Search Results (365)

Using a sample of 297 prefecture-level cities in China from 2010 to 2022 and drawing on green patent transfer data, this study constructs a directed weighted network and applies social network analysis, a modified gravity model, and quadratic assignment procedure (QAP) regression to examine the spatial structural evolution, node topology characteristics, and driving factors of China’s green technology transfer (GTT) network. The results show that: (1) From 2010 to 2022, the number of nodes grew from 249 to 292, network coverage increased from 83.8% to 98.3%, and the number of edges expanded by a factor of 14.47. Network density and average degree also rose markedly. The spatial structure evolved from an initially sparse and fragmented configuration into a polycentric complex network centered on the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Chengdu–Chongqing economic circle. (2) In terms of node topology, the intermediary and control capacities of cities exhibit dynamic changes, with central and western cities gaining growing influence within the network. (3) Cohesive subgroup analysis identifies four functional blocks, revealing a multi-level technology spillover path of “core—secondary—regional—peripheral.” (4) QAP regression further identifies the digital economy, geographic location, high-speed rail mileage, industrial structure, and government environmental concern as key drivers of network formation and evolution. This study offers a new perspective on understanding cross-regional green technology transfer and provides theoretical grounding and policy references for promoting regional collaborative innovation and green low-carbon development. Full article

The selective conversion of high-molecular-weight paraffins (C₂₀–C₄₀) into linear alpha-olefins is often hindered by severe diffusion limitations and secondary over-cracking. This study addresses these challenges by transforming low-value natural minerals into sophisticated catalytic systems. We present a “top-down” engineering strategy for designing hierarchical catalysts based on natural Kazakhstani clinoptilolite. The multi-stage modification involves synergistic demineralization and precision chelation (EDTA, sulfosalicylic acid) to generate a tailored mesoporous architecture. This framework serves as a host for the sub-nanometric immobilization of Keggin-type heteropolyacids (PW₁₂, PMo₁₂), ensuring optimal active-phase dispersion. The innovative dual-step modification successfully bypassed the “micropore barrier”, creating a high-surface-area hierarchical network that facilitates the transport of bulky paraffinic molecules. Precise localization of heteropolyacid clusters within the created mesopores resulted in the formation of superstrong Lewis acid sites, as confirmed via temperature-programmed ammonia desorption. These sites triggered a highly efficient monomolecular beta-scission mechanism, suppressing undesirable hydrogen transfer reactions. The resulting catalysts achieved a breakthrough in technical paraffin cracking, delivering a 70% liquid product yield with an unprecedented >50% selectivity toward the C₇–C₁₄ α-olefin fraction. This work demonstrates a sustainable pathway for upgrading natural zeolites into high-performance, green catalysts that rival expensive analogs in precision and efficiency. Full article

This study investigates the spatial association network structure of Green Transportation Development (GTD) in China to support coordinated regional development. Based on panel data from 30 major Chinese cities over the period 2011–2020, an entropy weighting method is used to evaluate urban GTD levels, while social network analysis (SNA) and the Quadratic Assignment Procedure (QAP) are employed to identify the spatial network topology, clustering patterns, and driving factors of GTD. The results show that GTD exhibits significant intercity spatial associations. The overall network structure is relatively stable and exhibits a loose hierarchical pattern, with network density fluctuating between 0.232 and 0.277. Shanghai, Yinchuan, and Nanjing play prominent roles in the core–periphery structure. Block modelling further classifies the network into four functional groups: “net spillover,” “bilateral spillover,” “net benefit,” and “broker” blocks. In 2020, the network contained 214 association ties, of which 176 were inter-block ties, indicating evident cross-block spillover effects but relatively weak intra-block communication. The QAP regression results further reveal that geographical distance inhibits network formation, whereas differences in economic development and transport-related employment promote intercity GTD associations; differences in technological innovation exert a negative effect. These findings suggest that policymakers should reduce administrative barriers, formulate differentiated GTD policies, strengthen regional linkages, and promote intercity cooperation based on complementary advantages to improve the overall performance of GTD. Full article

