数字农科院2.0

How does renewable energy transition promote food system resilience in China: The moderating role of artificial intelligence

文献类型： 外文期刊

作者： Jingyi Wang;Shuai Zhang;Haibo Zhu;Xin Dai;Xuebiao Zhang

作者机构：

关键词： Artificial intelligence;Energy transition;Food system esilience;Moderating effect

期刊名称： Journal of Agriculture and Food Research

ISSN：

年卷期： 2026 年 26 卷

页码：

收录情况： ESCI（2025版）

摘要： Investigating how energy transition facilitates food system resilience is crucial for improving food security. The study uses a panel dataset from 30 Chinese provinces over the period 2011–2021 to explore the influence of energy transition on food system resilience by using a series of regression models. The mechanism and heterogeneity analysis are also carried out. Furthermore, the moderating role of artificial intelligence (AI) is discussed. The findings of the feasible generalized least squares regression show that energy transition plays a positive role in promoting food system resilience, and the effect tends to be larger in major grain-producing areas than those in non-major grain-producing areas. AI acts as a moderating variable in the energy transition-food system resilience nexus. Based on these results, we recommend policy strategies that strengthen the efforts to promote energy transition to enhance food system resilience, and also improve the application of AI in this nexus.

分类号：

  • 相关文献

[1]Can fiscal expenditure for agriculture mitigate the impact of climate change on agricultural production?. Wang Hao,Jianqing Yang,Xiangdong Hu,Zhenxing Zhang,Zizhong Shi,Hui Zhou. 2024

[2]Breaking through the bottleneck in the promotion of artificial intelligence in agriculture: an analysis of the moderating role of individual's heterogeneity among farmers in Shandong Province. Cao, Kai,Wang, Ping,Kong, Siyu,Zhang, Chunzhen. 2025

[3]Urban Energy Transition In China: I.nsights From Trends, Socioeconomic D rivers, And Environmental Impacts Of Beijing. Zhang, Pengpeng,Zhang, Lixiao,Tian, Xin,Hao, Yan,Wang, Changbo. 2018

[4]Digital literacy and the rural cooking energy transition: Evidence from rural China. Lu, Hongwei,Li, Tingting,Li, Guojing,Luo, Qiyou,Gao, Mingjie. 2025

[5]Dynamic relationship between renewable energy, economic development, and energy security based on SVAR and ARDL-ECM models: Evidence from China. Taize Yu,Xi Chen,Xiaoran Liu,Hongbo Chen,Shiyi Tang,Lihang Cui,Haijiao Liu,Kunyu Niu,Xiaoshang Deng. 2025

[6]Ls-Svm Data Mining Analysis: How D.oes Biochar Influence Soil N et Nitrogen Mineralization In The Field?. Du, Zhenjie,Nan, Jiangkuan,Wang, Rui,Du, Zhenjie,Chen, Xiaomin,Deng, Jianqiang,Deng, Jianqiang. 2017

[7]LS-SVM data mining analysis: how does biochar influence soil net nitrogen mineralization in the field?. Deng, Jianqiang,Chen, Xiaomin,Nan, Jiangkuan,Du, Zhenjie,Deng, Jianqiang,Wang, Rui,Du, Zhenjie.

[8]Quantitative assessment of wheat quality using near-infrared spectroscopy: A comprehensive review. Du, Zhenjiao,Tian, Wenfei,Tilley, Michael,Wang, Donghai,Zhang, Guorong,Li, Yonghui. 2022

[9]High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing. Zhang, Huichun,Wang, Lu,Jin, Xiuliang,Bian, Liming,Ge, Yufeng. 2023

[10]Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation. Guangyue Li,Youcai Qin,Nicolas T. Fontaine,Matthieu Ng Fuk Chong,Miguel A. Maria‐Solano,Ferran Feixas,Xavier F. Cadet,Rudy Pandjaitan,Marc Garcia‐Borràs,Frederic Cadet,Manfred T. Reetz. 2021

[11]A bibliometric and visual analysis of artificial intelligence technologies-enhanced brain MRI research. Chen Xieling,Zhang Xinxin,Xie Haoran,Tao Xiaohui,Wang Fu Lee,Xie Nengfu,Hao Tianyong. 2021

[12]Harnessing Current Knowledge of DNA N6-Methyladenosine From Model Plants for Non-model Crops. Chachar Sadaruddin,Liu Jingrong,Zhang Pingxian,Riaz Adeel,Guan Changfei,Liu Shuyuan. 2021

[13]Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. Xu Y.,Zhang X.,Li H.,Zheng H.,Zhang J.,Olsen M.S.,Varshney R.K.,Prasanna B.M.,Qian Q.. 2022

[14]Experimental Study of Garlic Root Cutting Based on Deep Learning Application in Food Primary Processing. Yang K.,Yu Z.,Gu F.,Zhang Y.,Wang S.,Peng B.,Hu Z.. 2022

[15]Development potential of nanoenabled agriculture projected using machine learning. Peng Deng,Yiming Gao,Li Mu,Xiangang Hu,Fubo Yu,Yuying Jia,Zhenyu Wang,Baoshan Xing. 2023

[16]Exploring blockchain and artificial intelligence in intelligent packaging to combat food fraud: A comprehensive review. Yang Y.,Du Y.,Gupta V.K.,Ahmad F.,Amiri H.,Pan J.,Aghbashlo M.,Tabatabaei M.,Rajaei A.. 2024

[17]Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity. Ali Raza,Hajar Salehi,Shanza Bashir,Javaria Tabassum,Monica Jamla,Sidra Charagh,Rutwik Barmukh,Rakeeb Ahmad Mir,Basharat Ahmad Bhat,Muhammad Arshad Javed,Dong Xing Guan,Reyazul Rouf Mir,Kadambot H.M. Siddique,Rajeev K. Varshney. 2024

[18]Human inventions and its environmental challenges, especially artificial intelligence: New challenges require new thinking. Muhammad Adnan,Baohua Xiao,Muhammad Ubaid Ali,Shaheen Bibi,Hong Yu,Peiwen Xiao,Peng Zhao,Haiyan Wang,Xianjin An. 2024

[19]Fast-forwarding plant breeding with deep learning-based genomic prediction. Gao, Shang,Yu, Tingxi,Rasheed, Awais,Wang, Jiankang,Crossa, Jose,Hearne, Sarah,Li, Huihui. 2025

[20]Perspective on Current Advances and Future Directions in Food Safety and Health. Mingqian Tan,Maojun Jin,Maria Daglia,Esra Capanoglu,A. M. Abd El-Aty. 2025

作者其他论文 更多>>