A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network
Carbon trading is one of the important mechanisms used to reduce carbon dioxide emissions. The increasing interest in the carbon trading market has heightened the need to decrease the prediction error of the carbon price. In this paper, a new hybrid model for carbon price forecasting is proposed, an...
Uložené v:
| Vydané v: | Journal of cleaner production Ročník 243; s. 118671 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
10.01.2020
|
| Predmet: | |
| ISSN: | 0959-6526, 1879-1786 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Carbon trading is one of the important mechanisms used to reduce carbon dioxide emissions. The increasing interest in the carbon trading market has heightened the need to decrease the prediction error of the carbon price. In this paper, a new hybrid model for carbon price forecasting is proposed, and the secondary decomposition algorithm is innovatively introduced into carbon price forecasting. First, time series data were decomposed into several intrinsic modal functions by empirical mode decomposition (EMD). Second, the first intrinsic mode function (IMF1) was further decomposed by variational mode decomposition (VMD). Then, the model input was determined by partial autocorrelation analysis (PACF). Finally, the back propagation (BP) neural network model optimized by genetic algorithm (GA) was utilized for prediction. In the empirical analysis of the Hubei market, the proposed model outperforms other comparative models. The mean absolute percentage error (MAPE), goodness of fit (R2) and root mean square error (RMSE) of the model are 1.7577%, 0.9929 and 0.5441, respectively. In the complementary cases of the Beijing and Shanghai carbon markets, the model also performs best. The results suggest that the proposed model is effective and robust and could predict carbon prices more accurately.
[Display omitted]
•The secondary decomposition algorithm is suitable for carbon price prediction.•The decomposition algorithm significantly improves the prediction accuracy.•Combination of empirical and variational mode decomposition is effective.•This method could be applied to different carbon markets. |
|---|---|
| AbstractList | Carbon trading is one of the important mechanisms used to reduce carbon dioxide emissions. The increasing interest in the carbon trading market has heightened the need to decrease the prediction error of the carbon price. In this paper, a new hybrid model for carbon price forecasting is proposed, and the secondary decomposition algorithm is innovatively introduced into carbon price forecasting. First, time series data were decomposed into several intrinsic modal functions by empirical mode decomposition (EMD). Second, the first intrinsic mode function (IMF1) was further decomposed by variational mode decomposition (VMD). Then, the model input was determined by partial autocorrelation analysis (PACF). Finally, the back propagation (BP) neural network model optimized by genetic algorithm (GA) was utilized for prediction. In the empirical analysis of the Hubei market, the proposed model outperforms other comparative models. The mean absolute percentage error (MAPE), goodness of fit (R2) and root mean square error (RMSE) of the model are 1.7577%, 0.9929 and 0.5441, respectively. In the complementary cases of the Beijing and Shanghai carbon markets, the model also performs best. The results suggest that the proposed model is effective and robust and could predict carbon prices more accurately.
[Display omitted]
•The secondary decomposition algorithm is suitable for carbon price prediction.•The decomposition algorithm significantly improves the prediction accuracy.•Combination of empirical and variational mode decomposition is effective.•This method could be applied to different carbon markets. Carbon trading is one of the important mechanisms used to reduce carbon dioxide emissions. The increasing interest in the carbon trading market has heightened the need to decrease the prediction error of the carbon price. In this paper, a new hybrid model for carbon price forecasting is proposed, and the secondary decomposition algorithm is innovatively introduced into carbon price forecasting. First, time series data were decomposed into several intrinsic modal functions by empirical mode decomposition (EMD). Second, the first intrinsic mode function (IMF1) was further decomposed by variational mode decomposition (VMD). Then, the model input was determined by partial autocorrelation analysis (PACF). Finally, the back propagation (BP) neural network model optimized by genetic algorithm (GA) was utilized for prediction. In the empirical analysis of the Hubei market, the proposed model outperforms other comparative models. The mean absolute percentage error (MAPE), goodness of fit (R2) and root mean square error (RMSE) of the model are 1.7577%, 0.9929 and 0.5441, respectively. In the complementary cases of the Beijing and Shanghai carbon markets, the model also performs best. The results suggest that the proposed model is effective and robust and could predict carbon prices more accurately. |
