Search Results (7,340)

Amidst the global drive toward sustainable development, this study responds to China’s pressing imperative for a green and low-carbon transition. The research begins by theoretically examining the viability and intrinsic mechanisms through which the cultivation of new-pattern productive forces can foster environmentally sound, high-quality economic growth. Subsequently, by leveraging panel data from 30 Chinese provinces covering the period 2011–2023, a two-way fixed-effects model is deployed to empirically assess the linkage between new-pattern productive forces and green total factor productivity (GTFP). The empirical results demonstrate the following: (1) New-pattern productive forces exert a statistically significant positive influence on GTFP—a finding that withstands multiple robustness checks; (2) Heterogeneity tests reveal that the GTFP-enhancing effect is pronounced in provinces with relatively low carbon intensity, whereas it remains insignificant in high-carbon-intensity regions; (3) Mechanism analysis identifies green technology innovation as a pivotal mediator in the process through which new-pattern productivity improves GTFP; (4) A non-linear, dual-threshold effect characterizes the relationship, wherein the GTFP-promoting impact of new-pattern productive forces strengthens progressively as the development level of green finance crosses successive thresholds. Collectively, these insights advance the understanding of how new-pattern productive forces enable GTFP gains, furnish novel evidence for steering high-quality economic development, and thereby support the broader global sustainability agenda. Full article

The shift toward a circular economy (CE) holds a central position in solving, globally, the long-standing environmental degradation and resource scarcity. Innovative sustainable processes and products lie at the core of such a shift, but they often face challenges associated with technological, organizational, financial, and regulatory paradigms. To date, two leading facilitators have been identified: sophisticated digital technologies, such as Artificial Intelligence, the Internet of Things, and Big Data, together with the collaborative creation of value among diverse stakeholders. Although the implications of each of these enablers on sustainability are known to some extent, little is understood about how their interrelatedness can counteract implementation barriers and drive innovation. The systematic thematic literature review examines how organizations utilize digital technologies and stakeholder engagement to facilitate green innovation in Circular Economy (CE) systems. Based on Stakeholder Theory, the Technology-Organization-Environment (TOE) framework, and the Resource-Based View (RBV), this review examines how organizations leverage digital technologies and stakeholder engagement to foster green innovation within circular economy systems. Following the PRISMA 2020 guidelines, a structured search was conducted in Scopus and Web of Science, covering peer-reviewed journal articles published in English between 2013 and 2024. Using predefined inclusion and exclusion criteria, 84 studies were retained for analysis from an initial pool of 850 records. The review integrates findings from five thematic areas: collaborative innovation among stakeholders, the use of digital technology to advance sustainability, challenges associated with adopting circular-economy values, linkages between technology and stakeholders, and the consequences of innovation. The findings suggest that collaboration between diverse stakeholders, combined with integration with digital technologies, provides a synergistic approach to maximizing innovation outcomes, overcoming implementation challenges, and diffusing circular practice. Skillfully crafted initiatives augment organizational capacities, foster collaborative actions, and advance sustainability initiatives. Despite providing a comprehensive synthesis of existing research, this review is limited by its reliance on secondary data. A qualitative quality appraisal was conducted to support the interpretation of findings. This review was not registered and received no external funding. Future research should conduct empirical analyses of these relationships and develop inclusive frameworks to guide initiatives emerging from collaborative and digital platforms across a wide range of sectors within the circular economy. Full article

Car accidents remain a leading cause of death worldwide, with drowsiness and distraction accounting for roughly 25% of fatal crashes in Ecuador. This study presents a real-time driver monitoring system that uses computer vision and deep learning to detect fatigue, distraction, and emotions from facial expressions. It combines a MobileNetV2-based CNN trained on RAF-DB for emotion recognition and MediaPipe’s 468 facial landmarks to compute the EAR (Eye Aspect Ratio), the MAR (Mouth Aspect Ratio), the gaze, and the head pose. Tests with 27 participants in both real and simulated driving environments showed strong results. There was a 100% accuracy in detecting distraction, 85.19% for yawning, and 88.89% for eye closure. The system also effectively recognized happiness (100%) and anger/disgust (96.3%). However, it struggled with sadness and failed to detect fear, likely due to the subtlety of real-world expressions and limitations in the training dataset. Despite these challenges, the results highlight the importance of integrating emotional awareness into driver monitoring systems, which helps reduce false alarms and improve response accuracy. This work supports the development of lightweight, non-invasive technologies that enhance driving safety through intelligent behavior analysis. Full article

