Analysis of Land Use Gravity Center Change and Carbon Emission Impact in Chengdu Plain of China from 2006 to 2022 DOI Creative Commons

Yingga Wu,

Wanping Pu,

Jihong Dong

et al.

Land, Journal Year: 2024, Volume and Issue: 13(6), P. 873 - 873

Published: June 17, 2024

As the economic center and major grain-producing area in Southwest China, calculation of carbon budget protection cultivated land Chengdu Plain are vital significance for China to achieve a peak strategy ensure food security. For purpose clarifying trend use focus emissions Plain, level 33 counties was explored. Based on gravity model IPCC emission coefficient method, changing from 2006 2022 clarified. PLS regression LMDI were used explore main influencing factors cropland building land. PLUS simulate future patterns emissions. (1) The cropland, land, water, other unused shifted northeast by 4.23 km, 5.46 8.44 31.58 respectively, that forest grass southeast 11.12 km 3.41 respectively. crops, centers rice maize moved northeastward 15.47 7.52 while wheat southwestward 17.77 km. (2) From 2022, all rise, with total increase 13.552 million tons, sinks 31 continue decline, decrease 0.691 tons. (3) Under natural scenario, sink reduction 0.5391 3.4728 4.5265 tons Among 11 did not under 5 achieved scenario. During study period, there serious loss mainly central part forests within Longmen Mountain, Longquan Leshan City, is need strengthen this region future. can peak, will be more helpful peak.

Language: Английский

Machine learning for predicting urban greenhouse gas emissions: A systematic literature review DOI Creative Commons
Yukai Jin, Ayyoob Sharifi

Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 215, P. 115625 - 115625

Published: March 16, 2025

Language: Английский

Citations

2
Multi-step carbon emissions forecasting using an interpretable framework of new data preprocessing techniques and improved grey multivariable convolution model DOI
Song Ding, Juntao Ye, Zhijian Cai

et al.

Technological Forecasting and Social Change, Journal Year: 2024, Volume and Issue: 208, P. 123720 - 123720

Published: Sept. 4, 2024

Language: Английский

Citations

9
Carbon Emission Accounting Method for Coal-fired Power Units of Different Coal Types under Peak Shaving Conditions DOI
Haoyu Chen, Xi Chen,

Guanwen Zhou

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135314 - 135314

Published: Feb. 1, 2025

Language: Английский

Citations

1
Pathways to Carbon Neutrality: A Review of Life Cycle Assessment-Based Waste Tire Recycling Technologies and Future Trends DOI Open Access

Qingzi Zhao,

Ye Wu,

Junqing Xu

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(3), P. 741 - 741

Published: March 4, 2025

Waste tires (WTs) pose significant environmental challenges due to their massive volume, with millions of tons generated globally each year. Improper disposal methods, such as illegal burning, further aggravate these issues by releasing substantial quantities greenhouse gases (GHGs) and toxic pollutants into the atmosphere. To mitigate impacts, adoption environmentally friendly resource recovery technologies a thorough evaluation benefits are crucial. Against this backdrop, research reviews life cycle assessment (LCA)-based analyses WT recycling technologies, focusing on performance contributions GHG emission reduction. Key pathways, including pyrolysis, rubber reclaiming, energy recovery, evaluated in terms carbon emissions, alongside an in-depth analysis reduction opportunities across various stages process. Based findings, paper proposes feasible recommendations identifies future trends for advancing recovery. The objectives (1) systematically review existing LCA findings technological pathways recovery; (2) evaluate advantages disadvantages current from perspective reduction; (3) explore trends, proposing optimization development.

Language: Английский

Citations

1
Lifecycle CO2 analysis for urban emission reduction of hydrogen-fuelled and battery electric buses in the European Union current and future energetic scenarios DOI Creative Commons
Pier Paolo Brancaleoni, A. Ferretti, Enrico Corti

et al.

