Search Results (2,087)

Previous research has primarily focused on the restorative effects of environments on the general population, often overlooking the specific restorative capacity of urban settings for the disabled population. There is a lack of comprehensive investigation into the interaction between accessibility elements and urban restorativeness. This study, conducted in Shenzhen, Guangdong Province, China, categorizes streetscape accessibility elements for the disabled population and develops a recognition system based on an enhanced DeeplabV3+ framework. Semantic segmentation of streetscape accessibility elements was performed using 201,860 sampling points and 807,440 street view images. This study employed a combination of TrueSkill scoring, sentiment semantic analysis, LDA topic modeling, and LAB color clustering to quantify and visualize urban restorativeness. The impact of accessibility elements on urban restorativeness was explored using the XGBoost-SHAP model. Results indicate significant effects of architectural space constraints and high-density motor vehicle distribution on the safety of the disabled population’s mobility. The low pixel ratio of accessibility facilities and signs indicates insufficient infrastructure, while high landscape recognition rates exhibit significant spatial coverage heterogeneity. Detection rates for the disabled population in street views are nearly zero, highlighting a severe lack of inclusivity in pedestrian environments. Urban restorativeness exhibited a pattern of being higher in the south and east, and lower in the north and west. Among the accessibility elements, public green spaces (PGS) contributed the most to urban restorativeness, accounting for 25% of the impact, and the study elucidates the mechanisms through which various elements affect urban restorativeness. This absence stems from spatial competition, missing co-design, threshold effect conflicts, and color interference mechanisms. This research breaks away from traditional linear analytical frameworks and reveals the complex non-linear relationship between accessibility elements and urban restorativeness through the XGBoost-SHAP model, providing a quantitative decision-making tool for planning accessible environments in high-density cities. Full article

Agricultural carbon reduction is increasingly important for advancing low-carbon and sustainable agricultural development. Using provincial panel data for the Yangtze River Economic Belt (YEB) from 2007 to 2022, this study examines the spatiotemporal evolution of the digital economy (DE) and agricultural carbon emissions (ACE), and applies a two-way fixed-effects model with mediation and moderation analyses. The results show that digital economy (DE) increased steadily across the YEB, while agricultural carbon emissions (ACE) showed clear spatiotemporal variation. Digital economy (DE) is significantly negatively associated with agricultural carbon emissions (ACE), indicating that digital development can support agricultural carbon reduction. The Bootstrap results show that technological innovation and industrial agglomeration are statistically supported mediating pathways. Technological innovation is the primary mechanism, accounting for 44.02% of the total effect, while industrial agglomeration has a smaller but significant mediation share of 0.25%. Industrial structure optimization and fiscal investment are not confirmed as robust indirect pathways. The moderation results show that environmental regulation weakens the negative DE–ACE relationship, whereas agricultural fiscal expenditure strengthens it. These findings highlight the importance of green innovation, agglomeration effects, and supportive fiscal conditions in digital agricultural carbon reduction. Full article

Accurate diagnosis of potato nitrogen status is critical for optimized fertilizer management and sustaining productivity. We used data from nine field experiments (2010–2018) across major potato-producing regions in northern China to develop a regional critical nitrogen dilution curve via a Bayesian hierarchical model. The curve, Nc = 4.179 × DW^−0.417 (DW = whole-plant dry matter), provided the basis for calculating the nitrogen nutrition index (NNI), which was related to canopy spectral indices from a GreenSeeker sensor. Relationships between spectral indices and NNI were strongly growth-stage dependent. The tuber initiation–bulking period, approximately 29–70 days after emergence (DAE), represented the effective phenological window, with 29–55 DAE as the primary operational window for quantitative spectral diagnosis. Stage-specific ratio vegetation index (RVI) showed the most consistent association with NNI, whereas pooled whole-season models had low predictive power. The Bayesian framework quantified uncertainty, emphasizing that near-threshold NNI values require cautious interpretation. The resulting regional-average reference supports rapid field diagnosis of potato N status while accounting for cultivar, year, and site variability. These findings provide practical guidance for stage-specific N management and demonstrate the importance of growth-stage-aware spectral assessment in operational decision-making. Full article