Amid the dual pressures of the global energy transition and green technology upgrading, corporate environmental responsibility increasingly depends on interactions among firms rather than on isolated firm-level resources. From a systems perspective, this study focuses on the inter-firm green innovation linkages within the new energy sector, where knowledge diffusion, technological learning, and governance signals are jointly shaped by network structure. Using quarterly panel data from 52 listed Chinese new energy firms from 2018Q1 to 2023Q2, we employ the Adaptive Elastic Net Generalized Method of Moments approach to reconstruct a green innovation network from the observed dynamics of the panel data, and examine how firms’ positions within the network affect their environmental responsibility. The results show that the network exhibits a clear core–periphery spillover structure. Inter-firm ties are more likely to form when firms are located in the same province and when target firms have higher green patent citation impact and more executives with environmental backgrounds. Higher network centrality is associated with better corporate environmental responsibility, especially among firms facing intense market competition, state-owned firms, and non-key environmental regulatory units. These findings suggest that green innovation networks can alleviate innovation imbalances and strengthen informal inter-firm governance mechanisms in emerging green industries. Full article

Enhancing green development efficiency (GDE) is crucial for promoting ecological protection and high-quality growth in the Yellow River Basin (YRB). Using panel data from 48 prefecture-level cities in the YRB from 2010 to 2022, this study applies a Super-SBM model that accounts for undesirable outputs to measure GDE. Then, a modified gravity model and social network analysis (SNA) are used to identify the evolution of its spatial correlation. Additionally, a spatial Durbin model (SDM) is employed to examine the driving mechanisms from the dual perspectives of the innovation rebound effect and external micro-ESG (Environmental, Social, and Governance) reverse-forcing pressure. The results reveal the following: First, the spatial pattern of GDE in the YRB has changed significantly, showing an overall spatial imbalance, with efficiency improvements in the middle reaches and declines in the lower reaches. Notably, resource-based cities have improved GDE due to environmental regulations. Second, the spatial correlation network has evolved from a point-axis layout to a more complex network structure. However, spatial links among cities are mainly driven by geographic proximity, while collaborative ties between cities with similar economic features remain weak. Third, technological innovation has a significant negative effect on local GDE, likely due to the energy rebound effect. Meanwhile, the cross-regional transmission of the external supply chain ESG reverse-forcing mechanism remains weak, constrained by the carbon lock-in effect in the middle and upper reaches. These findings suggest that internal technological structures and external market constraints both influence GDE in the YRB. This research offers an empirical foundation for developing targeted, cross-regional collaborative governance policies. Full article

To address the decline in detection accuracy caused by the degradation of grayscale features under environmental interference, a lightweight detection model driven by grayscale characterization, YOLOv8n-CoalGangue, is proposed based on an in-depth analysis of the dynamic variations exhibited by grayscale features. First, grayscale histograms are used to quantitatively evaluate the effects of illumination changes and moisture conditions on feature distributions, revealing that global grayscale aliasing and local texture degradation are the key visual feature bottlenecks. Guided by these unique findings, targeted technological innovations are integrated into the developed architecture. HGNetV2-G, which incorporates the GhostNet principle, is used as the backbone to reduce the incurred computational cost while preserving the core feature extraction ability of the model. A mixed local channel attention (MLCA) mechanism is introduced in the neck to filter background noise and focus on local high-frequency features, which helps overcome global grayscale aliasing issues. In addition, a DGFPN-based feature fusion network is constructed by combining RepGFPN and DySample, together with lightweight shared convolution detection (LSCD), which compensates for the loss of multiscale grayscale details without increasing the imposed parameter burden. Furthermore, the PIoUv2 loss function improves the bounding-box regression process in dense overlapping scenarios. Experimental results show that the proposed model achieves an mAP@50 of 97.2% with a 32% reduction in the number of parameters required (only 2.1 M). It also demonstrates strong robustness under six extreme industrial conditions, such as low illumination and coal dust occlusion, confirming the effectiveness of the design driven by grayscale characterization for practical green mining applications. Full article