| ArticleNumber | 118671 |
| Author | Sun, Wei Huang, Chenchen |
| Author_xml | – sequence: 1 givenname: Wei surname: Sun fullname: Sun, Wei – sequence: 2 givenname: Chenchen surname: Huang fullname: Huang, Chenchen email: 973118497@qq.com |
| BookMark | eNqFkUFv1DAQhS1UJLalP6FSjlyyeOzETsQBVRUFpEpc6NlyxpPibRIH2wuFX193t6deevF4Ru89jb45ZSdLWIixC-Bb4KA-7rY7nGiNYSs49FuATml4wzbQ6b4G3akTtuF929eqFeodO01pxzlorpsNe7is0MYhLNUaPVJ5yXnMvgzm4GiqBpvIVaVNhGFxNv6rXPnNa0j-ILPTXYg-_5oruxThmv3s_xfLYPG-xIXV3tmDcKF9tFMp-W-I9-_Z29FOic6f6xm7vf7y8-pbffPj6_ery5saZSNyLUGMUqCUVg7a9oCDEOBGTSi7XlKvLY1t1yllG6UbJ_SAysHYNuhAQsvlGftwzC2r_N5Tymb2CWma7EJhn4yQsgUhO9UU6aejFGNIKdJo0OfD7jlaPxng5om32Zln3uaJtznyLu72hbsQnQuvV32fjz4qFP54iiahpwXLHSJhNi74VxIeAZa8ok4 |
| CitedBy_id | crossref_primary_10_1016_j_apenergy_2021_117623 crossref_primary_10_1016_j_energy_2025_136679 crossref_primary_10_1016_j_jclepro_2023_137791 crossref_primary_10_1016_j_inffus_2025_103720 crossref_primary_10_3390_ijerph20021503 crossref_primary_10_1016_j_jclepro_2024_144124 crossref_primary_10_1016_j_asoc_2025_113274 crossref_primary_10_1016_j_chaos_2023_113692 crossref_primary_10_1016_j_jenvman_2024_120967 crossref_primary_10_1155_2022_5488053 crossref_primary_10_1016_j_ribaf_2025_103063 crossref_primary_10_3390_electronics10091048 crossref_primary_10_1016_j_energy_2022_124167 crossref_primary_10_1016_j_eswa_2024_123171 crossref_primary_10_1007_s11356_021_16960_2 crossref_primary_10_1016_j_jclepro_2020_124519 crossref_primary_10_1007_s10479_023_05401_7 crossref_primary_10_1016_j_fecs_2025_100368 crossref_primary_10_1016_j_frl_2022_102809 crossref_primary_10_3390_su17125249 crossref_primary_10_1016_j_jclepro_2021_128024 crossref_primary_10_3389_fevo_2024_1362541 crossref_primary_10_1016_j_jclepro_2019_119386 crossref_primary_10_1016_j_cosrev_2020_100356 crossref_primary_10_1016_j_eneco_2022_106361 crossref_primary_10_1371_journal_pone_0322548 crossref_primary_10_1016_j_egyr_2020_08_010 crossref_primary_10_1016_j_jenvman_2024_121253 crossref_primary_10_1038_s41598_024_51524_7 crossref_primary_10_3390_su14138177 crossref_primary_10_1016_j_procs_2022_01_139 crossref_primary_10_3390_agriculture13091671 crossref_primary_10_1007_s12206_023_1225_8 crossref_primary_10_1016_j_energy_2024_132338 crossref_primary_10_1080_17583004_2023_2275576 crossref_primary_10_1108_JFM_03_2024_0027 crossref_primary_10_1155_2021_3052041 crossref_primary_10_3390_math12101428 crossref_primary_10_1016_j_eswa_2021_116267 crossref_primary_10_1016_j_egyr_2021_11_270 crossref_primary_10_1007_s10479_022_04858_2 crossref_primary_10_3390_plants12030601 crossref_primary_10_3390_math10142366 crossref_primary_10_3390_su16219332 crossref_primary_10_3390_math9202595 crossref_primary_10_1016_j_eswa_2023_122912 crossref_primary_10_1371_journal_pone_0326926 crossref_primary_10_1109_ACCESS_2021_3083928 crossref_primary_10_1016_j_jenvman_2024_121273 crossref_primary_10_1016_j_energy_2022_125609 crossref_primary_10_1016_j_knosys_2024_112037 crossref_primary_10_3390_en15103562 crossref_primary_10_1016_j_jhazmat_2024_136002 crossref_primary_10_1016_j_asoc_2022_108560 crossref_primary_10_1080_21642583_2023_2291409 crossref_primary_10_3390_fractalfract9050326 crossref_primary_10_1016_j_jclepro_2024_142932 crossref_primary_10_1016_j_apm_2021_03_020 crossref_primary_10_1007_s11356_023_31022_5 crossref_primary_10_1016_j_autcon_2022_104261 crossref_primary_10_1016_j_energy_2020_118773 crossref_primary_10_1016_j_apenergy_2023_121380 crossref_primary_10_3390_app12199512 crossref_primary_10_3390_math10224264 crossref_primary_10_33889_IJMEMS_2021_6_5_077 crossref_primary_10_1007_s10479_021_04392_7 crossref_primary_10_1016_j_scienta_2020_109528 crossref_primary_10_1016_j_asoc_2024_112648 crossref_primary_10_1002_isaf_1563 crossref_primary_10_1007_s10586_023_03979_y crossref_primary_10_3390_rs15112913 crossref_primary_10_1016_j_ijar_2022_04_002 crossref_primary_10_1016_j_energy_2021_120797 crossref_primary_10_1016_j_eneco_2022_106162 crossref_primary_10_3390_en13133471 crossref_primary_10_1002_ese3_799 crossref_primary_10_1016_j_engappai_2021_104564 crossref_primary_10_1016_j_jclepro_2023_139232 crossref_primary_10_3389_fenrg_2022_898620 