Quantitative Assessment of Regional Carbon Neutrality Policy Synergies Based on Deep Learning
This study presents a comprehensive quantitative assessment framework for evaluating regional carbon neutrality policy synergies using deep learning techniques. The research addresses the critical challenge of understanding complex interactions between multiple policy instruments in achieving carbo...
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| Format: | Article |
| Language: | English |
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Scientific Publication Center
2024-10-01
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| Series: | Journal of Advanced Computing Systems |
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| Online Access: | https://scipublication.com/index.php/JACS/article/view/116 |
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| author | Daiyang Zhang Enmiao Feng |
| author_facet | Daiyang Zhang Enmiao Feng |
| author_sort | Daiyang Zhang |
| collection | DOAJ |
| description |
This study presents a comprehensive quantitative assessment framework for evaluating regional carbon neutrality policy synergies using deep learning techniques. The research addresses the critical challenge of understanding complex interactions between multiple policy instruments in achieving carbon reduction goals. By working with neural network architectures and relevant tools, performance patterns, successes, and new management. The course indicates synergy index (psi) for providing the intervention that affects the impact of energy voluntarily than 1.17 for 1.71). Analysis of regional variations demonstrates that policy effectiveness is strongly influenced by local economic structures and energy systems, with manufacturing-dominated regions showing the highest responsiveness to carbon pricing mechanisms (PSI = 1.62). Temporal analysis indicates a typical 2-3 year lag before synergistic effects fully manifest. The deep learning model achieves robust prediction accuracy across diverse scenarios, with sensitivity analysis revealing technology learning rates as the most significant parameter influencing predictions (±24.3%). These findings provide approval to law enforcement officials of local structures, determination of local people and behavioural status of customary strategies.
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| format | Article |
| id | doaj-art-a73d93fcf5684c0381da4eb0d93db310 |
| institution | Kabale University |
| issn | 3066-3962 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Scientific Publication Center |
| record_format | Article |
| series | Journal of Advanced Computing Systems |
| spelling | doaj-art-a73d93fcf5684c0381da4eb0d93db3102025-08-26T06:07:53ZengScientific Publication CenterJournal of Advanced Computing Systems3066-39622024-10-0141010.69987/JACS.2024.41004Quantitative Assessment of Regional Carbon Neutrality Policy Synergies Based on Deep LearningDaiyang Zhang0Enmiao Feng1Communication, Culture & Technology, Georgetown University, DC, USAElectrical & Computer Engineering,Duke University,NC,USA This study presents a comprehensive quantitative assessment framework for evaluating regional carbon neutrality policy synergies using deep learning techniques. The research addresses the critical challenge of understanding complex interactions between multiple policy instruments in achieving carbon reduction goals. By working with neural network architectures and relevant tools, performance patterns, successes, and new management. The course indicates synergy index (psi) for providing the intervention that affects the impact of energy voluntarily than 1.17 for 1.71). Analysis of regional variations demonstrates that policy effectiveness is strongly influenced by local economic structures and energy systems, with manufacturing-dominated regions showing the highest responsiveness to carbon pricing mechanisms (PSI = 1.62). Temporal analysis indicates a typical 2-3 year lag before synergistic effects fully manifest. The deep learning model achieves robust prediction accuracy across diverse scenarios, with sensitivity analysis revealing technology learning rates as the most significant parameter influencing predictions (±24.3%). These findings provide approval to law enforcement officials of local structures, determination of local people and behavioural status of customary strategies. https://scipublication.com/index.php/JACS/article/view/116Carbon Neutrality PolicyDeep LearningPolicy Synergy AssessmentRegional Energy Systems |
| spellingShingle | Daiyang Zhang Enmiao Feng Quantitative Assessment of Regional Carbon Neutrality Policy Synergies Based on Deep Learning Journal of Advanced Computing Systems Carbon Neutrality Policy Deep Learning Policy Synergy Assessment Regional Energy Systems |
| title | Quantitative Assessment of Regional Carbon Neutrality Policy Synergies Based on Deep Learning |
| title_full | Quantitative Assessment of Regional Carbon Neutrality Policy Synergies Based on Deep Learning |
| title_fullStr | Quantitative Assessment of Regional Carbon Neutrality Policy Synergies Based on Deep Learning |
| title_full_unstemmed | Quantitative Assessment of Regional Carbon Neutrality Policy Synergies Based on Deep Learning |
| title_short | Quantitative Assessment of Regional Carbon Neutrality Policy Synergies Based on Deep Learning |
| title_sort | quantitative assessment of regional carbon neutrality policy synergies based on deep learning |
| topic | Carbon Neutrality Policy Deep Learning Policy Synergy Assessment Regional Energy Systems |
| url | https://scipublication.com/index.php/JACS/article/view/116 |
| work_keys_str_mv | AT daiyangzhang quantitativeassessmentofregionalcarbonneutralitypolicysynergiesbasedondeeplearning AT enmiaofeng quantitativeassessmentofregionalcarbonneutralitypolicysynergiesbasedondeeplearning |