To explore the life-cycle carbon emission characteristics of zero-carbon buildings and the economic feasibility of carbon reduction strategies, this study takes the Life Cycle Assessment (LCA) method as the core and constructs a full life-cycle carbon emission accounting system for buildings covering building material production, transportation, construction, operation and demolition in accordance with the standards. Taking the Jinan Zero-Carbon Operation Center Project as a case, this paper systematically calculates its carbon emissions at all stages of the life cycle, identifies the key carbon emission stages and core influencing factors, and comparatively analyzes the economic efficiency of two carbon offset strategies, namely increasing photovoltaic power generation and purchasing green electricity, for the two goals of zero carbon in the operation stage and zero carbon in the full life cycle by using the equivalent annual cost (EAC) method. The results show that the total life-cycle carbon emissions of the case project reach 149,974.04 tCO₂e, with the operation stage and building material production stage being the core carbon emission stages, accounting for 75.50% and 24.19% respectively, while the carbon emissions in the transportation, construction and demolition stages account for a negligible proportion. The economic analysis indicates that although the increase in photovoltaic power generation systems involves a high initial investment, its equivalent annual cost is significantly lower than that of the green electricity purchase strategy. Comparative analysis using equivalent annual costs shows that adding a photovoltaic system achieves equivalent annual costs of $206,589.58 and $273,630.84 for operation stage and life-cycle zero-carbon targets, respectively. In contrast, purchasing green power certificates annually to achieve the same goals incurs equivalent annual costs of $316,223.13 and $317,096.45, representing annual savings of 34.67% and 13.71%. The carbon emission accounting method constructed in this study can provide a reference for the life-cycle carbon quantification of zero-carbon buildings, and the conclusions on the economic efficiency of carbon reduction strategies can serve as an economic decision-making basis for the planning, design and carbon reduction scheme selection of zero-carbon buildings. Full article

This study examines the determinants of carbon dioxide (CO₂) emissions across 25 European Union countries over the period 2000–2021, with particular emphasis on the roles of environmental taxation and green innovation in shaping environmental sustainability. The analysis is grounded in ecological modernization theory, endogenous growth theory, and the Environmental Kuznets Curve hypothesis, which collectively explain the long-run and dynamic interactions between environmental policy, economic activity, structural transformation, and environmental outcomes. To ensure robust empirical inference, this study applies a comprehensive econometric framework that accounts for cross-sectional dependence, heterogeneity, non-stationarity, cointegration, and endogeneity. The empirical strategy begins with Pesaran cross-sectional dependence tests and slope heterogeneity diagnostics, followed by second-generation panel unit root tests (Pesaran CADF/CIPS) and Westerlund cointegration tests to establish the existence of long-run equilibrium relationships among the variables. Long-run coefficients are estimated using Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), Canonical Cointegrating Regression (CCR), and Common Correlated Effects Mean Group (CCEMG) estimators. In addition, the Panel Autoregressive Distributed Lag (ARDL) model is employed to capture both short-run dynamics and long-run adjustment processes, while the System Generalized Method of Moments (System GMM) estimator addresses potential endogeneity, reverse causality, omitted variable bias, and dynamic persistence in CO₂ emissions. The empirical results indicate that environmental taxation has a positive and statistically significant association with CO₂ emissions, suggesting that current fiscal environmental policies in EU-25 countries may not yet be sufficiently effective in discouraging pollution-intensive activities. In contrast, green innovation is found to significantly reduce CO₂ emissions, underscoring the critical role of innovation-driven environmental investment and technological progress in improving environmental quality. Economic growth, exports, and urbanization are associated with higher emissions, while imports contribute to emission reductions, reflecting differences between domestic production-based effects and trade-related structural adjustments. The System GMM results further confirm the persistence of CO₂ emissions over time and validate the robustness of the long-run relationships identified by alternative estimators. Likewise, the CCEMG and Panel ARDL results support the stability and consistency of the findings under conditions of cross-sectional dependence and heterogeneous country dynamics. Taken together, the results highlight the importance of integrating environmental taxation with green innovation policies, innovation-driven investment, and sustainable trade policies to achieve long-term emission reductions in the European Union. This study contributes to the environmental economics literature by providing robust empirical evidence using second-generation panel econometric techniques that explicitly address cross-sectional dependence, heterogeneity, and endogeneity in the analysis of environmental sustainability. Full article

