{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T18:18:28Z","timestamp":1768933108030,"version":"3.49.0"},"reference-count":44,"publisher":"Emerald","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,21]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to improve the accuracy of energy forecasting in China\u2019s food manufacturing industry by addressing the challenges posed by the fixed parameters of grey Gompertz models. A Kernel Function Augmented Dynamic Fractional-Order Grey Gompertz Model (KFDGGM) is proposed to overcome the limitations of existing grey forecasting approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/methodology\/approach<\/jats:title>\n                    <jats:p>The proposed KFDGGM model combines the Gompertz model with a fractional-order accumulation operator and a kernel function to enable dynamic parameter adjustment. Validation is conducted using energy consumption data from China\u2019s food manufacturing sector, and the model\u2019s performance is compared with that of other models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>Empirical results validate KFDGGM\u2019s superior accuracy over traditional models in forecasting China\u2019s food manufacturing energy consumption. Predictions show rising total energy demand but declining diesel\/gasoline use, reflecting a clean energy substitution trend.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Originality\/value<\/jats:title>\n                    <jats:p>This study proposes a novel integration of fractional-order accumulation operators and kernel functions into the grey Gompertz framework, overcoming traditional limitations of fixed parameters and large dataset dependencies. KFDGGM\u2019s dynamic mechanisms enable precise forecasting in data-scarce environments, offering methodological innovations for low-carbon transitions and data-driven energy policy optimization in food manufacturing.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/gs-04-2025-0036","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T03:16:49Z","timestamp":1764213409000},"page":"199-223","source":"Crossref","is-referenced-by-count":0,"title":["Kernel function augmented dynamic fractional-order grey Gompertz model for forecasting energy consumption in China\u2019s food manufacturing industry"],"prefix":"10.1108","volume":"16","author":[{"given":"Minlin","family":"Gu","sequence":"first","affiliation":[{"name":"School of Science, Jiangsu University of Science and Technology , ,","place":["Zhenjiang, China"]}]},{"given":"Jiahuang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Science, Jiangsu University of Science and 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