This paper selects the data of China's specialized,special and new“small giants”listed companies from 2011 to 2021,and starts from the key production factor and strategic asset of data assets,empirically examine...This paper selects the data of China's specialized,special and new“small giants”listed companies from 2011 to 2021,and starts from the key production factor and strategic asset of data assets,empirically examines the impact of data assetization on the supply chain resilience of SRDI SMEs,and examines the impact of data assetization on the supply chain resilience of SRDI SMEs using the role of the mechanism model.Through the mechanism model,the mediating effects of financing constraints and technological innovation are examined,and a path of action is drawn,which provides theoretical evidence and policy recommendations for promoting the digital transformation of SRDI SMEs and improving supply chain resilience.展开更多
At the beginning of 2025,China’s national carbon market carbon price trend exhibited a continuous unilateral downward trajectory,representing a departure from the overall steady upward trend in carbon prices since th...At the beginning of 2025,China’s national carbon market carbon price trend exhibited a continuous unilateral downward trajectory,representing a departure from the overall steady upward trend in carbon prices since the carbon market launched in 2021.The analysis suggests that the primary reason for the recent decline in carbon prices is the reversal of supply and demand dynamics in the carbon market,with increased quota supply amid a sluggish economy.It is expected that downward pressure on carbon prices will persist in the short term,but with more industries being included and continued policy optimization and improvement,a rise in China’s medium-to long-term carbon prices is highly probable.Recommendations for enterprises involved in carbon asset operations and management:first,refining carbon asset reserves and trading strategies;second,accelerating internal CCER project development;third,exploring carbon financial instrument applications;fourth,establishing and improving internal carbon pricing mechanisms;fifth,proactively planning for new industry inclusion.展开更多
Hidden champions play a critical role in China’s efforts to overcome technological and industrial“chokepoints”.These enterprises are pivotal for breaking free from Western technological embargoes,avoiding entrapmen...Hidden champions play a critical role in China’s efforts to overcome technological and industrial“chokepoints”.These enterprises are pivotal for breaking free from Western technological embargoes,avoiding entrapment in low-value-added production,and driving industrial upgrading.Given the distinct market environment in which China’s hidden champions have emerged,it is both timely and practically significant to examine their growth trajectories and underlying mechanisms.This study adopts a resource allocation perspective to investigate the development path of Chinese manufacturing enterprises into hidden champions,using a vertical case study of Hailiya Group.The findings reveal that such enterprises achieve hidden champion status by vertically concentrating on niche markets while harnessing technological potential and horizontally diversifying their technology application scenarios.Their growth follows a“T-shaped”strategy,combining vertical specialization in a focused market with horizontal expansion into new applications.Four critical mechanisms underpin the rise of manufacturing hidden champions:market niche positioning,innovation-driven focus,application scenario expansion,and ecosystem development.Specifically,these enterprises strategically target niche markets,establish a technology-oriented competitive edge,broaden technology applications to unlock new profit opportunities,and develop collaborative ecosystems to share resources and drive industrial advancement.This paper not only extends the interpretive boundaries of resource allocation theory but also offers fresh insights into the emergence of Chinese manufacturing enterprises as hidden champions,enriching our understanding of their unique growth dynamics.展开更多
This article analyzes the predictability of market betas concerning cryptocurrency assets and evaluates the efficiency of beta-hedged,market-neutral portfolios.We forecast 1-year-ahead market betas using various estim...This article analyzes the predictability of market betas concerning cryptocurrency assets and evaluates the efficiency of beta-hedged,market-neutral portfolios.We forecast 1-year-ahead market betas using various estimating methods,including ordinary least squares(OLS)and Vasicek’s Bayesian shrinkage estimator,and assess their impact on portfolio variance reduction across cryptomarket indices.Our findings indicate that while standard OLS betas explain significantly less of the variation in future betas for cryptoassets compared to US stocks,slope winsorization and Bayesian shrinkage improve prediction accuracy.The results suggest that beta-hedged portfolios reduce variance for approximately 17%of the universe,with the Broad Digital Market Index demonstrating the best hedging efficiency.These findings underscore the significant challenges of developing effective hedging strategies in the cryptocurrency market,emphasizing the importance of idiosyncratic risk in crypto returns and the need for appropriate market index representation.展开更多
Trend with an increase to EUR l67 mn(compared with minus EUR122.8 mn in 2023).Liquid assets decreased by 38.2 percent compared to December 3l,2023,to a level of EUR 451.7 mn as of December 31,2024,mainly dueto the rep...Trend with an increase to EUR l67 mn(compared with minus EUR122.8 mn in 2023).Liquid assets decreased by 38.2 percent compared to December 3l,2023,to a level of EUR 451.7 mn as of December 31,2024,mainly dueto the repayment of private placements and other loans and borrowings.展开更多
