Search Results (1,843)

Against the backdrop of global warming, China has set forth its ‘dual carbon’ goals, striving to achieve carbon neutrality by 2060. As a vital engine of economic development, the Yangtze River Delta region has formulated implementation plans, prioritizing carbon emission reduction. The transport industry, a major source of carbon emissions, plays a crucial role through its transition to clean energy, making it pivotal for advancing regional carbon neutrality. This study categorizes carbon emission drivers based on an assessment of current emissions and dynamic evolution analysis, integrating policy evolution and technological innovation trajectories. These drivers are classified into: transport structure, transport intensity, energy intensity, year-end resident population, per capita GDP, and industrial structure. Using the extended STIRPAT-Ridge model, quantitative analysis of carbon emission drivers is conducted. Employing the Tapio decoupling model, the decoupling state between carbon emissions and economic growth is deconstructed. Empirical findings reveal that carbon emissions from the transport industry in the Yangtze River Delta are influenced by multiple factors, with year-end resident population and industrial structure emerging as primary drivers. The decoupling between carbon emissions and economic growth exhibits fluctuating characteristics, but has been progressively strengthened in recent years by government policy initiatives and market mechanisms. Full article

The purpose of this study is to characterize, through an exploratory empirical diagnosis, the degree of development and preliminary association between circular economy capabilities and sustainability-oriented digital transformation capabilities in Chamber-affiliated SMEs in Lambayeque, Peru. Guided by three exploratory working hypotheses, the study expected intermediate levels of development, heterogeneous performance across dimensions, and a positive but non-confirmatory coupling between both capability families. A self-administered questionnaire with thirty Likert-type items measured four circular economy dimensions—circular design and eco-design, resource optimization, circular waste management, and circular business models—and four sustainability-oriented digital transformation dimensions—digital technology infrastructure, dynamic digital capabilities, sustainable digital strategy, and digital innovation culture. The initial database contained 111 complete Chamber-affiliated responses; however, seven large Chamber-affiliated firms were retained only as contextual comparators and were excluded from all statistical processing. Consequently, all descriptive, psychometric, and SEM results were calculated using the final analytical sample of 104 micro-, small-, and medium-sized enterprises. The findings show intermediate development in both constructs, higher perceived performance in digital innovation culture and resource optimization, and lower performance in digital technology infrastructure, reverse logistics, platforms enabling circularity, and monetization of circular models. The latent association between the two higher-order constructs was very high (β = 0.985, p < 0.001); however, because global fit indices were below conventional thresholds, this coefficient is interpreted as preliminary evidence of empirical overlap and capability co-occurrence rather than confirmatory evidence of a validated structural model or causal integration. Full article

Repeated sit-to-stand and stand-to-sit transitions load the knee extensors and may contribute to work-related musculoskeletal disorders. Reducing this load requires assistive devices and monitoring of knee function, which depend on real-time onset/offset detection and direction-aware classification of each transition. However, no prior wearable surface electromyographic system has delivered this capability for real-time. This study presents a deep learning method that computes both onset/offset detection and direction discrimination of sit-to-stand and stand-to-sit in a developed wearable surface electromyographic system in real-time. Two ESP32-S3 nodes and a hub record from the vastus lateralis and vastus medialis and run a per-burst convolutional detector, while the hub runs a dual-branch classifier with seventeen handcrafted features. Trained offline on the public Gait120 dataset, the networks are deployed unchanged with embedded-firmware parity to the MATLAB reference. Under leave-one-subject-out evaluation on Gait120, the offline classifier separated each transition with 99.6% accuracy and the detector achieved 96.6% completeness. In real-time recordings from thirty healthy adults, the system retained 85.6% classification and 82.0% detection accuracy, with ≈100 ms latency and a 618 KB network footprint. Results show that a low-power wearable delivers combined detection and phase discrimination in real-time, supporting the potential application in assistive-device control and knee-joint monitoring. Full article