This study draws on socio-technical transition theory to examine how multi-actor dynamics among producers, consumers, and the media within an experimental niche—Korea’s regulatory sandbox—shape policy responsiveness and the regulatory speed of governmental responses to emerging technologies, thereby influencing socio-technical transitions. We construct a longitudinal dataset of 2136 sandbox approvals between 2019 and 2025 and 1374 cases in which related legal or administrative adjustments have been completed. Changes in actor couplings before and after sandbox approval are first assessed using Pearson correlation analysis, while temporal lead–lag relationships are identified via vector autoregression (VAR) and Granger causality tests. Building on these dynamic analyses, the study subsequently investigates the determinants of regulatory response speed using ordered logistic regression, incorporating government policy orientation (progressive vs. conservative) as a moderating variable. The results show, first, that the strong producer–consumer coupling observed prior to sandbox approval weakens afterwards, whereas the consumer–media linkage becomes substantially stronger. Second, the time-series analysis of technologies within the regulatory sandbox reveals a typical technology-push pattern and a self-reinforcing feedback loop. Specifically, producer activity initiates the signal sequence, preceding consumer reactions; subsequently, media coverage significantly drives consumer engagement, and the resulting increase in consumer attention, in turn, stimulates further media coverage. Third, in the ordered logit model, media activity accelerates legal and regulatory reform, whereas consumer activity acts as a delaying factor, with producer activity showing no significant direct effect. Finally, government policy orientation systematically moderates the magnitude and direction of these effects. Overall, the study proposes an actor-centered mechanism in which learning generated in the sandbox is externalized through consumer–media channels and translated into regulatory pacing. Based on these findings, we derive practical implications for firms and regulators regarding proactive media engagement, transparent use of evidence, institutionalized channels for consumer input, and robust feedback standards that support sustainable commercialization of emerging technologies. Full article

The rapid global expansion of offshore wind energy (OWE) has established it as a critical component of the renewable energy transition; however, this development concurrently introduces significant underwater noise pollution into marine ecosystems. This paper provides a comprehensive review of the acoustic footprint of OWE across its entire lifecycle, rigorously distinguishing between the high-intensity, acute impulsive noise generated during pile-driving construction and the chronic, low-frequency continuous noise associated with decades-long turbine operation. We critically evaluate the engineering capabilities and limitations of current underwater acoustic monitoring architectures, including buoy-based real-time monitoring nodes, cabled high-bandwidth systems (e.g., cabled hydrophone arrays with DAQ/DSP and fiber-optic distributed acoustic sensing, DAS), and autonomous seabed archival recorders (PAM deployment). Furthermore, documented biological impacts are synthesized across diverse taxa, ranging from auditory masking and threshold shifts in marine mammals to the often-overlooked sensitivity of invertebrates and fish to particle motion—a key metric frequently missing from standard pressure-based assessments. Our analysis identifies a fundamental gap in current governance paradigms, which disproportionately prioritize the mitigation of short-term acute impacts while neglecting the cumulative ecological risks of long-term operational noise. This review synthesizes recent evidence on chronic operational noise and outlines a conceptual pathway from event-based compliance monitoring toward long-term, adaptive soundscape management. We propose the implementation of integrated, adaptive acoustic monitoring networks capable of quantifying cumulative noise exposure and informing real-time mitigation strategies. Such a paradigm shift is essential for optimizing mitigation technologies and ensuring the sustainable coexistence of marine renewable energy development and marine biodiversity. Full article

Photocatalytic degradation of tetracycline (TC) is considered a viable technology due to its stable molecular structure and resistance to absorption by biological organisms. As a promising photocatalyst, TiO₂ suffers from a wide bandgap and rapid charge recombination rates. In this work, the S-scheme heterojunctions of g-C₃N₄/TiO₂ (CNTOx, x = 10, 30, and 70) were synthesized via solvothermal, calcination, and impregnation methods. Furthermore, carbon quantum dots (CQDs) were incorporated into the CNTO30 samples, resulting in yCQDs-CNTO30 (y = 0.5, 1, and 3). The 1CQDs-CNTO30 demonstrat an impressive TC degradation efficiency of 76.7% in 60 min under visible light, which is higher than that of CNTO30 (59.8%). This enhanced efficiency is ascribed to the effective charge separation induced by the dual-effect of S-scheme heterojunction and the CQDs. The built-in electric field within the heterojunction drives the separation of electrons and holes. Meanwhile, the highly conductive CQDs accelerate the electron transport, thereby promoting the charge separation. Additionally, the CQDs improve the ability of absorption light. This research provides critical insights into the strategic development of efficient ternary photocatalytic S-scheme heterojunctions for environmental remediation. Full article

To address the instability of small coal pillars in gob-side entry driving under thick and hard roof conditions, this study proposes a synergistic control technology combining “pressure relief, bundle control, and strong support”. First, a segmented deflection curve model of the coal pillar was established to quantify the correlation between pillar deformation and dominant controlling factors. Numerical simulations (FLAC3D) were then performed to optimize the roof cutting parameters, determining an optimal cutting height of 23.2 m and a cutting angle of 9°. Based on these findings, a comprehensive control scheme was implemented in the Fucun Coal Mine. Field monitoring results indicate that the proposed technology effectively controlled the lateral displacement of the coal pillar to 264 mm and maintained the stability of the roadway. This study provides a theoretical basis and practical reference for deformation control in similar geological conditions. Full article