crossref_primary_10_1371_journal_pone_0296105 crossref_primary_10_1007_s00500_023_08172_2 crossref_primary_10_1016_j_eswa_2023_120647 crossref_primary_10_3390_electronics14122370 crossref_primary_10_1016_j_scitotenv_2022_160410 crossref_primary_10_1016_j_ins_2024_121235 crossref_primary_10_3390_en14051328 crossref_primary_10_1016_j_bios_2024_116076 crossref_primary_10_3390_su15129354 crossref_primary_10_1016_j_jksuci_2021_01_015 crossref_primary_10_1016_j_jclepro_2023_136192 crossref_primary_10_3390_en18164227 crossref_primary_10_1007_s11356_022_24570_9 crossref_primary_10_1016_j_energy_2022_123225 crossref_primary_10_3390_agriculture12081188 crossref_primary_10_1057_s41599_025_05482_8 crossref_primary_10_1016_j_energy_2025_138377 crossref_primary_10_1016_j_iref_2024_04_011 crossref_primary_10_1016_j_omega_2023_102922 crossref_primary_10_1016_j_engappai_2022_105172 crossref_primary_10_1016_j_asoc_2023_110948 crossref_primary_10_1142_S0129156425400609 crossref_primary_10_1016_j_ecolind_2021_108392 crossref_primary_10_1007_s42484_023_00115_2 crossref_primary_10_1016_j_cep_2021_108484 crossref_primary_10_1016_j_petsci_2022_03_013 crossref_primary_10_1371_journal_pone_0294269 crossref_primary_10_1007_s10614_023_10384_5 crossref_primary_10_3390_math13142323 crossref_primary_10_1002_ese3_1769 crossref_primary_10_1007_s10651_024_00619_5 crossref_primary_10_1016_j_apenergy_2023_121977 crossref_primary_10_3390_math11143126 crossref_primary_10_1155_jama_7706431 crossref_primary_10_1016_j_jes_2023_05_038 crossref_primary_10_1007_s11063_021_10607_6 crossref_primary_10_1016_j_apenergy_2022_118601 crossref_primary_10_1016_j_eswa_2024_123325 crossref_primary_10_1016_j_ins_2023_03_133 crossref_primary_10_1016_j_undsp_2024_01_004 crossref_primary_10_1016_j_apm_2022_09_004 crossref_primary_10_1007_s11356_022_21277_9 crossref_primary_10_1016_j_jclepro_2020_124124 crossref_primary_10_1007_s10668_023_03886_7 crossref_primary_10_1016_j_eneco_2025_108318 crossref_primary_10_1007_s11356_023_29196_z crossref_primary_10_3390_app10165700 crossref_primary_10_1088_1742_6596_2430_1_012009 crossref_primary_10_1007_s11356_023_27109_8 crossref_primary_10_1016_j_compag_2020_105844 crossref_primary_10_1016_j_jenvman_2023_119873 crossref_primary_10_1016_j_scitotenv_2021_148444 crossref_primary_10_1186_s40537_025_01240_4 crossref_primary_10_1016_j_energy_2023_127783 crossref_primary_10_1007_s41660_024_00428_0 crossref_primary_10_1016_j_psep_2023_07_015 crossref_primary_10_1016_j_suscom_2021_100578 crossref_primary_10_3390_smartcities7060132 crossref_primary_10_1002_ese3_1304 crossref_primary_10_1016_j_eswa_2023_122502 crossref_primary_10_1016_j_asoc_2024_111869 crossref_primary_10_1007_s11665_021_05536_3 crossref_primary_10_1016_j_energy_2021_120941 crossref_primary_10_1016_j_energy_2023_129954 crossref_primary_10_1038_s41598_021_97195_6 crossref_primary_10_1016_j_renene_2022_10_027 crossref_primary_10_1016_j_chaos_2021_111783 crossref_primary_10_1016_j_jhazmat_2025_138492 crossref_primary_10_3390_en16114444 crossref_primary_10_1016_j_eneco_2022_106502 crossref_primary_10_1016_j_seta_2023_103168 crossref_primary_10_1016_j_eneco_2021_105239 crossref_primary_10_3390_su15086571 crossref_primary_10_3390_e27080817 crossref_primary_10_1002_for_2831 crossref_primary_10_1007_s10791_025_09565_7 crossref_primary_10_1016_j_jclepro_2023_136701 crossref_primary_10_1016_j_scitotenv_2020_142052 crossref_primary_10_1007_s11356_023_28563_0 crossref_primary_10_1007_s11356_024_32169_5 crossref_primary_10_1016_j_resourpol_2022_102714 crossref_primary_10_1007_s00477_021_02100_2 crossref_primary_10_1007_s00521_023_09106_7 crossref_primary_10_1002_ese3_703 crossref_primary_10_1016_j_susoc_2023_08_002 crossref_primary_10_1016_j_jclepro_2020_123997 crossref_primary_10_3390_agronomy14030546 crossref_primary_10_1016_j_eneco_2025_108350 crossref_primary_10_1007_s11356_022_19388_4 crossref_primary_10_1007_s13762_021_03871_5 crossref_primary_10_1007_s11071_025_11185_1 crossref_primary_10_1016_j_inffus_2023_101966 crossref_primary_10_3389_fenrg_2022_991570 crossref_primary_10_1016_j_apenergy_2023_122515 crossref_primary_10_1016_j_energy_2023_128150 crossref_primary_10_1007_s11356_023_26661_7 crossref_primary_10_1016_j_apr_2021_101306 crossref_primary_10_1016_j_engappai_2024_108646 crossref_primary_10_1016_j_commatsci_2023_112737 crossref_primary_10_3390_su13158413 crossref_primary_10_1016_j_scitotenv_2025_178541 crossref_primary_10_1088_1674_1056_ad9454 crossref_primary_10_1016_j_jclepro_2023_139508 crossref_primary_10_1016_j_epsr_2021_107762 crossref_primary_10_1002_ep_14216 crossref_primary_10_1007_s10479_023_05443_x crossref_primary_10_3390_su14138002 crossref_primary_10_1016_j_resourpol_2022_102969 crossref_primary_10_1016_j_scitotenv_2021_149509 crossref_primary_10_1007_s42461_025_01190_8 crossref_primary_10_1109_TFUZZ_2023_3298970 crossref_primary_10_1016_j_apenergy_2024_123126 crossref_primary_10_1016_j_psep_2025_106772 crossref_primary_10_1016_j_energy_2021_120052 crossref_primary_10_1007_s10462_022_10199_0 crossref_primary_10_1016_j_energy_2024_132929 crossref_primary_10_1371_journal_pone_0285631 crossref_primary_10_1016_j_isatra_2020_03_012 crossref_primary_10_1007_s10644_024_09851_2 crossref_primary_10_1002_for_2916 |
