Continuously growing attention has been paid to potential of polarimetry to provide additional information of remote sounding of the earth and other planets and to detect some special targets. In the present paper the...Continuously growing attention has been paid to potential of polarimetry to provide additional information of remote sounding of the earth and other planets and to detect some special targets. In the present paper the polarimetric technique in space for all the four Stockes parameters is presented.展开更多
With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threat...With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threats to ecosystem stability.Understanding the current status of forest degradation and assessing potential carbon stocks in China are of strategic importance for making forest restoration efforts and enhancing carbon sequestration capacity.In this study,we used the national forest inventory data from 2009 to 2018 to develop a set of standard measures for assessing degraded forests across China,based on five key indicators:forest accumulation growth rate(FAGR),forest recruitment rate(FRR),tree species reduction rate(TSRR),forest canopy cover reduction rate(FCCRR),and forest disaster level(FDL).Additionally,we estimated standing carbon stock,potential carbon stock,and theoretical space to grow by developing a stand growth model,which accounts for stand density across different site classes,to evaluate the restoration potential of degraded forests.The results indicate that degraded forest area in China is 36.15 million hectares,accounting for 20.10% of a total forest area.Standing carbon stock and potential carbon stock of degraded forests in China are 23.93 million tons and 61.90 million tons,respectively.Overall,degraded forest varies significantly across different regions.The results highlight the important trade-offs among environmental factors,policy decisions,and forest conditions,providing a robust foundation for developing measures to enhance forest quality.展开更多
Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attent...Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.展开更多
The COVID-19 pandemic and its subsequent eradication precipitated a rare and unexpected stock market crash.This crisis presents a unique opportunity to evaluate environmental,social,sustainable,and management(ESSM)pol...The COVID-19 pandemic and its subsequent eradication precipitated a rare and unexpected stock market crash.This crisis presents a unique opportunity to evaluate environmental,social,sustainable,and management(ESSM)policy choices.This study contributes by demonstrating the application of ESSM concerns and investment decision investment horizons and showing the significance of the long-term perspective in ESSM investment decision-making.From January 2020 to December 2022,stocks with higher ESSM ratings had significantly greater returns,less return arbitrariness,and larger operational profit margins.ESSM ventures with more advertising expenditures had higher stock returns;however,investors more predisposed toward ESSM saw less return arbitrariness during market crises.This study emphasizes the importance of investor and consumer loyalty to the elasticity of ESSM stocks.展开更多
Based on market integration theory,we investigate the static and dynamic connectedness between nonfungible tokens(NFTs)and the Association of Southeast Asian Nations(ASEAN)equity markets using the Quantile Vector Auto...Based on market integration theory,we investigate the static and dynamic connectedness between nonfungible tokens(NFTs)and the Association of Southeast Asian Nations(ASEAN)equity markets using the Quantile Vector Auto Regressive model.We also compute optimal weights and hedge ratios for our variable of interest to establish their diversification and hedging potential.Our analysis infers a moderate level of return transmission at the median quantile,where equity markets evolved as the net recipients of return spillover from the system,while NFTs emerge as key transmitters.In extreme market conditions,transmission between variables is amplified,but the increase is symmetrical across extreme quantiles,suggesting a similar impact.However,the interlinkage among assets is symmetric across conditional quantiles.The dynamic analysis demonstrates that the system integration amplifies during uncertain times(e.g.,COVID-19 and the Russia–Ukraine conflict).Our portfolio analysis shows that NFTs provide diversification and hedging in all market conditions.However,the period of turmoil dampened the diversification potential,and hedging became expensive.Our study offers detailed and insightful information about the transmission mechanism and enables the participants of financial markets to diversify and hedge their portfolio.展开更多
Necessary actions should be taken to ensure stock market efficiency;thus,financial innovation-based criteria that affect stock market efficiency should be improved.However,simultaneously improving all criteria is diff...Necessary actions should be taken to ensure stock market efficiency;thus,financial innovation-based criteria that affect stock market efficiency should be improved.However,simultaneously improving all criteria is difficult;therefore,performing priority analysis is important for carrying out this process effectively and efficiently.Accordingly,this study aims to evaluate the financial innovation-based characteristics of stock market efficiency.This study’s main research question within this framework is identifying which factors should be prioritized to improve the stock market.In this scope,we created a novel fuzzy decision-making model consisting of two stages.First,selected