Search Results (283)

Ginkgo biloba is a dioecious species valued for landscaping and medicine, but rapid sex identification outside the flowering and fruiting stages remains challenging. Hyperspectral imaging offers a potential solution, though whether spectral sex markers are stable across phenological stages and can support a single year-round model is unclear. Here, leaf hyperspectral reflectance (400–1000 nm) was acquired from mature G. biloba at flowering (n = 360), green-leaf (n = 1395), and yellow-leaf (n = 243) stages. Sex identification models were built using multiple machine learning classifiers, with leaf flavonoid content as biochemical validation. Stage-specific models achieved optimal test accuracies of 96.67% (flowering, MSC + PLS-DA), 95.77% (green-leaf, raw spectra + LDA), and 97.12% (yellow-leaf, MSC + LDA). However, discriminative bands shifted from the visible (520–690 nm) at the green-leaf stage to the near-infrared (700–1000 nm) at the yellow-leaf stage, with almost no bands shared across all stages. Furthermore, cross-stage prediction accuracy dropped to near-chance levels (~50%). As t-SNE analysis revealed, the universal model had learned phenological rather than sex-specific information. In addition, flavonoid measurements revealed a highly significant sex × stage interaction (p < 0.001) and a reversal of the sex difference between green-leaf (male > female) and yellow-leaf (female > male) stages. Thus, the spectral and biochemical sex markers examined in this study varied substantially with phenology, and stage-specific models are required for practical sex identification in G. biloba. Full article

This study asks whether, and through which firm-level channels, the green data center (GDC) pilot improves corporate sustainability, and it aims to quantify the policy effect and identify its transmission pathways. Under the dual shift in digital transformation and low-carbon transition, the energy demand and emissions generated by data centers have become important constraints on the sustainability performance of firms. This study regards the 2015 “National Green Data Center Pilot Work Plan” as a quasi-experimental policy shock. In terms of methods, using panel observations of Chinese A-share companies listed in Shanghai or Shenzhen during 2010–2024, a difference-in-differences (DID) strategy is employed to estimate the effect of GDC policy and to identify its transmission pathways for corporate sustainability. In terms of results, the empirical estimates indicate that the GDC pilot improves corporate sustainability, and the effect is robust to fixed-effects specifications, an instrumental-variable strategy, propensity-score matching, and a placebo test. Mechanism tests show that the pilot works through three channels: greater green innovation output and technical value, reduced financing frictions, and enhanced green governance capacity. Heterogeneity tests further show that the effect is stronger among firms with a higher level of digital transformation, a larger share of skilled technical personnel, stronger internal control, and greater executive green awareness, and that it is clearer where regional environmental regulation is stricter and market competition is more intense. In terms of conclusions, by integrating institutional pressure theory with the resource-based view, this study explains how the GDC pilot is translated into a firm-level sustainability advantage, and it offers evidence for refining GDC policy design and advancing the coordinated digital and green transformation of enterprises. The novelty of the study lies in providing firm-level causal evidence within a unified “pressure-to-capability” framework, in opening the three transmission channels, and in specifying the technological, organizational, and environmental conditions under which the effect is stronger. Full article

Traditional financial distress early-warning models mostly rely on lagged structured financial indicators, which fail to capture the potential credit risks associated with the climate transition of green enterprises. Taking A-share listed green companies from 2015 to 2024 as research samples, this paper centers on the core research question of whether mandatory climate narratives in annual reports can deliver incremental risk warning information beyond accounting indicators. Based on textual data from annual reports, this study constructs a corporate climate resilience indicator by integrating word frequency statistics and sentiment analysis. Two data-partitioning schemes (random sampling and time-series extrapolation) are adopted to compare the predictive performance of four ensemble learning models. Extended tests are further conducted via SHAP values, partial dependence plots, polynomial Logit regression, interaction effect regression and grouped regression. The results indicate that the climate resilience indicator carries incremental information supplementary to financial indicators and possesses predictive power for financial distress. XGBoost demonstrates optimal adaptability to the hybrid feature framework, combining financial data and climate textual features. The climate resilience indicator exerts synergistic effects with financial variables and presents a non-linear statistical correlation with default probability. This study verifies that climate narratives disclosed in annual reports can serve as valid early-warning signals for credit risks. The conclusions provide empirical evidence for financial risk control, corporate disclosure management and the formulation of climate regulatory policies. Full article

