Search Results (41)

The rapid growth of Waste Electrical and Electronic Equipment (WEEE) in the European Union highlights the need for a rigorous understanding of its long-term dynamics and the role of innovation in shaping its trajectory. This study investigates how innovation influences the dynamics of WEEE generation in the European Union. We develop an innovation-adjusted mathematical model of e-waste as a stock flow system and prove the existence and global stability of a unique positive equilibrium. The model analytically generates an environmental Kuznets-type turning point and shows that innovation reduces waste accumulation by accelerating effective depreciation. To link the theoretical results with empirical patterns, we embed the model in a STIRPAT panel specification using annual data for 27 EU member states from 2013 to 2023, where EU Eco-innovation Index (EEI) serves as a composite index which directly captures policy-driven green technology and circular economy activities, aligning precisely with our theoretical framework. We also extend the quasi-demeaning transformation to panels with correlated shocks and establish its consistency under a factor structured error process. The empirical estimates confirm a positive effect of income on WEEE at lower development levels and a negative coefficient on its squared term, consistent with an inverted U pattern, while innovation is associated with lower waste intensity. These findings demonstrate how mathematical modeling can strengthen the interpretation of macro panel evidence on circularity and provide a basis for future optimization of innovation driven sustainability transitions. Full article

This study examines whether the external financing anomaly exists in an emerging-market setting. Using data on Korean listed firms from 1994 to 2023, we find that firms with higher net external financing subsequently earn significantly lower stock returns, consistent with behavioral misvaluation and market-timing explanations. A hedge portfolio long in net repurchasers and short in net issuers delivers an average annual return of about 12 percent. Decomposing financing flows show that both equity and debt issuance predict lower future returns, and further separating debt into bonds and loans reveals a stronger negative return association for bond-financed firms, consistent with greater sentiment sensitivity in market-based financing. We also document subsequent declines in operating performance, indicating that external financing aligns with temporary overvaluation rather than growth opportunities. Overall, our findings extend evidence on the external financing anomaly to an emerging market and provide further support for the behavioral interpretation of corporate financing decisions. Full article

This study analyzes the development and determinants of control premiums in mergers and acquisitions in the European market from 2009 to 2022 (i.e., stock volatility, liquidity via money supply, sectoral growth, transaction volume, market capitalization, free cash flows, presence of a toehold, public listing, cross-border transactions, payment types, and sectoral relatedness), whereby control premiums represent the premium that buyers pay above the current market value of a company to gain control. The empirical analysis implements linear as well as quantile regression analyses. Results reveal that the average and median premiums fluctuated notably between 2009 and 2022, with the lowest premiums paid in 2009 and the highest in 2022. Factors such as the volatility of the stock market, capital liquidity, and deal activity within certain sectors have a consistently significant influence on the level of premiums if a longer period of analysis is selected. Cross-border status, payment structure, stock market listing of the acquiring company, and the build-up of a toehold influence the premiums paid in shorter- and longer-term analyses. In contrast, neither the market capitalization nor the free cash flow of the target company has a significant influence on the premiums paid. Full article

Recirculating aquaculture systems (RAS) address pollution, disease, and sustainability in commercial fish farming, but marine RAS are limited by biofilter maturation and nitrification. This study investigated the effects of air nanobubbles on water quality, fish growth, and bacterial communities in marine RAS stocked with juvenile Malabar red snapper, barramundi and saline-tolerant hybrid tilapia. Flow cytometry was evaluated as a rapid management tool for non-culturable microbes, finding viable bacterial counts 30–100 times higher than conventional total plate counts. There were no significant differences in fish growth, survival, or Feed Conversion Ratio between groups, likely due to low stocking densities (<20 kg/m³) and high water exchange rates (>100%/hour), indicating low system stress. Air nanobubbles did not significantly increase dissolved oxygen levels. While bacterial abundance in water was consistently higher in nanobubble-treated RAS (RAS-N), tank walls showed less biofilm. RAS-N also exhibited a higher abundance of nitrifying bacteria like Nitrospira and Marinobacter, leading to improved nitrogenous waste breakdown and lower nitrite levels. Future research should investigate nanobubbles’ benefits at higher stocking densities and longer durations to fully assess their impact on intensive aquaculture. Full article

