Impact of nighttime light data saturation correction on the application of carbon emissions spatialization: A comparative study of the correction effect and application effect based on five methods in China

Sun, Jingyan; Qi, Yanbing; Guo, Jinwei; Zheng, Jiale; Zhang, Luhao; Yang, Xiangyun

doi:10.1016/j.jclepro.2024.140815

Journal of Cleaner Production

2024

DOI: 10.1016/j.jclepro.2024.140815

|View full text |Cite

Impact of nighttime light data saturation correction on the application of carbon emissions spatialization: A comparative study of the correction effect and application effect based on five methods in China

Jingyan Sun,

Yanbing Qi,

Jinwei Guo

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Dynamic spatiotemporal evolution and spatial effect of carbon emissions in urban agglomerations based on nighttime light data

Wu,

Yang,

2024

Sustainable Cities and Society

View full text Add to dashboard Cite

Dynamic spatiotemporal evolution and spatial effect of carbon emissions in urban agglomerations based on nighttime light data

Wu,

Yang,

2024

Sustainable Cities and Society

View full text Add to dashboard Cite

County-level carbon emissions in the guanzhong area of Shaanxi province: towards achieving China’s dual carbon goals

Gao,

He,

Zhang

et al. 2024

Front. Environ. Sci.

View full text Add to dashboard Cite

China’s rapid urbanization has significantly impacted carbon emissions in rural areas, driving the need for region-specific carbon management strategies to achieve the country’s dual carbon goals. However, previous research has primarily focused on large urban centers, leaving a gap in understanding the spatial and temporal patterns of carbon emissions at the county level in rural regions. This study focuses on the Guanzhong region of Shaanxi Province and develops a county-level carbon emission accounting system based on geographic, socio-economic, and land-use data. The carbon emissions are categorized into energy, industrial processes, agriculture, and waste management. Key findings indicate that industrial and residential sectors are the primary contributors to carbon emissions, with Xi’an being the largest emitter. Carbon emission intensity follows a ranking of Xi’an < Weinan < Baoji < Xianyang < Tongchuan. Spatial patterns show a “core-edge” distribution, with higher emissions in urban centers and lower emissions in rural areas. The study also highlights the carbon sink potential in the southern Qinling National Park. This research provides a valuable framework for rural low-carbon development and offers critical insights for policymakers aiming to balance carbon reduction and economic growth in rural China.

show abstract

Carbon Peak Control Strategies and Pathway Selection in Dalian City: A Hybrid Approach with STIRPAT and GA-BP Neural Networks

Zheng,

Sun,

2024

Sustainability

View full text Add to dashboard Cite

Mitigating the rate of global warming is imperative to preserve the natural environment upon which humanity relies for survival; greenhouse gas emissions serve as the principal driver of climate change, rendering the promotion of urban carbon peaking and carbon neutrality a crucial initiative for effectively addressing climate change and attaining sustainable development. This study addresses the inherent uncertainties and complexities associated with carbon dioxide emission accounting by undertaking a scenario prediction analysis of peak carbon emissions in Dalian, utilizing the STIRPAT model in conjunction with a GA-BP neural network model optimized through a genetic algorithm. An analysis of the mechanisms underlying the influencing factors of carbon emissions, along with the identification of the carbon emission peak, is conducted based on carbon emission accounting derived from nighttime lighting data. The GA-BP prediction model exhibits significant advantages in addressing the nonlinear and non-stationary characteristics of carbon emissions, attributable to its robust mapping capabilities and probabilistic analysis proficiency. The findings reveal that energy intensity, tertiary industry value, resident population, and GDP are positively correlated with carbon emissions in Dalian, ranked in order of importance. In contrast, population density significantly reduces emissions. The GA-BP model predicts carbon emissions with 99.33% accuracy, confirming its excellent predictive capability. The recommended strategy for Dalian to achieve its carbon peak at the earliest is to adopt a low-carbon scenario, with a forecasted peak of 191.79 million tons by 2033.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Impact of nighttime light data saturation correction on the application of carbon emissions spatialization: A comparative study of the correction effect and application effect based on five methods in China

Cited by 3 publications

References 38 publications

Dynamic spatiotemporal evolution and spatial effect of carbon emissions in urban agglomerations based on nighttime light data

Dynamic spatiotemporal evolution and spatial effect of carbon emissions in urban agglomerations based on nighttime light data

County-level carbon emissions in the guanzhong area of Shaanxi province: towards achieving China’s dual carbon goals

Carbon Peak Control Strategies and Pathway Selection in Dalian City: A Hybrid Approach with STIRPAT and GA-BP Neural Networks

Contact Info

Product

Resources

About