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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

An evolutionary cost‐sensitive support vector machine for carbon price trend forecasting
Bangzhu Zhu, Jingyi Zhang, Chunzhuo Wan, et al.
Journal of Forecasting (2022) Vol. 42, Iss. 4, pp. 741-755
Closed Access | Times Cited: 17

Showing 17 citing articles:

Impact of carbon emission trading and renewable energy development policy on the sustainability of electricity market: A stackelberg game analysis
Xiaochen Ma, Yanchun Pan, Manzi Zhang, et al.
Energy Economics (2023) Vol. 129, pp. 107199-107199
Closed Access | Times Cited: 37
Forecasting carbon price: A novel multi-factor spatial-temporal GNN framework integrating graph WaveNet and self-attention mechanism
Jin Cao, Xie Chi, Yang Zhou, et al.
Energy Economics (2025), pp. 108318-108318
Closed Access | Times Cited: 1
SimVGNets: Similarity-Based Visibility Graph Networks for Carbon Price Forecasting
Shengzhong Mao, Xiao‐Jun Zeng
Expert Systems with Applications (2023) Vol. 230, pp. 120647-120647
Open Access | Times Cited: 16
Predictive Recurrent Neural Networks Based Carbon Price Forecasting: A Generative Perspective
Zhong Zheng, Yan Zhang
Computational Economics (2025)
Closed Access
A study on the differentiation of carbon prices in China: insights from eight carbon emissions trading pilots
Tianshu Zhang, Menghua Deng
Journal of Cleaner Production (2025), pp. 145279-145279
Closed Access
Forecasting Carbon Prices: What Is the Role of Technology?
Ali Ben Mrad, Amine Lahiani, Salma Mefteh‐Wali, et al.
Journal of Forecasting (2025)
Closed Access
Interpretable short-term carbon dioxide emissions forecasting based on flexible two-stage decomposition and temporal fusion transformers
Binrong Wu, Huanze Zeng, Zhongrui Wang, et al.
Applied Soft Computing (2024) Vol. 159, pp. 111639-111639
Closed Access | Times Cited: 4
Using explainable deep learning to improve decision quality: Evidence from carbon trading market
Yang Zhao, Jianzhou Wang, Shuai Wang, et al.
Omega (2025), pp. 103281-103281
Closed Access
Interval time series forecasting: A systematic literature review
Piao Wang, Shahid Hussain Gurmani, Zhifu Tao, et al.
Journal of Forecasting (2023) Vol. 43, Iss. 2, pp. 249-285
Closed Access | Times Cited: 10
Cost prediction for water reuse equipment using interpretable machine learning models
Kan Chen, Yuezheng Zhang, Naixin Hu, et al.
Journal of Water Process Engineering (2024) Vol. 63, pp. 105474-105474
Closed Access | Times Cited: 2
An optimized and interpretable carbon price prediction: Explainable deep learning model
Gehad Ismail Sayed, Eman I. Abd El-Latif, Ashraf Darwish, et al.
Chaos Solitons & Fractals (2024) Vol. 188, pp. 115533-115533
Closed Access | Times Cited: 2
Interval Forecasting of Carbon Price With a Novel Hybrid Multiscale Decomposition and Bootstrap Approach
Bangzhu Zhu, Chunzhuo Wan, Ping Wang, et al.
Journal of Forecasting (2024)
Closed Access | Times Cited: 2
Elevating Univariate Time Series Forecasting: Innovative SVR-Empowered Nonlinear Autoregressive Neural Networks
Juan D. Borrero, Jesús Mariscal
Algorithms (2023) Vol. 16, Iss. 9, pp. 423-423
Open Access | Times Cited: 6
Extreme weather raises the prices of regional emission allowances in China
Tian-Hong Zhu, Chao Feng, Li-Yang Guo, et al.
Environmental Science and Pollution Research (2023) Vol. 30, Iss. 34, pp. 82189-82198
Closed Access | Times Cited: 5
An enhanced interval-valued PM2.5 concentration forecasting model with attention-based feature extraction and self-adaptive combination technology
Jiaming Zhu, Zheng Peng, Niu Li-li, et al.
Expert Systems with Applications (2024) Vol. 264, pp. 125867-125867
Closed Access | Times Cited: 1
Volatility forecasting incorporating intraday positive and negative jumps based on deep learning model
Yilun Zhang, Yuping Song, Ying Peng, et al.
Journal of Forecasting (2024) Vol. 43, Iss. 7, pp. 2749-2765
Closed Access
Carbon Price Forecasting for China's Eight Major Markets Based on GRU-Attention Model
M L Wang, Qingchun Hu, Wei Zhu, et al.
(2024), pp. 1-6
Closed Access
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