This study examines whether green factory certification improves corporate capacity utilization. As a voluntary regulatory instrument for promoting green manufacturing, green factory certification may enhance firms’ production efficiency by providing legitimacy incentives and external recognition. Using Chinese A-share listed firms from 2010 to 2024, we employ a staggered difference-in-differences model and find that green factory certification significantly increases firms’ capacity utilization. Heterogeneity analyses show that this effect is stronger for firms in regions with stricter command-and-control environmental regulation, weaker market-based environmental regulation, greater financing constraints, and weaker product advantages. Mechanism tests indicate that political legitimacy and market legitimacy are two important channels through which certification improves capacity utilization. Further analyses show that certification-induced improvements in capacity utilization increase firm value and stock liquidity. This study provides empirical evidence on the real effects of green factory certification and highlights the role of voluntary environmental governance in promoting both sustainable transformation and industrial efficiency. Full article

Accurate detection of citrus leaf and fruit diseases is important for precision orchard management. However, natural orchard images often contain small disease symptoms, leaf and fruit overlap, illumination variation, and cluttered backgrounds, making reliable detection challenging. This study proposes LDTC-YOLO (LDTC denotes Lightweight Detection for Typical Citrus Diseases), a lightweight YOLOv8n-based detection model for typical citrus leaf and fruit diseases under natural orchard conditions. To improve detection accuracy and model compactness, LDTC-YOLO integrates an Adaptive Feature Pyramid Network for cross-level feature fusion, Coordinate Attention for disease-region feature enhancement, a Lightweight Shared Convolutional Detection head for reducing parameter redundancy, and Wise-IoU for bounding-box regression optimization. In addition, a self-collected handheld citrus disease dataset, HOCD-4, was constructed using close-range smartphone images captured under natural illumination. The dataset covers leaf and fruit symptoms of four typical citrus diseases: Huanglongbing/citrus greening, black spot, canker, and melanose. On the HOCD-4 test set, averaged across the four disease categories, LDTC-YOLO achieved precision, recall, mAP@0.5, and mAP@0.5:0.95 values of 0.915, 0.843, 0.894, and 0.648, respectively. These results indicate that LDTC-YOLO improves detection performance while maintaining a compact and efficient model profile, providing a potential reference for citrus disease detection under natural orchard conditions; however, its performance on actual mobile or embedded edge devices remains to be validated. Full article

The realization of forest ecological product value has been promoted as an important pathway for reconciling ecological conservation with rural prosperity. However, it remains unclear whether the growth of forest ecological product value has been synchronized with improvements in farmers’ livelihoods. Using panel data from 31 Chinese provinces from 2011 to 2022, this study develops an integrated framework combining allometric growth analysis, a Bayesian hierarchical symbiotic coefficient model, a Lotka–Volterra interaction model, a multi-period difference-in-differences design and LightGBM-SHAP interpretation. The results show that 87% of provinces exhibit negative allometric growth, indicating that forest ecological product value has generally grown faster than farmers’ income. The national symbiotic coefficient increased before 2019 but declined thereafter, suggesting a weakening ecological-livelihood synergy. The multi-period DID results indicate that the 2017 Green Finance Reform and Innovation Pilot Policy significantly weakened the symbiotic relationship in pilot provinces. LightGBM-SHAP further shows that financial development, technological progress and transportation infrastructure are key variables associated with symbiotic equilibrium, with substantial regional heterogeneity. These findings suggest that ecological product value realization and green finance do not automatically translate into inclusive livelihood benefits. More targeted benefit-sharing, financial transmission and farmer-participation mechanisms are needed to promote forest-based ecological prosperity. Full article

