2022
DOI: 10.1002/ese3.1304
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Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN‐LSTM

Abstract: Global carbon dioxide emissions have become a great threat to economic sustainability and human health. The carbon market is recognized as the most promising mean to curb carbon emissions, furthermore, carbon price forecasting will promote the role of the carbon market in emissions reduction and achieve reduction targets at lower economic costs for emission entities. However, there are still some technical problems in carbon price prediction, such as mode mixing and larger reconstruction error for the traditio… Show more

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Cited by 28 publications
(8 citation statements)
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“…In accordance with the previous literature review, carbon price research can be categorized into two classifications: models based on historical data 7 9 and models based on influencing factors 10 13 .…”
Section: Introductionmentioning
confidence: 88%
“…In accordance with the previous literature review, carbon price research can be categorized into two classifications: models based on historical data 7 9 and models based on influencing factors 10 13 .…”
Section: Introductionmentioning
confidence: 88%
“…Wireless edge devices are networked with ZigBee or Lora, allowing data to be uploaded using satellite communications even in the event of network outages or fluctuations. In addition, a carbon query function is built into each node of the blockchain, allowing queries for specific carbon emissions, greenhouse gas emissions and hash values for a certain period of time [ [27] , [28] , [29] ]. Before deleting block records, most nodes are backed up and the backup information is not tampered with.…”
Section: Low Carbon Management Of Regional Energy Economies Based On ...mentioning
confidence: 99%
“…They achieved it by decomposing carbon price sequencing using EEMD into numerous intrinsic framework functions (IFFs) by dividing these IFFs into several high-frequency compartments, low-frequency compartments, and drift compartments after which the least square vector support machine (LSSVM), particle swarm optimization LSSVM (PSO-LSSVM), and bat algorithm-LSSVM (BA-LSSVM) were utilized to forecast three compartments, respectively. Similarly, Yun et al [ 8 ] built a robust carbon price-predicting model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-long short-term memory (LSTM) by blending the merits of CEEMDAN in decaying multiresolution time-frequency carbon price waves and the LSTM model by installing monetary waves.…”
Section: Introductionmentioning
confidence: 99%