This paper examines the environmental impact of green assets using machine learning and impulse responses by local projections.A series of 87 green assets from various classes are considered,namely firms providing ren...This paper examines the environmental impact of green assets using machine learning and impulse responses by local projections.A series of 87 green assets from various classes are considered,namely firms providing renewable energy and carbon offset solutions,carbon and sustainable investing ETFs and green cryptocurrencies.The dataset spans the period from 2015 to 2022 and comprises globally sourced environmental and financial data.The current study examines whether asset prices,returns and trading volumes have an impact on environmental indicators such as temperature(global mean and anomalies)and greenhouse gas concentration.The results indicate that adoption of these green assets does not have a significant environmental impact,suggesting that they should not be used as substitutes for real climate action.This work serves as a cautionary tale on the nexus between green assets and environmental indicators and the results can be used by governments and corporations when formulating climate and ESG strategies.展开更多
Against the backdrop of China’s“Dual Carbon”strategy,the rapid transformation of China’s energy structure is driving the need for structural adjustments in its carbon assets.Investigating the volatility spillovers...Against the backdrop of China’s“Dual Carbon”strategy,the rapid transformation of China’s energy structure is driving the need for structural adjustments in its carbon assets.Investigating the volatility spillovers between climate risks and carbon assets is therefore crucial for optimizing asset allocation,enhancing the green financial system,and mitigating cross-market risk contagion.Using a refined time-varying parameter vector autoregression(TVP-VAR)model,we analyze their time-varying spillover effects on high-carbon(WTI crude oil futures)and low-carbon(the NASDAQ Clean Edge Green Energy Index[CELS])assets.Our results reveal a significant bidirectional spillover relationship between climate risks and carbon assets.Statistically,the climate risks serve as the primary net transmitter of spillovers to both crude oil and the clean energy index.Dynamically,the influence of transition risk on low-carbon assets amplifies markedly during specific episodes,highlighting its acute sensitivity to policy and market signals.A key finding is the considerable hedging capacity of the CELS,which exhibits a 43.6%hedging efficiency against systemic risk in high-carbon assets,an effect that becomes particularly pronounced during periods of intensive policy implementation.This research provides a quantitative basis for investors to design“carbon quota and green bond”portfolios,and for regulators to develop a cross-market early warning mechanism for climate risks.展开更多
The objective of this article is to delve into the digital financial asset(DFA)portfolio price of institutional investors,such as hedge funds.The aim is to make a significant contribution by providing methods(statisti...The objective of this article is to delve into the digital financial asset(DFA)portfolio price of institutional investors,such as hedge funds.The aim is to make a significant contribution by providing methods(statistical methods and fuzzy logic)for investors to identify and select the best long-term portfolio from the pool of 218 digital financial assets that are available in the Russian market.Importantly,companies listed as digital financial asset operators often offer multiple classes of these assets for trading,and as such,investors are only able to trade floating digital financial assets.By the time we reach the conclusion of the year 2024,it has been estimated that the total volume of the Russian DFA market that is currently in circulation will amount to a staggering 1.54 billion USD.Additionally,it is worth noting that 708 different issues are currently actively circulating in the market,showcasing a rather extensive array of options for potential investors.In December alone,an impressive total of 147 new DFAs were introduced and successfully placed,contributing a notable 0.7 billion USD to the market,which is certainly a remarkable feat.The sustainability of the price premium remains uncertain,a consequence of the digital asset market’s inherent volatility and relatively short history.When this volatility is considered,the observed premium lacks statistical significance for the sample period.Therefore,the novelty of this study is the creation of new effective tools for researching the effectiveness of portfolio management for time series,which includes 416 daily observations for the period March 2022–October 2023.展开更多
In the metaverse,digital assets are essential to define identity,shape the virtual environment,and facilitate economic transactions.This study introduces a novel feature to the metaverse by capturing a fundamental asp...In the metaverse,digital assets are essential to define identity,shape the virtual environment,and facilitate economic transactions.This study introduces a novel feature to the metaverse by capturing a fundamental aspect of individuals–their conversations–and transforming them into digital assets.It utilizes natural language processing and machine learning methods to extract key sentences from user conversations and match them with emojis that reflect their sentiments.The selected sentence,which encapsulates the essence of the user’s statements,is then transformed into digital art through a generative visual model.This digital artwork is transformed into a non-fungible token,becoming a valuable digital asset within the blockchain ecosystem that is ideal for integration into metaverse applications.Our aim is to manage personality traits as digital assets to foster individual uniqueness,enrich user experiences,and facilitate more personalized services and interactions with both like-minded users and non-player characters,thereby enhancing the overall user journey.展开更多
This paper focuses on the optimization of the evaluation index system for the value of transportation infrastructure assets.It analyzes the shortcomings of the current system and explores the directions for optimizing...This paper focuses on the optimization of the evaluation index system for the value of transportation infrastructure assets.It analyzes the shortcomings of the current system and explores the directions for optimizing the index system from the perspectives of functionality,economy,social impact,environmental impact,and sustainability.The paper also discusses the application of the optimized index system in practical evaluation and the measures to ensure its effectiveness.The research aims to enhance the evaluation mechanism for the value of transportation infrastructure assets,providing a more scientific basis for decision-making,addressing challenges in asset management,improving the level of asset management in transportation infrastructure,and meeting the demands of high-quality development in the transportation sector in the new era.展开更多