Universities serve as catalysts for knowledge creation across territories, promoting innovation and economic development through different channels. This paper investigates the role of university graduates as a location determinant of new green-tech-based firms (NGTBFs) across Italian NUTS-3 regions over the period 2011–2017. We examine whether universities, as providers of high-skilled human capital, affect the spatial distribution of new green ventures. Adopting a patent-based definition of NGTBFs and an econometric framework accounting for regional heterogeneity, we analyse the impact of university graduates on green firm creation. The results show that higher education fosters green entrepreneurship primarily through the channel of producing doctoral and STEM-oriented graduates, who serve as key drivers of NGTBF formation. Interestingly, the analysis reveals marked spatial heterogeneity across Italy’s North–South divide, with stronger associations of PhD and STEM graduates in Southern regions, where specialised human capital appears to compensate for weaker innovation systems. These findings deliver clear policy implications, suggesting that strategies aimed at promoting green entrepreneurship should prioritise advanced, STEM-oriented human capital and explicitly account for regional contexts, rather than relying on uniform higher education expansion approaches. Full article

Reducing building energy consumption is central to EU climate-neutrality targets and to sustainable development goals: buildings account for around 40% of EU final energy consumption, placing Building Energy Management Systems (BEMS) at the intersection of the European Green Deal and the EU Artificial Intelligence Act. A scoping review following PRISMA-ScR guidelines charted 61 Machine Learning (ML) for BEMS papers (2020–2026) across three sub-domains (load forecasting and energy monitoring, HVAC control, and demand response), using a nine-point Technology Readiness Level (TRL) rubric and three Trustworthy AI (TAI) dimensions (Privacy & Data Governance, Robustness, and Transparency). The review finds that 90.2% of papers remain at the development stage (TRL 4–6), with no multi-site production deployment documented. TAI coverage is heterogeneous at publication level: transparency is addressed in only 3 of 61 papers (4.9%), and privacy provisions (the best-covered ALTAI dimension) are concentrated in demand-response papers (9 of 17, 52.9%), largely via Federated Learning (6 of 9 privacy-tagged papers). A three-level EU AI Act risk classification identifies 23 borderline-candidacy papers (37.7%), predominantly Reinforcement Learning-based HVAC control systems, whose high-risk proximity cannot be resolved at abstract level; explicit compliance engagement is absent from all 61 mapped sources, including the 22 papers published after the Act entered into force in August 2024. The findings document adouble readiness gap: a TRL ceiling co-located with limited documented engagement with TAI obligations and EU AI Act compliance at publication level. Closing this gap is necessary before AI-driven building energy management can be deployed at scale under EU governance requirements. Full article

In this paper, we investigate the existence and positivity of solutions for a class of twelfth-order nonlinear boundary value problems that naturally arise in the mathematical modeling of elastic and micro-mechanical systems. The considered model incorporates higher-order derivatives to account for nonlocal and gradient effects that commonly appear in the analysis of micro- and nano-scale elastic structures. By employing the Leray–Schauder nonlinear alternative and fixed point theorems, we establish sufficient conditions for the existence of at least one positive solution. The analysis relies on the explicit construction and properties of the associated Green’s function, which plays a fundamental role in deriving upper and lower bounds for the nonlinear term. The obtained results extend and generalize earlier works on sixth, eighth and tenth-order problems to the twelfth-order case. Finally, numerical examples are presented to illustrate the applicability and accuracy of the theoretical findings. The results provide a rigorous analytical foundation for the study of high-order elastic models and micro-scale structural stability. Full article

