A Multi-scale Indicators Carbon Emission Prediction Method Based on Decision Forests
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5666-7_17
Reference14 articles.
1. Yang, J., et al.: Driving forces of China’s CO2 emissions from energy consumption based on Kaya-LMDI methods. Sci. Total Environ. 711, 134569 (2020)
2. Maheen, R., et al.: Quantitative analysis of carbon dioxide emission reduction pathways: towards carbon neutrality in China’s power sector. Carbon Capture Sci. Technol. 7, 100112 (2023)
3. Ahn, D.Y., et al.: CO2 Emissions from C40 cities: citywide emission inventories and comparisons with global gridded emission datasets. Environ. Res. Lett. 18(3) (2023)
4. Li, Y., Sun, Y.W.: Modeling and predicting city-level CO2 emissions using open access data and machine learning. Environ. Sci. Pollut. Res. 28(15), 19260–19271 (2021)
5. Chen, J.D., et al.: A carbon emissions reduction index: integrating the volume and allocation of regional emissions. Appl. Energy 184, 1154–1164 (2016)
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