期刊：
Economic Systems Research,2025年37(1):1-29 ISSN：0953-5314
通讯作者：
Xie, R
作者机构：
[Zhao, Guomei] Cent South Univ Forestry & Technol, Sch Econ, Changsha, Peoples R China.;[Zhao, Guomei] Res Ctr High Qual Dev Ind Econ, Key Res Base Philosophy & Social Sci Hunan Prov, Changsha, Peoples R China.;[Xie, Rui] Hunan Univ, Sch Econ & Trade, Changsha, Peoples R China.;[Su, Bin] Natl Univ Singapore, Energy Studies Inst, Singapore, Singapore.;[Wang, Qunwei] Nanjing Univ Aeronaut & Astronaut, Sch Econ & Management, Nanjing, Peoples R China.
通讯机构：
[Xie, R ] H;Hunan Univ, Changsha 410079, Peoples R China.
摘要：
This paper constructs a comparative analysis framework on how the input-output (IO) model with technical differences affects the calculation of the pollution terms of trade (PTT) and the tests of the pollution haven hypothesis. Specifically, the CO 2 terms of trade (CTT) of the world's major economies are calculated based on five IO models, and chain additive structure decomposition analysis (SDA) is conducted to examine the roles of different factors in the changes in CTT. The economic phenomena reflected by the CTT measured by these IO models are found to be different, and a comparative analysis shows that different IO models are suitable for studying different economic problems. Suggestions are provided on the application of different IO models in the calculation of economic indicators and the study of economic issues. Policy makers need to be cautious about policy recommendations based on the results obtained from different IO models.
作者机构：
[Yaping Xiao] School of Economics, Central South University of Forestry and Technology, Changsha City, People’s Republic of China;[Linfeng Niu; Qiqi Li] College of Mechanical and Vehicle Engineering, Changsha University of Science & Technology, Changsha City, People’s Republic of China
通讯机构：
[Qiqi Li] C;College of Mechanical and Vehicle Engineering, Changsha University of Science & Technology, Changsha City, People’s Republic of China
关键词：
Artificial tree algorithm;Dominant operator dynamic following;Update operators;Two populations
摘要：
An artificial tree (AT) algorithm has been proposed recently, and the performance of AT has been enhanced because of the introduction of improved AT algorithm with two-population (IATTP). However, the branch update operators of IATTP cannot effectively balance exploration and exploitation, which limits the optimization accuracy and efficiency of IATTP. To further improve the performance of IATTP, this work proposes a two-population artificial tree algorithm based on adaptive updating strategy for dominant populations (TATAD). In TATAD, six operators named self-evolution operator 2, crossover operator 2, improved self-evolution operator, gradient descent update operator, Gauss and Cauchy variational operator, and random traceless Sigma variational operator are applied to form an operator library. A dominant operator dynamic following mechanism is proposed to assign these six operators to the two populations in an optimal pairing scheme. Both populations and operators compete with each other, and the advantages of all operators are fully utilized. Moreover, the combination of diverse operators and dominant operator dynamic following mechanism can effectively balance the exploration and exploitation of TATAD. The performance of TATAD is compared with four AT algorithms and six efficient algorithms through typical test functions, and their results are bested by Wilcoxon rank sum test (WRST) and Friedman ranking test. It is found that TATAD is the most competitive algorithm among these algorithms for solving these optimization problems.
期刊：
Economic Analysis and Policy,2024年82:389-399 ISSN：0313-5926
通讯作者：
Chang, TY
作者机构：
[Fan, Yi] Cent South Univ Forestry & Technol, Coll Econ, Changsha, Peoples R China.;[Chang, Tsangyao; Chang, TY] Feng Chia Univ, Dept Finance, Taichung, Taiwan.;[Chang, Tsangyao; Chang, TY] CTBC Business Sch, Dept Business Adm, Tainan, Taiwan.;[Ranjbar, Omid] Allameh Tabataba I Univ, Tehran, Iran.;[Ranjbar, Omid] Trade Promot Org Iran, Tehran, Iran.
通讯机构：
[Chang, TY ] F;Feng Chia Univ, Dept Finance, Taichung, Taiwan.;CTBC Business Sch, Dept Business Adm, Tainan, Taiwan.
