Search Results (2,798)

Global urban development has fully entered the stage of stock renewal, and the synergy between public health and historic heritage conservation has become a core issue of urban sustainable development in the post-pandemic era. As special spatial units carrying urban cultural memories, historic districts generally face problems such as chaotic traffic functions, a lack of slow traffic spaces, and insufficient public health support. Existing studies lack a public health-oriented special evaluation system and a sustainable renewal path adapted to their characteristics. This paper systematically sorts out eight core impact paths of street built environment elements on public health and constructs a healthy street evaluation system for historic districts, including six dimensions (transportation facilities, green squares, ancillary facilities, street-front commerce, urban furniture, and street network) and 30 core elements combined with the spatial and cultural characteristics of historic districts. Taking five typical streets in the Liuhe Area of Hankou Historic District as an empirical case, a comprehensive evaluation is carried out using a combination of quantitative surveys, questionnaire surveys, and spatial analyses. The results show that the overall health performance of street space in the study area is low, with extremely unbalanced development across dimensions. The core shortcomings are concentrated in incomplete slow traffic systems, lack of public spaces, prominent parking chaos, and fragmented historic styles, and the health problems of streets with different functional types show significant typological differentiation characteristics. Based on this, this paper proposes five systematic renewal strategies, transportation system optimization, public space improvement, landscape system perfection, historic style activation, and long-term mechanism construction, for achieving the synergistic goals of historic culture conservation, public health promotion, and urban sustainable development. This study not only enriches the theoretical system of research on healthy spaces in historic districts but also provides a referable evaluation framework and practical approach for modern historic districts in China and other similar historic districts with comparable spatial textures and functional characteristics. Full article

Time-series clustering is a prominent research area with extensive practical applications. Given the complexity and diversity of modern time-series data, this study proposes a novel time-series clustering method based on inter-network heterogeneity. First, each time-series is converted into a network by using two types of time-series segmentation techniques. Second, an inter-network clustering approach based on graph spectral theory is introduced: we calculate the total variation (TV) distance between the empirical spectral distributions of each network and identify distinct clusters using a hierarchical clustering algorithm. From the perspective of symmetry, networks constructed from similar time-series tend to exhibit comparable spectral structures, which reflect the underlying structural symmetries of their dynamics. Differences in spectral distributions correspond to symmetry breaking among networks, providing an effective mechanism for distinguishing heterogeneous time-series patterns. Our method effectively preserves more distinctive features inherent in the original time-series. To evaluate the performance of the proposed method, simulation studies are conducted, including the recognition of both stationary and non-stationary sequences. The method also performs well on real-world datasets, such as stock closing prices. These results demonstrate that our approach can handle non-stationary sequences and identify the intrinsic correlations in time-series. Full article

Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution network comprising a central warehouse, regional warehouses, and retailers. The model integrates a continuous-review (r,Q) replenishment policy, stochastic demand, safety stock requirements, transportation lead times, and stockout behaviour, enabling a detailed representation of operational dynamics under uncertainty and environmental concerns. Unlike most sustainable inventory models—which typically treat environmental impacts and replenishment control separately or rely on simplified service assumptions—this study provides an integrated framework that jointly embeds (r,Q) policies, stochastic demand, stockouts and distance-based CO₂ metrics within a unified optimisation structure. The model advances prior work by explicitly integrating continuous-review (r,Q) replenishment policies with distance-based CO₂ metrics under stochastic demand, a combination rarely addressed in sustainable multi-echelon inventory models. A multi-objective formulation captures the trade-off between economic performance and CO₂ emissions, allowing the identification of Pareto-efficient strategies that reconcile financial and environmental goals. Reducing emissions by over 90% requires an additional cost of only about 4%, demonstrating that substantial emission reductions can be achieved at relatively low additional cost. The findings offer practical insights for managers seeking to design more sustainable and cost-effective distribution policies, highlighting the value of integrated optimisation approaches in contemporary logistics systems. Full article

Background/Objectives: The COVID-19 pandemic imposed logistical challenges on health systems, particularly for mass vaccination campaigns under emergency conditions. In decentralized health systems, the absence of a structured preparedness phase may compromise coordination, allocation, and operational performance. This study analyzes the vaccination campaign in a municipality in southern Brazil, examining how the overlap of the preparedness and response phases affected outcomes and how alternative logistical scenarios could have altered campaign performance. Methods: An empirical analysis was conducted using scenario-based simulation with stock and flow structures. The model represents vaccine procurement, distribution across national, state, regional, and municipal levels, and municipal vaccination capacity. Real data from the 2021 vaccination campaign in the municipality were used to build a Business-as-Usual scenario, compared with alternative scenarios involving changes in procurement predictability, allocation rules, and operational capacity. Results: Vaccination outcomes were strongly conditioned by upstream allocation decisions, particularly at the national state level. Isolated adjustments at intermediate supply chain levels produced limited improvements when upstream constraints persisted. Scenarios combining improved alignment between forecasted and acquired doses with operational capacity showed higher vaccination potential, revealing a gap between observed performance and system capacity. Conclusions: The findings reinforce that preparedness is a critical determinant of vaccination performance and must precede response in emergency contexts. Supply predictability alone is insufficient without coordinated allocation mechanisms and operational readiness across governance levels. This study provides empirical evidence on how preparation-related decisions shape vaccination outcomes in decentralized health systems and inform logistical coordination in future emergencies. Full article