Against the backdrop of the gradual deepening of interest rate liberalization,the decline in effective credit demand,the intensification of competitive involution among commercial banks,and the complex international e...Against the backdrop of the gradual deepening of interest rate liberalization,the decline in effective credit demand,the intensification of competitive involution among commercial banks,and the complex international economic and trade situation,commercial banks in China's mainland have entered a stage of low interest rates and narrow interest margins.Coupled with the continuous exposure of risks in retail customer groups and small and micro enterprises,many commercial banks have chosen to phase in expanding and strengthening their corporate business segments to smoothly navigate economic cycles and enhance operational resilience and sustainability.How the corporate business segment optimizes its asset-liability structure through asset allocation to achieve high-quality development is a major issue worthy of consideration by the entire industry.From the perspective of a medium-sized national commercial bank,this paper explores and proposes four key basic customer groups,six asset allocation models,and fourteen key industries for layout,for reference,and research.展开更多
Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine ...Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine the internal conditions of sewage pipes.Due to the extensive inventory of pipes and associated costs,it is not practical for municipalities to conduct inspections on each sanitary sewage pipe section.According to the ASCE(American Society of Civil Engineers)infrastructure report published in 2021,combined investment needs for water and wastewater systems are estimated to be$150 billion during 2016-2025.Therefore,new solutions are needed to fill the trillion-dollar investment gap to improve the existing water and wastewater infrastructure for the coming years.ML(machine learning)based prediction model development is an effective method for predicting the condition of sewer pipes.In this research,sewer pipe inspection data from several municipalities are collected,which include variables such as pipe material,age,diameter,length,soil type,slope of construction,and PACP(Pipeline Assessment Certification Program)score.These sewer pipe data exhibit a severe imbalance in pipes’PACP scores,which is considered the target variable in the development of models.Due to this imbalanced dataset,the performance of the sewer prediction model is poor.This paper,therefore,aims to employ oversampling and hyperparameter tuning techniques to treat the imbalanced data and improve the model’s performance significantly.Utility owners and municipal asset managers can utilize the developed models to make more informed decisions on future inspections of sewer pipelines.展开更多
As urban areas expand and water demand intensifies,the need for efficient and reliable water distribution systems becomes increasingly critical.A widely used infrastructure management approach involves partitioning wa...As urban areas expand and water demand intensifies,the need for efficient and reliable water distribution systems becomes increasingly critical.A widely used infrastructure management approach involves partitioning water distribution networks(WDNs)into district metered areas(DMAs).However,suboptimal designs of DMA partitioning can lead to inefficiencies and increased costs.This study presents a core-peripheryinformed approach for DMA design that explicitly utilises the natural division between a densely connected core and a sparsely connected periphery.Incorporating this structural framework enhances network resilience,improves water pressure stability,and optimises boundary device placement.The proposed core-periphery-informed DMA design integrates hydraulic and topological analyses to identify central and peripheral network areas,applies a community structure detection algorithm conditioned by these areas,and uses an optimisation model to determine the optimal placement of boundary devices,enhancing network resilience and reducing costs.When applied to the Modena WDN in Italy,this approach demonstrates improved pressure stability and significant cost reductions compared to traditional methods.Overall,the findings highlight the practical benefits of the core-periphery-based DMA design,offering a scalable and data-driven solution for urban water distribution systems.展开更多
The COVID-19 pandemic precipitated a surge in the non-performing assets held by financial institutions,elevating systemic risk in financial networks.Therefore,developing strategies to alleviate this risk,with a focus ...The COVID-19 pandemic precipitated a surge in the non-performing assets held by financial institutions,elevating systemic risk in financial networks.Therefore,developing strategies to alleviate this risk,with a focus on non-performing assets,has become a research area of interest.Supported by policies related to the Chinese insurance market,this study proposes the establishment of a non-performing assets disposal fund backed by insurance capital.This fund will invest in the non-performing assets of financial institutions with the aim of mitigating systemic risk.Using a linear threshold model,we identify an asymptotically optimal scheme for disposing of nonperforming assets.Additionally,we construct a payment model integrated with nonperforming assets,from which we derive an optimal payment and clearing strategy.Our research also proposes a robust set of criteria to assist regulators in determining whether to use the non-performing assets disposal fund.To demonstrate the efficacy of the fund in reducing systemic risk,we conduct simulations and analyze data from the Chinese interbank financial network.Through this rigorous analysis,we confirm the role of the fund in enhancing the stability of the financial system.展开更多