Sustainable rural development requires households to move beyond defensive medical spending and emergency borrowing toward more productive, forward-looking resource allocation. This study uses panel data from the China Household Finance Survey (CHFS), covering the 2017, 2019, and 2021 waves plus a newly released 2023 green-channel wave. We examine whether improvements in safe drinking water, clean cooking energy, and sanitation are associated with lower rural household economic vulnerability. We employ a staggered difference-in-differences design with household and year fixed effects, complemented by event–study tests, mediation analysis, and robustness checks. Environmental infrastructure improvements are significantly associated with lower child hospitalization and out-of-pocket medical expenditure, reduced reliance on high-cost informal credit, and higher income-generating asset shares. Mechanism analysis supports a “health–credit–income” channel, in which environmental improvements reduce preventable health shocks, ease emergency borrowing, and relax liquidity constraints on productive asset allocation. Threshold results further show that these financial-resilience benefits are strongest among households with the lowest baseline resource endowments. The study focuses on rural China, yet the identified health–credit–income mechanism offers a broader, scalable framework. Environmental infrastructure first reduces preventable disease burden, then eases emergency informal borrowing, and finally frees liquidity for income-generating assets. This sequence helps explain how environmental investment can create the financial preconditions for sustainable consumption and investment across developing economies. These findings offer micro-level evidence for integrating environmental infrastructure, rural financial resilience, and ESG social-value assessment. Full article

As a core support for ecosystem service functions, biodiversity profoundly affects corporate resource acquisition and long-term value creation. Based on data from Chinese A-share listed companies from 2010 to 2023, this paper constructs a biodiversity attention dictionary using textual analysis and measures corporate biodiversity attention by the number of sentences containing biodiversity-related terms in annual reports. It empirically examines the impact of corporate biodiversity attention on sustainable development performance and its underlying mechanisms. This study demonstrates that corporate biodiversity attention significantly enhances sustainable development performance. Mechanism analysis reveals that corporate biodiversity attention primarily promotes sustainable development performance through three pathways: alleviating financing constraints, fostering green technology innovation, and accelerating digital transformation. Heterogeneity analysis further indicates that this positive effect is more pronounced in heavily polluting industries, non-high-tech enterprises, regulated industries, and firms with a high market share. Economic consequence analysis shows that improvements in corporate sustainable development performance significantly enhance corporate resilience, enabling stable operations and rapid recovery under external shocks. Therefore, firms should strengthen biodiversity disclosure, integrate biodiversity into strategic decision-making frameworks, and promote the coordinated advancement of green technology innovation and digital transformation. Regulatory authorities should accelerate the development of unified disclosure standards and implement differentiated policy guidance to facilitate the high-quality development of enterprises in the process of green transition. Full article

This review critically analyzes bioactive compounds derived from agro-industrial by-products, including polyphenols, natural pigments, dietary fiber, prebiotics, lipids, proteins, and bioactive peptides. The review examines their chemical characteristics, major agro-industrial sources, and recovery strategies, highlighting both conventional technologies and emerging green technologies, such as ultrasound-assisted extraction, supercritical fluids, and natural deep eutectic solvents (NADESs). Across compound classes, common patterns are identified, including the importance of external plant tissues as primary biological reservoirs, as well as a methodological convergence in extraction processes despite the wide chemical diversity of the molecules. Shared challenges related to compound stability, scalability, and process efficiency are also discussed. The results demonstrate that agro-industrial by-products should be understood as complex, integrated matrices rather than isolated sources of individual compounds, thereby supporting the development of unified biorefinery schemes. Unlike previous reviews focused on individual compound classes, this review integrates multiple classes of bioactive compounds, green extraction technologies, analytical characterization strategies, and industrial valorization approaches within a circular biorefinery framework. In conclusion, this review helps bridge the current fragmented understanding of waste valorization and highlights key opportunities for the sustainable development of high-value-added functional ingredients within the framework of the circular economy. Full article