关键词：
Energy security;Energy security risk index;Panel quantile unit root;Quantile regression;Sequential panel selection method;G7 countries
摘要：
Energy security is affected by extreme natural, human, domestic political, geopolitical, and fossil energy price shocks/events and green energy policies. The degree of persistence in energy security determines the magnitudes of social, economic, and environmental outcomes of the shocks/policies. In this paper, we examined the degree of persistence in energy security of G7 countries using a new proxy namely the energy security risk index, and a novel second-generation panel quantile unit root test over the period 1980–2018. In addition, we applied the sequential panel selection method (SPSM), to identify the stationary members within each quantile. Our results indicated the stochastic properties of the energy security risk indexes vary across the quantile and the countries. Among the G7 countries, the energy security risk index of the US displays unit root process within all quantiles. While the energy security risk indexes of other countries display stationary processes, especially within high quantiles. Our results have important policy implications regarding the effectiveness of green policies in improving the energy security of the G7 countries and the disturbance effects of anti-energy security shocks. According to our findings, the US has to constantly pursue the risks that threaten the country's energy system while other G7 countries likely do not have such severe concerns about shocks affecting energy security, and these shocks have a short-term effect on their energy security.
Energy security is affected by extreme natural, human, domestic political, geopolitical, and fossil energy price shocks/events and green energy policies. The degree of persistence in energy security determines the magnitudes of social, economic, and environmental outcomes of the shocks/policies. In this paper, we examined the degree of persistence in energy security of G7 countries using a new proxy namely the energy security risk index, and a novel second-generation panel quantile unit root test over the period 1980–2018. In addition, we applied the sequential panel selection method (SPSM), to identify the stationary members within each quantile. Our results indicated the stochastic properties of the energy security risk indexes vary across the quantile and the countries. Among the G7 countries, the energy security risk index of the US displays unit root process within all quantiles. While the energy security risk indexes of other countries display stationary processes, especially within high quantiles. Our results have important policy implications regarding the effectiveness of green policies in improving the energy security of the G7 countries and the disturbance effects of anti-energy security shocks. According to our findings, the US has to constantly pursue the risks that threaten the country's energy system while other G7 countries likely do not have such severe concerns about shocks affecting energy security, and these shocks have a short-term effect on their energy security.
摘要：
The "Annual Report 2021" from the United Nations Environment Programme (UNEP) highlights that the transportation sector is the fastest-growing greenhouse gas emissions sector, accounting for approximately 25% of energy-related emissions. What is even more concerning is that, at a time when carbon emissions need to be urgently reduced across various industries globally, carbon emissions from the transportation sector continue to rise. This is because the improvement in the efficiency of vehicle power combustion struggles to offset the increasing emissions resulting from the massive volume of travel. With the enhancement of transportation networks in various countries, it is projected that the growth rate of carbon emissions in the transportation sector will surpass that of the industrial and power sectors, presenting a significant challenge to achieving the emission reduction goals outlined in the Paris Agreement. Carbon emissions in the global transportation sector encompass various modes of transportation, including road, rail, aviation, and maritime, with road transportation being the largest contributor to carbon emissions. This study utilized the Stacking technique to build the X-MARL model for predicting
$$\hbox {CO}_{2}$$
emissions from vehicles and formulated recommendations for carbon reduction in the transportation industry. The model was tested using a dataset of vehicle
$$\hbox {CO}_{2}$$
emissions officially recorded by the Canadian government, comprising 7385 data points and covering 12 different vehicle parameter attributes. During the experimentation process, three statistical evaluation metrics were employed, namely mean squared error (MSE), root-mean-squared error (RMSE), and the coefficient of determination (R2). The dataset was randomly split into a training set (80% of the total data) and a testing set (20% of the total data). The experimental results demonstrated that the X-MARL model exhibited the highest prediction accuracy. This study provides an original strategy for accurately predicting carbon emissions from road transportation, which can offer support and guidance to decision-makers in formulating and implementing effective environmental policies.
作者机构：
[Zhu, Wenke; Zhao, Yunjing] Cent South Univ Forestry & Technol, Coll Bangor, Changsha 410004, Hunan, Peoples R China.;[Dai, Weisi; Zhou, Guoxiong; Liu, Zewei] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.;[Tang, Chunling] Cent South Univ Forestry & Technol, Coll Econ, Changsha 410004, Hunan, Peoples R China.
通讯机构：
[Zhou, GX ; Tang, CL ] C;Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.;Cent South Univ Forestry & Technol, Coll Econ, Changsha 410004, Hunan, Peoples R China.