Empirical research on the impact of the digital economy on sustainable development is hampered by severe methodological challenges. Discrepancies in the theoretical foundations and construction logic of measurement frameworks have led to diverse and often conflicting conclusions, hindering the systematic accumulation of knowledge. This study aims to address this critical gap by proposing a new, logically consistent measurement framework. To overcome the existing limitations, we construct a functional deconstruction framework grounded in General-Purpose Technology (GPT) theory and a “stock–flow” perspective. This framework deconstructs the digital economy into a neutral “digital infrastructure” (stock platform) and two forces reflecting its inherent duality: a “consumption force” (digital industrialization) and an “empowerment force” (industrial digitalization). Based on this, we develop a measurement system adhering to the principle of “logical purity” and apply a “two-step entropy weighting method with annual standardization” to assess 30 provinces in China from 2012 to 2023. Our analysis reveals a multi-scalar evolution. At the micro level, we identified four distinct provincial development models and three evolutionary paths. At the macro level, we found that the overall inter-provincial disparity followed an inverted U-shaped trajectory, with the core contradiction shifting from an “access gap” to a more profound “application gap.” Furthermore, the primary driver of this disparity has transitioned from being “empowerment-led” to a new phase of a “dual-force rebalancing.” The main contribution of this study is the provision of a new analytical tool that enables a paradigm shift from “aggregate assessment” to “structural diagnosis.” By deconstructing the digital economy, our framework allows for the identification of internal structural imbalances and provides a more robust and nuanced foundation for future causal inference studies and evidence-based policymaking in the field of digital sustainability Full article

Against the background of carbon emission reduction, this paper explores the driving factors of carbon footprint knowledge system toughness for building construction enterprises through the theory of constraints (TOC) and optimises the carbon footprint knowledge system toughness under static and dynamic perspectives, respectively. Under the static perspective, the fuzzy set qualitative comparative analysis method (fsQCA) is used to explore the development path of the carbon footprint knowledge system toughness for building construction enterprises, and the study finds three kinds of grouping paths. Under the dynamic perspective, system dynamics is used to analyse the causality of the driving factors of the carbon footprint knowledge system toughness and draw the causality diagram. The stock flow diagram is drawn according to the relationship between the factors, and G1 method is combined with the expert distribution to determine the weight of each factor, and then, the model equation is established to complete the construction of the system dynamics of the carbon footprint knowledge system toughness based on the control variable method of the four capabilities under the influence of the factors to simulate the comparison and to explore the extent of the influence of different factors on the carbon footprint knowledge system toughness. Through the two-dimensional analysis framework, we provide an integrated solution for path selection and dynamic regulation for building construction enterprises to help them achieve the adaptive optimisation of the carbon footprint knowledge system and promote the low-carbon transformation and sustainable development of the construction industry. Qualitative results show that three configuration paths affect resilience, with core factors including management, emission, predictive, and construction capabilities. Quantitative results indicate fsQCA overall consistency (0.861) and coverage (0.808); system dynamics simulation shows that management capability has the highest impact weight (0.355). Full article

Film-forming solutions were prepared using Tara gum (TG), with glycerol (GL) as a plasticizer and orange peel powder (OP) as a filler. A TG stock solution (10 g/L) was initially prepared to facilitate homogenization, from which appropriate dilutions were made to obtain final concentrations of 0.6%, 0.8%, and 1.0% (w/v). GL (30% and 50%) and OP (0%, 20%, and 50%) were incorporated based on the dry weight of TG, meaning their amounts were calculated relative to TG content to ensure consistent formulation ratios. Rheological parameters, including the flow behavior index, consistency coefficient, storage modulus (G′), and loss modulus (G″), were characterized via steady shear and oscillatory rheometry. Mechanical properties, such as the Young’s modulus, tensile strength, and elongation at break, were also evaluated. A strong positive correlation (R² = 0.840) was observed between G′ and the Young’s modulus, indicating that solutions with higher internal network strength yield films with greater stiffness. The synergistic interaction between TG and OP was critical: TG primarily enhanced stiffness and mechanical reinforcement, whereas OP improved structural cohesion and stability. GL functioned as a plasticizer, increasing film flexibility while reducing stiffness. These interactions led to a reduction in film solubility by up to 62.43%, particularly in formulations without orange peel powder. In contrast, mechanical strength increased by up to 50.21% in films containing orange peel powder, as those without it exhibited significantly lower tensile strength. Flexibility, expressed as elongation at break, was enhanced by up to 78.86% in formulations with higher glycerol content. Barrier properties were also improved, demonstrated by decreased water vapor permeability and increased hydrophobicity, attributed to the TG–OP synergy. A regression model (R² = 0.928) substantiated the contributions of TG to stiffness, OP to matrix reinforcement, and GL to flexibility modulation. This study underscores the pivotal role of rheological behavior in defining film performance and presents a novel analytical framework applicable to the design of sustainable, high-performance biopolymeric materials. Full article

