An innovative data-feature-driven approach for CO2 emission predictive analytics: A perspective from seasonality and nonlinearity characteristics-Reference-Cited by-同舟云学术

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An innovative data-feature-driven approach for CO2 emission predictive analytics: A perspective from seasonality and nonlinearity characteristics

Published:2024-06 Issue: Volume:192 Page:110195
ISSN:0360-8352
Container-title:Computers & Industrial Engineering
language:en
Short-container-title:Computers & Industrial Engineering

Author:

Ding Song,Shen Xingao^ORCID,Zhang Huahan,Cai Zhijian,Wang Yi

Publisher

Elsevier BV

Reference53 articles.

1. Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms;Ağbulut;Sustainable Production and Consumption.,2022

2. Energy saving and CO2 mitigation through restructuring Jordan's transportation sector: The diesel passenger cars scenario;Al-Hinti;Energy Policy.,2007

3. An interpretable forecasting framework for energy consumption and CO2 emissions;Aras;Applied Energy.,2022

4. Does monetary policy impact CO2 emissions? A GVAR analysis;Attílio;Energy Economics,2023

5. Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms;Bakay;Journal of Cleaner Production.,2021

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