Search Results (104)

In China, the recycling system for waste tires is characterized by high output but low standardized recovery rates. This study examines the environmental and health risks caused by non-compliant treatment by individual recyclers and explores the barriers to the large-scale adoption of Pyrolysis Technology. A Tripartite Evolutionary Game Model involving pyrolysis plants, waste tire recyclers, and government regulators is developed. The model incorporates pollutants from pretreatment and pyrolysis processes into a unified metric—Carbon Dioxide Equivalent (CO₂-eq)—based on Global Warming Potential (GWP), and designs a Differential Carbon Taxation mechanism accordingly. The strategy dynamics and stability conditions for Evolutionary Stable Strategies (ESS) are analyzed. Multi-scenario numerical simulations explore how key parameter changes influence evolutionary trajectories and equilibrium outcomes. Six typical equilibrium states are identified, along with the critical conditions for achieving environmentally friendly results. Based on theoretical analysis and simulation results, targeted policy recommendations are proposed to promote standardized waste tire pyrolysis: (1) Establish a phased dynamic carbon tax with supporting subsidies; (2) Build a green market cultivation and price stabilization system; (3) Implement performance-based differential incentives; (4) Strengthen coordination between central environmental inspections and local carbon tax enforcement. Full article

As a core strategy for carbon emission reduction, carbon trading plays a critical role in policy guidance and market stability. Accurate forecasting of carbon prices is essential, yet remains challenging due to the nonlinear, non-stationary, noisy, and uncertain nature of carbon price time series. To address this, this paper proposes a novel hybrid deep learning framework that integrates dual-mode decomposition and a TKMixer-BiGRU-SA model for carbon price prediction. First, external variables with high correlation to carbon prices are identified through correlation analysis and incorporated as inputs. Then, the carbon price series is decomposed using Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) to extract multi-scale features embedded in the original data. The core prediction model, TKMixer-BiGRU-SA Net, comprises three integrated branches: the first processes the raw carbon price and highly relevant external time series, and the second and third process multi-scale components obtained from VMD and EWT, respectively. The proposed model embeds Kolmogorov–Arnold Networks (KANs) into the Time-Series Mixer (TSMixer) module, replacing the conventional time-mapping layer to form the TKMixer module. Each branch alternately applies the TKMixer along the temporal and feature-channel dimensions to capture dependencies across time steps and variables. Hierarchical nonlinear transformations enhance higher-order feature interactions and improve nonlinear modeling capability. Additionally, the BiGRU component captures bidirectional long-term dependencies, while the Self-Attention (SA) mechanism adaptively weights critical features for integrated prediction. This architecture is designed to uncover global fluctuation patterns in carbon prices, multi-scale component behaviors, and external factor correlations, thereby enabling autonomous learning and the prediction of complex non-stationary and nonlinear price dynamics. Empirical evaluations using data from the EU Emission Allowance (EUA) and Hubei Emission Allowance (HBEA) demonstrate the model’s high accuracy in both single-step and multi-step forecasting tasks. For example, the e_MAPE of EUA predictions for 1–4 step forecasts are 0.2081%, 0.5660%, 0.8293%, and 1.1063%, respectively—outperforming benchmark models and confirming the proposed method’s effectiveness and robustness. This study provides a novel approach to carbon price forecasting with practical implications for market regulation and decision-making. Full article

The inherent uncertainty and information asymmetry in financial markets create significant challenges for accurate price forecasting. Although investor sentiment analysis has gained traction in recent research, the temporal dimension of sentiment dynamics remains underexplored. This study develops a novel framework that enhances stock price prediction by integrating time-partitioned investor sentiment, while improving model interpretability via Shapley additive explanations (SHAP) analysis. Employing the ERNIE (enhanced representation through knowledge integration) 3.0 model for sentiment extraction from China’s Eastmoney Guba stock forum, we quantitatively distinguish intraday and post-market investor sentiment then integrate these temporal components with technical indicators through neural network architecture. Our results indicate that temporal sentiment partitioning effectively reduces uncertainty. Empirical evidence demonstrates that our long short-term memory (LSTM) model integrating intraday and post-market sentiment indicators achieves better prediction accuracy, and SHAP analysis reveals the importance of intraday and post-market investor sentiment to stock price prediction models. Implementing quantitative trading strategies based on these insights generates significantly more annualized returns for representative stocks with controlled risk, outperforming sentiment-agnostic and non-temporal sentiment models. This research provides methodological innovations for processing temporal unstructured data in finance, while the SHAP framework offers regulators and investors actionable insights into sentiment-driven market dynamics. Full article