Environmental supply-chain governance increasingly requires firms to trace climate accountability across buyer–supplier relationships. This study examines whether downstream customer climate pressure is associated with suppliers’ green supply-chain management and value-chain carbon accountability among Chinese listed firms. We construct an exposure-weighted customer pressure measure by combining disclosed top-customer relationships with customer climate-accountability signals, and we decompose this measure into disclosure-based and non-disclosure-based components so that symbolic and substantive accountability can be separated. We then link this measure to supplier green supply-chain indicators, value-chain carbon-disclosure components, Scope 3 disclosure, environmental investment, and reported environmental performance indicators, including air emissions, water pollutant discharge, resource consumption, and environmental tax. Using firm-year panel regressions with fixed effects, alternative pressure measures, selection corrections, and extended outcome tests, we find an association between customer climate pressure and supplier value-chain disclosure. The depth of the association is concentrated where customer carbon-disclosure visibility is observed and is not separately identified in the smaller climate-only subsample, while the value-chain interaction association is positive but imprecisely estimated there. The value-chain disclosure associations are robust to a year-stratified randomization-inference placebo test. We do not find evidence that customer pressure is associated with supplier emissions, resource use, environmental investment, or environmental tax in the available matched samples. The pattern is consistent with symbolic compliance in supply-chain carbon accountability: customer disclosure visibility maps into supplier disclosure visibility, while we do not observe parallel movement in substantive environmental outcomes. The central finding is therefore that downstream customer climate pressure is associated with what suppliers disclose rather than with what they emit, shaping supplier disclosure behavior rather than substantive emission reduction. The estimates apply to supplier-year observations with disclosed and mappable listed-customer links, which we treat as the scope condition of the study rather than as an incidental data limitation. Full article

This study critically investigates the scientific literature on the intersection of management accounting and intellectual capital using a bibliometric performance analysis and science-mapping approach. Drawing on a sample of 59 publications from the Scopus and Web of Science databases, the paper maps the intellectual structure, key contributors, and thematic evolution of the field. This study conceptualizes management accounting not merely as a neutral technical system but as a socio-political mechanism that shapes how intellectual capital is rendered visible, measurable, and governable within organizations. The findings identify five dominant research clusters (intellectual capital and corporate strategy, management accounting and performance, green intellectual capital, digitalization and value creation, and management control and intangibles), revealing how accounting practices actively participate in constructing organizational realities and legitimizing particular forms of value and knowledge. The analysis highlights that measurement and reporting practices privilege certain dimensions of intellectual capital while potentially obscuring others, raising critical questions about power, visibility, and accountability in knowledge-based economies. In particular, the growing emphasis on digitalization and sustainability reflects shifting governance regimes in which accounting systems extend their influence over organizational conduct and strategic decision-making. By integrating bibliometric techniques with a critical interpretive lens, this study contributes to the literature by reframing management accounting as a key site where knowledge, control, and organizational value are negotiated. It also identifies gaps for future research, particularly regarding the ethical and political implications of accounting for intangible resources in increasingly digital and transparency-driven environments. Full article