In July 2025,Claudio Descalzi,CEO of Italian oil company Eni,stated in an interview with the Financial Times that Eni’s low-carbon business operating profits would equal those of its oil and gas business by 2035 and ...In July 2025,Claudio Descalzi,CEO of Italian oil company Eni,stated in an interview with the Financial Times that Eni’s low-carbon business operating profits would equal those of its oil and gas business by 2035 and exceed oil and gas business profits by 2040.Meanwhile,British oil company bp is scaling back its low-carbon business under shareholder pressure,returning to traditional oil and gas operations,and plans to gradually dispose of USD 20 billion worth of low-carbon business assets such as onshore wind farms,aiming to increase oil and gas production to 2.3−2.5 million boe per day by 2030.Two European oil companies are heading in completely different strategic directions.In the future,whether Eni can achieve its expected goals of making renewable energy business a key core business,as well as what development strategies oil and gas companies should implement and adhere to,are questions worthy of deep consideration.展开更多
This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power ...This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power of asset pricing models.Utilizing a long–short portfolio strategy and Fama–MacBeth cross-sectional regression,we find that trading-based anomalies outnumber accounting-based anomalies in the Chinese market.Our results demonstrate that conditional models significantly outperform their unconditional counterparts.Notably,investor sentiment is crucial for capturing the size anomaly when excluding observations from the COVID-19 pandemic period.Additionally,it substantially improves the ability of conditional Fama–French three-factor models to capture individual anomalies and enhances the return–prediction accuracy of conditional CAPMs.We suggest further investigating high-frequency investor sentiment-based conditional models to anticipate stock price fluctuations during extraordinary public health events.展开更多
Xin'ao Co.,Ltd.(603889),Iocated in Jiaxing City,Zhejiang Province,is a wellestablished enterprise in the wool textile industry.It focuses on the entire wool textile supply chain,integrating wool procurement,wool t...Xin'ao Co.,Ltd.(603889),Iocated in Jiaxing City,Zhejiang Province,is a wellestablished enterprise in the wool textile industry.It focuses on the entire wool textile supply chain,integrating wool procurement,wool top production,wool top modification,dyeing and finishing,and spinning,After more than thirty years of development,it has gradually formed a large-scale,market-oriented textile eco-system with Xin'ao Co.,Ltd. as the core,with its subsidia ries specializing in different production and sales functions.展开更多
The rapid development of vocational colleges in China brings about the explosive growth of the number and category of state-owned assets that guarantees the development of vocational colleges. The special equipment an...The rapid development of vocational colleges in China brings about the explosive growth of the number and category of state-owned assets that guarantees the development of vocational colleges. The special equipment and the general equipment included in the state-owned assets of vocational colleges are increasing at the fastest rate. Based on the problems from equipment inventory, this paper analyzes the problems of state-owned management, and puts forward countermeasures to improve the management of state-owned assets from formulating regulations and rules, strengthening the unified institution, applying the information technologies in building the team of administrators.展开更多
1.Introduction Microbiologically influenced corrosion(MIC)is the destruction of metal materials caused by the activity of microorganisms and the participation of biofilms[1].Global economic costs caused by marine corr...1.Introduction Microbiologically influenced corrosion(MIC)is the destruction of metal materials caused by the activity of microorganisms and the participation of biofilms[1].Global economic costs caused by marine corrosion come to hundreds of billion dollars per year,with approximately 20% of corrosion losses caused by MIC[2].The MIC poses a serious threat to the integrity and safety of assets in the oil and gas industry,water industry,and nuclear waste storage facili-ties[3-5].展开更多
This study explores the impact of board diversity on firm performance,with a focus on companies listed on the Singapore Stock Exchange(SGX).Board diversity is examined across various dimensions,including gender,age,et...This study explores the impact of board diversity on firm performance,with a focus on companies listed on the Singapore Stock Exchange(SGX).Board diversity is examined across various dimensions,including gender,age,ethnicity,and professional background,to understand its relationship with key performance indicators such as Return on Assets(ROA)and Return on Equity(ROE).Using a quantitative research approach,the study analyzes data from 90 publicly listed firms,employing descriptive statistics,correlation analysis,and multiple regression techniques.The findings reveal that the direct correlation between board diversity and financial performance,particularly in terms of ROA and ROE,is not statistically significant in the studied sample.Despite the lack of direct significance,the research underscores the nuanced and multifaceted role of diversity in corporate governance,suggesting that its impact may be more complex and influenced by various contextual factors.The study concludes by recommending that companies continue to enhance gender diversity,balance age structures,tailor professional backgrounds to industry needs,and manage board tenure effectively to optimize corporate governance and support sustainable growth.展开更多
基金National Undergraduate Training Program for Innovation and Entrepreneurship(D202410120257422558)。
文摘This paper selects the data of China's specialized,special and new“small giants”listed companies from 2011 to 2021,and starts from the key production factor and strategic asset of data assets,empirically examines the impact of data assetization on the supply chain resilience of SRDI SMEs,and examines the impact of data assetization on the supply chain resilience of SRDI SMEs using the role of the mechanism model.Through the mechanism model,the mediating effects of financing constraints and technological innovation are examined,and a path of action is drawn,which provides theoretical evidence and policy recommendations for promoting the digital transformation of SRDI SMEs and improving supply chain resilience.
