Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT Model
INTRODUCTION: It is of great research significance to explore whether China can achieve the "two-carbon target" on time. The MLP model combines nonlinear modeling principles with other techniques, possessing powerful adaptive learning capabilities, and providing a viable solution for carb...
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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2024-04-01
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| Series: | EAI Endorsed Transactions on Energy Web |
| Subjects: | |
| Online Access: | https://publications.eai.eu/index.php/ew/article/view/5808 |
| _version_ | 1797204278328164352 |
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| author | Ning Zhao Chengyu Li |
| author_facet | Ning Zhao Chengyu Li |
| author_sort | Ning Zhao |
| collection | DOAJ |
| description |
INTRODUCTION: It is of great research significance to explore whether China can achieve the "two-carbon target" on time. The MLP model combines nonlinear modeling principles with other techniques, possessing powerful adaptive learning capabilities, and providing a viable solution for carbon emission prediction.
OBJECTIVES: This study models and forecasts carbon emissions in Jiangsu Province, one of China's largest industrial provinces, aiming to forecast whether Jiangsu province will achieve the two-carbon target on time plan and provide feasible pathways and theoretical foundations for achieving dual carbon goals.
METHODS: Based on the analysis of the contributions of relevant indicators using the Grey Relational Analysis method, a comprehensive approach integrating the STIRPAT model, Logistic model, and ARIMA model is adopted. Ultimately, an MLP prediction model for carbon emission variations is established. Using this model, simulations are conducted to analyze the carbon emission levels in Jiangsu Province under different scenarios from 2021 to 2060.
RESULTS: The time to reach carbon peak and the likelihood of achieving carbon neutrality vary under three scenarios. Under the natural scenario of no human intervention, achieving carbon neutrality is not feasible. While under human-made intervention scenarios including baseline and intervention scenarios, Jiangsu Province is projected to achieve the carbon neutrality target as scheduled, attaining the peak carbon goal, however, proves challenging to realize by the year 2030.
CONCLUSION: The MLP model exhibits high accuracy in predicting carbon emissions. To expedite the realization of dual carbon goals, proactive government intervention is necessary.
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| first_indexed | 2024-04-24T08:32:41Z |
| format | Article |
| id | doaj.art-c01a5cbc883c4828b174c7ffefa4854a |
| institution | Directory Open Access Journal |
| issn | 2032-944X |
| language | English |
| last_indexed | 2024-04-24T08:32:41Z |
| publishDate | 2024-04-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Energy Web |
| spelling | doaj.art-c01a5cbc883c4828b174c7ffefa4854a2024-04-16T19:02:13ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Energy Web2032-944X2024-04-011110.4108/ew.5808Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT ModelNing Zhao0Chengyu Li1Liaoning Technical University Liaoning Technical University INTRODUCTION: It is of great research significance to explore whether China can achieve the "two-carbon target" on time. The MLP model combines nonlinear modeling principles with other techniques, possessing powerful adaptive learning capabilities, and providing a viable solution for carbon emission prediction. OBJECTIVES: This study models and forecasts carbon emissions in Jiangsu Province, one of China's largest industrial provinces, aiming to forecast whether Jiangsu province will achieve the two-carbon target on time plan and provide feasible pathways and theoretical foundations for achieving dual carbon goals. METHODS: Based on the analysis of the contributions of relevant indicators using the Grey Relational Analysis method, a comprehensive approach integrating the STIRPAT model, Logistic model, and ARIMA model is adopted. Ultimately, an MLP prediction model for carbon emission variations is established. Using this model, simulations are conducted to analyze the carbon emission levels in Jiangsu Province under different scenarios from 2021 to 2060. RESULTS: The time to reach carbon peak and the likelihood of achieving carbon neutrality vary under three scenarios. Under the natural scenario of no human intervention, achieving carbon neutrality is not feasible. While under human-made intervention scenarios including baseline and intervention scenarios, Jiangsu Province is projected to achieve the carbon neutrality target as scheduled, attaining the peak carbon goal, however, proves challenging to realize by the year 2030. CONCLUSION: The MLP model exhibits high accuracy in predicting carbon emissions. To expedite the realization of dual carbon goals, proactive government intervention is necessary. https://publications.eai.eu/index.php/ew/article/view/5808Carbon emissionsForecastPath planningMultilayer Perception ModelMLPScenario analysis method |
| spellingShingle | Ning Zhao Chengyu Li Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT Model EAI Endorsed Transactions on Energy Web Carbon emissions Forecast Path planning Multilayer Perception Model MLP Scenario analysis method |
| title | Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT Model |
| title_full | Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT Model |
| title_fullStr | Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT Model |
| title_full_unstemmed | Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT Model |
| title_short | Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT Model |
| title_sort | carbon emission forecast based on multilayer perceptron network and stirpat model |
| topic | Carbon emissions Forecast Path planning Multilayer Perception Model MLP Scenario analysis method |
| url | https://publications.eai.eu/index.php/ew/article/view/5808 |
| work_keys_str_mv | AT ningzhao carbonemissionforecastbasedonmultilayerperceptronnetworkandstirpatmodel AT chengyuli carbonemissionforecastbasedonmultilayerperceptronnetworkandstirpatmodel |