This paper focuses on joint production/inventory optimization in single and multiple horizons, respectively, within a complicated supply network (CSN) consisting of firm nodes with coupled demands and firm nodes with coupled demands. We first formulate the single-epoch joint optimal output model by allowing the production of extra quantity for stock underage, considering the fixed costs incurred by having inventory over demand and shortfalls. Then, the multi-temporal dynamic joint production model is further investigated to deal with stochastic demand fluctuations among CSN nodes by constructing a dynamic input–output model. The K-convexity defined in Rn space is proved to obtain the optimal control strategy. According to physical flow links, all demands associated to the nodes of CSN are categorized into the inter-node demand inside CSN (intermediate demand) and external demand outside CSN (final demand). We exploit the meliorated input–output matrix to describe demand relations, building dynamic input–output models where demand fluctuates randomly in single-cycle CSN and finite multi-cycle CSN. The novel monocyclic and multicyclic dynamic models have been developed to minimize system-wide operational costs. Unlike existent literature, we consider fixed costs incurred by overdemand and underdemand inventory into system operational cost functions and then demonstrate the convexity of objective functions. The cost function with two fixed penalty costs due to excess and shortage of inventory is developed in a multicycle model, and the K-convexity defined in Rn is proved to find out the optimal strategy for joint dynamic production of CSNs in the case of multi-products and multicycles. Full article

Establishing artificial sand-fixing shrubs is a key measure to curb dune flow and drive changes in the soil stocks and cycling of carbon and nitrogen. But our understanding of these dynamics across years of sand-fixing afforestation and the factors influencing them remains inadequate, making it hard to accurately assess its capacity to sequester carbon. To fill that knowledge gap, this study investigated soil organic carbon (SOC) and soil total nitrogen (STN) stocks in Mu Us Desert under artificial sand-fixing shrub stands of different ages (10, 30, 50, and 70 years old) vis-à-vis a mobile sand dune, to determine whether Caragana korshinskii afforestation improved stock characteristics and whether SOC and STN stocks were correlated during the restoration processes. The results showed that the pattern observed is consistent with an increase over time in the stocks of both SOC and STN. At 10, 30, 50, and 70 years, these stocks were found to be 1.8, 2.3, 3.2, and 5.5 times higher for SOC, and 1.3, 1.6, 2.1, and 2.7 times higher for STN, respectively, than those of the control (mobile sand) dune. Stocks of SOC and STN mainly increased significantly in the 0–10 cm soil layer. The SOC stock was correlated positively with the STN stock as well as the C:N ratio. The slope of the regression for the C:N ratio against stand age was positive, increasing slightly faster with afforestation age. Additionally, our findings suggest that during the establishment of artificial stands of shrubs, the size of the STN stock did not expand as fast as the SOC stock, resulting in an asynchronous N supply and demand that likely limits the accumulation of soil organic matter. This research provides important evidence for the sustainable development of desertified ecosystems. Full article