| Cites_doi | 10.3390/en9010054 10.1016/j.enconman.2017.09.034 10.1016/j.apenergy.2008.09.017 10.1016/j.eneco.2017.12.030 10.1016/j.jclepro.2017.11.133 10.1016/j.apenergy.2018.02.003 10.1016/j.apenergy.2017.01.076 10.1016/j.knosys.2013.11.015 10.1016/j.energy.2015.10.041 10.1016/j.csda.2011.05.015 10.1016/j.eswa.2008.05.024 10.1016/j.physleta.2005.06.116 10.1016/j.enconman.2017.08.014 10.1016/j.apenergy.2018.09.118 10.1016/j.eneco.2013.06.017 10.1016/j.physa.2018.12.017 10.1016/j.egypro.2015.07.609 10.1016/j.eswa.2004.05.018 10.1016/j.eswa.2014.12.047 10.1016/S0029-8018(03)00115-X 10.1007/s10462-011-9208-z 10.1016/j.energy.2019.01.009 10.1063/1.4996653 10.1109/TSP.2013.2288675 10.1016/j.applthermaleng.2011.02.031 10.1016/j.renene.2018.02.092 10.1016/j.renene.2011.06.023 10.1016/j.applthermaleng.2005.10.006 10.1007/s00521-012-1323-5 10.1016/j.econmod.2011.11.003 10.1016/j.apenergy.2009.12.019 10.1007/s10614-013-9417-4 |
| ContentType | Journal Article |
| Copyright | 2019 Elsevier Ltd |
| Copyright_xml | – notice: 2019 Elsevier Ltd |
| DBID | AAYXX CITATION 7S9 L.6 |
| DOI | 10.1016/j.jclepro.2019.118671 |
| DatabaseName | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1879-1786 |
| ExternalDocumentID | 10_1016_j_jclepro_2019_118671 S0959652619335413 |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GroupedDBID | --K --M ..I .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JM 9JN AABNK AACTN AAEDT AAEDW AAHCO AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARJD AAXUO ABFYP ABJNI ABLST ABMAC ABYKQ ACDAQ ACGFS ACRLP ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHEUO AHHHB AHIDL AIEXJ AIKHN AITUG AJOXV AKIFW ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BELTK BKOJK BLECG BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA HMC IHE J1W JARJE K-O KCYFY KOM LY9 M41 MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RNS ROL RPZ SCC SDF SDG SDP SES SPC SPCBC SSJ SSR SSZ T5K ~G- 29K 9DU AAHBH AAQXK AATTM AAXKI AAYWO AAYXX ABFNM ABWVN ABXDB ACLOT ACRPL ACVFH ADCNI ADHUB ADMUD ADNMO AEGFY AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION D-I EFKBS FEDTE FGOYB G-2 HVGLF HZ~ R2- SEN SEW WUQ ZY4 ~HD 7S9 L.6 |
| ID | FETCH-LOGICAL-c342t-312f32c33a3b7a91cb221df7ec3893e97aef58866a4674d27bc6d1f54cd131503 |
| ISICitedReferencesCount | 219 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000498805600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0959-6526 |
| IngestDate | Thu Oct 02 11:01:15 EDT 2025 Sat Nov 29 07:04:56 EST 2025 Tue Nov 18 21:08:18 EST 2025 Fri Feb 23 02:27:33 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Carbon price prediction Empirical mode decomposition Back propagation neural network Genetic algorithm Secondary decomposition |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c342t-312f32c33a3b7a91cb221df7ec3893e97aef58866a4674d27bc6d1f54cd131503 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PQID | 2335123864 |
| PQPubID | 24069 |
| ParticipantIDs | proquest_miscellaneous_2335123864 crossref_citationtrail_10_1016_j_jclepro_2019_118671 crossref_primary_10_1016_j_jclepro_2019_118671 elsevier_sciencedirect_doi_10_1016_j_jclepro_2019_118671 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-01-10 |
| PublicationDateYYYYMMDD | 2020-01-10 |
| PublicationDate_xml | – month: 01 year: 2020 text: 2020-01-10 day: 10 |
| PublicationDecade | 2020 |
| PublicationTitle | Journal of cleaner production |