Under the background of the steady advancement of the dual-carbon goal and the increasing improvement of the green financial system, green supply chain finance is like a bridge that closely links the capital of the financial market and the low-carbon transformation of the real economy. The following article chooses A-shares traded enterprises from 2014 to 2024 as the study sample, adopts multi-dimensional empirical methods to study the association in green supply chain finance along with corporate emission levels, and analyzes its transmission mechanisms and heterogeneity. The findings demonstrate that green supply chain finance has a substantial inhibitory impact with enterprise emission levels, a finding that remains robust across a series of tests, including parallel trend tests, placebo tests, and propensity score matching (PSM). Mechanism analysis demonstrates that green supply chain finance can indirectly reduce carbon emission intensity by improving both financing levels and supply chain stability. Looking at heterogeneity, we find that the emission-reducing effect tends to be stronger among state-owned firms, non-heavy polluters, enterprises with higher total factor productivity, and enterprises that are more financially oriented. Our theoretical value lies in clarifying the direct relationship between green supply chain finance and micro-enterprise carbon emissions, identifying two differentiated intermediary transmission paths, and defining the boundary conditions of the policy role across multiple dimensions, thereby better coordinating and promoting the digital and low-carbon transformation of enterprises. Full article

Promoting the green development of the tourism industry is a crucial pathway for achieving coordinated progress in ecological civilization development and industrial transformation and upgrading. Based on panel data for 16 prefecture-level cities in Anhui Province from 2011 to 2022, this study constructs an “inputs–desirable outputs–undesirable outputs” indicator system, measures city-level tourism eco-efficiency (TEE) using a super-efficiency SBM model incorporating undesirable outputs, decomposes provincial disparities and their sources using the Theil index and its decomposition, and further identifies city-specific heterogeneity in influencing factors by employing a panel variable-coefficient fixed-effects model. The results show that: (1) Anhui’s TEE exhibited an overall fluctuating upward trend during 2011–2022, with provincial efficiency values ranging from 1.465 (2016) to 1.500 (2022), and a more pronounced rebound after 2017; (2) spatially, TEE displays a pattern of “higher in the south, lower in the north, with a central uplift,” with southern Anhui cities such as Huangshan and Xuancheng performing relatively well, while many northern Anhui cities lag behind; (3) Theil decomposition indicates that overall disparities are driven mainly by within-region differences, whereas between-region differences contribute relatively little; and (4) influencing factors are markedly heterogeneous: scale- and affluence-related variables promote TEE in core cities such as Hefei, but tend to inhibit it in cities such as Bozhou, Anqing, Chuzhou, and Wuhu. The mechanisms associated with technology and structural variables are more complex; in particular, the expansion of energy consumption exerts a significantly negative effect on TEE in most cities and constitutes a common constraint on efficiency improvement, while the effects of R&D investment, digitalization, and the share of the tertiary sector vary across cities. Accordingly, policy efforts should prioritize energy-efficiency improvement and low-carbon substitution at the provincial level while implementing differentiated, city-specific pathways at the municipal level to jointly advance the low-carbon transition and high-quality development of the tourism industry. Full article

European sustainability transitions are increasingly defined by the convergence of blue, green, and circular economy agendas. Traditionally analysed and governed in isolation, these domains generate important interdependencies, trade-offs, and coordination challenges that remain insufficiently understood. Drawing on the multi-level perspective (MLP) and recent advances in multi-system dynamics, this article introduces the Resource Infrastructure Economy (RIE) as a novel integrative framework. The RIE differs from existing multi-system frameworks by explicitly integrating marine governance as a full socio-technical regime, theorising regulatory-driven regime coupling as a distinct transition pathway, and foregrounding the constitutive role of shared physical and digital infrastructures in shaping value creation, path dependencies, and distributional outcomes. The RIE conceptualises contemporary European transitions as processes of deep regime coupling and infrastructural integration, whereby energy, marine, and material regimes become tightly coordinated through shared physical and digital infrastructures and assertive regulatory steering. Through a systematic integrative literature review (58 core publications selected from over 450 records following PRISMA guidelines, analysed using abductive thematic analysis with MAXQDA 26 software) and comparative analysis of six countries—Portugal, Spain, Denmark, Germany, the Netherlands, and Norway—the study reveals persistent structural gaps between the three agendas alongside emerging patterns of pairwise and triadic regime coupling. While Northern and Central European frontrunners demonstrate more advanced infrastructural coordination, Southern peripheral regions face greater difficulties in governance integration and just transition outcomes. The RIE framework advances sustainability transitions theory in three ways: (1) systematically integrating blue economy scholarship into multi-system analysis; (2) theorising regulatory-driven regime coupling as a distinct transition pathway; and (3) foregrounding the constitutive role of physical and digital infrastructures and environmental data systems in shaping value creation, path dependencies, and distributional outcomes. By reframing European sustainability transitions through the lens of the Resource Infrastructure Economy, this article provides a new conceptual lens to understand uneven transition geographies and offers actionable insights for more integrated and just policy coordination across the European Green Deal. Full article