The rapid pace of urbanization and the increasing concentration of populations in urban areas have generated a substantial demand for architectural structures, resulting in a significant increase in building stock and continuous material flows that interact with the environment. This study emphasizes the importance of high-spatial-resolution mapping of residential building stock for effective urban-construction resource management, planning, and waste management. Focusing on Xi’an as a case study, the research develops a comprehensive framework for mapping urban residential building stock by integrating diverse data dimensions, including temporal, spatial, network, and multi-attribute aspects. The findings indicate that between 1990 and 2020, approximately 4758 residential communities were established in central Xi’an. The analysis of seven key residential construction materials revealed that the building stock escalated from 1.53 million tons to 731.12 million tons, with a steady spatial expansion of material distribution. The study attributes this growth to factors such as population increase, economic advancement, and policy initiatives, which, in turn, have driven the demand for residential building materials and reinforced the interdependence between urban expansion and residential construction development. Remarkably, from 1990 to 2020, the population surged by 2.1-fold, the economy by 66-fold, and the stock of residential building materials by 477-fold, indicating that the growth rate of material stock consistently outpaced that of both population and economic growth. Over the past three decades, the rapid expansion of residential buildings has led to the encroachment of urban ecological spaces by concrete structures. The methodology proposed in this study for quantifying building material offers valuable insights for policymakers and urban and environmental planners to foster responsible resource consumption and supports component-level circularity in the built environment. Full article

This paper examines the market responses to concurrent earnings and stock split announcements for evidence on the information content of stock splits. The majority of stock split research excludes splits announced with other information events due to confounding issues. However, it is difficult to extract the information content of splits by merely focusing on the standalone split announcement because stock splits are devoid of any information regarding firms’ future cash flows. This study explicitly considers how a stock split is evaluated in conjunction with current earnings. This study shows that the market reacts more positively to earnings news concurrently announced with stock splits, consistent with the idea that splits are favorable news. Furthermore, this study finds that stock returns of concurrent split–earnings announcers exhibit a greater association with future cash flows, suggesting that investors should value stock splits favorably for more persistent earnings ahead. Full article

This paper introduces a novel deep learning approach for intraday stock price direction prediction, motivated by the need for more accurate models to enable profitable algorithmic trading. The key problems addressed are effectively modelling complex limit order book (LOB) and order flow (OF) microstructure data and improving prediction accuracy over current state-of-the-art models. The proposed deep learning model, TrioFlow Fusion of Convolutional Layers and Gated Recurrent Units (TFF-CL-GRU), takes LOB and OF features as input and consists of convolutional layers splitting into three channels before rejoining into a Gated Recurrent Unit. Key innovations include a tailored input representation incorporating LOB and OF features across recent timestamps, a hierarchical feature-learning architecture leveraging convolutional and recurrent layers, and a model design specifically optimised for LOB and OF data. Experiments utilise a new dataset (MICEX LOB OF) with over 1.5 million LOB and OF records and the existing LOBSTER dataset. Comparative evaluation against the state-of-the-art models demonstrates significant performance improvements with the TFF-CL-GRU approach. Through simulated trading experiments, the model also demonstrates practical applicability, yielding positive returns when used for trade signals. This work contributes a new dataset, performance improvements for microstructure-based price prediction, and insights into effectively applying deep learning to financial time-series data. The results highlight the viability of data-driven deep learning techniques in algorithmic trading systems. Full article

Stump–root systems consist of aboveground stumps and underground coarse roots after timber harvesting. Stump–root systems are the primary source of coarse woody debris (CWD) in plantations, and they play a crucial role in the material cycle, energy flow, and biodiversity of Eucalyptus plantation ecosystems. However, there is limited knowledge about the changes in elemental stock within this CWD type during decomposition. To address this gap, we conducted a study on Eucalyptus urophylla × E. grandis stump–root systems at various times (0, 1, 2, 3, 4, 5, and 6 years) after clearcutting. Our aim was to investigate the stock changes in eight elements (K, Ca, Mg, S, Fe, Mn, Cu, and Zn) within the stumps and coarse roots over time and their decay levels, and we analyzed the relationship between elemental stocks and the physical, chemical, and structural components of stump–root systems. Our findings revealed the following: (1) The majority of each element’s stock within the stump–root system was found in the coarse roots. The elemental stocks in both stumps and coarse roots decreased as time passed after clearcutting and as decay progressed. (2) Notably, the elemental stocks in stumps and coarse roots were significantly higher than in other treatments during the initial 0–2 years after clearcutting and at decay classes I and II. In terms of elemental stocks, stumps from all clearcutting times or decay classes had the highest K stock, followed by Ca and Fe. Mg, Mn, and S stocks were lower than the first three, while Zn and Cu stocks were very low. The ordering of elemental stocks from high to low in the stump–root systems generally aligned with that of the coarse roots. (3) The residual rates of K, Mg, and Mn stocks in the stump–root systems fit the negative exponential model well. It took approximately 1 to 3.5 years for a 50% loss of the initial stocks of these elements and 5 to 10 years for a 95% loss. (4) The large amount of biomass in the stump–root system is the long-term nutrient reservoir of plantations, and any factor related to biomass loss affects the magnitude and duration of the nutrient reservoir, such as N, P, stoichiometric ratios, density, water-holding capacity, and hemicellulose. These findings contribute to a better understanding of the nutrient elemental dynamics and ecological functions of stump–root systems in Eucalyptus plantations. Full article