criteria for the financial innovation-based characteristics of stock market efficiency are weighted.In this process,quantum spherical fuzzy sets based on DEMATEL are considered.In the second stage,selected economies are ranked using the technique for order of preference by similarity to ideal solution(TOPSIS)approach.This study’s main contribution is that the DEMATEL technique in calculating criterion weights in the decision-making analysis process provides some advantages.With the help of this situation,the causal directions between these items can be considered;thus,it is possible to determine the most accurate strategies.The findings demonstrate that providing tax advantages is the most important factor in ensuring stock market efficiency.Moreover,the excellence of the financial system is critical in ensuring stock market efficiency.In this context,it is possible to provide tax advantages,especially for long-term investments.Thus,long-term investments can be increased,significantly increasing the market’s stability.展开更多
This study explores correlations and risk spillovers,essential concepts for financial risk management,among commodities(crude oil,gold,and a global commodities index)and emerging stock markets.Using the Asymmetric Dyn...This study explores correlations and risk spillovers,essential concepts for financial risk management,among commodities(crude oil,gold,and a global commodities index)and emerging stock markets.Using the Asymmetric Dynamic Conditional Correlation–Conditional Value-at-Risk(ADCC-CoVaR)model and a bootstrapped Kolmogorov–Smirnov(KS)test,we analyze the period from December 30,2005,to February 28,2024,examining correlations,downside and upside risk spillovers,and highlighting the effects of major events such as the global financial crisis of 2008,the COVID-19 pandemic,and the Russia-Ukraine war.The results show heightened correlations during crises and significant risk spillovers across market pairs,with downside risks often outweighing upside risks.Gold displays minimal risk spillover,highlighting its unique role as a haven asset.We find that spillovers between gold,global commodities,and stocks increased during the pandemic and the Russia-Ukraine conflict,while those involving crude oil remained stable.These findings provide valuable guidance for portfolio managers in navigating volatile markets.展开更多
In contrast to previous studies that investigated the impact of the investment groups’trading volume on the volatility of the stock index,this research,inspired by behavioral finance literature,aims to evaluate the d...In contrast to previous studies that investigated the impact of the investment groups’trading volume on the volatility of the stock index,this research,inspired by behavioral finance literature,aims to evaluate the dynamic bi-directional relationship between the trading behavior of investor groups(institutional and noninstitutional)and stock index fluctuations in different positions(long and short)and market conditions(the pre-COVID-19 and COVID-19 periods)in the Turkish stock market.The results indicate a bidirectional relationship between the stock index return(SIR)and the trading behavior of online individual traders(OIT)and equity mutual and pension funds(EMPF).However,this relationship varies depending on the trading positions of different investor groups.Also,there is a unidirectional relationship between the SIR and the trading behavior of the diversified equity funds(DEF).During the pandemic period,the role of online traders became more prominent,coinciding with their increased participation in the market,significantly affecting and being affected by stock index fluctuations.We also evaluated some behavioral biases(including overconfidence and asymmetric reaction)and the trading strategy of investor groups(with their performance).Results suggest that the online individual traders were less(more)overconfident than other groups in the prepandemic(pandemic)period.Additionally,all groups had an asymmetric reaction to the positive and negative SIR shocks.This research,contributing to the field of financial innovation and aligning with behavioral finance principles,reveals a fascinating finding:individual investors react to stock index fluctuations,largely driven by institutional investors,despite lacking access to new fundamental information about their portfolio stocks.These findings have significant implications for investors and market regulators.Recognizing and addressing behavioral biases is crucial for individual investors as they strive to make informed and successful financial decisions.It is concluded that the surge in retail investment is a phenomenon;hence,more effort is required for their investment stability in the Turkish stock market.展开更多
Tibetan Plateau,as one of the most carbon intensive regions in China,is crucial in the carbon cycle,and accurately estimating its vegetation carbon density(C_(VEG))is essential for assessing regional and national carb...Tibetan Plateau,as one of the most carbon intensive regions in China,is crucial in the carbon cycle,and accurately estimating its vegetation carbon density(C_(VEG))is essential for assessing regional and national carbon balance.However,the spatial distribution of regional C_(VEG)is not available remains highly uncertain due to lack of systematic research,especially for different organs.Here,we investigated the spatial distribution patterns and driving factors of C_(VEG)among different plant organs(leaf,branch,trunk and root)by systematically field grid-sampling 2040 field-plots of plant communities over the Tibetan Plateau from 2019 to 2020.The results showed that the carbon content of plant