Approximately 30% of the food produced worldwide is wasted, and a substantial share of municipal food waste still contains non-biodegradable packaging material after sorting. This study investigated an aerobic biodrying process for reducing the moisture content of mixed food-waste fractions, containing varying proportions of green biomass, vegetables, kitchen waste, and packaging-derived plastics, in order to increase their calorific value and obtain a refuse-derived fuel (RDF). Four substrate variants (K1–K4, including a control without added plastics) were biodried in laboratory-scale bioreactors. Process temperatures exceeded 70 °C in all variants, and the addition of plastics increased both the cumulative and the average temperature relative to the control. The plastic fraction recovered after biodrying showed the largest increase in calorific value, reaching over 15 MJ∙kg⁻¹, while the AT4 respiration activity of the separated compost fraction decreased to around 10 mg O₂ g⁻¹ DM in all variants, indicating good storage stability. The results suggest that pre-treated plastics did not adversely affect the biodrying process and, owing to their structuring properties, may support biological decomposition of the remaining biomass; these preliminary, single-run findings should be confirmed in replicated trials. Full article

We evaluated a multi-output neural network framework for jointly analyzing maize grain yield (GY) and root lodging percentage (LP) using above-ground morphological traits measured under defined environmental conditions. To address model robustness, the multi-output neural network was compared with linear regression, elastic net, random forest, and XGBoost using repeated five-fold cross-validation, an 80/20 holdout split, and independent year-wise validation. Under repeated cross-validation, XGBoost provided the strongest average predictive performance for both traits, with R² values of 0.57 for GY and 0.67 for LP. The multi-output neural network showed moderate performance, with R² values of 0.49 for GY and 0.57 for LP. Final holdout performance for the neural network for GY and LP was R² = 0.64 and R² = 0.92, respectively. Year-wise validation showed weak temporal transferability because the two seasons differed not only in environmental conditions, but also in lodging mechanism. Repeated permutation importance identified ear width (EW), kernel row number (RNE), thousand kernel mass (KM1000), and kernel number per ear (KNE) as important predictors of GY, while LP prediction was most strongly associated with internode major diameter (IDmajor), ear length (EL), and the number of green leaves (NGL). Across both permutation importance and SHAP, only RNE and NGL were consistently shared between GY and LP. Supplementary ALE diagnostics indicated that RNE showed increasing model-estimated effects for both predicted GY and LP, whereas NGL showed a positive association with predicted GY but a decreasing or nonlinear association with predicted LP. These results show that joint modeling can support exploratory trait interpretation, but the predictive relationships remain environment-specific and should not be interpreted as causal or broadly transferable without further multi-environment validation. Full article

The escalating global climate crisis has increased scholarly and practical attention to green investment as a key driver of corporate sustainability. From a systems perspective, enterprises can be viewed as complex socio-technical systems in which green resource allocation, technological innovation, and digital transformation interact dynamically. Against this background, this study examines how green investment (GI) affects corporate digital value (DV) and whether green technological innovation (GTI) serves as a transmission mechanism in this relationship. Using panel data from 15,244 firm-year observations of Chinese A-share listed companies from 2012 to 2024, this study applies panel data estimation methods to test the proposed relationships. The results show that GI significantly enhances DV, indicating that green resource allocation can strengthen firms’ digital value creation. GTI plays a partial mediating role in the relationship between GI and DV, suggesting that green investment contributes to digital value not only directly but also by stimulating technological innovation within the corporate system. Further heterogeneity analysis reveals that the positive effect of GI on DV is more pronounced among state-owned enterprises and firms located in eastern regions. These findings enrich the literature on green–digital transformation by highlighting the systemic linkage between green investment, green technological innovation, and digital value creation. They also provide practical implications for policymakers and corporate managers seeking to promote coordinated low-carbon and digital development through more effective green investment and innovation strategies. Full article