The Relationship Between Three-Dimensional Spatial Structure and CO2 Emission of Urban Agglomerations Based on CNN-RF Modeling: A Case Study in East China DOI Open Access
Banglong Pan,

Doudou Dong,

Zhuo Diao

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(17), P. 7623 - 7623

Published: Sept. 3, 2024

Good urban design helps mitigate carbon dioxide emissions and is important for achieving global low-carbon goals. Previous studies have mostly focused on the two-dimensional level of socio-economic activities, land use changes, morphology, neglecting importance three-dimensional spatial structure cities. This study takes 30 cities in East China as an example. By using building data emission datasets, four machine learning algorithms, BP, RF, CNN, CNN-RF, are established to build a CO2 prediction model based structure, main influencing factors further studied. The results show that CNN-RF performed optimally both testing validation phases, with coefficient determination (R2), root mean square error (RMSE), residual deviation (RPD) 0.85, 0.82; 10.60, 22.32; 2.53, 1.92, respectively. Meanwhile, unit, S, V, NHB, AN, BCR, SCD, FAR greater impact emissions. indicates strong correlation between can effectively evaluate relationship them, providing strategic support optimization

Language: Английский

Optimizing urban functional land towards “dual carbon” target: A coupling structural and spatial scales approach DOI

Yifei Yang,

Banghua Xie,

Jianjun Lv

et al.

Cities, Journal Year: 2024, Volume and Issue: 148, P. 104860 - 104860

Published: Feb. 8, 2024

Language: Английский

Citations

14
Optimization of Land Use Structure Integrating Ecosystem Service Function and Economic Development—A Case Study in Dongting Lake Ecological and Economic Zone, China DOI Creative Commons
Yifan Zhu, Min Zhou

Environmental and Sustainability Indicators, Journal Year: 2025, Volume and Issue: unknown, P. 100604 - 100604

Published: Jan. 1, 2025

Language: Английский

Citations

1
Spatiotemporal Effects and Optimization Strategies of Land-Use Carbon Emissions at the County Scale: A Case Study of Shaanxi Province, China DOI Open Access
Yahui Zhang, Jianfeng Li, Siqi Liu

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(10), P. 4104 - 4104

Published: May 14, 2024

Land use, as one of the major sources carbon emissions, has profound implications for global climate change. County-level land-use systems play a critical role in national emission management and control. Consequently, it is essential to explore spatiotemporal effects optimization strategies emissions at county scale promote achievement regional dual targets. This study, focusing on Shaanxi Province, analyzed characteristics land use from 2000 2020. By establishing evaluation model, county-level were clarified. Utilizing Geodetector K-means clustering methods, driving mechanisms elucidated, explored. The results showed that during 2000–2020, Province underwent significant changes, with constructed increasing by 97.62%, while cultivated grassland substantially reduced. overall exhibited pattern North > Central South. total within province increased nearly fourfold over 20 years, reaching 1.00 × 108 tons. Constructed was primary source forest contributed significantly sink study area. Interactions among factors had impacts spatial differentiation emissions. For counties different types differentiated recommended. Low-carbon should intensify ecological protection rational utilization, medium-carbon need strike balance between economic development environmental protection, high-carbon prioritize reduction structural transformation.

Language: Английский

Citations

2
Spatial Optimization of Land Use Allocation Based on the Trade-off of Carbon Mitigation and Economic Benefits: A Study in Tianshan North Slope Urban Agglomeration DOI Creative Commons
Jinmeng Lee,

Xiaojun Yin,

Honghui Zhu

et al.

Land, Journal Year: 2024, Volume and Issue: 13(6), P. 892 - 892

Published: June 20, 2024

The rational allocation of land use space is crucial to carbon emissions reductions and economic development. However, previous studies have either examined inter-objective trade-offs or intra-objective within a single objective lacked multilevel comprehensive studies. Therefore, this paper integrates inter- mitigation efficiency comprehensively study the interaction between pattern demand due policies. research methods were mainly multi-objective planning, gray model, patch-generating simulation area was less-developed urban agglomeration—the Tianshan north slope agglomeration. results show that total change from 2000 2020 5767.94 km2, grassland transferred out most, 3582.59 accounting for 62.11%, cultivated in 3741.01 km2. Compared with 2020, simulated obtained 2030 has significantly changed. In addition, benefits under low-carbon objectives changed opposite direction. four landscape patterns three scenarios same direction, degree fragmentation, agglomeration, regularity better than objective. are essential references future resource management, mitigation, sustainable development agglomerations.

Language: Английский

Citations

2
The Relationship Between Three-Dimensional Spatial Structure and CO2 Emission of Urban Agglomerations Based on CNN-RF Modeling: A Case Study in East China DOI Open Access
Banglong Pan,

Doudou Dong,

Zhuo Diao

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(17), P. 7623 - 7623

Published: Sept. 3, 2024

Good urban design helps mitigate carbon dioxide emissions and is important for achieving global low-carbon goals. Previous studies have mostly focused on the two-dimensional level of socio-economic activities, land use changes, morphology, neglecting importance three-dimensional spatial structure cities. This study takes 30 cities in East China as an example. By using building data emission datasets, four machine learning algorithms, BP, RF, CNN, CNN-RF, are established to build a CO2 prediction model based structure, main influencing factors further studied. The results show that CNN-RF performed optimally both testing validation phases, with coefficient determination (R2), root mean square error (RMSE), residual deviation (RPD) 0.85, 0.82; 10.60, 22.32; 2.53, 1.92, respectively. Meanwhile, unit, S, V, NHB, AN, BCR, SCD, FAR greater impact emissions. indicates strong correlation between can effectively evaluate relationship them, providing strategic support optimization

Language: Английский

Citations

0