Artificial intelligence is poised to transform not only how we live but also how we die, with emerging “Death Tech” applications—such as deadbots, deepfake memorials, and AI-driven resurrection/immortality projects—reshaping postmortem experiences. Global warning that AI dominance could make one the “ruler of the world” takes on new significance in this context, as these technologies raise profound ethical questions about the dignity of the dead, freedom, and the sacredness of death. To critically assess these challenges, this paper turns to two thinkers from the Russian Religious Renaissance (RRR)—Nikolai Fedorov (1829–1903) and Fr. Sergius Bulgakov (1871–1944)—whose theological engagement with technology, death, and resurrection offers a counterpoint to the consumerist logic driving the Death Tech industry. Fedorov’s vision of a “Common Task” to overcome death through science and Bulgakov’s warnings against mangodhood and criticism of Fedorov provide insights into evaluating what is gained and what is lost in digitizing the afterlife and attempts to resurrect the dead. Full article

Amid the growing global environmental challenges, precise and efficient vehicle emission management plays a critical role in achieving energy-saving and emission reduction goals. At the same time, the rapid development of connected vehicles and autonomous driving technologies has generated a large amount of high-dimensional vehicle operation data. This not only provides a rich data foundation for refined emission accounting but also raises higher demands for the construction of accounting models. Therefore, this study aims to develop an accurate and efficient emission accounting model to contribute to the precise nitrogen oxide (NO_x) emission accounting for hybrid electric vehicles (HEVs). A systematic approach is proposed that combines incremental dimensionality reduction with advanced regression algorithms to achieve refined and efficient emission accounting based on multiple variables. Specifically, the dimensionality of the real driving emission (RDE) data is first reduced using the feature selection and t-distributed stochastic neighbor embedding (t-SNE) feature extraction method to capture key parameter information and reduce subsequent computational complexity. Next, an incremental dimensionality reduction method based on dictionary learning is employed to efficiently embed new data into a low-dimensional space through straightforward matrix operations. Given the computational cost of the dictionary learning training process, this study introduces the FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) for accelerated iterative optimization and enhances the computational efficiency through parameter optimization, while maintaining the accuracy of dictionary learning. Subsequently, an NO_x emission factor correction factor prediction model is trained using the low-dimensional data obtained from t-SNE embeddings, enabling direct computation of the corresponding correction factor when presented with new incremental low-dimensional embeddings. Finally, validation on independent HEV datasets shows that parameter K improves to 1 ± 0.05 and R² increases up to 0.990, laying a foundation for constructing an emission accounting model with broad applicability based on multiple variables. Full article

Drivable area (DA) detection in unstructured off-road environments remains challenging for unmanned ground vehicles (UGVs) due to limited field-of-view, persistent occlusions, and the inherent limitations of individual sensors. While existing fusion approaches combine aerial and ground perspectives, they often struggle with misaligned spatiotemporal viewpoints, dynamic environmental changes, and ineffective feature integration, particularly at intersections or under long-range occlusion. To address these issues, this paper proposes a cooperative air–ground perception framework based on multi-source data fusion. Our three-stage system first introduces DynCoANet, a semantic segmentation network incorporating directional strip convolution and connectivity attention to extract topologically consistent road structures from UAV imagery. Second, an enhanced particle filter with semantic road constraints and diversity-preserving resampling achieves robust cross-view localization between UAV maps and UGV LiDAR. Finally, a distance-adaptive fusion transformer (DAFT) dynamically fuses UAV semantic features with LiDAR BEV representations via confidence-guided cross-attention, balancing geometric precision and semantic richness according to spatial distance. Extensive evaluations demonstrate the effectiveness of our approach: on the DeepGlobe road extraction dataset, DynCoANet attains an IoU of 61.14%; cross-view localization on KITTI sequences reduces average position error by approximately 10%; and DA detection on OpenSatMap outperforms Grid-DATrNet by 8.42% in accuracy for large-scale regions (400 m × 400 m). Real-world experiments with a coordinated UAV-UGV platform confirm the framework’s robustness in occlusion-heavy and geometrically complex scenarios. This work provides a unified solution for reliable DA perception through tightly coupled cross-modal alignment and adaptive fusion. Full article

