Search Results (1,107)

China’s economy is shifting from an era of rapid expansion to one focused on high-quality development, making it imperative to tackle environmental degradation linked to energy use. Understanding how New Quality Productive Forces (NQPF) interact with energy efficiency, along with the mechanisms driving this relationship, is essential for economic transformation and long-term sustainability. This study establishes an evaluation framework for NQPF, integrating technological, green, and digital dimensions. We apply fixed-effects models, the spatial Durbin model (SDM), a moderation model, and a threshold model to analyze the influence of NQPF on Green Total Factor Energy Efficiency (GTFEE) and its spatial implications. This underscores the necessity of distinguishing it from traditional productivity frameworks and adopting a new analytical perspective. Furthermore, by considering dimensions such as input, application, innovation capability, and market efficiency, we reveal the moderating role and heterogeneous effects of artificial intelligence (AI). The findings are as follows: The development of NQPF significantly enhances GTFEE, and the conclusion remains robust after tail reduction and endogeneity tests. NQPF has a positive spatial spillover effect on GTFEE; that is, while improving the local GTFEE, it also improves neighboring regions GTFEE. The advancement of AI significantly strengthens the positive impact of NQPF on GTFEE. AI exhibits a significant U-shaped threshold effect: as AI levels increase, its moderating effect transitions from suppression to facilitation, with marginal benefits gradually increasing over time. Full article

In the context of growing global climate change awareness and intensifying environmental degradation, technological innovation in renewable energy has become a key realization method for sustainable development. This paper uses data samples from 30 provinces, municipalities, and autonomous regions in China (excluding Tibet, Hong Kong, Macao, and Taiwan due to data availability) from 2007–2022, constructs an SFA model to measure carbon emission efficiency, and innovatively investigates the U-shaped impact of technological innovation in renewable energy on carbon emission efficiency along with the moderating effects of informatization level and fiscal decentralization. The empirical findings reveal the following: (1) Technological innovation in renewable energy demonstrates a U-shaped impact on carbon emission efficiency, with a negative impact before inflection point 2.596605 and a positive impact after the inflection point. (2) The informatization level plays a positive regulating role in the impact of technological innovation in renewable energy toward carbon emission efficiency, while fiscal decentralization exerts a negative regulating effect. (3) The impact of technological innovation in renewable energy concerning carbon emission efficiency varies depending on regional differences, industrial structure levels, and technological innovation levels in renewable energy. The conclusions of this paper are helpful for promoting the development of technological innovation in renewable energy, improving carbon emission efficiency, and advancing sustainable socio-economic development. Full article

Drawing on a panel dataset of 27,972 firm-year observations from Chinese A-share listed companies spanning 2009 to 2022, this study employs fixed-effects models to examine the nonlinear relationship between firm-level climate policy uncertainty (FCPU) and corporate green governance expenditure (GGE). The results reveal a robust inverted U-shaped pattern: moderate levels of FCPU encourage firms to increase GGE, while excessive uncertainty discourages it. Financing constraints mediate this relationship; specifically, FCPU exhibits a U-shaped impact on financing constraints, initially easing and then tightening them. Older top management teams accelerate the GGE downturn, while government environmental expenditure delays it, acting as a buffer. Heterogeneity analyses reveal the inverted U-shaped effect is more pronounced for non-polluting firms and state-owned enterprises (SOEs). This study highlights the complex dynamics of FCPU on corporate green behavior, underscoring the importance of climate policy stability and transparency for advancing corporate environmental engagement in China. Full article

This study examines the impact of board characteristics on capital structure decisions in the context of a transition economy, focusing on Algeria, where governance institutions are underdeveloped and the financial market remains immature. Using the Generalized Method of Moments (GMM) on a panel dataset of 120 firms over the period 2015 to 2019, we identify a U-shaped relationship between board size and leverage, and an inverted U-shaped relationship between board size and debt maturity. Furthermore, increased nationality diversity on boards is found to significantly reduce debt maturity. These findings highlight the critical role of board composition in shaping corporate financing strategies in transition economies and provide novel insights into corporate governance dynamics in a relatively underexplored institutional context. The results are particularly relevant for national entities such as COSOB and Hawkama El Djazaïr and may guide banking sector practices by promoting the integration of board governance criteria into credit evaluation processes. Full article