Amid escalating climate change and China’s “dual carbon” policy imperative, resource-based listed firms face urgent yet complex green transition challenges that single-factor approaches fail to adequately explain. Grounded in complex adaptive systems theory and the Technology-Organization-Environment (TOE) framework, this study employs dynamic fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze a panel of 160 Chinese resource-based A-share listed firms (2019–2024) to identify configurational pathways driving Environmental, Social, and Governance (ESG) performance. The results reveal eight high-performance pathways categorized into three archetypal patterns and six low-performance pathways reflecting distinct systemic misalignment traps. High- and low-performance pathways are causally asymmetric. Dynamic analysis further uncovers strong path-locking effects and divergent regional transition strategies. These findings advance configurational theory of corporate sustainability and offer strategic guidance for resource-dependent firms and policymakers. Full article

Against the dual backdrop of China’s rapid economic growth and the continuous expansion of the income gap between urban and rural areas, income mobility, as a dynamic indicator for measuring social opportunity equity, is of great significance for breaking through class solidification and promoting common prosperity. Based on the tracking data of the China Household Finance Survey (CHFS) from 2015 to 2019, this paper systematically examines the mechanism and heterogeneity of the impact of financial literacy on household income mobility from the perspective of urban–rural comparison by constructing the Markov transition matrix and the Ordered Probit model. The findings are as follows: first, financial literacy significantly enhances household income mobility in both urban and rural areas, but there is a significant urban–rural difference. The baseline regression shows that financial literacy has a stronger promoting effect on urban households, while the endogeneity test further reveals that there is an underestimation of urban–rural heterogeneity in its impact; second, the mechanism test shows that financial literacy promotes household income mobility by influencing financial behaviour; third, from the perspective of heterogeneity analysis, regional heterogeneity, the effect of urban households and the eastern and northeastern regions is more prominent. Income heterogeneity, the role of financial literacy in promoting income mobility, is particularly prominent among low-income groups in both urban and rural areas. This study not only provides an analytical framework from static to dynamic for understanding the economic empowerment effect of financial literacy, but also deepens the academic discussion on how financial capacity promotes opportunity equity, and its findings on the heterogeneity between urban and rural areas, regions, and income groups offer crucial micro-evidence for formulating more targeted differentiated policies that can effectively avoid the Matthew effect. Full article

The global proliferation of plastic waste has made the search for sustainable chemical recycling strategies imperative to transition toward a circular bioeconomy. This study presents a dual-valorization approach for polylactic acid (PLA) waste, utilizing it both as a sustainable precursor for g-C₃N₄ catalyst synthesis and as a sacrificial agent for green hydrogen production via photoreforming. Platinum-modified graphitic carbon nitride catalysts were synthesized and evaluated using pure lactic acid and commercial PLA waste under solar-simulated irradiation. Results identified C₃N₄-NaOH-Pt as the most active material, while the simultaneous one-pot depolymerization/photoreforming of macroscopic PLA fragments exhibited a peak H₂ production rate of 1.5 mmol·h⁻¹·g⁻¹, remarkably surpassing both the pure monomer model and pre-depolymerized solutions. This enhanced performance is tentatively attributed to a “controlled release” mechanism that prevents catalyst surface saturation and minimizes light scattering effects inherent to fine powders. The study concludes that maintaining the macroscopic integrity of PLA waste provides a strategic advantage for chemical reforming by eliminating energy-intensive grinding and pretreatment. Future research into diverse operational and chemical parameters, including temperature and base-addition strategies, will be essential for scaling solar-driven upcycling technologies. Full article

Natural product-based upconversion photosensitizers (PSs) have emerged as innovative agents in photodynamic therapy (PDT). Lanthanide ions such as Yb³⁺, Er³⁺, Nd³⁺, Gd³⁺, and Tm³⁺ have unique photophysical properties and biocompatibility, exhibiting sharp 4f–4f transitions and long-lived excited states involving the dual luminescence processes, upconversion and downshifting. Natural product photosensitizers (PSs), including coumarin, riboflavin, curcumin, chlorophyll derivatives, and hypocrellin, offer superior safety profiles compared with synthetic PSs. Recent advances in upconversion nanoparticles (UCNPs) and upconversion–downshifting nanoparticles (UDNPs) for the generation of ROS in PDT have been evaluated. This narrative review surveyed the literature published between 1995 and 2026 across multiple electronic databases, including WanFang Data, PubMed, ScienceDirect, Scopus, Web of Science, Springer Link, SciFinder, and the China National Knowledge Infrastructure (CNKI), without language restrictions. The search focused on studies related to photodynamic therapy, lanthanide photophysics, and natural product photosensitizers such as coumarin, riboflavin, curcumin, chlorophyll derivatives, and hypocrellin, as well as nanoplatforms involving upconversion (UCNPs) and upconversion–downshifting nanoparticles (UDNPs). Relevant publications were identified and synthesized to integrate advances in lanthanide photophysics, natural product PSs, and nanoplatform design into a conceptual framework. Natural product-based upconversion PSs for PDT have the advantages of low dark toxicity, biocompatibility, and multimodal actions. Lanthanide-enhanced systems overcome these issues, including shallow tissue penetration, photobleaching, and relatively low singlet oxygen quantum yields. Thus, natural product-based upconversion PSs in PDT are an innovative strategy, but bridging preclinical promise with clinical translation remains a critical future challenge. Full article