Enhancing the ESG performance of manufacturing enterprises represents a critical pathway for promoting high-quality economic development and achieving sustainable development goals. Leveraging the establishment of National New-Generation Artificial Intelligence Innovation and Development Pilot Zones as a quasi-natural experiment, this study examined A-share listed manufacturing enterprises on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2023, employing a multi-period difference-in-differences model to systematically evaluate the policy’s impact on enterprise ESG performance and its underlying mechanisms. The empirical results demonstrate that the Artificial Intelligence Innovation and Development Pilot Zone policy exerts a significant positive effect on manufacturing enterprises’ ESG performance, with the robustness of this conclusion validated through parallel trends tests, placebo tests, and multiple robustness checks. A mechanism analysis revealed that the policy primarily enhances manufacturing enterprises’ ESG performance through two transmission channels: intensifying the R&D expenditure intensity and strengthening environmental compliance pressures. Furthermore, the enterprise resource allocation and operational efficiencies significantly moderate the policy effect, amplifying the enabling effect of the policy on ESG performance. A heterogeneity analysis indicates that, from the perspectives of enterprise ownership and responsibility orientation, the policy demonstrates more pronounced enabling effects on non-state-owned enterprises and non-high-pollution enterprises; from the perspectives of technological endowment and factor structure, the policy effects are more evident among high-tech enterprises, non-capital-intensive enterprises, and non-labor-intensive enterprises. This study elucidates the multi-dimensional transmission mechanisms through which the Artificial Intelligence Innovation and Development Pilot Zone policy empowers ESG development in manufacturing enterprises, providing theoretical foundations and practical guidance for refining artificial intelligence policy frameworks and promoting manufacturing enterprise sustainable development. The research findings also contribute empirical evidence from emerging economies to comparative research on global AI governance. Full article

The latest research on tax avoidance indicates that the number of female directors on a board increases the accounting accuracy and company performance by decreasing tax avoidance. The empirical research illustrates that women’s higher risk aversion and more conservative characteristics are key for company decision-making, especially when considering a tax strategy. We posit that the risk avoidance of women and other board attributes that enhance diversity influence the company’s sustainability through their effects on the company’s taxpaying activities. To verify this relationship, an empirical analysis was conducted using data for the period from 2009 to 2025 for the non-financial enterprises listed on the Pakistan Stock Exchange. The results showed that enhancing diversity on the board by attributes such as gender inclusion paves the way for firms to achieve firm performance. The results showed that tax avoidance partially mediates the relationship between corporate board attributes and firm performance. Effective board diversity encourages firms to engage in more tax-paying activities, which leads to positive firm performance. The research outcomes strengthen the existing proof of the link between board diversity and company financial performance, with tax avoidance behavior serving as an intervening factor. This also provides insights for policy-making authorities, encouraging them to make tax-related regulations that better promote long-term growth and prosperity. This study fills a gap in the research by highlighting the influence of board diversity on tax avoidance behavior and corporate financial performance. Full article

Hybrid rocket technologies are gaining recognition as eco-friendly alternatives to traditional propulsion systems. Utah State University’s Propulsion Research Laboratory has developed a High-Performance Green Hybrid Propulsion (HPGHP) technology, leveraging 3D-printed ABS fuel for reliable, low-energy ignition. Among tested materials, only ABS shows suitable electrical-breakdown properties for arc ignition. Unfortunately, due to the proprietary formulations in commercial ABS blends, and its limited use as a rocket-propellant, related composition and combustion data are limited. This study uses spectroscopic evaluation and bomb calorimetry to estimate material compositions, enthalpies of formation, and combustion energies for multiple commercially available 3-D print feed stock ABS types, finding minimal differences amongst the samples tested. Based on these test results, “representative” ABS properties including chemical formula, mean molecular weight, enthalpy of formation, and Higher Heating Value, is recommended. Follow-on tests with 5 alternative, commonly used, 3D-printable thermoplastic feed stocks demonstrate that ABS has significantly higher energy content. This result supports ABS’s advantages and utility as a conveniently fabricated hybrid rocket fuel. Full article