Background: Environmental exposures, such as per- and polyfluoroalkyl substances (PFAS), in conjunction with social and behavioral factors, can significantly impact liver health. This research investigates the combined effects of PFAS (perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), alcohol consumption, smoking, income, and education on liver function among the U.S. population, utilizing data from the 2017–2018 National Health and Nutrition Examination Survey (NHANES). Methods: PFAS concentrations in blood samples were analyzed using online solid-phase extraction combined with liquid chromatography–tandem mass spectrometry (LC-MS/MS), a highly sensitive and specific method for detecting levels of PFAS. Liver function was evaluated using biomarkers such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyltransferase (GGT), total bilirubin, and the fatty liver index (FLI). Descriptive statistics and multivariable linear regression analyses were employed to assess the associations between exposures and liver outcomes. Bayesian Kernel Machine Regression (BKMR) was utilized to explore the nonlinear and interactive effects of these exposures. To determine the relative influence of each factor on liver health, Posterior Inclusion Probabilities (PIPs) were calculated. Results: Linear regression analyses indicated that income and education were inversely associated with several liver injury biomarkers, while alcohol use and smoking demonstrated stronger and more consistent associations. Bayesian Kernel Machine Regression (BKMR) further highlighted alcohol and smoking as the most influential predictors, particularly for GGT and total bilirubin, with posterior inclusion probabilities (PIPs) close to 1.0. In contrast, PFAS showed weaker associations. Regression coefficients were small and largely non-significant, and PIPs were comparatively lower across most liver outcomes. Notably, education had a higher PIP for ALT and GGT than PFAS, suggesting a more protective role in liver health. People with higher education levels tend to live healthier lifestyles, have better access to healthcare, and are generally more aware of health risks. These factors can all help reduce the risk of liver problems. Overall mixture effects demonstrated nonlinear trends, including U-shaped relationships for ALT and GGT, and inverse associations for AST, FLI, and ALP. Conclusion: These findings underscore the importance of considering both environmental and social–behavioral determinants in liver health. While PFAS exposures remain a long-term concern, modifiable lifestyle and structural factors, particularly alcohol, smoking, income, and education, exert more immediate and pronounced effects on hepatic biomarkers in the general population. Full article

This study investigates the relationship between economic growth and energy consumption using the Energy Kuznets Curve (EKC) framework. Spatial econometric models, including the Spatial Panel Lag Model and the Spatial Dynamic Panel Lag IV Model, are employed to capture both spatial and dynamic effects. The results indicate that energy consumption in Thailand is spatially clustered, with energy use tending to spill over into neighboring provinces and concentrating in specific regions. Key factors that positively influence energy consumption include gross provincial product (GPP) per capita, population density, and road density. Regions characterized by favorable climates, sufficient infrastructure, and high levels of economic activity exhibit higher per capita energy consumption. The EKC analysis reveals a U-shape relationship between GPP per capita and energy consumption in the BKK&VIC, CE, EA, WE, and NE regions. As many regions continue to experience rising energy consumption, the findings underscore the importance of Thailand adopting more efficient energy usage strategies in tandem with its economic development. Full article

AM fungi play a pivotal role in regulating ecosystem functioning and processes. However, the assembly of soil AM fungal communities and its drivers across Populus euphratica forests in extremely arid regions remain largely unclear. Here, we explored the composition and assembly processes of AM fungal communities in the soil of P. euphratica forests in northwest China. The results showed that soil salinity affected the composition, assembly processes, and network stability and complexity of AM fungal communities. Stochastic processes rather than deterministic processes dominated the community assembly of AM fungi. Habitat generalists were more susceptible to deterministic processes than specialists. In addition, the network analysis showed that fungal network complexity had a hump-shaped relationship with increasing soil salinity, while network stability had a U-shaped relationship. This research suggests that soil salinity plays an essential role in determining AM fungal community composition and assembly processes in P. euphratica forests of arid regions. Full article