organs ranged from 255.53 to 515.58 g kg^(-1),with the highest in branches and the lowest in roots.Among the different plant functional groups,the highest C_(VEG)was found in evergreen coniferous forests,and the lowest in desert grasslands,with an average C_(VEG)of 1603.98 g m^(-2).C_(VEG)increased spatially from northwest to southeast over the Tibetan Plateau,with MAP being the dominant factor.Furthermore,the total vegetation carbon stock on the Tibetan Plateau was estimated to be 1965.62 Tg for all vegetation types.Based on the comprehensive field survey dataset,the Random Forest model effectively predicted and mapped the spatial distribution of C_(VEG)(including aboveground,belowground,and the total biomass carbon density)over the Tibetan Plateau with notable accuracy(validation R2 values were 71%,56%,and 64%for C_(AGB),C_(BGB),and C_(VEG),respectively)at a spatial resolution of 1 km×1 km.Our findings can help improve the accuracy of regional carbon stock estimations and provide parameters for carbon cycle model optimization and remote sensing calibration in the future.展开更多
Sacred forests play a valuable role in the conservation of local biodiversity and provide numerous ecosystem services in Cameroon. The aim of this study was to estimate floristic diversity, stand structures and carbon...Sacred forests play a valuable role in the conservation of local biodiversity and provide numerous ecosystem services in Cameroon. The aim of this study was to estimate floristic diversity, stand structures and carbon stocks in the sacred forests of Bandrefam and Batoufam (western Cameroon). The floristic inventory and the stand structures were carried out in 25 m × 25 m plots for individuals with diameters greater than 10 cm;5 m × 5 m for individuals with diameters less than 10 cm. Carbon stocks were estimated using the non-destructive method and allometric equations. The floristic inventory identified 65 species divided into 57 genera and 30 families in the Bandrefam sacred forest and 45 species divided into 42 genera and 27 families in the Batoufam sacred forest. In the Bandrefam, the most important families are Phyllanthaceae (53.98%), Moraceae (21.69%), Lamiaceae (20.15%). At Batoufam, the most important families are Phyllanthaceae (39.73%), Fabaceae (28.47%), Araliaceae (23.77%). Malacantha alnifolia (55.14%), Vitex grandifolia (18.43%), Bosqueia angolensis (15.06%) were the most important species in Bandrefam. Otherwise, Malacantha alnifolia (28%), Polyscias fulva (22.73%), Psychotria sp. (21.28%) were the most important in Batoufam. The Bandrefam sacred forest has the highest tree density (2669 stems/ha). Total carbon stock is 484.88 ± 2.28 tC/ha at Batoufam and 313.95 ± 0.93 tC/ha at Bandrefam. The economic value varies between 5858.04 ± 27.62 USD/ha in Batoufam sacred forest and 3788.51 ± 11.26 USD/ha in Bandrefam sacred forest. The number of individuals and small-diameter trees has little influence on the carbon stocks in the trees. Medium-diameter trees store the most carbon, and very large-diameter trees, which are very poorly represented, store less carbon. In another way, wood density and the basal areas influence the carbon storage of the trees.展开更多
Quantifying material use in infrastructure development and analyzing its relationship with economic growth is essential for enhancing resource efficiency and steering regional resource management toward sustainable de...Quantifying material use in infrastructure development and analyzing its relationship with economic growth is essential for enhancing resource efficiency and steering regional resource management toward sustainable development.This study systematically assessed infrastructure related material use in 30 provinces,autonomous regions,and municipalities in China during 1978-2022.The result indicated that material stock has experienced significant growth,increasing from 16.91×10^(9)t in 1978 to 103.60×10^(9)t in 2022,with an average annual growth rate of 4.20%.However,from 1978 to 2015,material input followed a strong upward trend but saturated after 2015.At the national level,material input peaked in 2015,after which it began to decline.The central region reached its peak earlier in 2013,while the eastern and western regions peaked in 2015.Using a decoupling analysis framework,this study revealed that nationally,the elasticity value between material stock and gross domestic product(GDP)remained near or above 1.0,reflecting continued reliance on stock accumulation.Regionally,the elasticity value between material stock and GDP has increased in the central and western regions during 1978-2022,whereas elasticity value between material stock and GDP in the eastern region showed a slower growth rate but still struggled to achieve absolute decoupling.Moreover,the elasticity value between material input and GDP has declined at the national level,presenting a relative decoupling,with some regions already achieving absolute decoupling.The eastern region was closer to absolute decoupling,while the central and western regions,though still intensive in material input,exhibited faster declines in elasticity.Accelerating the transition from linear to circular economy is an essential step for China to achieve absolute decoupling and long-term sustainability.Finally,this research recommends promoting the adoption of renewable energy,driving industrial upgrading,implementing compact urban design,and extending the lifespan of infrastructure to reduce material dependency and achieve sustainable infrastructure transformation at the national level.展开更多