文摘At the beginning of 2025,China’s national carbon market carbon price trend exhibited a continuous unilateral downward trajectory,representing a departure from the overall steady upward trend in carbon prices since the carbon market launched in 2021.The analysis suggests that the primary reason for the recent decline in carbon prices is the reversal of supply and demand dynamics in the carbon market,with increased quota supply amid a sluggish economy.It is expected that downward pressure on carbon prices will persist in the short term,but with more industries being included and continued policy optimization and improvement,a rise in China’s medium-to long-term carbon prices is highly probable.Recommendations for enterprises involved in carbon asset operations and management:first,refining carbon asset reserves and trading strategies;second,accelerating internal CCER project development;third,exploring carbon financial instrument applications;fourth,establishing and improving internal carbon pricing mechanisms;fifth,proactively planning for new industry inclusion.
基金supported by the following projects:The Youth Project of the National Natural Science Foundation of China(NSFC)“Research on Chinese Multinational Companies’Componovation under Resource Constraint:From a Dynamic Circulation Logic of Home and Host Countries”(Grant No.72102030)The Research Project of Humanities and Social Sciences of the Ministry of Education“Research on Chinese Multinational Companies’Learning by Doing Mechanism under Resource Constraint”(Grant No.21C10173022)The General Project of the China Postdoctoral Science Foundation“Research on the Realization Path and Motivations for the Domestic Replacement of Core Technologies for Chinese Manufacturing Enterprises”(Grant No.2022M720975).
文摘Hidden champions play a critical role in China’s efforts to overcome technological and industrial“chokepoints”.These enterprises are pivotal for breaking free from Western technological embargoes,avoiding entrapment in low-value-added production,and driving industrial upgrading.Given the distinct market environment in which China’s hidden champions have emerged,it is both timely and practically significant to examine their growth trajectories and underlying mechanisms.This study adopts a resource allocation perspective to investigate the development path of Chinese manufacturing enterprises into hidden champions,using a vertical case study of Hailiya Group.The findings reveal that such enterprises achieve hidden champion status by vertically concentrating on niche markets while harnessing technological potential and horizontally diversifying their technology application scenarios.Their growth follows a“T-shaped”strategy,combining vertical specialization in a focused market with horizontal expansion into new applications.Four critical mechanisms underpin the rise of manufacturing hidden champions:market niche positioning,innovation-driven focus,application scenario expansion,and ecosystem development.Specifically,these enterprises strategically target niche markets,establish a technology-oriented competitive edge,broaden technology applications to unlock new profit opportunities,and develop collaborative ecosystems to share resources and drive industrial advancement.This paper not only extends the interpretive boundaries of resource allocation theory but also offers fresh insights into the emergence of Chinese manufacturing enterprises as hidden champions,enriching our understanding of their unique growth dynamics.
基金financial support from the Czech Science Foundation under the project`Deep dive into decentralized finance:Market microstructure,and behavioral and psychological patterns’[Grant No.23-06606S]supported by Charles University Research Centre program No.24/SSH/020+1 种基金the Cooperatio Program at Charles University,research area Economicsfinancial support from the Charles University Specific University Research scheme[Grant No.SVV 260843].