Industrial agglomeration (IA) is an important factor in promoting forestry development, which has a notable impact on green total factor productivity (GTFP). IA can generate a “scale effect”, but excessive agglomeration may also bring a “crowding effect”, ultimately leading to an inverted U-shaped impact of IA on GTFP. How do these two effects work? From the perspective of agglomeration externalities, this study explores the intermediate role of labor pooling, input sharing, and knowledge spillover to clarify the mechanism between IA and GTFP. This study calculates forestry GTFP of Chinese provinces from 2004 to 2021 and empirically tests the inverted U-shaped relationship between IA and GTFP. It further examines the mediating and moderating effects of agglomeration externalities. The findings reveal that most provinces are still in the “scale effect” stage, but as IA intensifies, the “crowding effect” gradually becomes increasingly evident. Additionally, “crowding effect” is most significant in the eastern region and forestry industrialization areas. Therefore, this study proposes policy measures based on regional differences to promote the green development of the forestry sector. Full article

The depletion of global fish stocks poses a major challenge to sustainable development, particularly in economies where marine resources are critical to livelihoods and food security. In this study, the relationship between economic development and the sustainability of fish stocks is examined using the Environmental Kuznets Curve (EKC). We use panel data from 32 economies between 2002 and 2020 and analyze the fish stock status indicator (EPI_FSS) from the Environmental Performance Index, which captures the proportion of national catches from overfished or collapsed stocks. Using a dynamic panel approach and the generalized method of moments (GMM), we investigate how the human development index (HDI) and other socio-economic factors influence changes in the state of fish stocks. Our results show a statistically significant inverted-U-shaped (∩-shaped) relationship between the HDI and the state of fish stocks, suggesting that the deterioration of fish stocks increases at lower levels of development, but improves beyond a certain threshold. In addition, higher levels of foreign direct investment (FDI), education, and research and development (R&D) spending are associated with better outcomes for fish stocks. These results suggest that while early economic growth may put pressure on marine resources, sustained investment in human capital, innovation, and global integration is critical to promoting long-term marine sustainability. Full article

In the context of the explosive growth of the digital economy, how inter-organizational connectivity affects corporate ambidextrous innovation has emerged as a pressing issue in the current digital economy. Based on the perspectives of the innovation ecosystem and digital coupling, this paper explores the inner mechanism of this issue through structural modeling by using the data of China’s high-tech enterprise alliance cooperation from 2015 to 2022. It is found in the empirical study that the local efficiency and reach rate of the digital innovation ecosystem have an inverted U-shaped relationship with exploratory innovation, and the local efficiency and reach rate of the digital innovation ecosystem have a negative effect on firm exploitative innovation. In addition, the level of firms’ digital transformation mediates the relationship between the local efficiency, reach rate, and ambidextrous innovation. The level of market development plays a moderating role in the relationship between the local efficiency, reach rate, and ambidextrous innovation. The findings provide a theoretical basis for the digital innovation ecosystem to realize the role of a “resource pool” through structural connections, which in turn provides important guidance for the digital transformation and innovation development of high-tech enterprises. Full article

Online reviews play a critical role in shaping consumer decisions and providing valuable insights to enhance the products and services for businesses. As visual content becomes increasingly prevalent in reviews, it is essential to understand how image complexity influences review helpfulness. Despite the growing importance of images, the impact of color diversity and texture homogeneity on review helpfulness remains underexplored. Grounded in Information Diagnosticity Theory and Dual Coding Theory, this study investigates the relationship between image complexity and review helpfulness, as well as the moderating role of review text readability. Using a large-scale dataset from the hotel and travel sectors, the findings reveal that color diversity has a positive effect on review helpfulness, while texture homogeneity follows an inverted U-shaped relationship with helpfulness. Furthermore, text readability strengthens the positive impact of texture homogeneity, making moderately homogeneous images more effective when paired with clear and well-structured text. Heterogeneity analysis demonstrates that these effects vary across product categories. The results advance the understanding of multimodal information processing in online reviews, providing actionable guidance for platforms and businesses to refine the review systems. Full article