| PublicationYear | 2020 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Canakoglu, Adiyeke, Agrali (bib3) 2018; 10 Najafi, Najafi, Hoseinpoori (bib18) 2011; 31 Hu, Wang (bib10) 2015; 93 Ding, Su, Yu (bib4) 2011; 36 Yildiz (bib25) 2005; 345 Yin, Dong, Chen, Ge, Lai, Vaccaro, Meng (bib26) 2017; 150 Zhao, Han, Ding, Kang (bib28) 2018; 216 Guo, Zhao, Lu, Wang (bib7) 2012; 37 Parlak, Islamoglu, Yasar, Egrisogut (bib19) 2006; 26 Ren, An, Wang, Li, Hu, Shang (bib20) 2014; 56 Zhu, Han, Wang, Wu, Zhang, Wei (bib29) 2017; 191 Liu, Zhang, Song (bib13) 2018; 172 Najafi, Ghobadian, Tavakoli, Buttsworth, Yusaf, Faizollahnejad (bib17) 2009; 86 Zhu, Wang, Chevallier, Wei (bib30) 2015; 45 Versace, Bhatt, Hinds, Shiffer (bib24) 2004; 27 Zhu, Wu, Chen, Liu, Zhou (bib32) 2019; 519 Arouri, Jawadi, Nguyen (bib1) 2012; 29 Mi, Liu, Li (bib15) 2017; 151 Hashim, Ramlan, Shiun, Siong, Kamyab, Majid, Lee (bib9) 2015; 75 Fan, Li, Tian (bib6) 2015; 42 Han, Ding, Zhao, Kang (bib8) 2019; 171 Moghtaderi, Flandrin, Borgnat (bib16) 2013; 58 Liu, Mi, Li (bib12) 2018; 123 Byun, Cho (bib2) 2013; 40 Sun, Zhang (bib23) 2018; 231 Dragomiretskiy, Zosso (bib5) 2014; 62 Lee (bib11) 2004; 31 Sedki, Ouazar, El Mazoudi (bib21) 2009; 36 Sun, Chen, Wei, Sun, Zang, Chen (bib22) 2016; 9 Zhang, Wei (bib27) 2010; 87 Liu, Sun, Zeng (bib14) 2014; 24 Zhu, Ye, Wang, He, Zhang, Wei (bib31) 2018; 70 Zhang (10.1016/j.jclepro.2019.118671_bib27) 2010; 87 Moghtaderi (10.1016/j.jclepro.2019.118671_bib16) 2013; 58 Ren (10.1016/j.jclepro.2019.118671_bib20) 2014; 56 Ding (10.1016/j.jclepro.2019.118671_bib4) 2011; 36 Liu (10.1016/j.jclepro.2019.118671_bib14) 2014; 24 Guo (10.1016/j.jclepro.2019.118671_bib7) 2012; 37 Zhao (10.1016/j.jclepro.2019.118671_bib28) 2018; 216 Sun (10.1016/j.jclepro.2019.118671_bib22) 2016; 9 Sun (10.1016/j.jclepro.2019.118671_bib23) 2018; 231 Canakoglu (10.1016/j.jclepro.2019.118671_bib3) 2018; 10 Zhu (10.1016/j.jclepro.2019.118671_bib29) 2017; 191 Hashim (10.1016/j.jclepro.2019.118671_bib9) 2015; 75 Yin (10.1016/j.jclepro.2019.118671_bib26) 2017; 150 Lee (10.1016/j.jclepro.2019.118671_bib11) 2004; 31 Parlak (10.1016/j.jclepro.2019.118671_bib19) 2006; 26 Zhu (10.1016/j.jclepro.2019.118671_bib32) 2019; 519 Byun (10.1016/j.jclepro.2019.118671_bib2) 2013; 40 Najafi (10.1016/j.jclepro.2019.118671_bib17) 2009; 86 Arouri (10.1016/j.jclepro.2019.118671_bib1) 2012; 29 Liu (10.1016/j.jclepro.2019.118671_bib13) 2018; 172 Versace (10.1016/j.jclepro.2019.118671_bib24) 2004; 27 Hu (10.1016/j.jclepro.2019.118671_bib10) 2015; 93 Zhu (10.1016/j.jclepro.2019.118671_bib30) 2015; 45 Mi (10.1016/j.jclepro.2019.118671_bib15) 2017; 151 Yildiz (10.1016/j.jclepro.2019.118671_bib25) 2005; 345 Liu (10.1016/j.jclepro.2019.118671_bib12) 2018; 123 Dragomiretskiy (10.1016/j.jclepro.2019.118671_bib5) 2014; 62 Najafi (10.1016/j.jclepro.2019.118671_bib18) 2011; 31 Fan (10.1016/j.jclepro.2019.118671_bib6) 2015; 42 Zhu (10.1016/j.jclepro.2019.118671_bib31) 2018; 70 Han (10.1016/j.jclepro.2019.118671_bib8) 2019; 171 Sedki (10.1016/j.jclepro.2019.118671_bib21) 2009; 36 |
| References_xml | – volume: 36 start-page: 4523 year: 2009 end-page: 4527 ident: bib21 article-title: Evolving neural network using real coded genetic algorithm for daily rainfall–runoff forecasting publication-title: Expert Syst. Appl. – volume: 27 start-page: 417 year: 2004 end-page: 425 ident: bib24 article-title: Predicting the exchange traded fund DIA with a combination of genetic algorithms and neural networks publication-title: Expert Syst. Appl. – volume: 29 start-page: 884 year: 2012 end-page: 892 ident: bib1 article-title: Nonlinearities in carbon spot-futures price relationships during Phase II of the EU ETS publication-title: Econ. Modell. – volume: 151 start-page: 709 year: 2017 end-page: 722 ident: bib15 article-title: Wind speed forecasting method using wavelet, extreme learning machine and outlier correction algorithm publication-title: Energy Convers. Manag. – volume: 36 start-page: 153 year: 2011 end-page: 162 ident: bib4 article-title: An optimizing BP neural network algorithm based on genetic algorithm publication-title: Artif. Intell. Rev. – volume: 86 start-page: 630 year: 2009 end-page: 639 ident: bib17 article-title: Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network publication-title: Appl. Energy – volume: 172 start-page: 2793 year: 2018 end-page: 2810 ident: bib13 article-title: Regional carbon emission evolution mechanism and its prediction approach driven by carbon trading - a case study of Beijing publication-title: J. Clean. Prod. – volume: 519 start-page: 140 year: 2019 end-page: 158 ident: bib32 article-title: Carbon price forecasting with variational mode decomposition and optimal combined model publication-title: Phys. A Stat. Mech. Appl. – volume: 345 start-page: 69 year: 2005 end-page: 87 ident: bib25 article-title: Layered feedforward neural network is relevant to empirical physical formula construction: a theoretical analysis and some simulation results publication-title: Phys. Lett. A – volume: 87 start-page: 1804 year: 2010 end-page: 1814 ident: bib27 article-title: An overview of current research on EU ETS: evidence from its operating mechanism and economic effect publication-title: Appl. Energy – volume: 70 start-page: 143 year: 2018 end-page: 157 ident: bib31 article-title: A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting publication-title: Energy Econ. – volume: 93 start-page: 1456 year: 2015 end-page: 1466 ident: bib10 article-title: Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression publication-title: Energy – volume: 45 start-page: 195 year: 2015 end-page: 206 ident: bib30 article-title: Carbon price analysis using empirical mode decomposition publication-title: Comput. Econ. – volume: 26 start-page: 824 year: 2006 end-page: 828 ident: bib19 article-title: Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine publication-title: Appl. Therm. Eng. – volume: 171 start-page: 69 year: 2019 end-page: 76 ident: bib8 article-title: Forecasting carbon prices in the Shenzhen market, China: the role of mixed-frequency factors publication-title: Energy – volume: 24 start-page: 973 year: 2014 end-page: 983 ident: bib14 article-title: A new short-term load forecasting method of power system based on EEMD and SS-PSO publication-title: Neural Comput. Appl. – volume: 56 start-page: 226 year: 2014 end-page: 239 ident: bib20 article-title: Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting publication-title: Knowl. Based Syst. – volume: 216 start-page: 132 year: 2018 end-page: 141 ident: bib28 article-title: Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS publication-title: Appl. Energy – volume: 123 start-page: 694 year: 2018 end-page: 705 ident: bib12 article-title: An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm publication-title: Renew. Energy – volume: 9 start-page: 54 year: 2016 ident: bib22 article-title: A carbon price forecasting model based on variational mode decomposition and spiking neural networks publication-title: Energies – volume: 42 start-page: 3945 year: 2015 end-page: 3952 ident: bib6 article-title: Chaotic characteristic identification for carbon price and an multi-layer perceptron network prediction model publication-title: Expert Syst. Appl. – volume: 191 start-page: 521 year: 2017 end-page: 530 ident: bib29 article-title: Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression publication-title: Appl. Energy – volume: 10 year: 2018 ident: bib3 article-title: Modeling of carbon credit prices using regime switching approach publication-title: J. Renew. Sustain. Energy – volume: 231 start-page: 1354 year: 2018 end-page: 1371 ident: bib23 article-title: Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm publication-title: Appl. Energy – volume: 40 start-page: 207 year: 2013 end-page: 221 ident: bib2 article-title: Forecasting carbon futures volatility using GARCH models with energy volatilities publication-title: Energy Econ. – volume: 37 start-page: 241 year: 2012 end-page: 249 ident: bib7 article-title: Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model publication-title: Renew. Energy – volume: 58 start-page: 114 year: 2013 end-page: 126 ident: bib16 article-title: Trend filtering via empirical mode decompositions publication-title: Comput. Stat. Data Anal. – volume: 31 start-page: 225 year: 2004 end-page: 238 ident: bib11 article-title: Back-propagation neural network for long-term tidal predictions publication-title: Ocean Eng. – volume: 150 start-page: 108 year: 2017 end-page: 121 ident: bib26 article-title: An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization publication-title: Energy Convers. Manag. – volume: 75 start-page: 2993 year: 2015 end-page: 2998 ident: bib9 article-title: An integrated carbon accounting and mitigation framework for greening the industry publication-title: Energy Procedia – volume: 31 start-page: 1839 year: 2011 end-page: 1847 ident: bib18 article-title: Energy and cost optimization of a plate and fin heat exchanger using genetic algorithm publication-title: Appl. Therm. Eng. – volume: 62 start-page: 531 year: 2014 end-page: 544 ident: bib5 article-title: Variational mode decomposition publication-title: IEEE Trans. Signal Process. – volume: 9 start-page: 54 issue: 1 year: 2016 ident: 10.1016/j.jclepro.2019.118671_bib22 article-title: A carbon price forecasting model based on variational mode decomposition and spiking neural networks publication-title: Energies doi: 10.3390/en9010054 – volume: 151 start-page: 709 year: 2017 ident: 10.1016/j.jclepro.2019.118671_bib15 article-title: Wind speed forecasting method using wavelet, extreme learning machine and outlier correction algorithm publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2017.09.034 – volume: 86 start-page: 630 issue: 5 year: 2009 ident: 10.1016/j.jclepro.2019.118671_bib17 article-title: Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network publication-title: Appl. Energy doi: 10.1016/j.apenergy.2008.09.017 – volume: 