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：
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.
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.
期刊：
Economic Systems Research,2024年: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 CO2 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.
期刊：
Finance Research Letters,2024年67 ISSN：1544-6123
通讯作者：
Zheng, Y
作者机构：
[Zhu, Yulin; Cui, Na; Liu, Hong] Cent South Univ Forestry & Technol, Sch Econ, Changsha 410004, Peoples R China.;[Cui, Na; Liu, Hong] Key Res Ctr Philosophy & Social Sci Hunan Prov Uni, Res Ctr High Qual Dev Ind Econ, Changsha 410004, Peoples R China.;[Zheng, Yan; Zheng, Y] Hainan Univ, Int Business Sch, Haikou 570228, Peoples R China.
通讯机构：
[Zheng, Y ] H;Hainan Univ, Int Business Sch, Haikou 570228, Peoples R China.
摘要：
This paper employs the TVP-VAR-DY model to investigate the spillover among the European carbon market (EUA), the Chinese oil futures market (INE), and the Brent oil futures market (Brent) and explores whether their spillovers affect their volatility. Furthermore, we explore the impact of global uncertainties about the economy, finance, and geopolitics on their spillovers. The static analysis shows INE as a spillover receiver and EUA and Brent as transmitters. Dynamic analysis reveals spillover peaks during the COVID-19 pandemic. Furthermore, volatilities are positively influenced by spillovers among them. Finally, economic and financial uncertainties increase spillovers, while geopolitical uncertainties decrease them.