By 2030, older workers (55–64) will comprise a quarter of the global labour force, making their retirement a critical policy concern. Yet, a gap remains regarding how informal caregiving shapes early retirement for economically vulnerable “ALICE” (Asset-Limited, Income-Constrained, Employed) populations. This qualitative descriptive study explores this intersection in Hong Kong through a theoretical framework integrating linked lives to highlight relational drivers of workforce exit, cumulative disadvantage to contextualize lifelong financial precarity, and the resource-based dynamic perspective to explain how fluctuating resources impact adjustment. A purposive subset of eight primary caregivers was selected for separate analysis from a broader study of 51 working-poor early retirees to specifically examine the intense physical and economic strains unique to full-time eldercare. We identified five themes through reflexive thematic analysis of interview data: (1) caregiving as a primary driver of early retirement; (2) adjusting to the loss of work and value; (3) life on standby; (4) anxiety over the future; and (5) multi-layered resources for adjustment. These findings illustrate how deeply caregiving can complicate the retirement transition for this demographic. Moving forward, a multilevel approach—integrating individual and environmental resources—can help address these complex challenges and inform practice and policy, such as strengthening community-level supports and statutory leave. Full article

Shared energy storage is emerging as a pivotal institutional and technological arrangement for increasing power-system flexibility, integrating renewable electricity, and improving the allocation of storage resources. Focusing on power generation enterprises, this study develops a Technology–Organization–Environment (TOE) model that incorporates perceived risk and perceived benefit to explain how participation intentions toward shared energy storage are formed. Structural equation modeling and fuzzy-set qualitative comparative analysis (fsQCA) show that technological compatibility, technological maturity, top-management support, organizational slack, subjective norms, policy support, and market competition shape participation intentions by reducing perceived risk and strengthening perceived benefit. Perceived risk significantly suppresses participation intention, whereas perceived benefit significantly promotes it. The fsQCA results identify three configurational pathways to high participation intention: benefit–risk co-activation, market competition and benefit-driven participation, and market–policy dual activation. These findings show that participation in shared energy storage is generated by interdependent technological, organizational, and environmental conditions rather than by any single determinant. The study offers evidence for refining a shared-energy-storage policy and improving business models in the transition to a new power system. Full article

Multi-asset portfolio optimization under non-stationary financial markets requires robust state representations across market phase transitions. This paper proposes distribution-robust graph representation learning for portfolio optimization (DR-GRL-PO), which learns asset-dependency graph representations as robust cross-asset structural priors for policy learning. DR-GRL-PO consists of a market-phase invariant graph contrastive encoding module (MPIGCE), a distribution-robust predictive coding module (DRPC), and a portfolio policy learning module (PPL). MPIGCE learns invariant structural priors from Spearman-based asset-dependency graphs, DRPC incorporates these priors into dual-scale predictive branches with invariant ranking consistency, and PPL integrates structural priors and predictive states for dynamic asset allocation. The model is evaluated on three separate datasets for portfolio construction, using daily data from CSI-300 (2011–2021), NASDAQ-100 (2011–2021), and Cryptocurrency (2017–2026) markets. The results show that DR-GRL-PO mainly improves wealth growth, annualized profitability, and risk-adjusted performance, while maintaining a certain degree of downside-risk control and a favorable upside–downside return balance. Its performance across separate market categories and market phases provides evidence of robustness under non-stationary market conditions. These findings indicate that robust cross-asset structural priors can support more reliable dynamic portfolio allocation. Full article

