Empirical assessment of carbon emissions in Guangdong Province within the framework of carbon peaking and carbon neutrality: a lasso-TPE-BP neural network approach

• Published in: Environmental science and pollution research international Vol. 30; no. 58; pp. 121647 - 121665
• Main Authors: Chen, Ruihan, Ye, Minhua, Li, Zhi, Ma, Zebin, Yang, Derong, Li, Sheng
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023; Springer Nature B.V
• Subjects: Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Back propagation networks; Carbon; Carbon content; Carbon footprint; Carbon neutrality; Clean technology; Development strategies; Earth and Environmental Science; Economic development; Economics; Ecotoxicology; Emission analysis; Emissions; Emissions control; Empirical analysis; Energy conservation; Environment; Environmental accounting; Environmental Chemistry; Environmental Health; Environmental protection; Green development; Greenhouse gases; Neural networks; Population dynamics; Population number; Research Article; Statistical analysis; Sustainability reporting; Sustainable development; Waste Water Technology; Water Management; Water Pollution Control; Guangdong China; China; BP neural network; Lasso regression; Carbon neutrality; Tree-structured Parzen estimator; Scenario setting; Carbon peaking; Carbon emissions

The escalating global greenhouse gas emission crisis necessitates a robust scientific carbon accounting framework and innovative development approaches. Achieving emission peaks remains the primary goal for emission reduction. Guangdong Province, a pivotal region in China, faces pressure to reduce c...