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Wen J, Chuai X, Xiang A, Liu Y, Wang T, Luo Y, Miao L, Zhang L, Li J, Zhao R. Re-identifying farmland carbon neutrality gap under a new carbon counting and the framework of regional interactions in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 952:175996. [PMID: 39233066 DOI: 10.1016/j.scitotenv.2024.175996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 08/09/2024] [Accepted: 09/01/2024] [Indexed: 09/06/2024]
Abstract
The farmland ecosystem, with its numerous material cycles and energy flows, is an important part of the carbon cycle in terrestrial ecosystems. Focusing on the carbon neutrality of farmland is meaningful for mitigating global warming and serving national low-carbon strategies. This study enriches the carbon accounting items of farmland and establishes a new research framework to check the carbon neutrality of farmland from the aspect of regional interactions and, subsequently, the inequality among China's provinces. The results revealed that there is still a great gap in the capability of China's farmland to reach carbon neutrality, with a gap value of up to 10,503 × 104 t C. All of the provinces presented net carbon emissions, and the per unit area carbon neutrality gaps showed spatial regularity decreasing from the coastal regions to the inland areas. Anthropogenic carbon emissions on farmland played a dominant role compared with soil organic carbon. Five provinces had reduced interior-regional carbon emissions through grain trade, and the amounts were especially high for developed regions, such as Guangdong, Zhejiang, Beijing, Shanghai and Jiangsu. Sixteen provinces gained external carbon emissions through trade; these were the less developed regions located mainly in the north, such as Inner Mongolia, Hebei, Jilin, Heilongjiang and Xinjiang. Under regional inequality, 15 provinces added to the net amount of the carbon emissions generated in external regions, with China's megacities adding the highest percentage, especially Beijing, with 389.95 % compared with its original emissions. Inequality showed that most provinces had a moderate status. Sichuan and Hunan experienced weak advantages, and six provinces had disadvantages. Therefore, constructing compensation and trade-based rights and responsibilities traceability mechanisms is important.
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Affiliation(s)
- Jiqun Wen
- School of Public Administration, Guangdong University of Finance and Economics, Guangzhou 510320, Guangdong Province, China
| | - Xiaowei Chuai
- School of Geography & Ocean Science, Nanjing University, Nanjing 210023, Jiangsu Province, China.
| | - Ai Xiang
- School of Geography & Ocean Science, Nanjing University, Nanjing 210023, Jiangsu Province, China
| | - Yonghua Liu
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, Jiangsu Province, China
| | - Tong Wang
- School of Geography & Ocean Science, Nanjing University, Nanjing 210023, Jiangsu Province, China
| | - Yuting Luo
- School of Geography & Ocean Science, Nanjing University, Nanjing 210023, Jiangsu Province, China
| | - Lijuan Miao
- School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu Province, China
| | - Libao Zhang
- Qianxi County Branch of Tangshan Ecological Environment Bureau, Qianxi 064300,Hebei Province, China
| | - Jianbao Li
- School of Public Administration, Nanjing University of Finance & Economics, Nanjing 210023, Jiangsu Province, China
| | - Rongqin Zhao
- School of Surveying and Geo-informatics, North China University of Water Resource and Electric Power, Zhengzhou 450046, Henan Province, China
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Tang C, Xie X, Wei G, Pan L, Qi Z. Exploring the Evolutionary Characteristics of Food Security in China and the United States from a Multidimensional Perspective. Foods 2024; 13:2272. [PMID: 39063356 PMCID: PMC11275272 DOI: 10.3390/foods13142272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 07/14/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024] Open
Abstract
Against the backdrop of global warming, intensifying regional conflicts, deglobalization, and the spread of diseases, global food security is facing severe challenges. Studying the food security situation in China and the United States in depth can provide practical experience for formulating food security policies for countries around the world and promoting global food security governance. On the basis of a meticulous review of the evolving connotations of food security, this study adopts six dimensions-quantity security, quality security, circulation security, economic security, ecological resource security, and policy security-as breakthrough points to construct a framework consisting of food security evaluation indicator system comprising 29 specific indicators. The CRITIC-MEREC-MARCOS model is applied to evaluate the status of food security in China and the United States from 2000 to 2022, while the obstacle degree model (ODM) model is utilized to identify factors impeding food security between the two countries. The results indicate that the level of food security in China has shown slight fluctuations initially, followed by a steady upward trend. The gap with the United States is continuously narrowing. However, significant differences between China and the United States still exist in terms of economic security, ecological resource security, and policy security. Furthermore, due to the limited productivity of agricultural labor, scarcity of water and soil resources, and low efficiency in the use of fertilizers and pesticides, China's food security is subject to economic and environmental constraints. The restrictions imposed by economic security and ecological resource security on China's food security are showing an increasing trend year by year. For the United States, with the obstruction of grain exports and the increasing frequency of drought disasters, the impact of circulation security and ecological resource security on food security is becoming increasingly prominent. In the future, China and the United States should join hands to address challenges, actively promote international cooperation in food security, and drive sustainable development for humanity.
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Affiliation(s)
- Chang Tang
- School of Science, Hunan University of Technology and Business, Changsha 410205, China; (X.X.)
- Changsha Social Laboratory of Artificial Intelligence, Changsha 410205, China
| | - Xiaoliang Xie
- School of Science, Hunan University of Technology and Business, Changsha 410205, China; (X.X.)