Improving the energy performance of existing multi-apartment residential buildings is critical for reducing energy consumption and greenhouse gas emissions in Central and Eastern Europe, where large stocks of post-war buildings with limited insulation are connected to district heating systems. This study evaluates façade insulation retrofit strategies for two representative typologies in Novi Beograd, Serbia—a high-rise tower and an elongated slab-type (‘lamella’) building—using calibrated dynamic energy models and cradle-to-use lifecycle assessment (LCA) over a 50-year service life. Models were calibrated against measured 2023–2024 heating consumption data (NMBE < 1%, CVRMSE < 15%) and normalized with Typical Meteorological Year weather for consistent scenario comparison. Retrofit scenarios applied expanded polystyrene (EPS) and cellulose insulation at 10, 12, and 15 cm thicknesses. Results show that external insulation reduces annual heating demand by approximately 19–20% compared to the uninsulated baseline (192 kWh/m²·a), with the majority of savings achieved at 10 cm and only marginal gains from additional thickness. Insulation thickness has a stronger influence on operational energy reduction than material choice, as differences between EPS and cellulose remain below 0.5%. LCA indicates 23.6–26.0% lower climate change impacts and 23.6–25.8% reduced cumulative energy demand in retrofit scenarios, with cellulose offering modest advantages due to lower embodied emissions and biogenic carbon storage. These findings support targeted envelope retrofits as an effective strategy for decarbonizing district-heated residential buildings in the region. Full article

Based on panel data of Chinese A-share companies listed on the Shanghai and Shenzhen stock exchanges from 2006 to 2023, this study employs a staggered difference-in-differences (DID) approach to examine the impact of the Innovative Industrial Cluster (IIC) pilot policy on corporate carbon performance. The research findings indicate that the IIC pilot policy significantly enhances corporate carbon performance, a conclusion that remains robust after a series of reliability tests, including PSM-DID. Mechanism analysis demonstrates that the policy primarily operates through channels such as fostering corporate green technological innovation, increasing public environmental concern, and attracting the entry of green investors. Heterogeneity analysis further reveals that the policy effect is more pronounced among firms located in the eastern region, those in non-heavy-polluting industries, and state-owned enterprises (SOEs). This study provides micro-level evidence for understanding the green effects of industrial agglomeration and offers references for optimizing cluster policy design to facilitate the low-carbon transition. Full article

This study assesses the elasticity between integrated media performance and corporate reputation by examining the relationship between Media Output Score (MOS) and RepScore™ in the Portuguese telecommunications sector (Altice/MEO, NOS, and Vodafone) between 2021 and 2023. Adopting a longitudinal observational design, the analysis compares inter-annual variation in communication output with corresponding changes in stakeholder-based reputation. Media performance is operationalized through MOS as a composite indicator of visibility, favorability, readership, targeting, and social amplification, while corporate reputation is measured using third-party RepScore™ data. The findings indicate directional alignment between media output and corporate reputation; however, the magnitude of reputational adjustment appears substantially lower than the amplitude of media volatility. Across heterogeneous crisis contexts, including cybersecurity incidents and governance-related events, reputational scores exhibit incremental and comparatively stable evolution despite pronounced fluctuations in media performance. These results suggest that the relationship between media output and corporate reputation is characterized by constrained responsiveness at the annual level, consistent with a stock–flow interpretation in which communication signals operate as high-variance flows and reputation evolves as a path-dependent stock. By empirically illustrating this asymmetry, the study contributes to media influence research by identifying a structural boundary condition in the translation of media exposure into stakeholder evaluation. The findings further clarify the analytical distinction between output-level communication metrics and outcome-level reputational constructs in digital media environments. Full article

This study aims to predict stock market risk and improve preparedness for potential economic crises by identifying sharp declines in stock returns using classification-based machine learning models. Using ten years of KOSPI 200 index data (2015 to 2024), a daily return series was constructed. A day was labeled a risk event (1) if its return fell below the 5th percentile of the returns observed over the preceding 100 trading days, indicating a sharp decline. Nine classification models—Logistic Regression, k-nearest Neighbor, Decision Tree, Random Forest, Linear Discriminant Analysis, Naive Bayes, Quadratic Discriminant Analysis, AdaBoost, and Gradient Boosting—were trained and validated. Among these, Logistic Regression demonstrated the strongest overall performance across multiple evaluation metrics, including accuracy, non-risk F1 score, risk F1 score, and AUC. Full article