This study employs the TVP-VAR-BK-DY spillover index model to investigate the risk spillover effects between China’s carbon emission trading system (ETS) pilots and A-share listed emission-regulated enterprises. The findings reveal that, due to the nascent stage of China’s carbon market, the overall risk spillover level within the “carbon-stock” system remains low; however, dynamic risk spillovers have shown an upward trend driven by the advancement of ETS pilots. In particular, during compliance periods, enterprises that exceed their emission limits must purchase sufficient allowances on the carbon trading market to avoid high penalties for non-compliance. This creates substantial demand, which drives a rapid increase in the spot prices of carbon allowances, triggering intense short-term price fluctuations and risk spillovers—a pronounced “compliance-driven trading” effect. Frequency domain analysis indicates that long-term shocks have a significantly greater impact on the market than short-term oscillations, reflecting moderate information processing efficiency within the “carbon-stock” system. Directional spillover analysis shows that A-share enterprises initially absorb risks from the carbon market in the short term, but over the long term, they transmit part of these risks back to the carbon market, forming a significant bidirectional risk transmission relationship. Furthermore, heterogeneity analysis reveals marked differences in risk spillover contributions among firms associated with different ETS pilots, as well as between enterprises with polluting behaviors and those with high ESG scores, with the latter contributing considerably higher spillovers to the overall carbon market. These findings offer nuanced insights into the dynamic, structural, and firm-level characteristics of risk spillovers, providing valuable guidance for policymakers and investors to enhance market stability and optimize investment strategies. Full article

This study investigates how the rise of commission-free FinTech platforms and the introduction of fractional trading (FT) have altered trading behavior and order book dynamics in the NASDAQ equity market. Leveraging high-frequency ITCH data from highly capitalized stocks—AAPL, AMZN, GOOG, and TSLA—we analyze market microstructure changes surrounding the implementation of FT. Our empirical findings show a statistically significant increase in price levels, average tick sizes, and price volatility in the post-FinTech-FT period, alongside elevated price impact factors (PIFs), indicating steeper and less liquid limit order books. These shifts reflect greater participation by non-professional investors with limited order placement precision, contributing to noisier price discovery and heightened intraday risk. The altered liquidity landscape and increased volatility raise important questions about the resilience and informational efficiency of modern equity markets under democratized access. Our findings contribute to the growing literature on retail trading and provide actionable insights for market regulators and exchanges evaluating the design and oversight of evolving trading mechanisms. Full article

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models’ relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties. Full article

In response to the challenges of imbalanced economic efficiency of charging stations caused by disorderly charging of large-scale electric vehicles (EVs), rising electricity expenditure of users, and increased risk of stable operation of the power grid, this study designs a user-side vehicle pile resource interaction strategy considering source load clustering to enhance the economy and safety of electric vehicle energy management. Firstly, by constructing a dynamic traffic flow distribution network coupling architecture, a bidirectional interaction model between charging facilities and transportation/power systems is established to analyze the dynamic correlation between charging demand and road network status. Next, an EV charging and discharging electricity price response model is established to quantify the load regulation potential under different scenarios. Secondly, by combining urban transportation big data and prediction networks, high-precision inference of the spatiotemporal distribution of charging loads can be achieved. Then, a multidimensional optimization objective function covering operator revenue, user economy, and grid power quality is constructed, and a collaborative decision-making model is established. Finally, the IEEE69 node system is validated through joint simulation with actual urban areas, and the non-dominated sorting genetic algorithm II (NSGA-II) based on reference points is used for the solution. The results show that the optimization strategy proposed by NSGA-II can increase the operating revenue of charging stations by 33.43% while reducing user energy costs and grid voltage deviations by 18.9% and 68.89%, respectively. Full article

This study examines the market reaction to the approval of the Nature Restoration Law, a key component of the EU Biodiversity Strategy, and its implications for biodiversity-related financial risks. Using an event study methodology, we analyze the equity price movements of companies listed in the MSCI Europe Index that are equally weighted in relation to the announcement. We select the RepRisk Due Diligence Score, focusing on incidents linked to landscapes, ecosystems, and biodiversity, as a measure of biodiversity risk. At first, it seems that companies with a high RepRisk Due Diligence Score show limited or positive abnormal returns, suggesting that biodiversity risks are already priced for companies that have experienced incidents linked to this issue. Conversely, firms with lower biodiversity risk exposure see null or negative impacts, reflecting heightened investor concerns about new environmental regulations or compliance costs. Although the event does not have a systemic impact on European companies in the index, it seems that some sectors are affected when analyzed using parametric and non-parametric distributions. Full article