70 start-page: 143 year: 2018 ident: 10.1016/j.jclepro.2019.118671_bib31 article-title: A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting publication-title: Energy Econ. doi: 10.1016/j.eneco.2017.12.030 – volume: 172 start-page: 2793 year: 2018 ident: 10.1016/j.jclepro.2019.118671_bib13 article-title: Regional carbon emission evolution mechanism and its prediction approach driven by carbon trading - a case study of Beijing publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2017.11.133 – volume: 216 start-page: 132 year: 2018 ident: 10.1016/j.jclepro.2019.118671_bib28 article-title: Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS publication-title: Appl. Energy doi: 10.1016/j.apenergy.2018.02.003 – volume: 191 start-page: 521 year: 2017 ident: 10.1016/j.jclepro.2019.118671_bib29 article-title: Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression publication-title: Appl. Energy doi: 10.1016/j.apenergy.2017.01.076 – volume: 56 start-page: 226 year: 2014 ident: 10.1016/j.jclepro.2019.118671_bib20 article-title: Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2013.11.015 – volume: 93 start-page: 1456 year: 2015 ident: 10.1016/j.jclepro.2019.118671_bib10 article-title: Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression publication-title: Energy doi: 10.1016/j.energy.2015.10.041 – volume: 58 start-page: 114 year: 2013 ident: 10.1016/j.jclepro.2019.118671_bib16 article-title: Trend filtering via empirical mode decompositions publication-title: Comput. Stat. Data Anal. doi: 10.1016/j.csda.2011.05.015 – volume: 36 start-page: 4523 issue: 3 year: 2009 ident: 10.1016/j.jclepro.2019.118671_bib21 article-title: Evolving neural network using real coded genetic algorithm for daily rainfall–runoff forecasting publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2008.05.024 – volume: 345 start-page: 69 issue: 1–3 year: 2005 ident: 10.1016/j.jclepro.2019.118671_bib25 article-title: Layered feedforward neural network is relevant to empirical physical formula construction: a theoretical analysis and some simulation results publication-title: Phys. Lett. A doi: 10.1016/j.physleta.2005.06.116 – volume: 150 start-page: 108 year: 2017 ident: 10.1016/j.jclepro.2019.118671_bib26 article-title: An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2017.08.014 – volume: 231 start-page: 1354 year: 2018 ident: 10.1016/j.jclepro.2019.118671_bib23 article-title: Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm publication-title: Appl. Energy doi: 10.1016/j.apenergy.2018.09.118 – volume: 40 start-page: 207 year: 2013 ident: 10.1016/j.jclepro.2019.118671_bib2 article-title: Forecasting carbon futures volatility using GARCH models with energy volatilities publication-title: Energy Econ. doi: 10.1016/j.eneco.2013.06.017 – volume: 519 start-page: 140 year: 2019 ident: 10.1016/j.jclepro.2019.118671_bib32 article-title: Carbon price forecasting with variational mode decomposition and optimal combined model publication-title: Phys. A Stat. Mech. Appl. doi: 10.1016/j.physa.2018.12.017 – volume: 75 start-page: 2993 year: 2015 ident: 10.1016/j.jclepro.2019.118671_bib9 article-title: An integrated carbon accounting and mitigation framework for greening the industry publication-title: Energy Procedia doi: 10.1016/j.egypro.2015.07.609 – volume: 27 start-page: 417 issue: 3 year: 2004 ident: 10.1016/j.jclepro.2019.118671_bib24 article-title: Predicting the exchange traded fund DIA with a combination of genetic algorithms and neural networks publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2004.05.018 – volume: 42 start-page: 3945 issue: 8 year: 2015 ident: 10.1016/j.jclepro.2019.118671_bib6 article-title: Chaotic characteristic identification for carbon price and an multi-layer perceptron network prediction model publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.12.047 – volume: 31 start-page: 225 issue: 2 year: 2004 ident: 10.1016/j.jclepro.2019.118671_bib11 article-title: Back-propagation neural network for long-term tidal predictions publication-title: Ocean Eng. doi: 10.1016/S0029-8018(03)00115-X – volume: 36 start-page: 153 issue: 2 year: 2011 ident: 10.1016/j.jclepro.2019.118671_bib4 article-title: An optimizing BP neural network algorithm based on genetic algorithm publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-011-9208-z – volume: 171 start-page: 69 year: 2019 ident: 10.1016/j.jclepro.2019.118671_bib8 article-title: Forecasting carbon prices in the Shenzhen market, China: the role of mixed-frequency factors publication-title: Energy doi: 10.1016/j.energy.2019.01.009 – volume: 10 issue: 3 year: 2018 ident: 10.1016/j.jclepro.2019.118671_bib3 article-title: