ForestAdvisor: A multi-modal forest decision-making system based on carbon emissions DOI
Tong Ji, Yifeng Lin, Yuer Yang

et al.

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 181, P. 106190 - 106190

Published: Aug. 24, 2024

Language: Английский

Assessment of operational carbon emissions for residential buildings comparing different machine learning approaches: A study of 34 cities in China DOI

Rongming Huang,

Xiaocun Zhang, Kaihua Liu

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: 250, P. 111176 - 111176

Published: Jan. 9, 2024

Language: Английский

Citations

31
Industrial carbon emission forecasting considering external factors based on linear and machine learning models DOI
Ye Liang, Pei Du, Shubin Wang

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 434, P. 140010 - 140010

Published: Dec. 2, 2023

Language: Английский

Citations

32
Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review DOI Creative Commons
Manuel Jaramillo, Wilson Pavón, L.F. Jaramillo

et al.

Data, Journal Year: 2024, Volume and Issue: 9(1), P. 13 - 13

Published: Jan. 11, 2024

This paper addresses the challenges in forecasting electrical energy current era of renewable integration. It reviews advanced adaptive methodologies while also analyzing evolution research this field through bibliometric analysis. The review highlights key contributions and limitations models with an emphasis on traditional methods. analysis reveals that Long Short-Term Memory (LSTM) networks, optimization techniques, deep learning have potential to model dynamic nature consumption, but they higher computational demands data requirements. aims offer a balanced view advancements methods, guiding researchers, policymakers, industry experts. advocates for collaborative innovation enhance accuracy support development resilient, sustainable systems.

Language: Английский

Citations

14
Analysis of spatial and temporal carbon emission efficiency in Yangtze River Delta city cluster — Based on nighttime lighting data and machine learning DOI
Qingqing Sun, Hong Chen, Yujie Wang

et al.

Environmental Impact Assessment Review, Journal Year: 2023, Volume and Issue: 103, P. 107232 - 107232

Published: Aug. 10, 2023

Language: Английский

Citations

21
Predicting carbon futures prices based on a new hybrid machine learning: Comparative study of carbon prices in different periods DOI
Xi Zhang, Kailing Yang, Qin Lu

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 346, P. 118962 - 118962

Published: Sept. 13, 2023

Language: Английский

Citations

20
Coupling LSTM and CNN Neural Networks for Accurate Carbon Emission Prediction in 30 Chinese Provinces DOI Open Access
Zhonghua Han,

Bingwei Cui,

Liwen Xu

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(18), P. 13934 - 13934

Published: Sept. 20, 2023

Global warming is a major environmental issue facing humanity, and the resulting climate change has severely affected environment daily lives of people. China attaches great importance to actively responds issues. In order achieve “dual carbon” goal, it necessary clearly define emission reduction path scientifically predict future carbon emissions, which basis for setting targets. To ensure accuracy data, this study applies coefficient method calculate emissions from energy consumption in 30 provinces, regions, cities 1997 2021. Considering spatial correlation between different regions China, we propose new machine learning prediction model that incorporates weighting, namely, an LSTM-CNN combination with weighting. The weighting explains combined used analyze 2022 2035 under scenarios. results show four convolutional layers performs best. Compared other models, best predictive performance, MAE 8.0169, RMSE 11.1505, R2 0.9661 on test set. Based scenario predictions, found most can peaking before 2030. Some need adjust their development rates based specific circumstances as early possible. This provides research direction deep time series forecasting proposes forecasting.

Language: Английский

Citations

19
A data-driven rule-base approach for carbon emission trend forecast with environmental regulation and efficiency improvement DOI
Long-Hao Yang, Fei-Fei Ye, Haibo Hu

et al.

Sustainable Production and Consumption, Journal Year: 2024, Volume and Issue: 45, P. 316 - 332

Published: Jan. 4, 2024

Language: Английский

Citations

9
An innovative data-feature-driven approach for CO2 emission predictive analytics: A perspective from seasonality and nonlinearity characteristics DOI
Song Ding, Xingao Shen,

Huahan Zhang

et al.

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 192, P. 110195 - 110195

Published: May 6, 2024

Language: Английский

Citations

9
How can the Pearl River Delta urban agglomeration achieve the carbon peak target: Based on the perspective of an optimal stable economic growth path DOI

Yanchun Rao,

Xiuli Wang, Hengkai Li

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 439, P. 140879 - 140879

Published: Jan. 22, 2024

Language: Английский

Citations

8
Analysis of carbon emission drivers and peak carbon forecasts for island economies DOI
Geng Wang,

Yan Feng

Ecological Modelling, Journal Year: 2024, Volume and Issue: 489, P. 110611 - 110611

Published: Jan. 4, 2024

Language: Английский

Citations

5