The research on a novel multivariate grey model and its application in carbon dioxide emissions prediction-Reference-Cited by-同舟云学术

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The research on a novel multivariate grey model and its application in carbon dioxide emissions prediction

Published:2024-02-24 Issue:14 Volume:31 Page:21986-22011
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Xu Yan,Lin Tong,Du Pei,Wang Jianzhou

Funder

Humanities and Social Sciences of Ministry of Education Planning Fund

Soft science project of Shaanxi Province

Humanities and Social Sciences Foundation of Ministry of Education of China

Fundamental Research Funds for the Central Universities

National Key Research and Development Program of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11356-024-32262-9.pdf

Reference67 articles.

1. Ağbulut Ü (2022) Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms. Sustain Prod Consum 29:141–157. https://doi.org/10.1016/j.spc.2021.10.001

2. Aslam B, Hu J, Shahab S, Ahmad A, Saleem M, Shah SSA, ... Hassan M (2021) The nexus of industrialization, GDP per capita and CO2 emission in China. Environ Technol Innov 23:101674. https://doi.org/10.1016/j.eti.2021.101674.

3. Cai P, Zhang C, Chai J (2023) Forecasting hourly PM2. 5 concentrations based on decomposition-ensemble-reconstruction framework incorporating deep learning algorithms. Data Sci Manag 6(1):46–54. https://doi.org/10.1016/j.dsm.2023.02.002

4. Chen HB, Pei LL, Zhao YF (2021) Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach. Energy 222:119952. https://doi.org/10.1016/j.energy.2021.119952

5. Chen J, Chen Y, Mao B, Wang X, Peng L (2022a) Key mitigation regions and strategies for CO2 emission reduction in China based on STIRPAT and ARIMA models. Environ Sci Pollut Res 29(34):51537–51553. https://doi.org/10.1007/s11356-022-19126-w

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