Hatchery release is a common and effective practice for protecting and restoring wild resources,and the success of the practice is commonly assessed using mark-recapture technology.We investigated the use of different...Hatchery release is a common and effective practice for protecting and restoring wild resources,and the success of the practice is commonly assessed using mark-recapture technology.We investigated the use of different dimensional X-ray imaging techniques for the strontium(Sr)marking of fish fin rays for stocking.Megalobrama amblycephala juveniles were marked by culturing specimens in 800-mg/L SrCl_(2)·6 H_(2)O solution,the cross-sections of dorsal fin rays were subsequently obtained,and the concentrations Sr was analyzed by 2 D imaging using an electron probe X-ray microanalyzer.Our preliminary findings indicate that the immersion marking method is effective for the Sr marking of fin rays in experimental fish.Moreover,we generated a bird’s-eye-view 3 D mesh image of the Sr concentrations,which can provide a more comprehensive information for fish stocking than that using normal 2 D imaging.展开更多
Biomass is among the most important state variables used to characterize ecosystems. Estimation of tree biomass involves the development of species-specific “allometric equations” that describe the relationship betw...Biomass is among the most important state variables used to characterize ecosystems. Estimation of tree biomass involves the development of species-specific “allometric equations” that describe the relationship between tree biomass and tree diameter and/or height. While many allometric equations were developed for northern hemisphere and tropical species, rarely have they been developed for trees in arid ecosystems, limiting, amongst other things, our ability to estimate carbon stocks in arid regions. Acacia raddiana and A. tortilis are major components of savannas and arid regions in the Middle East and Africa, where they are considered keystone species. Using the opportunity that trees were being uprooted for land development, we measured height (H), north-south (C1) and east-west (C2) canopy diameters, stem diameter at 1.3 meters of the largest stem (D1.3 or DBH), and aboveground fresh and dry weight (FW and DW, respectively) of nine trees (n = 9) from each species. For A. tortilis only, we recorded the number of trunks, and measured the diameter of the largest trunk at ground level (D0). While the average crown (canopy) size (C1 + C2) was very similar among the two species, Acacia raddiana trees were found to be significantly taller than their Acacia tortilis counterparts. Results show that in the arid Arava (southern Israel), an average adult acacia tree has ~200 kg of aboveground dry biomass and that a typical healthy acacia ecosystem in this region, may include ~41 tons of tree biomass per km2. The coefficients of DBH (tree diameter at breast height) to biomass and wood volume, could be used by researchers studying acacia trees throughout the Middle East and Africa, enabling them to estimate biomass of acacia trees and to evaluate their importance for carbon stocks in their arid regions. Highlights: 1) Estimations of tree biomass in arid regions are rare. 2) Biomass allometric equations were developed for A. raddiana and A. tortilis trees. 3) Equations contribute to the estimation of carbon stocks in arid regions.展开更多
As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitat...As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.展开更多
Scientific research and technological innovation are driving modern economies;however,a new form of property rights is required to compensate knowledge workers for their contributions.In 1994,the Science and Technolog...Scientific research and technological innovation are driving modern economies;however,a new form of property rights is required to compensate knowledge workers for their contributions.In 1994,the Science and Technology Bureau of Shenzhen,China implemented a policy to encourage scientists and engineers to develop innovative technologies that would provide them a share of the profits earned from their innovations.This created a new“shared property rights”system.China’s shared property model is so new that the conditions under which it can improve enterprise profits remain unclear.To answer this question,we obtained data from the China Stock Market and Accounting Research database for 904 Chinese enterprises that had implemented shared property rights for the first time between 2009 and 2021 and used a propensity score matching method and econometric models to evaluate their performance.The results indicated that shared property incentives improved corporate financial performance and that benefits increased gradually over time.The new approach showed a stronger positive effect than restricted stock options during the study period.The strength of the incentive was greater for core technical staff than for senior executives,suggesting that scientists,engineers,and computer programmers should be the targets of a shared property rights incentive program.To take full advantage of the new shared property rights institution,enterprise managers should set the implementation period at a reasonable length(5 to 10 years,based on our study results).Enterprises can also test two or more simultaneous approaches that account for the specific needs of each category of workers,based on a careful examination of their current situation and expected or desired future situations.展开更多
文摘Continuously growing attention has been paid to potential of polarimetry to provide additional information of remote sounding of the earth and other planets and to detect some special targets. In the present paper the polarimetric technique in space for all the four Stockes parameters is presented.
