Following the publication of the above paper, it was drawn to the Editor's attention by a concerned reader that, in Fig. 2E on p. 264, the Transwell invasion assay results shown in the "T98G‑AHIF KD" data panel appeared to potentially contain an overlapping section with the "U251‑AHIF OE" data panel in Fig. 3E. The authors were contacted by the Editorial Office to offer an explanation for this apparent anomaly in the presentation of the data in this paper; however, up to this time, no response from them has been forthcoming. Owing to the fact that the Editorial Office has been made aware of potential issues surrounding the scientific integrity of this paper, we are issuing an Expression of Concern to notify readers of this potential problem while the Editorial Office continues to investigate this matter further. [International Journal of Oncology 54: 261‑270, 2019; DOI: 10.3892/ijo.2018.4621].

Terrorism clusters and inbound tourism flows: Unravelling the complex relationship using panel data and explainable machine learning

Digital technology-driven transformations in global production methods and consumption patterns are profoundly reshaping the world economic landscape, with digital-real integration (DRI) having become a critical pathway for promoting high-quality economic development in the new development stage. Using panel data from 335 Chinese prefecture-level cities spanning 2000-2023 as the research sample, this study comprehensively employs factor analysis, the coupling coordination degree model, and double machine learning methods to systematically examine the impact effects and mechanisms of the "Broadband China" strategy (BCS) on DRI. The empirical results demonstrate that BCS has a significant positive on DRI. Heterogeneity analysis reveals that the policy effects are more pronounced in resource-based cities, old industrial base cities, highly developed cities, and integration-advantaged cities. Mechanism testing indicates that BCS primarily promotes digital-real integration through four pathways: improving capital misallocation, alleviating labor misallocation, industrial structure upgrading, and adjusting government expenditure.

Beyond case studies: A city-level quantitative analysis of payments for ecosystem services system and atmospheric environmental quality

Research on the coupling coordination and spatial influence effect of the digital economy and carbon emission

Study on spatiotemporal evolution and regional control of financial risks in the Yangtze river delta based on SDM-GWR

The ability to learn from past disasters is crucial for adaptation and disaster risk reduction, which is also fundamental to achieving Sustainable Development Goals (SDGs). However, empirical evidence on how disaster losses interact with adaptive capacity remains limited. Drawing on panel data from 31 provincial regions in mainland China during 2012-2022, the study constructed a disaster loss index integrating population, economy, and agriculture dimensions and applied a capital approach to assess adaptive capacity. The coupling coordination degree model, combined with the fixed effects model, was applied to examine their interrelationship. The results indicate that (1) the disaster loss showed an overall stable trend but spiked in certain years due to extreme events, while adaptive capacity remained underdeveloped yet exhibited gradual improvement; (2) significant regional variations existed in the two systems, where only the central region achieved marginal coordination, while the eastern and western regions displayed opposite patterns of incoordination; and (3) disaster loss was not always a negative factor, as a positive association with adaptive capacity was primarily observed in the economically developed eastern region, suggesting a regional-specific wealth effect. These findings enhance the understanding of the dynamic interactions between disaster loss and adaptive capacity, offering insights for strengthening disaster resilience and promoting sustainable, high-quality development.

How local officials moderate the spatial impact of politically and economically driven environmental regulations on green economic efficiency?

How does digitalization affect regional carbon emission efficiency? The role of carbon emission network

Peer firm’s ESG pressure, executives’ green perception and sustainable development- An empirical study from Chinese listed companies

China's transition to high-quality development requires decoupling economic growth from environmental degradation. This study investigates the drivers of Green Total Factor Productivity (GTFP) across 30 Chinese provinces from 2005 to 2020, specifically examining the non-linear transmission mechanisms of technological change. Employing a dynamic panel System GMM estimator to control for endogeneity and persistence, we uncover a robust U-shaped relationship between green technology innovation (GTI) and GTFP. This finding indicates that provinces must overcome an initial “innovation trap” characterized by high adjustment costs before innovation yields net productivity gains (threshold GTI ≈ 5.077). Furthermore, our results validate the Porter Hypothesis, showing that environmental regulation significantly boosts GTFP, whereas traditional infrastructure expansion and high energy intensity currently exert negative structural drags. These insights challenge linear growth models and inform a differentiated policy framework: we advocate for “bridge financing” to support lagging regions through the innovation valley and a strategic pivot from physical road density to digital and green infrastructure investments. • Dynamic analysis of Green Total Factor Productivity (GTFP) across 30 Chinese provinces (2005 – 2020). • System GMM estimation reveals a U-shaped relationship between green innovation and GTFP. • Identifies a critical innovation threshold (GTI ≈ 5.077) separating adjustment costs from productivity gains. • Environmental regulation acts as a positive driver, validating the Porter Hypothesis in China. • High energy intensity and traditional infrastructure expansion negatively impact green productivity.