The synergistic development of digitalization and green transition has become a key driver for promoting China’s high-quality economic development. To elucidate the impact and mechanism of digital–green policy synergy on urban carbon emissions, this paper utilizes the intersection of the “National Big Data Comprehensive Pilot Zones” (BDPZ) and “Low-Carbon City Pilot” (LCCP) programs as a quasi-natural experiment. Based on panel data from 300 prefecture-level cities in China from 2005 to 2023, a multi-period DID model is constructed for empirical research. The empirical results indicate the following: (1) The synergy between digital and green policies significantly curbs urban carbon emissions, and this conclusion remains robust after parallel trend tests and a series of robustness checks. (2) Compared with single digital or green policies, the digital–green synergy exhibits a significantly superior carbon reduction effect. (3) Mechanism analysis reveals that digital–green synergy promotes low-carbon transition primarily through three pathways: driving green technology innovation, promoting the agglomeration of scientific and technological talent, and optimizing the allocation efficiency of capital factors. (4) Heterogeneity analysis reveals stronger emission reduction effects in non-resource-based, eastern, and developed cities, highlighting how structural rigidities and the digital divide constrain the policy’s effectiveness. We suggest strengthening policy integration and adopting differentiated strategies to break path dependence and achieve “Dual Carbon” goals. Full article

With the rapid advancement of autonomous driving technology, Traffic Sign Detection and Recognition (TSDR) has become a critical component for ensuring vehicle safety. However, existing TSDR systems still face significant challenges in accurately detecting partially occluded traffic signs, which poses a substantial risk in real-world applications. To address this issue, this study proposes a comprehensive solution from three perspectives: data augmentation, model architecture enhancement, and dataset construction. We propose an innovative network framework tailored for occluded traffic sign detection. The framework enhances feature representation through a dual-path convolutional mechanism (DualConv) that preserves information flow even when parts of the sign are blocked, and employs a spatial attention module (SEAM) that helps the model focus on visible sign regions while ignoring occluded areas. Finally, we construct the Jinzhou Traffic Sign (JZTS) occlusion dataset to provide targeted training and evaluation samples. Extensive experiments on the public Tsinghua-Tencent 100K (TT-100K) dataset and our JZTS dataset demonstrate the superior performance and strong generalisation capability of our model under occlusion conditions. This work not only advances the robustness of TSDR systems for autonomous driving but also provides a valuable benchmark for future research. Full article

Climate change has become the most severe challenge among current global crises, with harsh environmental conditions profoundly impacting inclusive green growth. This study employs data envelopment analysis (DEA) to measure the levels of inclusive green growth in 280 Chinese cities from 2010 to 2022, utilizing a difference-in-differences (DID) model to examine the channels and spatial effects of the pilot policy for the construction of climate-resilient cities (CCRC) on inclusive green growth. The findings reveal that CCRC significantly promotes inclusive green growth. In addition, CCRC drives inclusive green growth through incentivizing technological innovation, with regional economic agglomeration and public environmental participation exerting positive moderating effects. CCRC also generates positive spatial spillover effects on the inclusive green growth of neighboring cities. Full article

Autonomous vehicles, often described as “computers on wheels,” must collect extensive data, including personal information, and employ data analysis to enhance their self-learning capabilities. In this process, users’ personal information is particularly vulnerable to excessive collection, leakage, and misuse. Accordingly, establishing a robust legal framework for the protection of personal information in the context of autonomous driving is of critical importance. China has not yet implemented an Autonomous Driving Law, and the related legal provisions on protecting of personal information in the field of autonomous vehicles still unclear. We conducted a comparative analysis of the policies and legislation on automated driving and personal information protection in various countries and regions. The results indicate that China could benefit from the EU’s approach to expanding protection. Considering the current state of China’s legal system and legislative trends, it is more suitable to guide the legal application of personal information protection for automated driving through legal interpretation, alongside the existing laws on personal information protection. Full article

As an important industrial organizational form in the era of the digital economy, digital industry agglomeration exerts a profound impact on urban ecological resilience. Using panel data of 281 prefecture-level cities in China from 2011 to 2021, this study measures the level of digital industry agglomeration by means of the location entropy method, and constructs an urban ecological resilience evaluation system based on the “Pressure-State-Response (PSR)” model. It systematically examines the impact effects and action mechanisms of digital industry agglomeration on urban ecological resilience. The results show that: (1) The spatio-temporal evolution of the two presents a gradient pattern of “eastern leadership and central-western catch-up”, and their spatial correlation deepens over time, with the synergy maturity in the eastern region being significantly higher than that in the central and western regions. (2) Digital industry agglomeration significantly promotes the improvement in urban ecological resilience, and this conclusion remains valid after endogeneity treatment and robustness tests. (3) The promotional effect is more prominent in central cities, coastal cities, and key environmental protection cities, whose advantages stem from digital infrastructure and innovation endowments, industrial synergy and an open environment, and the adaptability of green technologies under strict environmental regulations, respectively. (4) Digital industry agglomeration empowers ecological resilience by driving green innovation and improving the efficiency of land resource allocation, while the construction of digital infrastructure plays a positive regulatory role. Full article