African swine fever (ASF), caused by African swine fever virus (ASFV), is a highly destructive transboundary disease in domestic pigs. The circulating virus in this study belonged to ASFV genotype II, commonly associated with high virulence. In endemic regions such as Thailand, limited vaccine availability and shortages of naïve breeding stock necessitate reliance on early detection, surveillance, and the retention of convalescent sows, thereby raising concerns regarding viral persistence and reproductive performance. This study evaluated the long-term reproductive performance of convalescent sows compared with naïve cohorts under co-habitation conditions, while assessing the efficacy of passive surveillance and strict biosecurity in preventing viral transmission from both internal and external sources. Convalescent sows showed reproductive performance comparable to naïve cohorts across two parities. Long-term co-habitation with naïve sentinel pigs was not associated with detectable viral transmission, although low-level viral persistence or intermittent shedding cannot be excluded. From a disease control perspective, the transition from delayed detection to enhanced passive surveillance facilitated early clinical recognition and targeted removal (“tooth extraction”) of infected animals, effectively limiting intra-herd transmission without full depopulation. Importantly, irrespective of the uncertain carrier status, strict biosecurity and rapid response protocols appeared effective in mitigating both external introduction and within-farm transmission of ASFV. These findings suggest that, under appropriate management and biosecurity conditions, convalescent sows may be reintegrated into production systems with caution. Full article

This study provides a comparative evaluation of three state-of-the-art large language models (LLMs), namely OpenAI’s (San Francisco, CA, USA) GPT-4.0, Google’s (Google LLC, Mountain View, CA, USA) Gemini 2.0 Flash, and Meta’s (Meta Platforms, Menlo Park, CA, USA) LLaMA-4-Scout-17B-16E, in a decision-oriented framework in which the models generate structured outputs based only on historical closing-price data. The evaluation covers 150 stocks sampled from three countries (India, the United States, and South Africa) across ten economic sectors, including Information Technology, Banking, and Pharmaceuticals. Unlike many prior studies that combine numerical and textual inputs, this study relies solely on three years of numerical time series data and examines model responses in terms of decision labels such as buy, sell, or hold. The LLMs were provided with historical closing-price sequences and prompted with three types of finance-related questions: (a) whether to buy a stock, (b) whether to sell or hold a stock, and (c) in a pairwise comparison, which stock to buy or hold. These prompts were evaluated across two investment horizons: 1 month and 3 months. Model outputs were compared against realized market outcomes during the corresponding test periods. Performance was assessed across four key dimensions: country, sector, annualized volatility, and question type. The models were not given any supplementary financial information or instructions on specific analytical methods. The results indicate that GPT-4.0 achieves the highest average accuracy (56%), followed by LLaMA-4-Scout-17B-16E (48%) and Gemini 2.0 Flash (39%). Overall performance remains moderate and varies across market conditions, with relatively higher accuracy observed in high-volatility regimes (51%). This work evaluates how LLMs behave when presented with structured numerical price sequences in a controlled decision-labeling setting and contributes to the broader discussion on the potential and limitations of LLMs for numerical decision tasks in finance. Full article

In the context of a rapidly evolving data-driven economy and increasingly stringent carbon reduction policies, the impact of supply chain digitalization (SCD) on corporate carbon emission intensity (CEI) has become an important research topic. Using panel data on Chinese A-share listed firms from the Shanghai and Shenzhen stock exchanges over the period 2013–2023, this study employs Python-based text analysis of corporate annual reports to explore the effect of SCD on corporate CEI. The results show that SCD significantly reduces corporate CEI. Mechanism analysis further indicates that this effect operates through three channels: alleviating financing constraints, promoting green innovation, and reducing supply chain disruption risk. Heterogeneity analysis reveals that the mitigating effect of SCD on corporate CEI is more pronounced among non-state-owned firms, large-scale firms, firms in non-high-tech industries, firms in highly environmentally sensitive industries, and firms located in regions with more developed digital infrastructure. Further analysis shows that SCD contributes to improvements in both firms’ sustainability and financial performance. Overall, this study provides important policy implications for both governments and firms. Full article

Against the backdrop of the comprehensive advancement of the law-based governance of China and the “dual carbon” strategic goals, existing research still lacks a systematic discussion on how corporate compliance management affects ESG performance, and few studies have constructed targeted compliance management indicators from a textual perspective. To fill this research gap, this paper aims to explore the influence of corporate compliance management on ESG performance. Using Chinese A-share listed firms on the Shanghai and Shenzhen Stock Exchanges from 2010 to 2023 as research samples, this study adopts text mining techniques, combined with a panel regression model and a mediating effect model, to construct an indicator of corporate compliance management and examine its impact on ESG performance. The empirical results show that compliance management significantly improves corporate ESG performance and functions mainly through three channels: promoting corporate green innovation, fostering corporate ethical culture, and reducing agency costs. Heterogeneity tests indicate that the positive relationship is more pronounced in state-owned enterprises and in firms with higher managerial power. Further analysis reveals that compliance management also helps reduce the divergence in ESG ratings among Chinese firms, and the construction of all dimensions of compliance management can contribute to the improvement of corporate ESG performance. These findings enrich the literature on the economic consequences of compliance management and the determinants of ESG performance and provide theoretical guidance for Chinese firms to enhance ESG performance via compliance management. Full article