China's apparel exports increased in value by 7.05 per cent to$167.876 billion in 2022,but this was due to a rise in per unit price rather than an increase in volume.Similarly,in 2021,due to the rising unit price,...China's apparel exports increased in value by 7.05 per cent to$167.876 billion in 2022,but this was due to a rise in per unit price rather than an increase in volume.Similarly,in 2021,due to the rising unit price,clothing exports increased by 25.80%to US$156.764 billion.The average unit price of Chinese apparel exports declined until 2020.展开更多
The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the...The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.展开更多
After many years of exploitation,onshore oil and gas resources are about to enter a recession period.Oil and gas will mainly come from oceans in the future.Generally speaking,the exploration and production(E&P)cos...After many years of exploitation,onshore oil and gas resources are about to enter a recession period.Oil and gas will mainly come from oceans in the future.Generally speaking,the exploration and production(E&P)cost of oil from offshore is much higher than that of oil from onshore,so it is more sensitive to oil price.However,in recent years,oil price has been hovering at a low level for a long time,almost close to or even lower than the E&P cost of oil,which directly affects the development of oilfields.Besides the influence of oil price,some oilfields present the characteristics of marginal reserve scale,short peak production period and output rapidly declining.There leads to short economic life period and makes the economic benefit close to or lower than oilfield’s hurdle rate,which increases the difficulty of offshore oilfield development.As an important part of oilfield development,Floating Production Storage and Offloading unit,its investment mode and rent mode directly affect overall oilfield’s rate of return and the economic life.This paper chooses lease mode as the research object based on the analysis of investment mode,and further puts forward rent mode related with oil price through the analysis of traditional rent mode,and illustrates the advantages and disadvantages of various rent modes and their applicability so that the lessor chooses the right mode to achieve Win-Win with Oil Company and promotes the development of oilfields under low oil price.展开更多
Accurate carbon price forecasting is essential to provide the guidance for production and investment.Current research is mainly dependent on plenty of historical samples of carbon prices,which is impractical for the n...Accurate carbon price forecasting is essential to provide the guidance for production and investment.Current research is mainly dependent on plenty of historical samples of carbon prices,which is impractical for the newly launched carbon market due to its short history.Based on the idea of transfer learning,this paper proposes a novel price forecasting model,which utilizes the correlation between the new and mature markets.The model is firstly pretrained on large data of mature market by gated recurrent unit algorithm,and then fine-tuned by the target market samples.An integral framework,including complexity decomposition method for data pre-processing,sample entropy for feature selection,and support vector regression for result post-processing,is provided.In the empirical analysis of new Chinese market,the root mean square error,mean absolute error,mean absolute percentage error,and determination coefficient of the model are 0.529,0.476,0.717%and 0.501 respectively,proving its validity.展开更多
文摘China's apparel exports increased in value by 7.05 per cent to$167.876 billion in 2022,but this was due to a rise in per unit price rather than an increase in volume.Similarly,in 2021,due to the rising unit price,clothing exports increased by 25.80%to US$156.764 billion.The average unit price of Chinese apparel exports declined until 2020.
基金supported by China Southern Power Grid Science and Technology Innovation Research Project(000000KK52220052).
文摘The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.
文摘After many years of exploitation,onshore oil and gas resources are about to enter a recession period.Oil and gas will mainly come from oceans in the future.Generally speaking,the exploration and production(E&P)cost of oil from offshore is much higher than that of oil from onshore,so it is more sensitive to oil price.However,in recent years,oil price has been hovering at a low level for a long time,almost close to or even lower than the E&P cost of oil,which directly affects the development of oilfields.Besides the influence of oil price,some oilfields present the characteristics of marginal reserve scale,short peak production period and output rapidly declining.There leads to short economic life period and makes the economic benefit close to or lower than oilfield’s hurdle rate,which increases the difficulty of offshore oilfield development.As an important part of oilfield development,Floating Production Storage and Offloading unit,its investment mode and rent mode directly affect overall oilfield’s rate of return and the economic life.This paper chooses lease mode as the research object based on the analysis of investment mode,and further puts forward rent mode related with oil price through the analysis of traditional rent mode,and illustrates the advantages and disadvantages of various rent modes and their applicability so that the lessor chooses the right mode to achieve Win-Win with Oil Company and promotes the development of oilfields under low oil price.
文摘Accurate carbon price forecasting is essential to provide the guidance for production and investment.Current research is mainly dependent on plenty of historical samples of carbon prices,which is impractical for the newly launched carbon market due to its short history.Based on the idea of transfer learning,this paper proposes a novel price forecasting model,which utilizes the correlation between the new and mature markets.The model is firstly pretrained on large data of mature market by gated recurrent unit algorithm,and then fine-tuned by the target market samples.An integral framework,including complexity decomposition method for data pre-processing,sample entropy for feature selection,and support vector regression for result post-processing,is provided.In the empirical analysis of new Chinese market,the root mean square error,mean absolute error,mean absolute percentage error,and determination coefficient of the model are 0.529,0.476,0.717%and 0.501 respectively,proving its validity.