The global proliferation of microplastics demands sustainable remediation alternatives to energy-intensive conventional disposal methods, shifting research focus toward polymer-degrading microbial communities within the “plastisphere.” The primary objectives of this study are twofold: first, to systematically decode the sequential biophysical mechanisms underlying microplastic colonization and enzymatic degradation; and second, to establish an empirically validated, scalable treatment framework that employs both a novel biological isolate and a hybrid engineering architecture. Experimentally, we investigate the multi-stage colonization process and demonstrate that “Phase Zero” conditioning films modulate the surface zeta potential (ζ) to anchor pioneer r-strategists. To evaluate degradative efficacy under accelerated conditions without abiotic pretreatment, the newly isolated carp gut strain Hafnia paralvei UUNT_MP29 was exposed to pristine low-density polyethylene (LDPE) and polystyrene (PS). Over a 16-day biotic incubation period, structural and chemical alterations were distinctly polymer-specific: bacterial action on the polyolefin LDPE yielded a Carbonyl Index of 0.4594 and a 10.95 °C reduction in thermal stability (T_max), whereas the aromatic PS matrix exhibited a Carbonyl Index of 0.3235 alongside a 10.80 °C decrease in T_max, with both substrates showing intense surface pitting. To standardize these complex tracking metrics across the field, a universal four-pillar Biodegradability Index (BI) was formulated. Based on these findings, we recommend an immediate transition from passive waste containment to a closed-loop engineering approach. Specifically, we propose integrating an artificial intelligence (AI)-managed hybrid bioprocess configuration that couples Advanced Oxidation Processes (AOPs) with Membrane Bioreactors (MBRs). This dual-stage configuration is recommended to overcome polyolefin crystallinity, accelerate stoichiometric mineralization, and actively mitigate additive-mediated toxicity at the industrial scale, providing a vital blueprint for the circular bio-economy. Full article

The low-carbon bus transition depends on the electricity and hydrogen pathways that support vehicle operation. This study develops a regionalized WTW mechanism diagnosis for six provincial electricity–hydrogen–bus systems in Northwest China and an adjacent energy-output region. Localized GREET pathway intensities, fleet-level aggregation, Logarithmic Mean Divisia Index (LMDI) decomposition, B0-upstream HFCB exposure tests, local A/B/C perturbations and HFCB intensity-sensitivity checks are combined to evaluate greenhouse gas (GHG) emissions, primary-energy consumption and primary-water burden. The results show that B0–S1 fleet-level GHG reductions range from 9.86% in Shaanxi to 73.81% in Qinghai, while Gansu records the largest absolute decrease, from 126.52 to 41.21 kg CO₂-eq/hkm. GHG, primary-energy and primary-water responses diverge: in Qinghai, S1–S2 GHG intensity decreases by 28.76%, while primary-energy consumption and primary-water burden increase by 1.99% and 21.31%, respectively. LMDI results reveal different attribution mechanisms, including dual-driver reduction in Gansu and a counteracting composition effect in Shaanxi. Exposure, perturbation and sensitivity tests indicate that hydrogen-related outcomes depend on pathway intensity, fleet share and break-even margins. The findings support pathway-conditioned screening that coordinates BEB and HFCB expansion with electricity decarbonization, renewable-hydrogen availability and multi-indicator burden assessment. Full article