The electrification of public transport, particularly Bus Rapid Transits (BRT), is a significant step toward achieving sustainable urban mobility and reducing dependency on fossil fuels. However, rapid adoption of Electric Vehicles Smart Charging (EVSC) infrastructure presents grid stability, economic and environmental concerns. The rising demand for electric cars, particularly in developing nations such as Nigeria, highlights the urgent need for a sustainable hybrid renewable energy charging infrastructure for BRT systems. This study presents a techno-economic assessment of an off-grid hybrid systems that use photovoltaic (PV), wind turbines (WTs), hydrogen (H₂), fuel cell (FC) and battery technologies to power Electric Vehicles Smart Charging within Bus Rapid Transits networks. The Lagos BRT charging system at City Mall Station (CMS) serves as a case study, with hourly renewable resources obtained from National Aeronautics and Space Administration database (NASA). Using the HOMER pro-optimization tool, a multi-criteria analysis is performed to evaluate system viability, with special focus on key metrics such as levelized cost of energy (LCOE), net present cost (NPC), renewable energy fraction (REF), and greenhouse gas (GHG) emissions. The simulation results demonstrate that the hybrid PV/wind/FC/battery configuration is exceptionally economical, with an LCOE as low as $0.222/kWh, $2.03M NPC, 51.3% REF, and 159,209 kg of carbon dioxide emissions per year compared to grid-dependent charging. The study shows that integrated renewable-hydrogen systems are not only financially feasible, but also provide significant insights for policymakers, transportation authorities, and energy planners seeking to accelerate the transition to green public transportation infrastructure through innovative hybrid energy schemes. Full article

Under the dual-carbon goals and energy transition backdrop, the photovoltaic cell has become a crucial pillar for optimizing China’s energy structure and promoting green development. From the perspective of patent intelligence, this study systematically investigates the spatiotemporal evolution paths, coupling characteristics, and driving mechanisms of China’s photovoltaic cell industry and innovation chains, using nationwide photovoltaic cell enterprise and patent data from 2005 to 2024 and integrating spatial gravity center modeling, location quotient analysis, and spatial Durbin models. The findings reveal the following: (1) the spatiotemporal evolution of the dual chains exhibits distinct phases, with a notable developmental leap after 2015. The industry chain shows a pattern of “westward shift and eastern optimization,” while the innovation chain evolves from eastern dominance toward a nationally coordinated, multipolar network. (2) At the macro level, the dual chains demonstrate a coupling trend characterized by “coordinated gravity center migration and spatial distance convergence,” yet significant spatial heterogeneity and mismatch persist at the city scale. (3) Industrial agglomeration has an inverted U-shaped effect on innovation, with regional heterogeneity in its impact, driven synergistically by multidimensional factors such as economic foundation, the innovation environment, and openness. Based on these insights, this study proposes recommendations for optimizing the spatial layout of these dual chains, strengthening multifactor synergy, and implementing regionally differentiated policies, aiming to provide decision-making references for achieving sustainable and high-quality development in the photovoltaic cell. Full article

Given the urgent need for corporate green transformation in the context of global climate governance, the sustainable development goals, and China’s dual carbon goals, this study examines the spillover effects of venture capital networks formed through common shareholder ties on green technology innovation from a complex network perspective. Based on regression analysis of panel data from Chinese A-share STAR and ChiNext Market listed companies between 2015 and 2023, we find the following: (1) Within venture capital networks, enterprises with higher centrality and structural hole positions exhibit more significant green technology innovation performance. (2) This facilitation effect varies across firm types. Private enterprises, foreign-invested enterprises and enterprises with weaker ESG performance rely more heavily on network advantage for innovation. (3) The mechanism analysis shows that occupying advantageous positions in venture capital networks enables firms to increase R&D personnel and R&D expenditure, thereby strengthening their ability to absorb external knowledge and transform innovation resources, which further enhances green technology innovation output. Full article