关键词：
Deep learning;Informer;Stock price prediction;Time series forecasting model
摘要：
Forecasting stock movements is a crucial research endeavor in finance, aiding traders in making informed decisions for enhanced profitability. Utilizing actual stock prices and correlating factors from the Wind platform presents a potent yet intricate forecasting approach. While previous methodologies have explored this avenue, they encounter challenges including limited comprehension of interrelations among stock data elements, diminished accuracy in extensive series, and struggles with anomaly points. This paper introduces an advanced hybrid model for stock price prediction, termed PMANet. PMANet is founded on Multi-scale Timing Feature Attention, amalgamating Multi-scale Timing Feature Convolution and Ant Particle Swarm Optimization. The model elevates the understanding of dependencies and interrelations within stock data sequences through Probabilistic Positional Attention. Furthermore, the Encoder incorporates Multi-scale Timing Feature Convolution, augmenting the model's capacity to discern multi-scale and significant features while adeptly managing lengthy input sequences. Additionally, the model's proficiency in addressing anomaly points in stock sequences is enhanced by substituting the optimizer with Ant Particle Swarm Optimization. To ascertain the model's efficacy and applicability, we conducted an empirical study using stocks from four pivotal industries in China. The experimental outcomes demonstrate that PMANet is both feasible and versatile in its predictive capability, yielding forecasts closely aligned with actual values, thereby fulfilling application requirements more effectively.
期刊：
ENERGY ECONOMICS,2024年136:107713 ISSN：0140-9883
通讯作者：
Wu, ZG
作者机构：
[Li, Di] City Univ Macau, Fac Finance, Macau, Peoples R China.;[Wu, Zhige] Cent South Univ Forestry & Technol, Coll Econ, Res Ctr High Qual Dev Ind Econ, Changsha, Peoples R China.;[Tang, Yixuan] Cent South Univ Forestry & Technol, Bangor Coll, Changsha, Peoples R China.;[Li, Di] City Univ Macau, Ave Padre Tomas Pereira Taipa, Macau, Peoples R China.;[Wu, Zhige] 498 Shaoshan Rd South, Changsha, Hunan, Peoples R China.
通讯机构：
[Wu, ZG ] C;Cent South Univ Forestry & Technol, Coll Econ, Res Ctr High Qual Dev Ind Econ, Changsha, Peoples R China.;498 Shaoshan Rd South, Changsha, Hunan, Peoples R China.
关键词：
Clean energy stock prices;Dirty energy stock prices;Dynamic conditional correlations;Climate risks
摘要：
Prior studies have extensively exhibited an interest in exploring the connectedness between dirty and clean energy stock prices alongside the drivers of such price connectedness, shedding light on hedging strategies for finance practitioners. Nevertheless, no empirical research has examined whether climate risks, the emerging indicator for investors to handle the divestment of dirty energy stocks, have affected the time-varying dirty–clean energy equity price nexus. This study fills this gap by innovatively identifying dynamic conditional correlations (DCCs) between dirty and clean energy stock prices. An ARDL/NARDL model is applied to assess whether the climate risks affect such correlations by controlling for business cycles, funding liquidity, USD values, and oil market sentiments. Overall, we detect an undeniable negative impact of climate risks on the positive dirty–clean energy price dynamic correlations. Additionally, the NARDL model results reveal that a rise in federal fund rates exerts higher effects on the dirty–clean energy stock price comovements. Our findings suggest the strengthened potential of hedging clean energy stocks against dirty energy equities in case of escalating climate risks and heightened fossil fuel price volatilities. Furthermore, substantial attention is required to account for monetary policies' asymmetric effects on clean energy investment.
Prior studies have extensively exhibited an interest in exploring the connectedness between dirty and clean energy stock prices alongside the drivers of such price connectedness, shedding light on hedging strategies for finance practitioners. Nevertheless, no empirical research has examined whether climate risks, the emerging indicator for investors to handle the divestment of dirty energy stocks, have affected the time-varying dirty–clean energy equity price nexus. This study fills this gap by innovatively identifying dynamic conditional correlations (DCCs) between dirty and clean energy stock prices. An ARDL/NARDL model is applied to assess whether the climate risks affect such correlations by controlling for business cycles, funding liquidity, USD values, and oil market sentiments. Overall, we detect an undeniable negative impact of climate risks on the positive dirty–clean energy price dynamic correlations. Additionally, the NARDL model results reveal that a rise in federal fund rates exerts higher effects on the dirty–clean energy stock price comovements. Our findings suggest the strengthened potential of hedging clean energy stocks against dirty energy equities in case of escalating climate risks and heightened fossil fuel price volatilities. Furthermore, substantial attention is required to account for monetary policies' asymmetric effects on clean energy investment.
关键词：
green funds;long-term performance;short-term performance;media attention;individual investors;Chinese financial market
摘要：
This study investigates the relationship between online media attention and the performance of China's green funds. The results show that increased media attention can boost the performance of green funds in the short term, however, this effect is short-lived. The mechanism of short-term positive effects may be due to increased media attention leading to larger purchases, which may undermine funds' long-term performance. In particular, online media attention has a greater impact on larger and older funds. Moreover, it indicates that media attention reduces the returns of individual investor -dominated funds, but has little effect on institutional investor -dominated funds.
