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关键农业要素对温室气体排放的线性和非线性影响。

Linear and non-linear impact of key agricultural components on greenhouse gas emissions.

作者信息

Ahmed Nazeer, Xinagyu Guo, Alnafissa Mohamad, Ali Arshad, Ullah Hafeez

机构信息

School of Economics and Management, Northeast Agricultural University, Harbin, China.

Department of Agricultural Economics, College of Food and Agricultural Sciences, King Suad University, Riyadh, Saudi Arabia.

出版信息

Sci Rep. 2025 Feb 13;15(1):5314. doi: 10.1038/s41598-025-88159-1.

Abstract

Agriculture significantly impacts the global environment, contributing to greenhouse gas (GHG) emissions, air and water pollution, and biodiversity loss. As the global population grows and demands higher agricultural output, these environmental impacts are expected to intensify. Among global contributors, China, with its vast population and prominent agricultural sector, plays a leading role in GHG emissions. Understanding and mitigating these impacts in China is crucial for addressing broader global environmental challenges. To address these key issues, we conducted a study on the dynamic impact of agricultural key variables (agricultural land, fertilizer consumption, energy use for agriculture, agricultural value-added, forest land, livestock, fisheries, and crop production) on GHG emissions by utilizing the data from 1990 to 2020, and employed linear and non-linear linear autoregressive distributed lag (ARDL and NARDL) models. In the study, co-integration analysis confirms the long-run relationship between variables, and the long-term findings from the ARDL model reveal important insights, increased agricultural land use, fertilizer consumption, agricultural energy use, crop production, livestock production, and fishery production increases GHG emissions in China and GHG emissions can be reduced by increasing forest land in the long term. Furthermore, with the asymmetric NARDL regression applied to three key variables, the positive shock analysis results confirm that agricultural land use (AGL+), fertilizer consumption (FC+), and agricultural energy use (EUA+) can significantly contribute to long-term GHG emissions. However, adverse shocks to (AGL-), (FC-), and (EUA-) could significantly compress GHG emissions. These findings offer valuable implications for Chinese authorities' focus on expanding forest land, using more renewable energy, and minimizing the usage of chemicals in agriculture. These measures can help to mitigate emissions while promoting sustainable agricultural practices.

摘要

农业对全球环境有着重大影响,导致温室气体排放、空气和水污染以及生物多样性丧失。随着全球人口增长以及对农业产出的需求提高,这些环境影响预计会加剧。在全球排放贡献国中,中国人口众多且农业部门突出,在温室气体排放方面起着主导作用。了解并减轻中国的这些影响对于应对更广泛的全球环境挑战至关重要。为解决这些关键问题,我们利用1990年至2020年的数据,对农业关键变量(农业用地、化肥消费、农业能源使用、农业增加值、林地、牲畜、渔业和作物生产)对温室气体排放的动态影响进行了研究,并采用了线性和非线性自回归分布滞后(ARDL和NARDL)模型。在该研究中,协整分析证实了变量之间的长期关系,ARDL模型的长期研究结果揭示了重要见解,即农业用地使用增加、化肥消费、农业能源使用、作物生产、牲畜生产和渔业生产会增加中国的温室气体排放,从长期来看,增加林地可减少温室气体排放。此外,通过将非对称NARDL回归应用于三个关键变量,正向冲击分析结果证实,农业用地使用(AGL+)、化肥消费(FC+)和农业能源使用(EUA+)会对长期温室气体排放产生显著影响。然而,(AGL-)、(FC-)和(EUA-)的负向冲击可显著压缩温室气体排放。这些发现为中国当局注重扩大林地、使用更多可再生能源以及尽量减少农业化学品使用提供了有价值的启示。这些措施有助于在促进可持续农业实践的同时减轻排放。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7cf3/11821880/520d1ef46b92/41598_2025_88159_Fig1_HTML.jpg

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