Proton exchange membrane water electrolysis is a promising technology for large-scale green hydrogen production due to its high efficiency, compact design, and rapid dynamic response. However, its commercialization is strictly limited by high material costs, durability issues, and complex multiphysics coupling within the membrane electrode assembly. This work provides a comprehensive and critical review of key physicochemical processes and advanced predictive modeling approaches for PEMWEs. To capture recent paradigm shifts, we introduce an innovative multi-dimensional classification framework—incorporating spatial resolution, temporal dynamics, and methodological paradigms—to critically evaluate lumped-parameter, continuum, microscale, and multiscale models, explicitly defining their applicability bounds and inherent limitations. The fundamental mechanisms governing electrode kinetics, membrane water transport, and gas–liquid two-phase flow are analyzed, establishing state-of-the-art quantitative benchmarks for microstructural parameters and advanced 3D flow field topologies under high-current-density and high-pressure regimes. Furthermore, we systematically examine model validation rigor, typical prediction errors, and the critical failure of static models in capturing dynamic property shifts during extreme bubble breakthrough. Recent breakthroughs integrating in situ diagnostics, pore-scale simulations, density functional theory, and Physics-Informed Neural Networks are extensively discussed. Future efforts must prioritize mechanical–electrochemical–thermal coupling, transient degradation prognostics, and machine learning-driven predictive digital twin technologies to overcome current empirical limitations and accelerate the gigawatt-scale deployment of PEMWE systems. Full article

Carbon Information Disclosure (CID) has emerged as an essential tool for achieving global sustainable development. While existing literature has extensively examined firm-level and institutional drivers of CID, the impact of supply chain network structure remains underexplored, particularly in developing economies. To bridge this gap, this study investigates the impact of supply chain network centrality on CID using a sample of Chinese A-share listed companies from 2008 to 2023. Our empirical results reveal a negative relationship between centrality and CID, suggesting that central firms tend to reduce carbon information disclosure levels to avoid proprietary costs, rather than signaling their environmental legitimacy. Mechanism analysis indicates that centrality inhibits CID through two suggested pathways: by crowding out green technology innovation and by reducing the participation of green investors. However, we find that strong external supervision, such as government environmental attention and media attention, can effectively weaken this inhibitory effect. This effect is also mitigated when firms are subject to heightened regulatory monitoring through China’s Carbon Emissions Trading Scheme pilot. Furthermore, heterogeneity analysis shows that this negative impact is more pronounced in non-heavily polluting sectors where regulatory constraints are softer, while market concentration does not yield a significant heterogeneous impact. Full article

Digital transformation calls for new models of sustainable regional development, particularly in spatially heterogeneous emerging economies where interregional cooperation and network effects play an important role in reducing disparities and supporting the gradual formation of integrated development systems. This study aims to develop a conceptual governance model based on innovative hyperclusters defined as transregional multi-sector network structures integrating digital platforms with circular economy principles. To achieve this goal, a composite ESG index was constructed by aggregating 26 statistical indicators (Environmental, Social, Governance) using a generalized Minkowski mean, and applied to empirically assessment of 85 Russian regions sustainable development. Regions were classified into five sustainability groups, from vulnerable to integrated. Most Russian regions fall into intermediate categories, while some lack the critical mass required for traditional cluster formation. The proposed hypercluster model functions as a digital bridge, allowing lagging regions to integrate into distributed value chains, access advanced competencies, and co-develop green technologies. This study offers a threefold contribution to the literature: first, a robust composite ESG indicator adapted to the Russian statistical framework; second, a novel governance model of innovative hyperclusters as a systemic tool for overcoming structural imbalances; third, empirically grounded differentiated application scenarios for Russian regions. Full article