Corrosion of carbon steel components represents a major economic and safety challenge in industrial applications, motivating the development of protective thin film coatings with optimized deposition parameters. This study investigates the deposition of nickel nitride (Ni-N) thin films on AISI 1016 carbon steel and silicon (111) wafers by reactive radio-frequency (r.f.) magnetron sputtering at three power levels: 150, 175, and 200 W. Surface color, film thickness, roughness, crystal structure, mechanical properties, and electrochemical behavior were evaluated using optical microscopy, stylus profilometry, atomic force microscopy (AFM), X-ray diffraction (XRD), nanoindentation, and potentiodynamic polarization combined with electrochemical impedance spectroscopy (EIS). Increasing r.f.-power produced systematic surface color changes consistent with variations in film thickness, which ranged from approximately 25.0 to 50.7 nm. Higher deposition power promoted smoother surfaces, with average roughness (Ra) decreasing from 64.28 nm at 150 W to 20.62 nm at 200 W. XRD analysis revealed a monocrystalline Ni₃N hexagonal close-packed (HCP) phase at 150 W, transitioning to a dual-phase Ni₃N (HCP) and Ni₄N face-centered cubic (FCC) microstructure at 175 and 200 W. The highest hardness (11.80 ± 3.34 GPa) was recorded at 150 W, accompanied by pop-in events attributed to dislocation nucleation in the HCP lattice. Electrochemical evaluation in 3.5 wt.% NaCl solution demonstrated that films deposited at 150 and 175 W exhibited corrosion current densities and rates exceeding those of bare steel, confirming that these conditions accelerate rather than inhibit corrosion. Only the film deposited at 200 W achieved superior corrosion protection, with a corrosion current density and rate approximately 50% lower than bare steel, attributed to its denser microstructure and smoother surface morphology. These findings demonstrate that r.f. power is a critical parameter governing the properties of Ni-N thin films, and that careful optimization of deposition conditions is essential before recommending such coatings for industrial corrosion-protective applications. Full article

Terrestrial ecosystems serve as key carbon reservoirs and contribute substantially to global carbon cycling and climate regulation. Shaanxi Province (SP) is located along China’s north–south geographical boundary and climatic transition zone, making it crucial to understand how carbon stocks change within its ecosystems. This study analyzed the land-use patterns, influencing factors, and spatiotemporal dynamics of carbon storage across three regions (Shanbei, Guanzhong, and Shannan) in SP. The results indicated that: (1) From 2000 to 2020, cropland and barren land areas in SP decreased significantly, while the forest land area increased markedly. Total carbon storage in SP increased from 1688.55 Tg in 2000 to 1726.12 Tg in 2020, with the highest accumulation observed in Shannan, followed by Shanbei and Guanzhong. (2) Forest land acted as the most significant carbon sink; its contribution to SP’s total carbon storage increased from 54.98% in 2000 to 60.28% in 2020. (3) Carbon storage across the three regions was positively correlated with elevation, slope, soil silt content, and precipitation, but negatively correlated with soil sand content, gross domestic product, and population distribution. (4) Geographical detector analysis identified precipitation as the key influencing factor for carbon storage in Shanbei and Guanzhong, whereas the primary factors in Shannan were temperature, elevation, and slope. This study recommends future land use priorities: maintaining grassland dominance in Shanbei, scientifically optimizing the planning of cropland and impervious land in Guanzhong, and sustaining current forest protection and management in Shannan. These results provide vital quantitative support and important references for ecologically sustainable development and the realization of China’s dual-carbon goals in SP. Full article

To achieve dual-carbon goals and advance the sustainable development of the automotive industry, China’s Dual-Credit Policy serves as the core long-term mechanism for the low-carbon transition of the automotive industry. Given the coexistence of fuel vehicles (FVs) and new energy vehicles (NEVs) in China, existing research often overemphasizes production output while neglecting energy consumption control, and focuses predominantly on NEVs at the expense of FV optimization. To address these gaps, this paper treats FV fuel consumption and NEV energy efficiency as core endogenous decision variables. We construct profit-maximizing optimal production decision models for both types of manufacturers under the Dual-Credit Policy. Through mathematical derivation, numerical simulations, and empirical tests using actual industrial parameters, this study verifies the existence and uniqueness of optimal solutions. It clarifies the influence mechanisms of policy and market factors on corporate energy decisions and identifies the rules of strategy dominance. The findings reveal that the optimal fuel consumption decisions of FV manufacturers exhibit distinct piecewise patterns and critical threshold effects. Specifically, credit prices, NEV quotas, and fuel consumption standards determine the dominance of compliant (low-consumption) versus non-compliant (high-consumption) strategies. Furthermore, the policy exerts a significant market-oriented positive incentive on the energy efficiency upgrading of NEV manufacturers, with credit prices, market demand, and R&D costs acting as core constraints. Notably, the transition-guiding effect of the policy has clear effective boundaries, and its efficacy highly depends on the alignment between parameter design and market conditions. This research provides theoretical support for manufacturers to formulate energy-optimized production decisions and offers actionable references for the continuous optimization of the Dual-Credit Policy system and the sustainable low-carbon transformation of China’s automotive sector. Full article