Sustainable Process Study on Emergy and Carbon Emission Analysis of Building System Based on Neural Network Algorithm

Sustainable building systems can effectively reduce environmental pressures and mitigate the deterioration of the global climate. The sustainability of complex building systems is influenced by various factors. This article quantitatively analyzes building systems from an ecological emergy and carbo...
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Autor*in:	Ye Wang [verfasserIn] Hairuo Wang [verfasserIn] Junxue Zhang [verfasserIn] Meng Jia [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	sustainable process emergy and carbon emission neural network algorithm building system

Übergeordnetes Werk:	In: Processes - MDPI AG, 2013, 11(2023), 2829, p 2829
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:2829, p 2829

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/pr11102829

Katalog-ID:	DOAJ098338641

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spelling	10.3390/pr11102829 doi (DE-627)DOAJ098338641 (DE-599)DOAJcf9753f7cf4943edb85c0ffbea4a2ca0 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Ye Wang verfasserin aut Sustainable Process Study on Emergy and Carbon Emission Analysis of Building System Based on Neural Network Algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sustainable building systems can effectively reduce environmental pressures and mitigate the deterioration of the global climate. The sustainability of complex building systems is influenced by various factors. This article quantitatively analyzes building systems from an ecological emergy and carbon emissions perspective, and considers typical feedback structures’ impact. A neural network algorithm is employed for sustainability prediction analysis. The results demonstrate that both from an emergy and carbon emissions perspective, the operational phase of the building and the production phase of building materials are the main contributors (accounting for over 90%). Among the three types of feedback subsystems, the cross-feedback structure has a more significant impact and yields the best corrective effect. For example, the correction proportion of the building’s emergy sustainability parameter reaches 11.3%, while it is 15.8% for carbon emissions. The neural network model predicts a decreasing trend in the energy sustainability of buildings and increasing carbon emissions over time. To improve the sustainability of building systems, measures such as ecological landscape design and carbon sequestration in building materials are considered, which can enhance the sustainability of buildings to a certain extent. sustainable process emergy and carbon emission neural network algorithm building system Chemical technology Chemistry Hairuo Wang verfasserin aut Junxue Zhang verfasserin aut Meng Jia verfasserin aut In Processes MDPI AG, 2013 11(2023), 2829, p 2829 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:11 year:2023 number:2829, p 2829 https://doi.org/10.3390/pr11102829 kostenfrei https://doaj.org/article/cf9753f7cf4943edb85c0ffbea4a2ca0 kostenfrei https://www.mdpi.com/2227-9717/11/10/2829 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 2829, p 2829
allfields_unstemmed	10.3390/pr11102829 doi (DE-627)DOAJ098338641 (DE-599)DOAJcf9753f7cf4943edb85c0ffbea4a2ca0 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Ye Wang verfasserin aut Sustainable Process Study on Emergy and Carbon Emission Analysis of Building System Based on Neural Network Algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sustainable building systems can effectively reduce environmental pressures and mitigate the deterioration of the global climate. The sustainability of complex building systems is influenced by various factors. This article quantitatively analyzes building systems from an ecological emergy and carbon emissions perspective, and considers typical feedback structures’ impact. A neural network algorithm is employed for sustainability prediction analysis. The results demonstrate that both from an emergy and carbon emissions perspective, the operational phase of the building and the production phase of building materials are the main contributors (accounting for over 90%). Among the three types of feedback subsystems, the cross-feedback structure has a more significant impact and yields the best corrective effect. For example, the correction proportion of the building’s emergy sustainability parameter reaches 11.3%, while it is 15.8% for carbon emissions. The neural network model predicts a decreasing trend in the energy sustainability of buildings and increasing carbon emissions over time. To improve the sustainability of building systems, measures such as ecological landscape design and carbon sequestration in building materials are considered, which can enhance the sustainability of buildings to a certain extent. sustainable process emergy and carbon emission neural network algorithm building system Chemical technology Chemistry Hairuo Wang verfasserin aut Junxue Zhang verfasserin aut Meng Jia verfasserin aut In Processes MDPI AG, 2013 11(2023), 2829, p 2829 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:11 year:2023 number:2829, p 2829 https://doi.org/10.3390/pr11102829 kostenfrei https://doaj.org/article/cf9753f7cf4943edb85c0ffbea4a2ca0 kostenfrei https://www.mdpi.com/2227-9717/11/10/2829 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 2829, p 2829