文摘This article analyzes the predictability of market betas concerning cryptocurrency assets and evaluates the efficiency of beta-hedged,market-neutral portfolios.We forecast 1-year-ahead market betas using various estimating methods,including ordinary least squares(OLS)and Vasicek’s Bayesian shrinkage estimator,and assess their impact on portfolio variance reduction across cryptomarket indices.Our findings indicate that while standard OLS betas explain significantly less of the variation in future betas for cryptoassets compared to US stocks,slope winsorization and Bayesian shrinkage improve prediction accuracy.The results suggest that beta-hedged portfolios reduce variance for approximately 17%of the universe,with the Broad Digital Market Index demonstrating the best hedging efficiency.These findings underscore the significant challenges of developing effective hedging strategies in the cryptocurrency market,emphasizing the importance of idiosyncratic risk in crypto returns and the need for appropriate market index representation.
文摘Trend with an increase to EUR l67 mn(compared with minus EUR122.8 mn in 2023).Liquid assets decreased by 38.2 percent compared to December 3l,2023,to a level of EUR 451.7 mn as of December 31,2024,mainly dueto the repayment of private placements and other loans and borrowings.
文摘This paper examines the environmental impact of green assets using machine learning and impulse responses by local projections.A series of 87 green assets from various classes are considered,namely firms providing renewable energy and carbon offset solutions,carbon and sustainable investing ETFs and green cryptocurrencies.The dataset spans the period from 2015 to 2022 and comprises globally sourced environmental and financial data.The current study examines whether asset prices,returns and trading volumes have an impact on environmental indicators such as temperature(global mean and anomalies)and greenhouse gas concentration.The results indicate that adoption of these green assets does not have a significant environmental impact,suggesting that they should not be used as substitutes for real climate action.This work serves as a cautionary tale on the nexus between green assets and environmental indicators and the results can be used by governments and corporations when formulating climate and ESG strategies.
基金financially supported by the National Natural Science Foundation of China(No.12501656)the National Social Science Foundation of China(No.22FGLB075).
文摘Against the backdrop of China’s“Dual Carbon”strategy,the rapid transformation of China’s energy structure is driving the need for structural adjustments in its carbon assets.Investigating the volatility spillovers between climate risks and carbon assets is therefore crucial for optimizing asset allocation,enhancing the green financial system,and mitigating cross-market risk contagion.Using a refined time-varying parameter vector autoregression(TVP-VAR)model,we analyze their time-varying spillover effects on high-carbon(WTI crude oil futures)and low-carbon(the NASDAQ Clean Edge Green Energy Index[CELS])assets.Our results reveal a significant bidirectional spillover relationship between climate risks and carbon assets.Statistically,the climate risks serve as the primary net transmitter of spillovers to both crude oil and the clean energy index.Dynamically,the influence of transition risk on low-carbon assets amplifies markedly during specific episodes,highlighting its acute sensitivity to policy and market signals.A key finding is the considerable hedging capacity of the CELS,which exhibits a 43.6%hedging efficiency against systemic risk in high-carbon assets,an effect that becomes particularly pronounced during periods of intensive policy implementation.This research provides a quantitative basis for investors to design“carbon quota and green bond”portfolios,and for regulators to develop a cross-market early warning mechanism for climate risks.
文摘The objective of this article is to delve into the digital financial asset(DFA)portfolio price of institutional investors,such as hedge funds.The aim is to make a significant contribution by providing methods(statistical methods and fuzzy logic)for investors to identify and select the best long-term portfolio from the pool of 218 digital financial assets that are available in the Russian market.Importantly,companies listed as digital financial asset operators often offer multiple classes of these assets for trading,and as such,investors are only able to trade floating digital financial assets.By the time we reach the conclusion of the year 2024,it has been estimated that the total volume of the Russian DFA market that is currently in circulation will amount to a staggering 1.54 billion USD.Additionally,it is worth noting that 708 different issues are currently actively circulating in the market,showcasing a rather extensive array of options for potential investors.In December alone,an impressive total of 147 new DFAs were introduced and successfully placed,contributing a notable 0.7 billion USD to the market,which is certainly a remarkable feat.The sustainability of the price premium remains uncertain,a consequence of the digital asset market’s inherent volatility and relatively short history.When this volatility is considered,the observed premium lacks statistical significance for the sample period.Therefore,the novelty of this study is the creation of new effective tools for researching the effectiveness of portfolio management for time series,which includes 416 daily observations for the period March 2022–October 2023.
文摘In the metaverse,digital assets are essential to define identity,shape the virtual environment,and facilitate economic transactions.This study introduces a novel feature to the metaverse by capturing a fundamental aspect of individuals–their conversations–and transforming them into digital assets.It utilizes natural language processing and machine learning methods to extract key sentences from user conversations and match them with emojis that reflect their sentiments.The selected sentence,which encapsulates the essence of the user’s statements,is then transformed into digital art through a generative visual model.This digital artwork is transformed into a non-fungible token,becoming a valuable digital asset within the blockchain ecosystem that is ideal for integration into metaverse applications.Our aim is to manage personality traits as digital assets to foster individual uniqueness,enrich user experiences,and facilitate more personalized services and interactions with both like-minded users and non-player characters,thereby enhancing the overall user journey.