基金supported by National Key Research and Development Program of China(No.2021YFD2200405(S.R.L.))Natural Science Foundation of China(Grant No.31971653).
文摘With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threats to ecosystem stability.Understanding the current status of forest degradation and assessing potential carbon stocks in China are of strategic importance for making forest restoration efforts and enhancing carbon sequestration capacity.In this study,we used the national forest inventory data from 2009 to 2018 to develop a set of standard measures for assessing degraded forests across China,based on five key indicators:forest accumulation growth rate(FAGR),forest recruitment rate(FRR),tree species reduction rate(TSRR),forest canopy cover reduction rate(FCCRR),and forest disaster level(FDL).Additionally,we estimated standing carbon stock,potential carbon stock,and theoretical space to grow by developing a stand growth model,which accounts for stand density across different site classes,to evaluate the restoration potential of degraded forests.The results indicate that degraded forest area in China is 36.15 million hectares,accounting for 20.10% of a total forest area.Standing carbon stock and potential carbon stock of degraded forests in China are 23.93 million tons and 61.90 million tons,respectively.Overall,degraded forest varies significantly across different regions.The results highlight the important trade-offs among environmental factors,policy decisions,and forest conditions,providing a robust foundation for developing measures to enhance forest quality.
基金funded by the project supported by the Natural Science Foundation of Heilongjiang Provincial(Grant Number LH2023F033)the Science and Technology Innovation Talent Project of Harbin(Grant Number 2022CXRCCG006).
文摘Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.
文摘The COVID-19 pandemic and its subsequent eradication precipitated a rare and unexpected stock market crash.This crisis presents a unique opportunity to evaluate environmental,social,sustainable,and management(ESSM)policy choices.This study contributes by demonstrating the application of ESSM concerns and investment decision investment horizons and showing the significance of the long-term perspective in ESSM investment decision-making.From January 2020 to December 2022,stocks with higher ESSM ratings had significantly greater returns,less return arbitrariness,and larger operational profit margins.ESSM ventures with more advertising expenditures had higher stock returns;however,investors more predisposed toward ESSM saw less return arbitrariness during market crises.This study emphasizes the importance of investor and consumer loyalty to the elasticity of ESSM stocks.
文摘Based on market integration theory,we investigate the static and dynamic connectedness between nonfungible tokens(NFTs)and the Association of Southeast Asian Nations(ASEAN)equity markets using the Quantile Vector Auto Regressive model.We also compute optimal weights and hedge ratios for our variable of interest to establish their diversification and hedging potential.Our analysis infers a moderate level of return transmission at the median quantile,where equity markets evolved as the net recipients of return spillover from the system,while NFTs emerge as key transmitters.In extreme market conditions,transmission between variables is amplified,but the increase is symmetrical across extreme quantiles,suggesting a similar impact.However,the interlinkage among assets is symmetric across conditional quantiles.The dynamic analysis demonstrates that the system integration amplifies during uncertain times(e.g.,COVID-19 and the Russia–Ukraine conflict).Our portfolio analysis shows that NFTs provide diversification and hedging in all market conditions.However,the period of turmoil dampened the diversification potential,and hedging became expensive.Our study offers detailed and insightful information about the transmission mechanism and enables the participants of financial markets to diversify and hedge their portfolio.