Modeling of carbon credit prices using regime switching approach publication-title: J. Renew. Sustain. Energy doi: 10.1063/1.4996653 – volume: 62 start-page: 531 issue: 3 year: 2014 ident: 10.1016/j.jclepro.2019.118671_bib5 article-title: Variational mode decomposition publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2013.2288675 – volume: 31 start-page: 1839 issue: 10 year: 2011 ident: 10.1016/j.jclepro.2019.118671_bib18 article-title: Energy and cost optimization of a plate and fin heat exchanger using genetic algorithm publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2011.02.031 – volume: 123 start-page: 694 year: 2018 ident: 10.1016/j.jclepro.2019.118671_bib12 article-title: An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm publication-title: Renew. Energy doi: 10.1016/j.renene.2018.02.092 – volume: 37 start-page: 241 issue: 1 year: 2012 ident: 10.1016/j.jclepro.2019.118671_bib7 article-title: Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model publication-title: Renew. Energy doi: 10.1016/j.renene.2011.06.023 – volume: 26 start-page: 824 issue: 8–9 year: 2006 ident: 10.1016/j.jclepro.2019.118671_bib19 article-title: Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2005.10.006 – volume: 24 start-page: 973 issue: 3–4 year: 2014 ident: 10.1016/j.jclepro.2019.118671_bib14 article-title: A new short-term load forecasting method of power system based on EEMD and SS-PSO publication-title: Neural Comput. Appl. doi: 10.1007/s00521-012-1323-5 – volume: 29 start-page: 884 issue: 3 year: 2012 ident: 10.1016/j.jclepro.2019.118671_bib1 article-title: Nonlinearities in carbon spot-futures price relationships during Phase II of the EU ETS publication-title: Econ. Modell. doi: 10.1016/j.econmod.2011.11.003 – volume: 87 start-page: 1804 issue: 6 year: 2010 ident: 10.1016/j.jclepro.2019.118671_bib27 article-title: An overview of current research on EU ETS: evidence from its operating mechanism and economic effect publication-title: Appl. Energy doi: 10.1016/j.apenergy.2009.12.019 – volume: 45 start-page: 195 issue: 2 year: 2015 ident: 10.1016/j.jclepro.2019.118671_bib30 article-title: Carbon price analysis using empirical mode decomposition publication-title: Comput. Econ. doi: 10.1007/s10614-013-9417-4 |
| SSID | ssj0017074 |
| Score | 2.6501093 |
| Snippet | Carbon trading is one of the important mechanisms used to reduce carbon dioxide emissions. The increasing interest in the carbon trading market has heightened... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 118671 |
| SubjectTerms | algorithms autocorrelation Back propagation neural network carbon dioxide carbon markets Carbon price prediction China emissions Empirical mode decomposition empirical research Genetic algorithm prediction Secondary decomposition time series analysis |
| Title | A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network |
| URI | https://dx.doi.org/10.1016/j.jclepro.2019.118671 https://www.proquest.com/docview/2335123864 |
| Volume | 243 |
| WOSCitedRecordID | wos000498805600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-1786 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017074 issn: 0959-6526 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELbQlgMcEE9RXjIStyqFOE4cH1dVq4JQhUSBvVmO7bDZlqTa3aIVv54Z20nKoyogcYl2Izmx_H0eT8bjbwh54SPGVeoS6VKb8EqbREthElirdGFTWVf-bNXHt-LoqJzN5Lu40b7y5QRE25abjTz7r1DDPQAbj87-BdzDQ-EG_AbQ4Qqww_WPgJ-i2nTVYeoVGAEUAbBNqAfuq97s4LplcY9ghd_CFrPmrMPM8pi-taNPP3fLZj0PxTM6sClfmm_oqGpzgvlcYIICa1ALExBuQyb5JW4u9E63bokNbVCqHfehvMX75JqRWzF6vTeHEZ3HU2oxJsEwtS2J2ak-UNYflhkzk_qIY5GzqHwd7G0pZJKKqIYdDTILwk2_GPcQZ1jsLqDn0GnMy5Ng8lGhb1zNhhzD9_g-fB24qFnOsbLxFhO5LCdka_p6f_Zm2GwSr4JYd9-_8aDXy9--7DIX5qfF3Hsox7fJrTjmdBoocYdcc-1dcvOC4OQ9spnSQA7qyUFHclBPDurJQeHvQA76AznoQA4K5KADOSiSg14gBw3koJEc98mHg_3jvcMk1t5ITMbZGpZmVmfMZJnOKqFlairGUlsLZ9DDdVJoV-dlWRQay9VYJioDU7vOubFpBh8Z2QMyabvWPSTUFamrmAbH1llesFoL8OIl1xmXpsiF2Ca8H05lojA91kc5VX0G4kJFFBSioAIK22R3aHYWlFmualD2WKnoXga3UQHBrmr6vMdWgfnFPTWYOt35SjHgFjh_ZcEf_fvjH5Mb4yx6Qibr5bl7Sq6br-tmtXwW6foda4Kzlw |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+carbon+price+prediction+model+based+on+secondary+decomposition+algorithm+and+optimized+back+propagation+neural+network&rft.jtitle=Journal+of+cleaner+production&rft.au=Sun%2C+Wei&rft.au=Huang%2C+Chenchen&rft.date=2020-01-10&rft.pub=Elsevier+Ltd&rft.issn=0959-6526&rft.eissn=1879-1786&rft.volume=243&rft_id=info:doi/10.1016%2Fj.jclepro.2019.118671&rft.externalDocID=S0959652619335413 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0959-6526&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0959-6526&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0959-6526&client=summon |