Spatio-Temporal Difference in Agricultural Eco-Efficiency and Its Influencing Factors Based on the SBM-Tobit Models in the Yangtze River Delta, China

Shi, Lin; Shi, Xiaofei; Yang, Fan; Zhang, Lixue

doi:10.3390/ijerph20064786

Cited by 2 publications

(1 citation statement)

References 67 publications

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“…According to the Food and Agriculture Organization (FAO) of the United Nations, global carbon emissions from agricultural and food systems increased by 16% between 1990 and 2020, reaching 17 billion tons of carbon dioxideequivalent (CO 2 -eq) in 2020, accounting for 30% of global anthropogenic carbon emissions. As a major agricultural country, China's agricultural carbon emissions account for approximately 24% of China's greenhouse gas emissions, and this proportion is even increasing (Shi et al, 2023). Indeed, agricultural development has reached an important period when it is necessary to change the "environment for growth" development model.…”

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.

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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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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.

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Decomposition and scenario analysis of agricultural carbon emissions in Heilongjiang, China

Zhang,

Chu,

You

et al. 2024

PeerJ

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Background As a key agricultural region in China, Heilongjiang Province has experienced significant carbon emissions over the past few decades. To understand the underlying factors and future trends in these emissions, a comprehensive analysis was conducted from 1993 to 2030. Methods The agricultural carbon emissions from 1993 to 2020 were estimated using the emission factor method. To analyze the influencing factors and future trends of these emissions, the study employed the Logarithmic Mean Divisia Index (LMDI) and integrated it with the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. Results Results showed that (1) the agricultural carbon emissions in Heilongjiang were primarily driven by rice cultivation, followed by fertilizer production and irrigation electricity. (2) The economic and labor structure effects were the main driving factors of agricultural carbon emissions, while the population, demographic, and intensity effects were the main inhibitors. (3) Agricultural carbon emissions in Heilongjiang Province peaked in 2016 with 69.6 Mt CO2-eq and could subsequently decline by −3.92% to −4.52% between 2020 and 2030 in different scenario simulations. In the future, Heilongjiang Province should prioritize the reduction of agricultural carbon emissions from rice production. Adjusting the planting structure, managing the layout of rice paddies, and promoting the cultivation of dry rice varieties would significantly contribute to mitigating agricultural carbon emissions.

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Spatio-Temporal Difference in Agricultural Eco-Efficiency and Its Influencing Factors Based on the SBM-Tobit Models in the Yangtze River Delta, China

Cited by 2 publications

References 67 publications

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

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

Decomposition and scenario analysis of agricultural carbon emissions in Heilongjiang, China

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