Deep eutectic solvent (DES) extraction is a green and efficient technology. As a substitute for organic reagents, DESs are widely used to extract active ingredients from traditional Chinese medicine. This study established an environmentally friendly and efficient method for extracting astaxanthin (AST) from Phaffia rhodozyma (PR) using ultrasound-assisted deep eutectic solvents (DESs-UAE). The astaxanthin content was determined by high-performance liquid chromatography (HPLC). Six types of deep eutectic solvents composed of DL-menthol and selected hydrogen bond donors were prepared and evaluated, among which the DL-menthol–acetic acid system showed superior extraction performance. Response surface methodology (RSM) was employed to optimize extraction parameters (ultrasonic power, time, and temperature), and the optimal conditions were determined as follows: ultrasonic power 420 W, ultrasonic time 20 min, and ultrasonic temperature 60 °C, achieving an AST extraction rate of 62% (2.49 mg/g). Compared with conventional organic solvent extraction, DESs exhibited a significantly higher AST extraction rate from PR, except for dimethyl sulfoxide (DMSO). Scanning electron microscopy (SEM) analysis demonstrated that DES-UAE treatment disrupted the cellular structure of PR, resulting in numerous surface pores; this facilitated the release of intracellular bioactive components and significantly improved AST extraction efficiency. The PR extract showed no significant cytotoxicity and could effectively promote L929 cell proliferation. It concentration-dependently increased superoxide dismutase (SOD) activity and decreased malondialdehyde (MDA) content in H₂O₂-induced oxidative stress L929 cells, thereby alleviating oxidative damage. Additionally, it concentration-dependently upregulated type I collagen expression in these cells, ameliorated the decline in collagen synthesis function, and exerted a protective effect against cellular oxidative damage. This study provides a green alternative to toxic solvents and offers important theoretical and chemical support for the extraction of natural products and the high-value utilization of Phaffia rhodozyma (PR). Deep eutectic solvents have emerged as promising green alternatives to hazardous organic solvents, yet hydrophobic DESs tailored for lipophilic astaxanthin extraction from Phaffia rhodozyma and the linkage between extraction performance and anti-aging bioactivity remain insufficiently explored. Here, an ultrasound-assisted hydrophobic deep eutectic solvent extraction strategy was constructed to acquire astaxanthin, aiming to overcome low efficiency and environmental risks of conventional organic extraction techniques. Six DL-menthol-based DESs were prepared and screened, and DL-menthol–acetic acid possessed the optimal extraction capacity. Key extraction parameters were optimized via response surface methodology, and the maximum astaxanthin extraction recovery reached 62% (2.49 mg/g) under 420 W ultrasonic power, 20 min treatment and 60 °C. This yield was markedly higher than that of most common organic solvents; though comparable extraction effect was obtained with DMSO, the adopted DES possessed outstanding low-toxic and biodegradable superiorities that DMSO cannot match. SEM characterization verified that the combined treatment destroyed yeast cell structure and formed porous morphology, which accelerated intracellular astaxanthin release and accounted for improved extraction efficiency. Biological assays proved the extract possessed good biosafety and proliferation-promoting effect on L929 cells. It effectively relieved cellular oxidative injury by elevating the SOD level and reducing MDA accumulation in oxidative damaged cells, and upregulated type I collagen expression to mitigate aging-related collagen loss. This work develops an eco-friendly and high-efficiency extraction route for lipophilic active substance, confirms the practical value of hydrophobic DES, and provides experimental basis for high-value utilization of Phaffia rhodozyma resources. Full article

Given the mounting pressure on agricultural water resources in China, which poses a threat to agricultural production safety, this study focuses on Hunan Province and analyzes five major crops over the period 2012–2022. Using a water footprint (WF) accounting method, it quantifies grey water from non-point source pollution and optimizes planting structures under 5%, 10%, and 15% water-saving scenarios. The results indicate that crop water footprints per unit mass follow the descending order: oilseeds, leaf tobacco, rice, fruits, and vegetables. Regarding water footprint components, green water footprint accounts for the largest proportion, playing a dominant role in crop water use, followed by grey water footprint. Blue water footprint and irrigation losses contribute the least. After optimization, under the 5% and 10% water-saving scenarios, the cultivated areas for rice, oilseeds, and leaf tobacco decreased compared to 2021, while those for vegetables and fruits increased. Under the 15% water-saving scenario, all crop planting areas were reduced relative to 2021. The optimized crop planting structure enhanced water use efficiency by 0.35%, 0.58% and 0.77%, respectively, under water-saving scenarios of 5%, 10% and 15%. These results provide a scientific basis for sustainable agricultural water management in Hunan Province. Full article