文摘Necessary actions should be taken to ensure stock market efficiency;thus,financial innovation-based criteria that affect stock market efficiency should be improved.However,simultaneously improving all criteria is difficult;therefore,performing priority analysis is important for carrying out this process effectively and efficiently.Accordingly,this study aims to evaluate the financial innovation-based characteristics of stock market efficiency.This study’s main research question within this framework is identifying which factors should be prioritized to improve the stock market.In this scope,we created a novel fuzzy decision-making model consisting of two stages.First,selected criteria for the financial innovation-based characteristics of stock market efficiency are weighted.In this process,quantum spherical fuzzy sets based on DEMATEL are considered.In the second stage,selected economies are ranked using the technique for order of preference by similarity to ideal solution(TOPSIS)approach.This study’s main contribution is that the DEMATEL technique in calculating criterion weights in the decision-making analysis process provides some advantages.With the help of this situation,the causal directions between these items can be considered;thus,it is possible to determine the most accurate strategies.The findings demonstrate that providing tax advantages is the most important factor in ensuring stock market efficiency.Moreover,the excellence of the financial system is critical in ensuring stock market efficiency.In this context,it is possible to provide tax advantages,especially for long-term investments.Thus,long-term investments can be increased,significantly increasing the market’s stability.
文摘This study explores correlations and risk spillovers,essential concepts for financial risk management,among commodities(crude oil,gold,and a global commodities index)and emerging stock markets.Using the Asymmetric Dynamic Conditional Correlation–Conditional Value-at-Risk(ADCC-CoVaR)model and a bootstrapped Kolmogorov–Smirnov(KS)test,we analyze the period from December 30,2005,to February 28,2024,examining correlations,downside and upside risk spillovers,and highlighting the effects of major events such as the global financial crisis of 2008,the COVID-19 pandemic,and the Russia-Ukraine war.The results show heightened correlations during crises and significant risk spillovers across market pairs,with downside risks often outweighing upside risks.Gold displays minimal risk spillover,highlighting its unique role as a haven asset.We find that spillovers between gold,global commodities,and stocks increased during the pandemic and the Russia-Ukraine conflict,while those involving crude oil remained stable.These findings provide valuable guidance for portfolio managers in navigating volatile markets.
文摘In contrast to previous studies that investigated the impact of the investment groups’trading volume on the volatility of the stock index,this research,inspired by behavioral finance literature,aims to evaluate the dynamic bi-directional relationship between the trading behavior of investor groups(institutional and noninstitutional)and stock index fluctuations in different positions(long and short)and market conditions(the pre-COVID-19 and COVID-19 periods)in the Turkish stock market.The results indicate a bidirectional relationship between the stock index return(SIR)and the trading behavior of online individual traders(OIT)and equity mutual and pension funds(EMPF).However,this relationship varies depending on the trading positions of different investor groups.Also,there is a unidirectional relationship between the SIR and the trading behavior of the diversified equity funds(DEF).During the pandemic period,the role of online traders became more prominent,coinciding with their increased participation in the market,significantly affecting and being affected by stock index fluctuations.We also evaluated some behavioral biases(including overconfidence and asymmetric reaction)and the trading strategy of investor groups(with their performance).Results suggest that the online individual traders were less(more)overconfident than other groups in the prepandemic(pandemic)period.Additionally,all groups had an asymmetric reaction to the positive and negative SIR shocks.This research,contributing to the field of financial innovation and aligning with behavioral finance principles,reveals a fascinating finding:individual investors react to stock index fluctuations,largely driven by institutional investors,despite lacking access to new fundamental information about their portfolio stocks.These findings have significant implications for investors and market regulators.Recognizing and addressing behavioral biases is crucial for individual investors as they strive to make informed and successful financial decisions.It is concluded that the surge in retail investment is a phenomenon;hence,more effort is required for their investment stability in the Turkish stock market.
基金supported by CAS Project for Young Scientists in Basic Research(YSBR-037)the National Natural Science Foundation of China(42141004,32430067)by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP,2019QZKK060602).
