Autoregressive models in environmental forecasting time series: a theoretical and application review

J Kaur, KS Parmar, S Singh - Environmental Science and Pollution …, 2023 - Springer
Though globalization, industrialization, and urbanization have escalated the economic
growth of nations, these activities have played foul on the environment. Better understanding …

Tackling climate change with machine learning

D Rolnick, PL Donti, LH Kaack, K Kochanski… - ACM Computing …, 2022 - dl.acm.org
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

Carbon price forecasting based on CEEMDAN and LSTM

F Zhou, Z Huang, C Zhang - Applied energy, 2022 - Elsevier
Abstract After signing the Paris Agreement and piloting carbon trading for many years, China
has taken a significant step toward carbon neutrality. Carbon price forecasting is helpful to …

A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China

H Shi, A Wei, X Xu, Y Zhu, H Hu, S Tang - Journal of environmental …, 2024 - Elsevier
Accurately predicting carbon trading prices using deep learning models can help
enterprises understand the operational mechanisms and regulations of the carbon market …

Environmental regulation and green innovation: Evidence from China's carbon emissions trading policy

M Liu, Y Li - Finance Research Letters, 2022 - Elsevier
This paper investigates the impact of environmental regulation on green innovation to
explore how to achieve a win-win situation of environmental protection and economic …

[HTML][HTML] On the accuracy of ARIMA based prediction of COVID-19 spread

H Alabdulrazzaq, MN Alenezi, Y Rawajfih… - Results in Physics, 2021 - Elsevier
COVID-19 was declared a global pandemic by the World Health Organization in March
2020, and has infected more than 4 million people worldwide with over 300,000 deaths by …

A hybrid model for carbon price forecasting using GARCH and long short-term memory network

Y Huang, X Dai, Q Wang, D Zhou - Applied Energy, 2021 - Elsevier
The reform of the EU ETS markets in 2017 has induced new carbon price forecasting
challenges. This study proposes a novel decomposition-ensemble paradigm VMD …

Forecasting carbon price trends based on an interpretable light gradient boosting machine and Bayesian optimization

S Deng, J Su, Y Zhu, Y Yu, C **ao - Expert Systems with Applications, 2024 - Elsevier
The future carbon price is crucial to relevant companies, investors, and carbon
policymakers, and the significance of carbon price prediction research is self-evident …

Computational intelligence and financial markets: A survey and future directions

RC Cavalcante, RC Brasileiro, VLF Souza… - Expert Systems with …, 2016 - Elsevier
Financial markets play an important role on the economical and social organization of
modern society. In these kinds of markets, information is an invaluable asset. However, with …

Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks

D Li, Y Li, C Wang, M Chen, Q Wu - Applied Energy, 2023 - Elsevier
Recently, global attention has been paid to climate change. On this account, the market-
based carbon pricing scheme is developed to limit greenhouse gas emissions, where a …