Although the European Union positions the bioeconomy at the core of its sustainability transition and the European Green Deal, the cross-country distribution of bioeconomy value-added associated with mission-oriented public R&D support remains highly uneven. This paper investigates how national researcher capacity (as a proxy of absorptive capacity) shapes the macroeconomic effectiveness of bioeconomy-oriented public R&D support, and how societal climate-oriented environmental concern acts as a direct structural driver of bioeconomy value-added. Using a panel dataset of 27 EU Member States from 2008 to 2020, the study constructs an original bioeconomy-specific measure of government budget appropriations for R&D (GBARD) and estimates two-way fixed-effects models with Driscoll–Kraay standard errors to account for cross-sectional dependence. The findings reveal a clear capacity-dependent conditional moderation effect: public R&D support is significantly associated with higher bioeconomy value-added only when a critical mass of researcher capacity is present. Sectoral disaggregation demonstrates that business enterprise researcher capacity acts as the primary transmission channel linking public funds to the market, whereas higher-education capacity shows no statistically significant short-to-medium-term moderating effect, consistent with the academic research commercialisation time lags documented in the literature. Additionally, societal climate-oriented environmental concern is positively associated with bioeconomy value-added in the baseline models, consistent with its role as a demand-side factor fostering receptive conditions for bio-based transitions. The study concludes that increasing mission-oriented R&D funding alone is likely insufficient; to successfully cross the “valley of death,” public R&D should be accompanied by complementary policies that build private-sector absorptive capacity and cultivate green market demand. Full article

Bifurcaria bifurcata R. Ross 1958 is a perennial brown seaweed belonging to the Sargassaceae family and represents an underexploited marine bioresource with promising applications in the sustainable food and pharmaceutical sectors. To support its sustainable valorization as an alternative to synthetic additives, this study provides a comprehensive evaluation of the chemical composition, bioactivity, and mineral profile of B. bifurcata to support its valorization. The essential oil, obtained by hydrodistillation and characterized by GC—MS, revealed a rich chemical profile comprising 51 compounds, of which 42 were identified, accounting for 95.37% of the total composition. The major constituents included 2′-hydroxy-4′,5′-dimethylacetophenone (13.11%), benzene, 1-ethyl-3,5-dimethyl- (9.79%), 1,5,9-cyclododecatriene, 1,5,9-trimethyl- (8.57%), and benzene, 1-(1,1-dimethylethyl)-4-methoxy- (8.52%). The essential oil exhibited moderate antioxidant activity under in vitro conditions, with a total antioxidant activity of 74.85 ± 2.78 mg E α-tocopherol/g and IC₅₀ values of 0.103 ± 0.004 (DPPH), 0.106 ± 0.002 (FRAP), and 2.672 ± 0.123 mg/mL (β-carotene bleaching assay). In addition, notable antibacterial activity was observed against Escherichia coli, Staphylococcus aureus, Bacillus subtilis, and Listeria monocytogenes, with inhibition zones ranging from 10 to 12 mm. Mineral analysis of the seaweed powder highlighted its nutritional importance, with high levels of organic matter, proteins, and sugars, along with substantial concentrations of essential macroelements (K, Ca, Mg, and Na) and trace elements (Fe, Mn, and Zn). Overall, these findings demonstrate the potential of B. bifurcata as a sustainable marine resource for the development of natural antioxidants, antimicrobial agents, and mineral-rich ingredients, contributing to environmentally friendly food systems and green pharmaceutical applications. Full article

Narrowing urban–rural disparities and advancing the green transition are two key imperatives for sustainable development, yet the relationship between green finance and urban–rural integration remains insufficiently examined. Using panel data for 30 Chinese provinces from 2013 to 2022, this study investigates how green finance is related to urban–rural integration, with particular attention to nonlinear patterns, institutional conditions, and spatial dependence. The results show a positive association between green finance and urban–rural integration. This result remains stable across alternative specifications and various sensitivity checks, including lagged specifications, the Oster bounds test, and placebo tests. Panel threshold models show stronger evidence of nonlinear heterogeneity for innovation capability, while the rationalization of industrial structure threshold provides only suggestive evidence. Interaction models indicate that the interaction terms involving intellectual property protection intensity and marketization level are positive and statistically significant. Estimates from the spatial Durbin model further show that green finance is associated with significant positive net spatial spillovers across neighboring regions, with the model-based indirect effect exceeding the direct effect as a net spatial association after accounting for negative conditional spatial dependence in urban–rural integration. Overall, this suggests that green finance is relevant not only to environmental objectives but also to coordinated and sustainable urban–rural development, with implications for SDGs 10 and 11. Full article