Current Status and Peak Prediction of China's Carbon Emissions: An Empirical Analysis Based on BP Neural Network Model DOI
Qian Ke, Yan Wang, Shuzhen Lei

et al.

Published: Sept. 22, 2023

Clarifying the current carbon emission situation and future emissions under existing policy basis is crucial to achieving dual goal. This paper uses relevant data of from 1999∼2018, combined with five factors economic growth, population, industrial output value, social consumption level energy consumption, linear (nonlinear) fitting predict major variables 2019 2030, further BP neural network model China's 2019∼2030. The results show that in Beijing, Tianjin, Jiangsu, Zhejiang Shanghai, Guangdong, Guangxi other places have reached peak state, western energy-oriented regions cannot achieve target before 2030 according policies, need more effective low-carbon transformation promotion support.

Language: Английский

Spatially explicit carbon emissions from land use change: Dynamics and scenario simulation in the Beijing-Tianjin-Hebei urban agglomeration DOI
Yuanyuan Yang,

Mingying Yang,

Boxuan Zhao

et al.

Land Use Policy, Journal Year: 2025, Volume and Issue: 150, P. 107473 - 107473

Published: Jan. 9, 2025

Language: Английский

Citations

1
Spatiotemporal evolution of land use carbon emissions and multi scenario simulation in the future-Based on carbon emission fair model and PLUS model DOI Creative Commons

Tianqi Rong,

Mingzhou Qin, Pengyan Zhang

et al.

Environmental Technology & Innovation, Journal Year: 2025, Volume and Issue: unknown, P. 104087 - 104087

Published: Feb. 1, 2025

Language: Английский

Citations

0
Optimizing Land Use for Carbon Neutrality: Integrating Photovoltaic Development in Lingbao, Henan Province DOI Creative Commons
Xiaohuan Xie,

Haifeng Deng,

Shengyuan Li

et al.

Land, Journal Year: 2024, Volume and Issue: 13(1), P. 97 - 97

Published: Jan. 15, 2024

This study aims to examine the impact of land use variations on carbon emissions by incorporating development photovoltaics as a scenario. To meet this end, we investigate fluctuations resulting from different scenarios: natural development, low-carbon strategies, and widespread adoption photovoltaic technology. We identify important influencing factors related these changes utilize multi-objective optimization PLUS model simulate patterns in Lingbao City projected for 2035, with focus achieving neutrality. Through multiple scenarios, analyze differences emissions, economic benefits, ecological impacts, allocations. Our findings demonstrate that scenario leads substantial 3500-ton reduction boosts overall benefits RMB 85 million compared highlights significant role systems inefficient utilization, meeting emission targets, generating gains. research explores relationship between alterations aiming achieve ambitious objectives integrating applications across diverse types. It provides fresh perspectives examining urban utilization strategies reduce emissions.

Language: Английский

Citations

3
Spatiotemporal evolution of carbon budget and carbon compensation zoning of urban agglomerations in the Yellow River Basin DOI Creative Commons
Zhongwu Zhang, Shiyu Wang, Jinyuan Zhang

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 3, 2024

As a crucial industrial, agricultural, and energy base in China, the urban agglomerations Yellow River Basin (YRB) have faced increasingly significant pressure for carbon emission reduction since implementation of "Dual Carbon" strategy. This study focuses on 615 counties within major YRB, analyzing spatiotemporal evolution budget land use from 2000 to 2020. Methods such as normalized revealed comparative advantage (NRCA) index SOM-K-means model are employed explore compensation zoning YRB perspective main functional zones. The results show that: (1) From 2020, there was continuous widening gap between emissions absorption area. total increased significantly 3.64 × 10

Language: Английский

Citations

2
Carbon neutrality transformation pathway in ecoregions: An empirical study of Chongming District, Shanghai, China DOI Creative Commons
Yuhao Zhang, Ru Guo, Kaiming Peng

et al.