allfieldsGer	10.3390/pr11102829 doi (DE-627)DOAJ098338641 (DE-599)DOAJcf9753f7cf4943edb85c0ffbea4a2ca0 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Ye Wang verfasserin aut Sustainable Process Study on Emergy and Carbon Emission Analysis of Building System Based on Neural Network Algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sustainable building systems can effectively reduce environmental pressures and mitigate the deterioration of the global climate. The sustainability of complex building systems is influenced by various factors. This article quantitatively analyzes building systems from an ecological emergy and carbon emissions perspective, and considers typical feedback structures’ impact. A neural network algorithm is employed for sustainability prediction analysis. The results demonstrate that both from an emergy and carbon emissions perspective, the operational phase of the building and the production phase of building materials are the main contributors (accounting for over 90%). Among the three types of feedback subsystems, the cross-feedback structure has a more significant impact and yields the best corrective effect. For example, the correction proportion of the building’s emergy sustainability parameter reaches 11.3%, while it is 15.8% for carbon emissions. The neural network model predicts a decreasing trend in the energy sustainability of buildings and increasing carbon emissions over time. To improve the sustainability of building systems, measures such as ecological landscape design and carbon sequestration in building materials are considered, which can enhance the sustainability of buildings to a certain extent. sustainable process emergy and carbon emission neural network algorithm building system Chemical technology Chemistry Hairuo Wang verfasserin aut Junxue Zhang verfasserin aut Meng Jia verfasserin aut In Processes MDPI AG, 2013 11(2023), 2829, p 2829 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:11 year:2023 number:2829, p 2829 https://doi.org/10.3390/pr11102829 kostenfrei https://doaj.org/article/cf9753f7cf4943edb85c0ffbea4a2ca0 kostenfrei https://www.mdpi.com/2227-9717/11/10/2829 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 2829, p 2829
allfieldsSound	10.3390/pr11102829 doi (DE-627)DOAJ098338641 (DE-599)DOAJcf9753f7cf4943edb85c0ffbea4a2ca0 DE-627 ger DE-627 rakwb eng TP1-1185 QD1-999 Ye Wang verfasserin aut Sustainable Process Study on Emergy and Carbon Emission Analysis of Building System Based on Neural Network Algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sustainable building systems can effectively reduce environmental pressures and mitigate the deterioration of the global climate. The sustainability of complex building systems is influenced by various factors. This article quantitatively analyzes building systems from an ecological emergy and carbon emissions perspective, and considers typical feedback structures’ impact. A neural network algorithm is employed for sustainability prediction analysis. The results demonstrate that both from an emergy and carbon emissions perspective, the operational phase of the building and the production phase of building materials are the main contributors (accounting for over 90%). Among the three types of feedback subsystems, the cross-feedback structure has a more significant impact and yields the best corrective effect. For example, the correction proportion of the building’s emergy sustainability parameter reaches 11.3%, while it is 15.8% for carbon emissions. The neural network model predicts a decreasing trend in the energy sustainability of buildings and increasing carbon emissions over time. To improve the sustainability of building systems, measures such as ecological landscape design and carbon sequestration in building materials are considered, which can enhance the sustainability of buildings to a certain extent. sustainable process emergy and carbon emission neural network algorithm building system Chemical technology Chemistry Hairuo Wang verfasserin aut Junxue Zhang verfasserin aut Meng Jia verfasserin aut In Processes MDPI AG, 2013 11(2023), 2829, p 2829 (DE-627)750371439 (DE-600)2720994-5 22279717 nnns volume:11 year:2023 number:2829, p 2829 https://doi.org/10.3390/pr11102829 kostenfrei https://doaj.org/article/cf9753f7cf4943edb85c0ffbea4a2ca0 kostenfrei https://www.mdpi.com/2227-9717/11/10/2829 kostenfrei https://doaj.org/toc/2227-9717 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 2829, p 2829
language	English
source	In Processes 11(2023), 2829, p 2829 volume:11 year:2023 number:2829, p 2829
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topic_facet	sustainable process emergy and carbon emission neural network algorithm building system Chemical technology Chemistry
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container_title	Processes
authorswithroles_txt_mv	Ye Wang @@aut@@ Hairuo Wang @@aut@@ Junxue Zhang @@aut@@ Meng Jia @@aut@@
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callnumber-first	T - Technology
author	Ye Wang
spellingShingle	Ye Wang misc TP1-1185 misc QD1-999 misc sustainable process misc emergy and carbon emission misc neural network algorithm misc building system misc Chemical technology misc Chemistry Sustainable Process Study on Emergy and Carbon Emission Analysis of Building System Based on Neural Network Algorithm
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topic_title	TP1-1185 QD1-999 Sustainable Process Study on Emergy and Carbon Emission Analysis of Building System Based on Neural Network Algorithm sustainable process emergy and carbon emission neural network algorithm building system