Traffic control in mass transit consists of the regulation of both vehicle dynamics and passenger flows. While most of the existing approaches focus on the optimization of vehicle dwell time, vehicle time headway, and passenger stocks, we propose in this article an approach which also includes the optimization of the passenger inflows to the platforms. We developed in this work a deep reinforcement Q-learning model for the traffic control in a mass transit line. We first propose a new mathematical traffic model for the train and passengers dynamics. The model combines a discrete-event description of the vehicle dynamics, with a macroscopic model for the passenger flows. We use this new model as the environment of the traffic in mass transit for the reinforcement learning optimization. For this aim, we defined, under the new traffic model, the state variables as well as the control ones, including in particular the number of running vehicles, the vehicle dwell times at stations, and the passenger inflow to platforms. Second, we present our new deep Q-network (DQN) model for the reinforcement learning (RL) with the state representation, action space, and reward function definitions. We also provide the neural network architecture as well as the main hyper-parameters. Finally, we give an evaluation of the model under multiple scenarios. We show in particular the efficiency of the control of the passenger inflows into the platforms. Full article

Despite a significant increase in global clean energy investments, as part of the decarbonization process, it remains insufficient to meet the demand for energy services in a sustainable manner. This study investigates the performance of sustainable energy equity investments, with focus on environmental markets, using monthly equity index data from 31 August 2009 to 30 December 2022. The main contributions of our study are (i) assessment of the performance of trading strategies based on the trend, momentum, and volatility of Environmental Opportunities (EO) and Environmental Technologies (ET) equity indices; and (ii) comparison of the performance of sustainable equity index investments to fossil fuel-based and major global equity indices. Market performance evaluation based on technical analysis tools such as the Relative Strength Index (RSI), Moving Averages, and Average True Range (ATR) is captured through the Sharpe and the Sharpe per trade. The analysis is divided according to regional, sector, and global EO indices, fossil fuel-based indices, and the key global stock market indices. Our findings reveal that a momentum-based strategy performed best for the MSCI Global Alternative Energy index with the highest excess return per unit of risk, followed by the fossil fuel-based indices. A trend-based strategy worked best for the MSCI Global Alternative Energy and EO 100 indices. The use of volatility-based information yielded the highest Sharpe ratio for EO Europe, followed by the Oil and Gas Exploration and Production industry, and MSCI Global Alternative Energy. We further find that a trader relying on a system which simultaneously provides momentum, trend, or volatility information would yield positive returns only for the MSCI Global Alternative Energy, the S&P Oil and Exploration and Production industry, NYSE Arca Oil, and FTSE 100 indices. Overall, despite the superior performance of the MSCI Global Alternative Energy index when using momentum and trend strategies, most region and sector EOs performed poorly compared to fossil fuel-based indices. The results suggest that the existing crude oil prices continue to allow fossil fuel-based equity investments to outperform most environmentally sustainable equity investments. These findings support that sustainable investments, on average, have yet to demonstrate consistent superior performance over non-renewable energy investments which demonstrates the need for continued, rigorous, and accommodating regulatory policy actions from government bodies in order to reorient significant capital flows towards sustainable equity investments. Full article