文摘This paper focuses on the optimization of the evaluation index system for the value of transportation infrastructure assets.It analyzes the shortcomings of the current system and explores the directions for optimizing the index system from the perspectives of functionality,economy,social impact,environmental impact,and sustainability.The paper also discusses the application of the optimized index system in practical evaluation and the measures to ensure its effectiveness.The research aims to enhance the evaluation mechanism for the value of transportation infrastructure assets,providing a more scientific basis for decision-making,addressing challenges in asset management,improving the level of asset management in transportation infrastructure,and meeting the demands of high-quality development in the transportation sector in the new era.
文摘Against the backdrop of the gradual deepening of interest rate liberalization,the decline in effective credit demand,the intensification of competitive involution among commercial banks,and the complex international economic and trade situation,commercial banks in China's mainland have entered a stage of low interest rates and narrow interest margins.Coupled with the continuous exposure of risks in retail customer groups and small and micro enterprises,many commercial banks have chosen to phase in expanding and strengthening their corporate business segments to smoothly navigate economic cycles and enhance operational resilience and sustainability.How the corporate business segment optimizes its asset-liability structure through asset allocation to achieve high-quality development is a major issue worthy of consideration by the entire industry.From the perspective of a medium-sized national commercial bank,this paper explores and proposes four key basic customer groups,six asset allocation models,and fourteen key industries for layout,for reference,and research.
文摘Sewer pipe condition assessment by performing regular inspections is crucial for ensuring the systems’effective operation and maintenance.CCTV(closed-circuit television)is widely employed in North America to examine the internal conditions of sewage pipes.Due to the extensive inventory of pipes and associated costs,it is not practical for municipalities to conduct inspections on each sanitary sewage pipe section.According to the ASCE(American Society of Civil Engineers)infrastructure report published in 2021,combined investment needs for water and wastewater systems are estimated to be$150 billion during 2016-2025.Therefore,new solutions are needed to fill the trillion-dollar investment gap to improve the existing water and wastewater infrastructure for the coming years.ML(machine learning)based prediction model development is an effective method for predicting the condition of sewer pipes.In this research,sewer pipe inspection data from several municipalities are collected,which include variables such as pipe material,age,diameter,length,soil type,slope of construction,and PACP(Pipeline Assessment Certification Program)score.These sewer pipe data exhibit a severe imbalance in pipes’PACP scores,which is considered the target variable in the development of models.Due to this imbalanced dataset,the performance of the sewer prediction model is poor.This paper,therefore,aims to employ oversampling and hyperparameter tuning techniques to treat the imbalanced data and improve the model’s performance significantly.Utility owners and municipal asset managers can utilize the developed models to make more informed decisions on future inspections of sewer pipelines.
文摘As urban areas expand and water demand intensifies,the need for efficient and reliable water distribution systems becomes increasingly critical.A widely used infrastructure management approach involves partitioning water distribution networks(WDNs)into district metered areas(DMAs).However,suboptimal designs of DMA partitioning can lead to inefficiencies and increased costs.This study presents a core-peripheryinformed approach for DMA design that explicitly utilises the natural division between a densely connected core and a sparsely connected periphery.Incorporating this structural framework enhances network resilience,improves water pressure stability,and optimises boundary device placement.The proposed core-periphery-informed DMA design integrates hydraulic and topological analyses to identify central and peripheral network areas,applies a community structure detection algorithm conditioned by these areas,and uses an optimisation model to determine the optimal placement of boundary devices,enhancing network resilience and reducing costs.When applied to the Modena WDN in Italy,this approach demonstrates improved pressure stability and significant cost reductions compared to traditional methods.Overall,the findings highlight the practical benefits of the core-periphery-based DMA design,offering a scalable and data-driven solution for urban water distribution systems.
基金supported by the National Social Science Fund of China(No.22BTJ027).
文摘The COVID-19 pandemic precipitated a surge in the non-performing assets held by financial institutions,elevating systemic risk in financial networks.Therefore,developing strategies to alleviate this risk,with a focus on non-performing assets,has become a research area of interest.Supported by policies related to the Chinese insurance market,this study proposes the establishment of a non-performing assets disposal fund backed by insurance capital.This fund will invest in the non-performing assets of financial institutions with the aim of mitigating systemic risk.Using a linear threshold model,we identify an asymptotically optimal scheme for disposing of nonperforming assets.Additionally,we construct a payment model integrated with nonperforming assets,from which we derive an optimal payment and clearing strategy.Our research also proposes a robust set of criteria to assist regulators in determining whether to use the non-performing assets disposal fund.To demonstrate the efficacy of the fund in reducing systemic risk,we conduct simulations and analyze data from the Chinese interbank financial network.Through this rigorous analysis,we confirm the role of the fund in enhancing the stability of the financial system.
