Investigating Environmental Kuznets Curve: A Panel Data Analysis for India

Pandey, Sweety; Mishra, Mrutyunjaya

doi:10.1177/09722661211043595

Cited by 1 publication

(1 citation statement)

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“…The third is about the trend prediction of agricultural carbon emissions. Scholars mainly rely on traditional forecasting methods to predict the peak value of agricultural carbon emissions, such as the environmental Kuznets curve (Pandey and Mishra, 2021;Ojaghlou et al, 2023), IPAT identity (Du et al, 2012;Yang et al, 2023), support vector machine model (Gao et al, 2022;, low-emission analysis platform (Sun et al, 2022b;Chen et al, 2023), gray forecasting model (Wang et al, 2023a;Saxena et al, 2023), and various combination models. On the basis of the extreme learning machine model (ELM), Wang et al (2020b) used the whale algorithm to optimize it and used the WOA-ELM model to predict China's carbon emissions, and the prediction results were more accurate.…”

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confidence: 99%

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

Guo,

Yang,

Shen

et al. 2023

Front. Energy Res.

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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”.

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