Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385575430> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W4385575430 abstract "Abstract Scientific analysis of regional agricultural carbon emission prediction models and empirical studies are of great practical significance to the realization of low-carbon agriculture, which can help revitalize and build up ecological and beautiful countryside in China. This paper takes agriculture in Guangdong Province, China, as the research object, and uses the extended STIPAT model to construct an indicator system for the factors influencing agricultural carbon emissions in Guangdong. Based on this system, a combined Isomap–ACO–ET prediction model combing the isometric mapping algorithm (Isomap), ant colony algorithm (ACO) and extreme random tree algorithm (ET) was used to predict agriculture carbon emissions in Guangdong Province under five scenarios. Effective predictions can be made for agricultural carbon emissions in Guangdong Province, which are expected to fluctuate between 11,142,200 tons and 11,386,000 tons in 2030. And compared with other machine learning and neural network models, the Isomap–ACO–ET model has a better prediction performance with an MSE of 0.00018 and an accuracy of 98.7%. To develop low-carbon agriculture in Guangdong Province, we should improve farming methods, reduce the intensity of agrochemical application, strengthen the development and promotion of agricultural energy-saving and emission reduction technologies and low-carbon energy sources, reduce the intensity of carbon emissions from agricultural energy consumption, optimize the agricultural planting structure, and develop green agricultural products and agro-ecological tourism according to local conditions. This will promote the development of agriculture in Guangdong Province in a green and sustainable direction." @default.
- W4385575430 created "2023-08-05" @default.
- W4385575430 creator A5001578547 @default.
- W4385575430 creator A5061024528 @default.
- W4385575430 creator A5082823301 @default.
- W4385575430 date "2023-08-04" @default.
- W4385575430 modified "2023-09-25" @default.
- W4385575430 title "Prediction model and demonstration of regional agricultural carbon emissions based on Isomap–ACO–ET: a case study of Guangdong Province, China" @default.
- W4385575430 cites W1974096764 @default.
- W4385575430 cites W1993900146 @default.
- W4385575430 cites W2115768128 @default.
- W4385575430 cites W2555173499 @default.
- W4385575430 cites W2741850261 @default.
- W4385575430 cites W2885094732 @default.
- W4385575430 cites W2915062141 @default.
- W4385575430 cites W2967446842 @default.
- W4385575430 cites W3007539280 @default.
- W4385575430 cites W3011541512 @default.
- W4385575430 cites W3013890861 @default.
- W4385575430 cites W3023113633 @default.
- W4385575430 cites W3037188201 @default.
- W4385575430 cites W3082842353 @default.
- W4385575430 cites W3083583719 @default.
- W4385575430 cites W3121998869 @default.
- W4385575430 cites W3123779334 @default.
- W4385575430 cites W3131112610 @default.
- W4385575430 cites W3172365033 @default.
- W4385575430 cites W3188144366 @default.
- W4385575430 cites W3199979947 @default.
- W4385575430 cites W4200275228 @default.
- W4385575430 cites W4200288687 @default.
- W4385575430 cites W4214601843 @default.
- W4385575430 cites W4282976603 @default.
- W4385575430 cites W4283591267 @default.
- W4385575430 cites W4285147516 @default.
- W4385575430 cites W4293036424 @default.
- W4385575430 cites W4296743457 @default.
- W4385575430 cites W4303685906 @default.
- W4385575430 cites W4308206546 @default.
- W4385575430 cites W4309312245 @default.
- W4385575430 doi "https://doi.org/10.1038/s41598-023-39996-5" @default.
- W4385575430 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37542116" @default.
- W4385575430 hasPublicationYear "2023" @default.
- W4385575430 type Work @default.
- W4385575430 citedByCount "0" @default.
- W4385575430 crossrefType "journal-article" @default.
- W4385575430 hasAuthorship W4385575430A5001578547 @default.
- W4385575430 hasAuthorship W4385575430A5061024528 @default.
- W4385575430 hasAuthorship W4385575430A5082823301 @default.
- W4385575430 hasBestOaLocation W43855754301 @default.
- W4385575430 hasConcept C118518473 @default.
- W4385575430 hasConcept C127413603 @default.
- W4385575430 hasConcept C18903297 @default.
- W4385575430 hasConcept C39432304 @default.
- W4385575430 hasConcept C41008148 @default.
- W4385575430 hasConcept C47737302 @default.
- W4385575430 hasConcept C552854447 @default.
- W4385575430 hasConcept C86803240 @default.
- W4385575430 hasConcept C88463610 @default.
- W4385575430 hasConceptScore W4385575430C118518473 @default.
- W4385575430 hasConceptScore W4385575430C127413603 @default.
- W4385575430 hasConceptScore W4385575430C18903297 @default.
- W4385575430 hasConceptScore W4385575430C39432304 @default.
- W4385575430 hasConceptScore W4385575430C41008148 @default.
- W4385575430 hasConceptScore W4385575430C47737302 @default.
- W4385575430 hasConceptScore W4385575430C552854447 @default.
- W4385575430 hasConceptScore W4385575430C86803240 @default.
- W4385575430 hasConceptScore W4385575430C88463610 @default.
- W4385575430 hasIssue "1" @default.
- W4385575430 hasLocation W43855754301 @default.
- W4385575430 hasLocation W43855754302 @default.
- W4385575430 hasOpenAccess W4385575430 @default.
- W4385575430 hasPrimaryLocation W43855754301 @default.
- W4385575430 hasRelatedWork W1566112789 @default.
- W4385575430 hasRelatedWork W2059242528 @default.
- W4385575430 hasRelatedWork W2112121335 @default.
- W4385575430 hasRelatedWork W2617375142 @default.
- W4385575430 hasRelatedWork W2899084033 @default.
- W4385575430 hasRelatedWork W2943011868 @default.
- W4385575430 hasRelatedWork W3121998869 @default.
- W4385575430 hasRelatedWork W3130201959 @default.
- W4385575430 hasRelatedWork W4296312342 @default.
- W4385575430 hasRelatedWork W4367299281 @default.
- W4385575430 hasVolume "13" @default.
- W4385575430 isParatext "false" @default.
- W4385575430 isRetracted "false" @default.
- W4385575430 workType "article" @default.