Regarded as a panacea in non-traditional medicine, Spirulina (“Arthrospira platensis”) refers to cyanobacteria that are highly consumed due to their mineral and bioactive compounds. Despite its wide popularity and availability, Spirulina is often present on the market as an insufficiently regulated dietary supplement with scarce quality control and has high batch-to-batch variability. The present study aims to provide the first comprehensive survey of the Spirulina commercialized on the Romanian market. Therefore, a highly diverse sample set, including an in-house cultivated sample, was analyzed and compared in regard to the ¹³C isotopic signature and the elemental profile of twenty-three elements, including macro-nutrients (Na, Mg, Ca, and K), essential trace elements (e.g., Fe, Zn, Co, and Mn), and possible toxic contaminants (e.g., As, Cd, Cr, and Pb). Results confirmed the potency of Spirulina as a proper mineral supplement source. The in-depth analysis performed in the present work takes into account several critical factors, like formulation, packaging type and material, geographical origin, and labeled growing system, in order to assess whether these marketing strategies are supported. Additionally, the statistical relationships among the price, isotope, and elemental determinations were assessed by Pearson correlation coefficients and subsequently discussed in regard to the biochemical and physiological processes. Full article

This study assesses the impact of two regulated deficit irrigation (RDI) and one sustained deficit irrigation (SDI) strategies on the fruit quality characteristics of pomegranate (Punica granatum L.) compared to a fully irrigated control in a Mediterranean climate. Field trials were conducted over two growing seasons at the Cajamar Experimental Center in Paiporta, Valencia, Spain. The SDI strategy, which achieved considerable water savings of approximately 50%, led to a reduction in yield (both total and marketable), as well as a decrease in the size and unit weight of the fruits. However, it also produced arils with higher dry matter content and aril juice with higher soluble solids content, all without altering the maturity index. Notably, the SDI approach resulted in increased non-marketable production due to a higher incidence of cracking, particularly during the exceptionally hot and dry summer of 2023. Although the maturity index remained unchanged across the irrigation strategies, the SDI yielded a greater percentage of pink-red rind on marketable fruits compared to the other strategies. This is important because ‘Mollar de Elche’ pomegranates are typically harvested based on their external colour. Thus, the SDI strategy could allow for earlier harvesting, potentially enhancing the commercial value, as earlier harvests often command higher prices, which may partially offset some of the reduction in marketable yield. Conversely, both RDI strategies achieved a slight water saving without compromising marketable yield or the quality characteristics of the fruit. Full article

In the realm of real estate, each property owns a unique set of characteristics that distinguish it from others. While each property has its own distinctive features, the appraisal process prioritises only those qualities that meaningfully affect the value in the given market context. However, in the dynamically evolving market situation, expectations of real estate buyers can also transform. This study aims to explore how the surrounding environment and micro-location aspects affect the property value, which can deliver valuable outcomes for real estate market participants and researchers. For that purpose, the authors selected nine factors, called non-market attributes, that may affect the estimated value: air quality, noise emissions, green areas, rivers and water reservoirs, kindergartens and primary schools, universities, medical facilities, shopping centres and religious buildings. Moreover, apart from non-market attributes, the authors selected six market attributes usually used for the determination of residential real estate values according to the Polish regulations in this field. The detailed analysis of factors influencing the property value has been conducted based on the residential apartments in the district Zwięczyca in Rzeszów. Specifically, with the use of Pearson’s total correlation coefficients, authors explored market and non-market attributes and examined their relationships with unit transaction prices, attempting to answer the research question on whether non-market attributes can differentiate market values of residential apartments, when local real estate markets are considered. The results demonstrate that all selected market factors have a visible effect on analysed real estate prices and might be adopted for appraisal. Among nine non-market factors, only three of them have a pronounced effect on prices and might be used for the valuation of residential properties on the local market. The combined database of market and non-market factors reveals eight attributes (five market and three non-market) affecting prices of residential apartments. Full article

