity patent transfer.

Using the policy package pilot implemented in Hubei Province, China, in April 2020 as a natural experiment, we use the synthetic control (SC) and synthetic difference in differences (SDID) methods to estimate the impact of the Chinese government’s support policy on the economic resilience and to analyze the mechanisms by which it impacts. This study finds that the policy package has contributed to the growth of economic resilience in the pilot provinces, with the policy package increasing the average economic resilience of the pilot provinces by 0.062 compared to their potential resilience. The validity and robustness of the above conclusions are objectively confirmed by multidimensional quantitative outcomes such as placebo tests, ranking tests, and replacements in calculating resilience. The mechanism analysis shows that the investment in real estate development, the stimulus for consumption, and the core industry development are virtual channels for the policy package to promote economic resilience growth in the pilot provinces. Moreover, traditional investment in transportation fixed assets plays a minor role. This paper quantitatively corroborates the academic idea that government governance capacity affects regional economic resilience (RER), and research can provide empirical support for regional economic recovery and policy support under a major crisis.