The effectiveness and heterogeneity of carbon emissions trading scheme in China DOI
Kai Tang, Ye Zhou, Xiaoyu Liang

et al.

Environmental Science and Pollution Research, Journal Year: 2021, Volume and Issue: 28(14), P. 17306 - 17318

Published: Jan. 4, 2021

Language: Английский

Forecasting Chinese provincial CO2 emissions: A universal and robust new-information-based grey model DOI
Song Ding,

Huahan Zhang

Energy Economics, Journal Year: 2023, Volume and Issue: 121, P. 106685 - 106685

Published: April 25, 2023

Language: Английский

Citations

50
Forecasting carbon price in China using a novel hybrid model based on secondary decomposition, multi-complexity and error correction DOI
Hong Yang, Xiaodie Yang, Guohui Li

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 401, P. 136701 - 136701

Published: March 9, 2023

Language: Английский

Citations

46
Analysis of CO2 pipeline regulations from a safety perspective for offshore carbon capture, utilization, and storage (CCUS) DOI

Ahmed Hamdy El‐Kady,

Md. Tanjin Amin, Faisal Khan

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 439, P. 140734 - 140734

Published: Jan. 21, 2024

Language: Английский

Citations

34
Thermal coal futures trading volume predictions through the neural network DOI
Bingzi Jin, Xiaojie Xu,

Yun Zhang

et al.

Journal of Modelling in Management, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Purpose Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose this study is concentrate on the energy sector explore volume prediction issue for thermal coal traded in Zhengzhou Commodity Exchange China with daily data spanning January 2016–December 2020. Design/methodology/approach nonlinear autoregressive neural network adopted performance examined based upon variety settings over algorithms model estimations, numbers hidden neurons delays ratios splitting series into training, validation testing phases. Findings A relatively simple setting arrived at that leads predictions good accuracy stabilities maintains small errors up 99.273 th quantile observed volume. Originality/value results could, one hand, serve as standalone technical predictions. They other be combined different (fundamental) forming perspectives trends carrying out policy analysis.

Language: Английский

Citations

33
Forecasting carbon price with attention mechanism and bidirectional long short-term memory network DOI
Chaoyong Qin,

Dongling Qin,

Qiuxian Jiang

et al.

Energy, Journal Year: 2024, Volume and Issue: 299, P. 131410 - 131410

Published: April 23, 2024

Language: Английский

Citations

29
The impact of green innovation on carbon reduction efficiency in China: Evidence from machine learning validation DOI

Qiuyun Zhao,

Mei Jiang, Zuoxiang Zhao

et al.

Energy Economics, Journal Year: 2024, Volume and Issue: 133, P. 107525 - 107525

Published: April 3, 2024

Language: Английский

Citations

20
Utilizing Artificial Intelligence for Stakeholder Engagement and Social Innovation in Addressing Climate Change DOI Open Access
Surajit Bag, Susmi Routray, Santosh Kumar Shrivastav

et al.

Journal of Global Information Management, Journal Year: 2025, Volume and Issue: 32(1), P. 1 - 31

Published: Jan. 4, 2025

This study employs Systematic Literature Review (SLR) and thematic analysis to explore the topics of Artificial Intelligence (AI), Stakeholder Engagement (SE) social innovation. To enhance methodological rigor, integrated literature media recognize within texts using Latent Dirichlet Allocation (LDA), an unsupervised machine learning method. The highlights AI's influence on engagement, aligning with diffusion theory, stressing need emphasize benefits for faster adoption.

Language: Английский

Citations

2
Volatility in Carbon Futures Amid Uncertainties: Considering Geopolitical and Economic Policy Factors DOI
Xiaoqing Wang,

Wenxin Jin,

Baochang Xu

et al.

Journal of Futures Markets, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 20, 2025

ABSTRACT This study uses a quantile autoregressive distributed lag model to quantitatively evaluate the effects of economic policy uncertainty (EPU) and geopolitical risk (GPR) on volatility in carbon futures (carbon trading price [CTP]), considering both time asymmetries. The findings show that long‐term GPR CTP are more significant than short‐term effects, contrary EPU. Both EPU have predominantly positive CTP, while negatively affects factors mixed influences short term. location asymmetry reveals impacts most pronounced at higher quantiles, whereas exhibit subtle variations across different quantiles. intensify during structural shifts owing heightened events. Moreover, is proven as dominant contributor influencing fluctuation long terms. provide targeted recommendations for policymakers stabilize contribute towards achieving sustainable development.

Language: Английский

Citations

2
Carbon dioxide transport via pipelines: A systematic review DOI
Hongfang Lü, Xin Ma, Kun Huang

et al.

Journal of Cleaner Production, Journal Year: 2020, Volume and Issue: 266, P. 121994 - 121994

Published: May 4, 2020

Language: Английский

Citations

130
Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower DOI
Hongfang Lü, Fei-Fei Cheng, Xin Ma

et al.

Energy, Journal Year: 2020, Volume and Issue: 203, P. 117756 - 117756

Published: May 5, 2020

Language: Английский

Citations

109