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：
[Liu, Rui; Tan, Juan; Tan, J] Beijing Technol & Business Univ, Business Sch, Beijing 100048, Peoples R China.;[Lu, Jianle] Beijing natl accounting inst, Acad Affairs Dept, Beijing 101312, Peoples R China.;[Tan, Q; Tan, Qiong] Cent South Univ Forestry & Technol, Sch Econ, Changsha 410004, Peoples R China.
通讯机构：
[Tan, Q ] C;[Tan, J ] B;Beijing Technol & Business Univ, Business Sch, Beijing 100048, Peoples R China.;Cent South Univ Forestry & Technol, Sch Econ, Changsha 410004, Peoples R China.
关键词：
digital economy;carbon emissions;SMMEs
摘要：
In recent years, the digital economy (DE) has gained significant attention for its potential in reducing carbon emissions (CE). This paper intends to explore the regional carbon reduction effect of the DE and the entrepreneurship of small and medium-sized manufacturing enterprises (SMMEs), as well as disclose the mechanism through which the entrepreneurship of SMMEs functions. To this end, this paper employs an extended STIRPAT model to analyze the panel data of 30 provinces in China spanning from 2011 to 2018. The empirical analysis shows that (1) the DE has a positive effect on reducing regional total carbon emissions (TCE) and carbon emissions intensity (CEI); (2) the entrepreneurship of SMMEs has a negative influence on reducing regional CE; (3) the entrepreneurship of SMMEs fully mediates the link between the DE and TCE and partially mediates the relationship between the DE and the CEI; and (4) the DE has a stronger carbon reduction effect in regions with low urbanization levels and low institutional quality, as well as non-industrial pilot areas. The findings provide empirical evidence to policymakers on promoting CE reduction and the DE. This study has practical value for SMMEs to improve competitiveness and survival under the current environment.
作者机构：
[Yongliang Liu; Chunling Tang; Aiying Zhou; Huaiyu Yuan] School of Economics, Central South University of Forestry and Technology, Changsha, China;[Kai Yang] Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
通讯机构：
[Chunling Tang; Huaiyu Yuan] S;School of Economics, Central South University of Forestry and Technology, Changsha, China<&wdkj&>School of Economics, Central South University of Forestry and Technology, Changsha, China
摘要：
An accurate calculation method of carbon trading price is of great significance to strengthening energy saving and emission reduction. Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new hybrid model for carbon trading price forecasting. The model fuses complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) with extreme gradient boosting (XGBoost) and long short-term memory (LSTM) networks, and leverages SnowNLP to derive sentiment scores from news text and the Baidu Index. To demonstrate the superiority of the proposed model, 5 chinese carbon emissions trading markets are selected for the predictions. The model shows better performance across all markets, improving by 4.20% to 17.89% over the CEEMDAN-LSTM model and outperforming other benchmarks. Furthermore, ablation experiments and parametric sensitivity analyses were carried out to verify the contribution of each component and the overall model’ s robustness. It offers a reliable and stable forecasting tool for stakeholders in the carbon market.
期刊：
Journal of theoretical and applied electronic commerce research,2025年20(3):158- ISSN：0718-1876
通讯作者：
Rui Liu<&wdkj&>Juan Tan
作者机构：
Authors to whom correspondence should be addressed.;[Wenjing Zhao; Yanling Dong] Business School, Beijing Technology and Business University, Beijing 100048, China;[Qiong Tan] School of Economics, Central South University of Forestry and Technology, Changsha 410004, China;[Rui Liu; Juan Tan] Authors to whom correspondence should be addressed.<&wdkj&>Business School, Beijing Technology and Business University, Beijing 100048, China
通讯机构：
[Rui Liu; Juan Tan] A;Authors to whom correspondence should be addressed.<&wdkj&>Business School, Beijing Technology and Business University, Beijing 100048, China
关键词：
social capital;perceived value;flow experience;user stickiness
摘要：
Amid the rapid growth of social media and live streaming platforms, streamers, who serve as a crucial link between products and users, have garnered significant attention from both academia and industry. This study explores the impact of the streamer’s social capital (S) on user stickiness (R), as well as the mediating roles of perceived value and flow experience (O) in light of the Stimuli-Organism-Response (SOR) framework and social capital theory. A total of 322 valid samples were analyzed through Structural Equation Modeling (SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). The results from the SEM indicate that the structural capital, cognitive capital, and relational capital of streamers in e-commerce live streaming significantly influence users’ perceived value, while structural capital and relational capital substantially impact users’ flow experience. Furthermore, both perceived value and flow experience are found to have a significant effect on user stickiness, with chained mediating effects observed between perceived value and flow experience. The fsQCA results further identify three configurational paths influencing user stickiness: the perceived value-oriented path, the flow experience-oriented path, and a hybrid path. This study offers valuable insights and practical implications for e-commerce merchants and companies involved in live streaming activities.
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.
作者机构：
[Sun, Haiwen; Tang, Chunling] Cent South Univ Forestry & Technol, Coll Econ & Management, Changsha 410004, Hunan, Peoples R China.;[Gao, Ping] Univ Manchester, Global Dev Inst, Oxford Rd, Manchester M13 9PL, England.;[Zhou, Guoxiong] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.
通讯机构：
[Tang, CL ] C;Cent South Univ Forestry & Technol, Coll Econ & Management, Changsha 410004, Hunan, Peoples R China.