文摘Tibetan Plateau,as one of the most carbon intensive regions in China,is crucial in the carbon cycle,and accurately estimating its vegetation carbon density(C_(VEG))is essential for assessing regional and national carbon balance.However,the spatial distribution of regional C_(VEG)is not available remains highly uncertain due to lack of systematic research,especially for different organs.Here,we investigated the spatial distribution patterns and driving factors of C_(VEG)among different plant organs(leaf,branch,trunk and root)by systematically field grid-sampling 2040 field-plots of plant communities over the Tibetan Plateau from 2019 to 2020.The results showed that the carbon content of plant organs ranged from 255.53 to 515.58 g kg^(-1),with the highest in branches and the lowest in roots.Among the different plant functional groups,the highest C_(VEG)was found in evergreen coniferous forests,and the lowest in desert grasslands,with an average C_(VEG)of 1603.98 g m^(-2).C_(VEG)increased spatially from northwest to southeast over the Tibetan Plateau,with MAP being the dominant factor.Furthermore,the total vegetation carbon stock on the Tibetan Plateau was estimated to be 1965.62 Tg for all vegetation types.Based on the comprehensive field survey dataset,the Random Forest model effectively predicted and mapped the spatial distribution of C_(VEG)(including aboveground,belowground,and the total biomass carbon density)over the Tibetan Plateau with notable accuracy(validation R2 values were 71%,56%,and 64%for C_(AGB),C_(BGB),and C_(VEG),respectively)at a spatial resolution of 1 km×1 km.Our findings can help improve the accuracy of regional carbon stock estimations and provide parameters for carbon cycle model optimization and remote sensing calibration in the future.
文摘Sacred forests play a valuable role in the conservation of local biodiversity and provide numerous ecosystem services in Cameroon. The aim of this study was to estimate floristic diversity, stand structures and carbon stocks in the sacred forests of Bandrefam and Batoufam (western Cameroon). The floristic inventory and the stand structures were carried out in 25 m × 25 m plots for individuals with diameters greater than 10 cm;5 m × 5 m for individuals with diameters less than 10 cm. Carbon stocks were estimated using the non-destructive method and allometric equations. The floristic inventory identified 65 species divided into 57 genera and 30 families in the Bandrefam sacred forest and 45 species divided into 42 genera and 27 families in the Batoufam sacred forest. In the Bandrefam, the most important families are Phyllanthaceae (53.98%), Moraceae (21.69%), Lamiaceae (20.15%). At Batoufam, the most important families are Phyllanthaceae (39.73%), Fabaceae (28.47%), Araliaceae (23.77%). Malacantha alnifolia (55.14%), Vitex grandifolia (18.43%), Bosqueia angolensis (15.06%) were the most important species in Bandrefam. Otherwise, Malacantha alnifolia (28%), Polyscias fulva (22.73%), Psychotria sp. (21.28%) were the most important in Batoufam. The Bandrefam sacred forest has the highest tree density (2669 stems/ha). Total carbon stock is 484.88 ± 2.28 tC/ha at Batoufam and 313.95 ± 0.93 tC/ha at Bandrefam. The economic value varies between 5858.04 ± 27.62 USD/ha in Batoufam sacred forest and 3788.51 ± 11.26 USD/ha in Bandrefam sacred forest. The number of individuals and small-diameter trees has little influence on the carbon stocks in the trees. Medium-diameter trees store the most carbon, and very large-diameter trees, which are very poorly represented, store less carbon. In another way, wood density and the basal areas influence the carbon storage of the trees.
基金supported by the Shanghai Committee of Science and Technology Fund(22ZR1419300)the Academic Year 2025 Ritsumeikan Asia Pacific University Academic Research Subsidy(Grants-in-Aid Reapplication Type).
文摘Quantifying material use in infrastructure development and analyzing its relationship with economic growth is essential for enhancing resource efficiency and steering regional resource management toward sustainable development.This study systematically assessed infrastructure related material use in 30 provinces,autonomous regions,and municipalities in China during 1978-2022.The result indicated that material stock has experienced significant growth,increasing from 16.91×10^(9)t in 1978 to 103.60×10^(9)t in 2022,with an average annual growth rate of 4.20%.However,from 1978 to 2015,material input followed a strong upward trend but saturated after 2015.At the national level,material input peaked in 2015,after which it began to decline.The central region reached its peak earlier in 2013,while the eastern and western regions peaked in 2015.Using a decoupling analysis framework,this study revealed that nationally,the elasticity value between material stock and gross domestic product(GDP)remained near or above 1.0,reflecting continued reliance on stock accumulation.Regionally,the elasticity value between material stock and GDP has increased in the central and western regions during 1978-2022,whereas elasticity value between material stock and GDP in the eastern region showed a slower growth rate but still struggled to achieve absolute decoupling.Moreover,the elasticity value between material input and GDP has declined at the national level,presenting a relative decoupling,with some regions already achieving absolute decoupling.The eastern region was closer to absolute decoupling,while the central and western regions,though still intensive in material input,exhibited faster declines in elasticity.Accelerating the transition from linear to circular economy is an essential step for China to achieve absolute decoupling and long-term sustainability.Finally,this research recommends promoting the adoption of renewable energy,driving industrial upgrading,implementing compact urban design,and extending the lifespan of infrastructure to reduce material dependency and achieve sustainable infrastructure transformation at the national level.