Nanophotonics, an interdisciplinary field merging nanotechnology and photonics, has enabled transformative advancements across diverse sectors, including green energy, biomedicine, and optical computing. This review comprehensively examines recent progress in nanophotonic principles and applications, highlighting key innovations in material design, device engineering, and system integration. In renewable energy, nanophotonics allows the use of light-trapping nanostructures and spectral control in perovskite solar cells, concentrating solar power systems, and thermophotovoltaics. This has significantly enhanced solar conversion efficiencies, approaching theoretical limits. In biosensing, nanophotonic platforms achieve unprecedented sensitivity in detecting biomolecules, pathogens, and pollutants, enabling real-time diagnostics and environmental monitoring. Medical applications leverage tailored light–matter interactions for precision photothermal therapy, image-guided surgery, and early disease detection. Furthermore, nanophotonics underpins next-generation optical neural networks and neuromorphic computing, offering ultrafast, energy-efficient alternatives to von Neumann architectures. Despite rapid growth, challenges in scalability, fabrication costs, and material stability persist. Future advancements will rely on novel materials, AI-driven design optimization, and multidisciplinary approaches to enable scalable, low-cost deployment. This review summarizes recent progress and highlights future trends, including novel material systems, multidisciplinary approaches, and enhanced computational capabilities, paving the way for transformative applications in this rapidly evolving field. Full article

Multi-source environmental data are increasingly used for measurement, reporting and verification, and for coordinating low-carbon innovation across interorganizational networks. However, voluntary data sharing remains limited because participants face asymmetric costs, leakage and compliance risks, and uncertainty in value capture. This study develops a network evolutionary game model to examine how cooperative data sharing emerges and stabilizes in green innovation networks. We specify a two-strategy game in which heterogeneous agents choose between sharing and withholding. The payoff structure integrates private innovation gains from their own data, cross-partner synergy, external incentives, fixed governance costs, and stock-scaled sharing and risk burdens. Agents interact on a Barabási–Albert scale-free network and update strategies via local imitation under a Fermi rule. Simulations show that cooperation can diffuse from low initial participation and converge to a high-sharing regime when benefit allocation and incentive intensity jointly offset cost and risk frictions. Several governance levers exhibit threshold-type effects, including the allocation share, the opportunity loss of non-sharing, and the marginal cost–risk burden. Multi-source synergy and subsidies further raise the attainable cooperation level, but with diminishing marginal returns. Degree heterogeneity accelerates diffusion once hub organizations adopt sharing, while also raising fairness concerns when benefits concentrate on central nodes. Overall, the findings provide green-innovation-specific governance conditions that translate threshold regions into implementable design targets for sustainable environmental data sharing. Full article

As a core ground-based coronal observation facility in the low-latitude and high-altitude regions of China, the Lijiang Coronagraph takes advantage of the natural endowments of the Lijiang Astronomical Observation Station, such as an altitude of 3200 m and low atmospheric turbulence. It has gone through a complete development process from introduction through Chinese–Japanese cooperation to independent innovation and iteration. This paper systematically summarizes the core technological innovation achievements of this facility, including the upgrade of the automatic operating system, the integration of the dual-band observation system, the stray light suppression technology based on the image difference method before and after cleaning, and the high-precision image calibration and registration technology. These innovations have significantly improved observation efficiency and data quality, laying a solid foundation for high-quality observations. At the scientific research level, the observation data reveal that 1.1 R_⊙ (solar radius) is a highly correlated region between coronal green line brightness and magnetic field intensity. This study also confirms a strong correlation between the coronal green line and the SDO/AIA 211 Å extreme ultraviolet band (correlation coefficient: 0.89–0.99), which can support the research on early warning of Coronal Mass Ejections (CMEs). These achievements provide key data support for the verification of coronal heating mechanisms and the exploration of the origin of the slow solar wind. The technical experience accumulated from the Lijiang Coronagraph has not only laid a solid foundation for the research and development of China’s next-generation large-aperture coronagraphs, but also facilitated and accelerated substantial progress in China’s technical capabilities for low coronal observation, enabling the country to establish internationally parallel competitive capabilities in this field. This system has also become an important part of the global coronal observation network. Full article