文摘In July 2025,Claudio Descalzi,CEO of Italian oil company Eni,stated in an interview with the Financial Times that Eni’s low-carbon business operating profits would equal those of its oil and gas business by 2035 and exceed oil and gas business profits by 2040.Meanwhile,British oil company bp is scaling back its low-carbon business under shareholder pressure,returning to traditional oil and gas operations,and plans to gradually dispose of USD 20 billion worth of low-carbon business assets such as onshore wind farms,aiming to increase oil and gas production to 2.3−2.5 million boe per day by 2030.Two European oil companies are heading in completely different strategic directions.In the future,whether Eni can achieve its expected goals of making renewable energy business a key core business,as well as what development strategies oil and gas companies should implement and adhere to,are questions worthy of deep consideration.
基金financially supported by:National Natural Science Foundation of China(72261002,72141304)Youth Foundation for Humanities and Social Sciences Research of the Ministry of Education(22YJC790190)+1 种基金National Key Research and Development Program of China(2022YFC3303304)Student Research Program of Guizhou University of Finance and Economics(2022ZXS).
文摘This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power of asset pricing models.Utilizing a long–short portfolio strategy and Fama–MacBeth cross-sectional regression,we find that trading-based anomalies outnumber accounting-based anomalies in the Chinese market.Our results demonstrate that conditional models significantly outperform their unconditional counterparts.Notably,investor sentiment is crucial for capturing the size anomaly when excluding observations from the COVID-19 pandemic period.Additionally,it substantially improves the ability of conditional Fama–French three-factor models to capture individual anomalies and enhances the return–prediction accuracy of conditional CAPMs.We suggest further investigating high-frequency investor sentiment-based conditional models to anticipate stock price fluctuations during extraordinary public health events.
文摘Xin'ao Co.,Ltd.(603889),Iocated in Jiaxing City,Zhejiang Province,is a wellestablished enterprise in the wool textile industry.It focuses on the entire wool textile supply chain,integrating wool procurement,wool top production,wool top modification,dyeing and finishing,and spinning,After more than thirty years of development,it has gradually formed a large-scale,market-oriented textile eco-system with Xin'ao Co.,Ltd. as the core,with its subsidia ries specializing in different production and sales functions.
基金Supported by College-level Research Project of Hangzhou Vocational&Technical College(ky202514).
文摘The rapid development of vocational colleges in China brings about the explosive growth of the number and category of state-owned assets that guarantees the development of vocational colleges. The special equipment and the general equipment included in the state-owned assets of vocational colleges are increasing at the fastest rate. Based on the problems from equipment inventory, this paper analyzes the problems of state-owned management, and puts forward countermeasures to improve the management of state-owned assets from formulating regulations and rules, strengthening the unified institution, applying the information technologies in building the team of administrators.
基金supported by the National Natural Science Foun-dation of China(Nos.52371071,51971228,and 51771212).
文摘1.Introduction Microbiologically influenced corrosion(MIC)is the destruction of metal materials caused by the activity of microorganisms and the participation of biofilms[1].Global economic costs caused by marine corrosion come to hundreds of billion dollars per year,with approximately 20% of corrosion losses caused by MIC[2].The MIC poses a serious threat to the integrity and safety of assets in the oil and gas industry,water industry,and nuclear waste storage facili-ties[3-5].
文摘This study explores the impact of board diversity on firm performance,with a focus on companies listed on the Singapore Stock Exchange(SGX).Board diversity is examined across various dimensions,including gender,age,ethnicity,and professional background,to understand its relationship with key performance indicators such as Return on Assets(ROA)and Return on Equity(ROE).Using a quantitative research approach,the study analyzes data from 90 publicly listed firms,employing descriptive statistics,correlation analysis,and multiple regression techniques.The findings reveal that the direct correlation between board diversity and financial performance,particularly in terms of ROA and ROE,is not statistically significant in the studied sample.Despite the lack of direct significance,the research underscores the nuanced and multifaceted role of diversity in corporate governance,suggesting that its impact may be more complex and influenced by various contextual factors.The study concludes by recommending that companies continue to enhance gender diversity,balance age structures,tailor professional backgrounds to industry needs,and manage board tenure effectively to optimize corporate governance and support sustainable growth.