基金Supported by the National Key Research and Development Program of China(No.2022 YFF 0608203)the Graduate Student Scientific Research Innovation Projects in Jiangsu Province(No.KYCX 22-0706)。
文摘Hatchery release is a common and effective practice for protecting and restoring wild resources,and the success of the practice is commonly assessed using mark-recapture technology.We investigated the use of different dimensional X-ray imaging techniques for the strontium(Sr)marking of fish fin rays for stocking.Megalobrama amblycephala juveniles were marked by culturing specimens in 800-mg/L SrCl_(2)·6 H_(2)O solution,the cross-sections of dorsal fin rays were subsequently obtained,and the concentrations Sr was analyzed by 2 D imaging using an electron probe X-ray microanalyzer.Our preliminary findings indicate that the immersion marking method is effective for the Sr marking of fin rays in experimental fish.Moreover,we generated a bird’s-eye-view 3 D mesh image of the Sr concentrations,which can provide a more comprehensive information for fish stocking than that using normal 2 D imaging.
文摘Biomass is among the most important state variables used to characterize ecosystems. Estimation of tree biomass involves the development of species-specific “allometric equations” that describe the relationship between tree biomass and tree diameter and/or height. While many allometric equations were developed for northern hemisphere and tropical species, rarely have they been developed for trees in arid ecosystems, limiting, amongst other things, our ability to estimate carbon stocks in arid regions. Acacia raddiana and A. tortilis are major components of savannas and arid regions in the Middle East and Africa, where they are considered keystone species. Using the opportunity that trees were being uprooted for land development, we measured height (H), north-south (C1) and east-west (C2) canopy diameters, stem diameter at 1.3 meters of the largest stem (D1.3 or DBH), and aboveground fresh and dry weight (FW and DW, respectively) of nine trees (n = 9) from each species. For A. tortilis only, we recorded the number of trunks, and measured the diameter of the largest trunk at ground level (D0). While the average crown (canopy) size (C1 + C2) was very similar among the two species, Acacia raddiana trees were found to be significantly taller than their Acacia tortilis counterparts. Results show that in the arid Arava (southern Israel), an average adult acacia tree has ~200 kg of aboveground dry biomass and that a typical healthy acacia ecosystem in this region, may include ~41 tons of tree biomass per km2. The coefficients of DBH (tree diameter at breast height) to biomass and wood volume, could be used by researchers studying acacia trees throughout the Middle East and Africa, enabling them to estimate biomass of acacia trees and to evaluate their importance for carbon stocks in their arid regions. Highlights: 1) Estimations of tree biomass in arid regions are rare. 2) Biomass allometric equations were developed for A. raddiana and A. tortilis trees. 3) Equations contribute to the estimation of carbon stocks in arid regions.
文摘As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.
基金supported by the National R&D Program China(No.2021xjkk0405).
文摘Scientific research and technological innovation are driving modern economies;however,a new form of property rights is required to compensate knowledge workers for their contributions.In 1994,the Science and Technology Bureau of Shenzhen,China implemented a policy to encourage scientists and engineers to develop innovative technologies that would provide them a share of the profits earned from their innovations.This created a new“shared property rights”system.China’s shared property model is so new that the conditions under which it can improve enterprise profits remain unclear.To answer this question,we obtained data from the China Stock Market and Accounting Research database for 904 Chinese enterprises that had implemented shared property rights for the first time between 2009 and 2021 and used a propensity score matching method and econometric models to evaluate their performance.The results indicated that shared property incentives improved corporate financial performance and that benefits increased gradually over time.The new approach showed a stronger positive effect than restricted stock options during the study period.The strength of the incentive was greater for core technical staff than for senior executives,suggesting that scientists,engineers,and computer programmers should be the targets of a shared property rights incentive program.To take full advantage of the new shared property rights institution,enterprise managers should set the implementation period at a reasonable length(5 to 10 years,based on our study results).Enterprises can also test two or more simultaneous approaches that account for the specific needs of each category of workers,based on a careful examination of their current situation and expected or desired future situations.