Reducing carbon dioxide emissions (CO2e) is essential to achieving sustainable development objectives, safeguarding the environment, reducing the effects of climate change, and maintaining biodiversity for a future that is cleaner and more resilient. Nowadays, environmentalists also focus on how the environment reacts to society's increasing level of education. Increasing public awareness of environmental deterioration through environmental education, moral sermons, and higher tertiary enrollment can be a crucial policy in the fight against global warming, along with other measures to reduce CO2e. The effort to combat climate change necessitates improving energy efficiency (EE) and information and communication technology (ICT). Therefore, this study examines the impact of higher education (HED), EE and ICT on CO2e under the N-shaped EKC hypothesis. Using the panel data for five BRICS nations between 1991 and 2023, an empirical analysis is carried out, and the coefficients of the variables are estimated using the Second generation techniques (cross-sectional augmented distributed lag (CS-ARDL), Common Correlated Effects Mean Group (CCEMG) and Augmented mean group (AMG) approach. The estimates confirm the Inverted N-shaped EKC hypothesis between the GDP and CO2e. Moreover, the long-run estimates reveal that higher education, energy efficiency and ICT have negative effects on CO2e. BRICS countries should promote environmental education across all tiers, with an emphasis on conservation, climate change mitigation, and sustainable development, to help improve environmental awareness and literacy. Moreover, they should decouple energy use from economic growth to simultaneously achieve both economic and environmental goals, which can be facilitated by increasing ICT utilization, promoting higher tertiary enrollment, and improving energy efficiency. • This study examines the impact of Digitalization, Higher Education, and Energy Efficiency on environmental sustainability. • This study investigates the the N-Shaped Environmental Kuznets Curve in the BRICS economies. • This study utilizes the CS-ARDL, CCEMG and AMG approaches. • The finding shows there exist an Inverted N-shaped EKC hypothesis between the GDP and CO2 emissions. • The digitalization, energy efficiency, and higher education have negative effect on CO2 emissions.

A panel data analytics framework for financial development, global integration, and environmental performance across industrial supply chains

Sustainability strategies and capital costs: A study of non-financial disclosure in the GCC agri-food industry

This study examines the impact of operational leanness on the credit ratings of manufacturing firms listed on the Pakistan Stock Exchange, focusing specifically on inventory and property, plant, and equipment (PPE) leanness. Utilizing panel data from 80 firms rated by the Pakistan Credit Rating Agency (PACRA) over the period 2008 to 2021, the analysis employs panel regression models that control for firm size, leverage, interest coverage, capital intensity, and dummy variables for financial loss and subordinate debt. The results indicate a significant relationship between operational leanness and credit ratings. In particular, firms demonstrating more efficient inventory management receive higher credit ratings than those with less efficient inventory practices, highlighting lean inventory as a positive signal of financial discipline and operational robustness. These findings offer important implications for corporate managers, investors, and rating agencies within emerging market contexts.

From siblings to skills: How does sibling structure shape children's academic achievement and non-cognitive development in rural Northwest China?

Liquidity shock mitigation and mental health: Evidence from pension withdrawals during the COVID-19 pandemic in Chile.

Although drug shortages for outpatient chronic conditions commonly occur, population-level data on how they impact patients' ability to refill prescriptions is scarce. We sought to identify distinct patterns of refill adherence following drug shortages and patient- and prescription-level factors associated with adherence trajectories reflecting potential shortage-related treatment disruption. We retrospectively analyzed panel data assembled from 2017 to 2020 Veterans Health Administration (VHA) electronic health record data. Patients were included if they were baseline users of medications subject to a shortage within VHA. Group-based trajectory modeling was applied to users' monthly proportion of days covered (PDC) values from 6-months before to 6-months after the reported drug supply chain disruption. Patient demographics and medication characteristics were compared between identified trajectory groups using multivariable logistic regression. Among 1.5 million episodes of medication use (representing 1.3 million unique Veterans) for 29 medications in shortage in VHA, 6.3% were for female patients and the mean age was 66.4 ± 12.8 years. A 4-group trajectory model had the best fit: High Adherence (69.2% of observations), Moderate Adherence (14.1%), Potential Shortage-Related Disruption (8.5%), and Pre-Shortage Disruption (8.3%). Drug characteristics (drug class, number of manufacturers) were more strongly associated than patient characteristics with having Potential Shortage-Related Treatment Disruption vs. High Adherence. We identified 4 trajectories of refill adherence for medications subject to VHA drug shortages, with 8.5% of users of affected drugs exhibiting a trajectory consistent with shortage-related treatment disruption. Drug characteristics may modify whether drug shortages lead to treatment disruption in VHA.

Technical standards and renewable energy exports: Governance pathways for sustainable futures

This study examines the interrelation among gold, oil, and cryptocurrency markets and their implications for economic growth in the context of geopolitical turmoil. Employing panel data from 2000 to 2023 of exporter, importer, and mixed economies, we employ Nonlinear Autoregressive Distributed Lag (NARDL) and Panel Vector Autoregression (PVAR) to ascertain asymmetric as well as dynamic relations. Evidence shows that oil and gold price shocks exert significant effects on growth with geopolitical risk increasing volatility, while cryptocurrencies are heterogeneously resilient in panels. The results provide fresh evidence of cross-asset linkages, risk transmission mechanisms, and provide policy implications for policymakers and investors under volatile geopolitical environments.