topic	misc TP1-1185 misc QD1-999 misc sustainable process misc emergy and carbon emission misc neural network algorithm misc building system misc Chemical technology misc Chemistry
topic_unstemmed	misc TP1-1185 misc QD1-999 misc sustainable process misc emergy and carbon emission misc neural network algorithm misc building system misc Chemical technology misc Chemistry
topic_browse	misc TP1-1185 misc QD1-999 misc sustainable process misc emergy and carbon emission misc neural network algorithm misc building system misc Chemical technology misc Chemistry
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title	Sustainable Process Study on Emergy and Carbon Emission Analysis of Building System Based on Neural Network Algorithm
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title_full	Sustainable Process Study on Emergy and Carbon Emission Analysis of Building System Based on Neural Network Algorithm
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title_sort	sustainable process study on emergy and carbon emission analysis of building system based on neural network algorithm
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title_auth	Sustainable Process Study on Emergy and Carbon Emission Analysis of Building System Based on Neural Network Algorithm
abstract	Sustainable building systems can effectively reduce environmental pressures and mitigate the deterioration of the global climate. The sustainability of complex building systems is influenced by various factors. This article quantitatively analyzes building systems from an ecological emergy and carbon emissions perspective, and considers typical feedback structures’ impact. A neural network algorithm is employed for sustainability prediction analysis. The results demonstrate that both from an emergy and carbon emissions perspective, the operational phase of the building and the production phase of building materials are the main contributors (accounting for over 90%). Among the three types of feedback subsystems, the cross-feedback structure has a more significant impact and yields the best corrective effect. For example, the correction proportion of the building’s emergy sustainability parameter reaches 11.3%, while it is 15.8% for carbon emissions. The neural network model predicts a decreasing trend in the energy sustainability of buildings and increasing carbon emissions over time. To improve the sustainability of building systems, measures such as ecological landscape design and carbon sequestration in building materials are considered, which can enhance the sustainability of buildings to a certain extent.
abstractGer	Sustainable building systems can effectively reduce environmental pressures and mitigate the deterioration of the global climate. The sustainability of complex building systems is influenced by various factors. This article quantitatively analyzes building systems from an ecological emergy and carbon emissions perspective, and considers typical feedback structures’ impact. A neural network algorithm is employed for sustainability prediction analysis. The results demonstrate that both from an emergy and carbon emissions perspective, the operational phase of the building and the production phase of building materials are the main contributors (accounting for over 90%). Among the three types of feedback subsystems, the cross-feedback structure has a more significant impact and yields the best corrective effect. For example, the correction proportion of the building’s emergy sustainability parameter reaches 11.3%, while it is 15.8% for carbon emissions. The neural network model predicts a decreasing trend in the energy sustainability of buildings and increasing carbon emissions over time. To improve the sustainability of building systems, measures such as ecological landscape design and carbon sequestration in building materials are considered, which can enhance the sustainability of buildings to a certain extent.
abstract_unstemmed	Sustainable building systems can effectively reduce environmental pressures and mitigate the deterioration of the global climate. The sustainability of complex building systems is influenced by various factors. This article quantitatively analyzes building systems from an ecological emergy and carbon emissions perspective, and considers typical feedback structures’ impact. A neural network algorithm is employed for sustainability prediction analysis. The results demonstrate that both from an emergy and carbon emissions perspective, the operational phase of the building and the production phase of building materials are the main contributors (accounting for over 90%). Among the three types of feedback subsystems, the cross-feedback structure has a more significant impact and yields the best corrective effect. For example, the correction proportion of the building’s emergy sustainability parameter reaches 11.3%, while it is 15.8% for carbon emissions. The neural network model predicts a decreasing trend in the energy sustainability of buildings and increasing carbon emissions over time. To improve the sustainability of building systems, measures such as ecological landscape design and carbon sequestration in building materials are considered, which can enhance the sustainability of buildings to a certain extent.
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title_short	Sustainable Process Study on Emergy and Carbon Emission Analysis of Building System Based on Neural Network Algorithm
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