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

Linghui Zheng, Yanli Sun () and Yang Yu ()
Additional contact information
Linghui Zheng: College of Management, Shenyang Jianzhu University, Shenyang 110168, China
Yanli Sun: College of Management, Shenyang Jianzhu University, Shenyang 110168, China
Yang Yu: Centre for Infrastructure Engineering, Western Sydney University, Penrith, NSW 2751, Australia

Sustainability, 2024, vol. 16, issue 19, 1-23

Abstract: 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.

Keywords: nighttime lighting data; STIRPAT model; GA-BP model; scenario analysis; carbon emission prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/19/8657/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/19/8657/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:19:p:8657-:d:1493445

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().