Background: Rejected medical claims pose a significant challenge for healthcare facilities accredited by Tanzania’s National Health Insurance Fund (NHIF). Despite the NHIF’s role in reducing out-of-pocket costs, claim rejections have been a persistent issue, largely due to documentation errors, coding mistakes, and non-compliance with NHIF regulations. This study determined the patterns of rejected claims and the strategies employed by NHIF-accredited hospitals to mitigate these challenges. Methodology: This cross-sectional study was conducted between July and August 2024 and used quantitative and qualitative approaches. The study utilized secondary data (August 2023 to January 2024) on the rejected claims from 46 healthcare facilities (HFs) and key informant interviews from the respective selected facilities. Descriptive data analysis was carried out using STATA version 15 and qualitative data analysis was conducted using NViVo2 version 12 software. Results: A total of 46 public (27) and private (19) HFs were included in this study. The data revealed significant variation in the average number of items rejected per claim across HFs, ranging from 0.21 in a regional referral hospital to 1.21 in a zonal hospital. Non-adherence to standard treatment guidelines (STGs) was significantly more common (p < 0.001) in polyclinics, accounting for 17.2% of the items rejected, and with the lowest number (0.8%) seen in zonal hospitals. Overutilization (drugs and investigations) was commonly reported in all HFs, ranging from 12.5% in polyclinics to 31.8% in district hospitals (p < 0.001). Non-applicable consultation charges were only reported in one zonal hospital. To mitigate these rejections, HFs implemented strategies such as immediate error verification, regular communication with NHIF, staff training, technology use, and regular supervision by the internal audit units. Despite these efforts, challenges persisted, particularly those stemming from complex NHIF policies, which account for most rejections in zonal health facilities. Conclusions: There are significant variations in rejection patterns among HFs, with attendance date anomalies, non-adherence to STGs, NHIF pricing, and overutilization being the most common reasons across all HFs. Strategies to address rejections should be tailored to specific health facilities, coupled with electronic systems that will detect errors during patient management. Full article

This study examines how telematics variables such as annual percentage driven, total miles driven, and driving patterns influence the distributional behaviour of conventional rating factors when incorporated into predictive models for capturing auto insurance risk in rate regulation. To effectively manage the complexity inherent in telematics data, we advocate for the adoption of non-negative sparse principal component analysis (NSPCA) as a structured approach for data dimensionality reduction. By emphasizing sparsity and non-negativity constraints, NSPCA enhances the interpretability and predictive power of models concerning both loss severity and claim counts. This methodological innovation aims to advance statistical analyses within insurance pricing frameworks, ensuring the robustness of predictive models and providing insights crucial for rate regulation strategies specific to the auto insurance sector. Results show that, to enhance auto insurance risk pricing models, it is essential to address data dimension reduction challenges when integrating telematics data variables. Our findings underscore that integrating telematics variables into predictive models maintains the integrity of risk relativity estimates associated with traditional policy variables. Full article

During the last two decades, an increased number of molecules with multiple mechanisms of action have been approved for the treatment of inflammatory bowel disease (IBD), with a substantial increase in the costs related to therapy, which has become a concern for payers, regulators, and healthcare professionals. Biosimilars are biologic medical products that are highly structurally similar to their reference products; have no clinically meaningful differences in terms of immunogenicity, safety, or effectiveness; and are available at a lower price. Materials and Methods: This was an observational prospective study conducted in two IBD centres in Bucharest and included 53 patients, 27 male (M) and 26 female (F), diagnosed with IBD according to standard clinical, endoscopic, radiological, and histological criteria, who were non-medically switched at the indication of the National Insurance House to a biosimilar of Adalimumab. Aims: The aim was to determine the rates of clinical remission, adverse effects, and treatment persistence at one year. Results: No significant differences were found in terms of the faecal calprotectin (FC) and C-reactive protein (CRP) levels 6 and 12 months after changing from the originator biologic treatment to a biosimilar. Only one patient required a change in their biological treatment following the clinical and biological loss of response. The main adverse effect reported by the patients was pain at the injection site. Of the 53 patients, only 2 reported pain at the injection site, and 1 patient reported experiencing abdominal pain and rectal bleeding immediately after the switch, but no recurrence was observed clinically or endoscopically. Conclusions: This observational study is the first to be carried out in Romania that shows that, after a non-medical switch, biosimilars of Adalimumab are as efficient and safe as the originator Adalimumab in the clinical treatment of patients with IBD. Full article

Green total factor productivity (GTFP) is critical to both the economic and ecological objectives of pig breeding. This research utilizes the SBM-ML model to calculate the GTFP of pig breeding in 381 counties within the Yangtze River Basin from 2014 to 2021. Then the GTFP is further decomposed into technical efficiency (MLEC) and technical progress (MLTC) to conduct in-depth exploration. The regression results reveal that: (1) Environmental regulation (ER) has significant double-threshold effects on GTFP, MLEC, and MLTC. (2) MLTC is the main force of GTFP growth, and stronger ER does not always lead to better GTFP growth. (3) GTFP is boosted by mechanization enhancement and industrial agglomeration limitation. (4) Counties in non-provincial capital cities and those closer to the river exhibit greater ER threshold effects. (5) Both pig price and transportation efficiency play a moderating role. (6) Further analysis demonstrates that ER simultaneously reduces pig production capacity and carbon emissions, as well as improves the water quality. And the reduction of ER, although beneficial for capacity, has a significant negative impact on GTFP. Finally, this study concludes with policy recommendations to boost the new quality productivity in the pig industry. Full article