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 123, P. 335 - 353

Published: April 1, 2025

Language: Английский

Citations

1
A graph-factor-based random forest model for assessing and predicting carbon emission patterns - Pearl River Delta urban agglomeration DOI
Y.K. Ding, Yongping Li, Heran Zheng

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 469, P. 143220 - 143220

Published: July 20, 2024

Language: Английский

Citations

6
Carbon emission prediction of 275 cities in China considering artificial intelligence effects and feature interaction: A heterogeneous deep learning modeling framework DOI

Gongquan Zhang,

Fangrong Chang,

Jie Liu

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 114, P. 105776 - 105776

Published: Aug. 26, 2024

Language: Английский

Citations

6
Scenario simulation of carbon balance in carbon peak pilot cities under the background of the "dual carbon" goals DOI
Jinting Zhang, Kui Yang,

Jingdong Wu

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 105910 - 105910

Published: Oct. 1, 2024

Language: Английский

Citations

4
Deep learning model based prediction of vehicle CO2 emissions with eXplainable AI integration for sustainable environment DOI Creative Commons
Gazi Mahabubul Alam, Sharia Arfin Tanim,

Sumit Sarker

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 29, 2025

The transportation industry contributes significantly to climate change through carbon dioxide ( $$\hbox {CO}_{2}$$ ) emissions, intensifying global warming and leading more frequent severe weather phenomena such as flooding, drought, heat waves, glacier melting, rising sea levels. This study proposes a comprehensive approach for predicting emissions from vehicles using deep learning techniques enhanced by eXplainable Artificial Intelligence (XAI) methods. Utilizing dataset the Canadian government's official open data portal, we explored impact of various vehicle attributes on emissions. Our analysis reveals that not only do high-performance engines emit pollutants, but fuel consumption under both city highway conditions also higher We identified skewed distributions in number produced different manufacturers trends across types. used construct CO2 emission prediction model, specifically light multilayer perceptron (MLP) architecture called CarbonMLP. proposed model was optimized hyperparameter tuning achieved excellent performance metrics, high R-squared value 0.9938 low Mean Squared Error (MSE) 0.0002. employs XAI approaches, particularly SHapley Additive exPlanations (SHAP), improve interpretation ability provide information about importance features. findings this show methodology accurately predicts vehicles. Additionally, suggests areas further research, increasing dataset, integrating additional improving interpretability, investigating real-world applications. Overall, design effective strategies reducing promoting environmental sustainability.

Language: Английский

Citations

0
Exploration of Dual-Carbon Target Pathways Based on Machine Learning Stacking Model and Policy Simulation—A Case Study in Northeast China DOI Creative Commons
Xuezhi Ren,

Jianya Zhao,

Shu Wang

et al.

Land, Journal Year: 2025, Volume and Issue: 14(4), P. 844 - 844

Published: April 12, 2025

Northeast China, a traditional heavy industrial base, faces significant carbon emissions challenges. This study analyzes the drivers of in 35 cities from 2000–2022, utilizing machine-learning approach based on stacking model. A model, integrating random forest and eXtreme Gradient Boosting (XGBoost) as base learners support vector machine (SVM) meta-model, outperformed individual algorithms, achieving coefficient determination (R2) 0.82. Compared to methods, model significantly improves prediction accuracy stability by combining strengths multiple algorithms. The Shapley additive explanations (SHAP) analysis identified key drivers: total energy consumption, urbanization rate, electricity population positively influenced emissions, while sulfur dioxide (SO2) smoke dust average temperature, humidity showed negative correlations. Notably, green coverage exhibited complex, slightly positive relationship with emissions. Monte Carlo simulations three scenarios (Baseline Scenario (BS), Aggressive De-coal (ADS), Climate Resilience (CRS)) projected peak 2030 under ADS, lowest fluctuation (standard deviation 5) largest reduction (17.5–24.6%). Baseline indicated around 2039–2040. These findings suggest important role de-coalization. Targeted policy recommendations emphasize accelerating transition, promoting low-carbon transformation, fostering urbanization, enhancing sequestration China’s sustainable development achievement dual-carbon goals.

Language: Английский

Citations

0