Water-Energy Nexus, Journal Year: 2024, Volume and Issue: 7, P. 200 - 212

Published: June 3, 2024

In the context of global efforts to address climate change, research into regional carbon neutrality strategies has become especially critical. For developing countries and regions, scientifically rationally assessing paths for small-scale transformations under imperatives is essential effective implementation low-carbon transition measures. This study uses Chongming District in Shanghai, China, as a case construct emission sink forecasting framework from multi-dimensional natural-social perspective, facilitating simulation optimization pathways transformation. The results indicate:(1)From 2000 2020, total emissions exhibited rising trend, while initially declined then increased, located potential enhancement zone, significant space development.(2) Enhanced management ecological spaces land use planning led notable increases sink. Strategic measures such consumption reductions, alongside energy transitions, effectively controlled growth facilitated comprehensive decarbonization. (3) Under combinations priority with enhanced control balanced development control, region can achieve neutrality, showcasing role policy regulation high-quality carbon-neutral transformations. (4)Effective ecosystem robust reduction enable at county level, offering model methodological support other regions facing twin challenges economic environmental sustainability.

Language: Английский

Citations

1
Mapping carbon–thermal environments for comprehending real-time scenarios DOI
C. M. Srivastava, Alka Bharat

Acta Geophysica, Journal Year: 2024, Volume and Issue: unknown

Published: June 13, 2024

Language: Английский

Citations

1
Characteristics of Spatial Correlation Network Structure and Carbon Balance Zoning of Land Use Carbon Emission in the Tarim River Basin DOI Creative Commons

Zhe Gao,

Jianming Ye,

Xiaoxue Zhu

et al.

Land, Journal Year: 2024, Volume and Issue: 13(11), P. 1952 - 1952

Published: Nov. 19, 2024

An accurate understanding of the structure spatial correlation networks land use carbon emissions (LUCEs) and balance zoning plays a guiding role in promoting regional emission reductions achieving high-quality coordinated development. In this study, 42 counties Tarim River Basin from 2002 to 2022 were chosen as samples (Corps cities excluded due missing statistics). The LUCE network characteristics analyzed by using Ecological Support Coefficient (ESC), Social Network Analysis (SNA), Spatial Clustering Data (SCDA), targeted optimization strategy was proposed for each zone. results study indicate following: (1) LUCEs showed an overall upward trend, but increase gradually slowed down, presenting characteristic “high mid-north low at edges”. addition, ESC decreasing with opposite that LUCEs. (2) With increasingly close Basin, presented better accessibility stability, individual differed significantly. Aksu City, Korla Bachu County, Shache Hotan Kuqa which center network, displayed remarkable ability control master correlation. (3) Based on analysis, subdivided into six functional zones synergistic reduction strategies zone promote fair efficient low-carbon transformational development among regions.

Language: Английский

Citations

1
Spatio-temporal characteristics and scenario prediction of carbon emissions from land use in Jiangxi Province, China DOI
Lian Duo, Yanfei Zhong, Wang Ju

et al.

International Journal of Environmental Science and Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 21, 2024

Language: Английский

Citations

0
Current Status and Peak Prediction of China's Carbon Emissions: An Empirical Analysis Based on BP Neural Network Model DOI
Qian Ke, Yan Wang, Shuzhen Lei

et al.

Published: Sept. 22, 2023

Clarifying the current carbon emission situation and future emissions under existing policy basis is crucial to achieving dual goal. This paper uses relevant data of from 1999∼2018, combined with five factors economic growth, population, industrial output value, social consumption level energy consumption, linear (nonlinear) fitting predict major variables 2019 2030, further BP neural network model China's 2019∼2030. The results show that in Beijing, Tianjin, Jiangsu, Zhejiang Shanghai, Guangdong, Guangxi other places have reached peak state, western energy-oriented regions cannot achieve target before 2030 according policies, need more effective low-carbon transformation promotion support.

Language: Английский

Citations

0