It is increasingly recognized that the twin transitions of digitalization and green transformation are pivotal to achieving sustainable development. This study examines how digital trade affects corporate green total factor productivity (GTFP), using panel data from Chinese A-share listed firms and 287 prefecture-level cities in Mainland China from 2012 to 2022. The results demonstrate that digital trade exerts a significant positive impact on GTFP, primarily through improvements in technical efficiency, with heterogeneous effects across different stages of the corporate life cycle. Endogeneity concerns are carefully addressed through instrumental variable estimation and quasi-experimental designs, and robustness checks confirm the reliability of the findings. Mechanism analyses further reveal that digital trade enhances GTFP by stimulating green technological innovation and optimizing supply chain management. Importantly, threshold regression reveals non-linear effects. Both the level of digital trade and institutional factors, such as environmental regulation, intellectual property protection, and market integration, moderate the relationship between digital trade and GTFP in U-shaped, N-shaped, and other positive non-linear patterns. These insights enhance the understanding of how digitalization interacts with institutional contexts to drive sustainable productivity growth, providing practical implications for policymakers seeking to optimize digital trade strategies and complementary regulatory frameworks. Full article

Background/Objectives: We aimed to determine how baseline TSH levels relate to clinical outcomes over a three-year follow-up in euthyroid patients with T2DR. Methods: This single-center retrospective cohort study included 363 euthyroid T2DR patients who were followed for three years after baseline TSH measurement. Patients were stratified into tertiles based on TSH values belonging to the standard clinical limits (0.35–4.50 mIU/L). Binary and multivariate logistic regression analyses, along with non-linear modeling, were used to evaluate the prognostic impact of TSH and its interaction with age on mortality. The study adhered to the STROBE guidelines. Results: In the first year of follow-up, Group 1 (TSH 0.35–1.24 mIU/L) had significantly higher rates of mortality and combined outcomes compared to Group 2 (TSH 1.24–1.94 mIU/L; p = 0.025 and p = 0.041, respectively). Group 2 had a lower risk (OR for mortality = 0.349, p = 0.004; OR for combined outcome = 0.358, p = 0.007). Between TSH and TSH tertiles, a non-linear, inverted U-shaped relationship was observed, with the lowest mortality risk near 2.0 mIU/L. A significant interaction between TSH and age was found for third-year mortality (p = 0.016). Conclusions: TSH values showed a non-linear association with outcomes in euthyroid T2DR patients. Group 2 was linked to the lowest risk. Given the significantly higher mortality and combined complications identified within Group 1, closer monitoring and individualized follow-up strategies may be warranted for these patients. Additionally, TSH’s impact on long-term mortality increased with age, supporting its use alongside age for risk stratification in T2DR. Full article

Although group network characteristics significantly influence a group’s ability to exchange and absorb knowledge by listening to group members’ opinions, previous research on voice behavior has not yet fully addressed the social and relational factors in work groups that affect group-level voice. Specifically, in line with the “dark side of social capital” argument, this study examined the effects of group network density on group voice. In addition, drawing on the notion of status conflict, we further examined the moderating role of status conflict on the relationship between group network density and group voice. Using data from 55 work groups, we found an inverted U-shaped relationship between group network density and group voice. Moreover, we found that status conflict moderated the inverted-U effect of group density on group voice, such that when status conflict was high, (1) the overall level of group voice was reduced and (2) group voice decreased faster on the downward side of the inverted-U curve. Herein, we discuss the theoretical and practical implications of these results with relation to effective group management. Full article

Based on relevant data from WIOD database from 2010 to 2014, this article calculates the trade-implied carbon emission intensity of various industrial sectors in China, analyzes the impact of GVC embedding on the trade-implied carbon emission intensity of Chinese industrial sectors, and further explores the moderating effect of industrial digitalization on this basis. Research has shown that, on an overall level, as the degree of forward embedding of the GVC deepens, the trade-implied carbon emission intensity of China’s industrial sectors shows an inverted “U”-shaped change of first increasing and then decreasing, while the backward embedding of the GVC promotes trade-implied carbon emissions. From the perspective of industry heterogeneity, there is an inverted “U”-shaped relationship between forward participation in non-pollution-intensive and non-technology-intensive industries and trade-implicit carbon emissions intensity. In technology-intensive industries, there is a positive “U”-shaped relationship between forward participation in the GVC and trade-implicit carbon emissions intensity. The increase in forward participation in pollution-intensive industries effectively suppresses and promotes trade-implicit carbon emissions. At the same time, the improvement of industrial digitalization can promote the early entry of China’s industrial sector’s trade-implicit carbon emission intensity into the decline stage. Therefore, enhancing the forward participation of the GVC and the level of industrial digitalization is an effective measure to promote the low-carbon development of trade in China’s industrial sectors. Full article