关键词：
digital economy;energy consumption;carbon emissions;non-linear relationships;spatial spillover effect
摘要：
The intensifying challenge of global climate change has made accelerating energy conservation and emission reduction an urgent global imperative. As one of the world’s largest carbon emitters, China plays a pivotal role in global decarbonization efforts. The digital economy, emerging as a key driver of China’s economic transformation, provides novel pathways for advancing carbon reduction. This paper employs kernel density estimation and ArcGIS 10.8 to analyze the spatiotemporal dynamics of the digital economy and urban carbon emissions in China. Using panel data from 278 prefecture-level cities, the study applies fixed-effects models, mediation effect models, and spatial Durbin models to explore the mechanisms and spatial impacts of the digital economy on carbon reduction. The findings reveal that: (1) the development of the digital economy exerts a significant “inverted U-shaped” influence on urban carbon emission reduction; (2) energy consumption intensity is the critical mechanism underlying the nonlinear relationship between the digital economy and carbon emissions; (3) the digital economy's impact on carbon emissions exhibits spatial spillover effects, following a similar “inverted U-shaped” trajectory. These results contribute valuable empirical insights into the dual objectives of digital economic growth and carbon emission reduction, offering policymakers guidance on leveraging digitalization to achieve sustainable and coordinated regional development.
The intensifying challenge of global climate change has made accelerating energy conservation and emission reduction an urgent global imperative. As one of the world’s largest carbon emitters, China plays a pivotal role in global decarbonization efforts. The digital economy, emerging as a key driver of China’s economic transformation, provides novel pathways for advancing carbon reduction. This paper employs kernel density estimation and ArcGIS 10.8 to analyze the spatiotemporal dynamics of the digital economy and urban carbon emissions in China. Using panel data from 278 prefecture-level cities, the study applies fixed-effects models, mediation effect models, and spatial Durbin models to explore the mechanisms and spatial impacts of the digital economy on carbon reduction. The findings reveal that: (1) the development of the digital economy exerts a significant “inverted U-shaped” influence on urban carbon emission reduction; (2) energy consumption intensity is the critical mechanism underlying the nonlinear relationship between the digital economy and carbon emissions; (3) the digital economy's impact on carbon emissions exhibits spatial spillover effects, following a similar “inverted U-shaped” trajectory. These results contribute valuable empirical insights into the dual objectives of digital economic growth and carbon emission reduction, offering policymakers guidance on leveraging digitalization to achieve sustainable and coordinated regional development.
摘要：
A simple theoretical model is proposed to predict how climate change adaptation affects energy productivity. Using a longitudinal panel dataset encompassing 251 Chinese cities from 2005 to 2020, this study conducts an empirical assessment of climate change adaptation policy (CCAP) on energy productivity through a quasi-natural experimental framework. Empirical findings suggest that the implementation of CCAP has significantly increased energy productivity, with an estimated effect of approximately 13.3%. Moreover, heterogeneity analysis shows that energy productivity improvements are more pronounced in eastern coastal regions, non-TCZ, and high-temperature cities compared to their counterparts. Finally, mechanism analysis indicates that CCAP increases energy productivity through improved financial inclusion.
期刊：
International Review of Financial Analysis,2025年:104554 ISSN：1057-5219
通讯作者：
Hong Liu
作者机构：
[Jihong Xiao; Wen Xu] School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China;[Hong Liu] School of Economics, Central South University of Forestry and Technology, Changsha 410004, China;[Yunning Zhao] Business School, Central South University, Changsha 410083, China
通讯机构：
[Hong Liu] S;School of Economics, Central South University of Forestry and Technology, Changsha 410004, China
摘要：
The effective transfer entropy approach offers a novel perspective for measuring information spillover. This study employs this method to investigate the spillover effects of oil price uncertainty (OPU), proxied by the oil implied volatility index, on Chinese sectoral stock returns. Empirical results reveal significant spillover effects of OPU on sectoral stock returns in China, and OPU is the primary information transmitter across the net spillover network. These spillover effects exhibit prominent time-varying characteristics, often increasing during important unexpected events. We find that economic policy uncertainty and climate policy uncertainty in China amplify and mitigate the OPU spillover effects on sectoral stock returns. Finally, these spillover effects can reduce stock return dispersion due to non-fundamental information, indicating the emergence of herd behavior driven by non-fundamentals. By linking methodological innovation, policy risk factors, and investor behavior, this study sheds light on how OPU shapes sectoral stock returns. The findings inform risk control and sector allocation for investors, and guide policymakers in reducing oil-induced market contagion.
The effective transfer entropy approach offers a novel perspective for measuring information spillover. This study employs this method to investigate the spillover effects of oil price uncertainty (OPU), proxied by the oil implied volatility index, on Chinese sectoral stock returns. Empirical results reveal significant spillover effects of OPU on sectoral stock returns in China, and OPU is the primary information transmitter across the net spillover network. These spillover effects exhibit prominent time-varying characteristics, often increasing during important unexpected events. We find that economic policy uncertainty and climate policy uncertainty in China amplify and mitigate the OPU spillover effects on sectoral stock returns. Finally, these spillover effects can reduce stock return dispersion due to non-fundamental information, indicating the emergence of herd behavior driven by non-fundamentals. By linking methodological innovation, policy risk factors, and investor behavior, this study sheds light on how OPU shapes sectoral stock returns. The findings inform risk control and sector allocation for investors, and guide policymakers in reducing oil-induced market contagion.
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.
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.
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.
