An approach for analyzing urban carbon emissions using machine learning models

Gao, Peidao; Zhu, Chaoyong; Zhang, Yang; Chen, Bo

doi:10.1177/1420326x231162253

Cited by 6 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most scholars use the emission coefficient method (Guo et al, 2022a), agricultural ecosystem model (Zhao et al, 2019;Sun et al, 2023), methane emission model of paddy field (Wang et al, 2018), and regional nitrogen cycle model (Mao et al, 2018) to measure and analyze agricultural carbon emissions in different regions. Among them, it has become the mainstream practice to modify agricultural carbon emission sources and related emission coefficients on the basis of the emission coefficient method (Liu et al, 2013;Wójcik-Gront and Gront, 2014;Ghosh, 2018;Gao et al, 2023). The second is the research on the influencing factors of agricultural carbon emissions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of agricultural carbon emissions in China based on a GA-ELM model

Guo,

Yang,

Shen

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

Introduction: Strengthening the early warning of greenhouse gas emissions from agriculture is an important way to achieve Goal 13 of the Sustainable Development Goals. Agricultural carbon emissions are an important part of greenhouse gases, and accelerating the development of green and low-carbon agriculture is of great significance for China to achieve high-quality economic development and the goal of “carbon neutrality in peak carbon dioxide emissions”.Methods: By measuring the total agricultural carbon emissions in China and seven administrative regions from 2000 to 2021, the paper analyzes the influencing factors of agricultural carbon emissions in China by using STIRPAT environmental pressure model, and on this basis, predicts the peak trend of agricultural carbon emissions in China under different development scenarios by using the extreme learning machine model optimized by genetic algorithm.Results: The results showed that the extreme learning machine model improved by the genetic algorithm can overcome the shortcoming that the extreme learning machine model is easy to fall into the local optimal solution, thus obtaining higher prediction accuracy. At the same time, from 2000 to 2021, the total agricultural carbon emissions in China showed a continuous fluctuation trend, and due to the constraints of the agricultural economic level, agricultural industrial structure, and agricultural human capital, the agricultural carbon emissions showed spatial differentiation. It is worth noting that, in the context of green development, the agricultural carbon emissions of the seven regions in China all have the potential to achieve the “peak carbon dioxide emissions” goal in 2030, with only a slight difference at the peak.Discussion: The research results of this paper provide evidence for the government to formulate flexible, accurate, reasonable and appropriate agricultural carbon reduction policies, which is helpful to strengthen the exchanges and cooperation of regional agricultural and rural carbon reduction and fixation, and actively and steadily promote China's agriculture to achieve the goal of “peak carbon dioxide emissions carbon neutrality”.

show abstract