{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T06:51:25Z","timestamp":1776840685433,"version":"3.51.2"},"reference-count":37,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T00:00:00Z","timestamp":1776729600000},"content-version":"vor","delay-in-days":110,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012337","name":"Nanhu Scholars Program for Young Scholars of Xinyang Normal University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012337","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    Accurate forecasting of CO\n                    <jats:sub>2<\/jats:sub>\n                    emissions can provide theoretical support for the Chinese government in formulating carbon reduction policies. However, China\u2019s CO\n                    <jats:sub>2<\/jats:sub>\n                    emission sequences are constrained by limited available samples and complex characteristics. To address these issues, this study develops a novel structure\u2010adaptive conformable fractional gray Bernoulli model (SAFGBM (1, 1)). Specifically, the newly designed model incorporates a new fractional\u2010order accumulation operator to extract the sequence features and further uses the Simpson formula to optimize the background value. Subsequently, the particle swarm optimization (PSO) algorithm is employed to solve the nonlinear parameters in the model. Comparative verification with various competing models demonstrates that the proposed model exhibits significant accuracy advantages in forecasting China\u2019s carbon emission sequences. Finally, this model is applied to predict China\u2019s CO\n                    <jats:sub>2<\/jats:sub>\n                    emissions from 2025 to 2030. The results indicate that although the growth rate of China\u2019s CO\n                    <jats:sub>2<\/jats:sub>\n                    emissions has slowed down, the dual carbon goals still face substantial challenges; the government needs to accelerate the implementation of relevant policies to advance the realization of the carbon peaking target and high\u2010quality green economic development.\n                  <\/jats:p>","DOI":"10.1155\/cplx\/7925189","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T05:52:26Z","timestamp":1776837146000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Forecasting China\u2019s Carbon Dioxide Emissions Using a Novel Structure\u2010Adaptive Conformable Fractional Gray Bernoulli Model"],"prefix":"10.1155","volume":"2026","author":[{"given":"Zesheng","family":"Li","sequence":"first","affiliation":[]},{"given":"Jiayang","family":"Kong","sequence":"additional","affiliation":[]},{"given":"Ye","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2038-8455","authenticated-orcid":false,"given":"Kuangxi","family":"Su","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2026,4,21]]},"reference":[{"key":"e_1_2_13_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/su141610216"},{"key":"e_1_2_13_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.124572"},{"key":"e_1_2_13_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-023-30428-5"},{"key":"e_1_2_13_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2019.05.004"},{"key":"e_1_2_13_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/su15107932"},{"key":"e_1_2_13_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13762-022-04609-7"},{"key":"e_1_2_13_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.128908"},{"key":"e_1_2_13_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clce.2023.100095"},{"key":"e_1_2_13_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/jmse9080871"},{"key":"e_1_2_13_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106314"},{"key":"e_1_2_13_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/en11092475"},{"key":"e_1_2_13_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-022-21277-9"},{"key":"e_1_2_13_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11356-022-20615-1"},{"key":"e_1_2_13_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2016.08.067"},{"key":"e_1_2_13_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.110968"},{"key":"e_1_2_13_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111310"},{"key":"e_1_2_13_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10668-023-03325-7"},{"key":"e_1_2_13_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/en13112924"},{"key":"e_1_2_13_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2022.112867"},{"key":"e_1_2_13_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2023.106685"},{"key":"e_1_2_13_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.126005"},{"key":"e_1_2_13_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121627"},{"key":"e_1_2_13_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cnsns.2006.08.008"},{"key":"e_1_2_13_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2019.03.006"},{"key":"e_1_2_13_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.120714"},{"key":"e_1_2_13_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124534"},{"key":"e_1_2_13_27_2","first-page":"1","article-title":"Forecasting 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