- Changsha Social Laboratory of Artificial Intelligence, Changsha 410205, China
| | - Guo Wei
- Department of Mathematics and Computer Science, University of North Carolina at Pembroke, Pembroke, NC 28372, USA;
| | - Linglong Pan
- School of Science, Hunan University of Technology and Business, Changsha 410205, China; (X.X.)
- Changsha Social Laboratory of Artificial Intelligence, Changsha 410205, China
| | - Zihan Qi
- School of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan 250014, China
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Lee CC, Li J, Zeng M. Construction of China's food security evaluation index system and spatiotemporal evolution. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:25014-25032. [PMID: 38460035 DOI: 10.1007/s11356-024-32633-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/20/2024] [Indexed: 03/11/2024]
Abstract
Food security is a vital material foundation for a nation's development and has been a topic of significant concern on the international stage in recent years. With a population exceeding 1.4 billion, China is not only a major producer but also a substantial consumer of food. Ensuring food security in China is not only a top priority for its socio-economic development but also a driving force in maintaining the stability of the global food supply chain and reducing the number of hungry people worldwide. However, a lack of comprehensive research into the Chinese food security system remains. This study addresses this gap by constructing a comprehensive evaluation framework encompassing four dimensions: food supply, accessibility, production stability, and sustainability. Utilizing the Moran's Index and generating LISA (Local Indicators of Spatial Association) maps, we analyze the spatial correlations of food security. The Dagum Gini coefficient and kernel density estimation are applied to assess heterogeneity and spatial disparities. Furthermore, this research employs the Exponential Smoothing (ETS) model to forecast food security trends. The findings reveal that the overall composite food security score exhibited fluctuations, initially increasing and reaching its peak of 0.407 in 2003, followed by a subsequent sharp decline after 2019. Spatially, food security exhibits correlations, with the Huang-Huai-Hai Plain and Northeast regions consistently showing high-high clustering. In contrast, the Western and Southern regions exhibit low-low clustering at specific periods. The Dagum Gini coefficient indicates that overall food security disparities are relatively small. However, these disparities have gradually expanded in recent years, with inter-group differences becoming predominant after 2005. As indicated by the kernel density estimation, the dynamic distribution of food security initially widens and then narrows, suggesting a shift from dispersed to concentrated data distribution. This phenomenon is accompanied by polarization and convergence trends, particularly evident after 2015. According to the ETS model, the study forecasts a substantial risk of declining food security in China over the next decade, largely influenced by the ongoing pandemic. In conclusion, this research provides a comprehensive assessment of the changing status of food security in China. It offers early warnings through predictive analysis, addressing the existing research gaps in the field of food security.
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Affiliation(s)
- Chien-Chiang Lee
- School of Economics and Management, Nanchang University, Nanchang, Jiangxi, China.
- Research Center of Central China for Economic and Social Development, Nanchang University, Nanchang, China.
- Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon.
| | - Jiangnan Li
- School of Qianhu, Nanchang University, Nanchang, China
| | - Mingli Zeng
- Shanghai Baolong Automotive Corporation, Shanghai, China
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Zhang Y, Wu X, Ge H, Jiang Y, Sun Z, Ji X, Jia Z, Cui G. A Blockchain-Based Traceability Model for Grain and Oil Food Supply Chain. Foods 2023; 12:3235. [PMID: 37685168 PMCID: PMC10486922 DOI: 10.3390/foods12173235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 08/07/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
The structure of the grain-and-oil-food-supply chain has the characteristics of complexity, cross-regionality, a long cycle, and numerous participants, making it difficult to maintain the safety of supply. In recent years, some phenomena have emerged in the field of grain procurement and sale, such as topping the new with the old, rotating grains, the pressure of grades and prices, and counterfeit oil food, which have seriously threatened grain-and-oil-food security. Blockchain technology has the advantage of decentralization and non-tampering Therefore, this study analyzes the characteristics of traceability data in the grain-and-oil-food-supply chain, and presents a blockchain-based traceability model for the grain-and-oil-food-supply chain. Firstly, a new method combining blockchain and machine learning is proposed to enhance the authenticity and reliability of blockchain source data by constructing anomalous data-processing models. In addition, a lightweight blockchain-storage method and a data-recovery mechanism are proposed to reduce the pressure on supply-chain-data storage and improve fault tolerance. The results indicate that the average query delay of public data is 0.42 s, the average query delay of private data is 0.88 s, and the average data-recovery delay is 1.2 s. Finally, a blockchain-based grain-and-oil-food-supply-chain traceability system is designed and built using Hyperledger Fabric. Compared with the existing grain-and-oil-food-supply chain, the model constructed achieves multi-source heterogeneous data uploading, lightweight storage, data recovery, and traceability in the supply chain, which are of great significance for ensuring the safety of grain-and-oil food in China.
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Affiliation(s)
- Yuan Zhang
- Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China (Y.J.)
- Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou 450001, China
- College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Xuyang Wu
- Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China (Y.J.)
- Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou 450001, China
- College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Hongyi Ge
- Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China (Y.J.)
- Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou 450001, China
- College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Yuying Jiang
- Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China (Y.J.)
- Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou 450001, China
- School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China
| | - Zhenyu Sun
- Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China (Y.J.)
- Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou 450001, China
- College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Xiaodi Ji
- Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China (Y.J.)
- Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou 450001, China
- College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Zhiyuan Jia
- Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China (Y.J.)
- Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou 450001, China
- College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
| | - Guangyuan Cui
- Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China (Y.J.)
- Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou 450001, China
- College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
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Wang Y, Sarkar A. Evaluating the influencing factors of food imports within belt and road initiatives (BRI) countries: An economic threshold model approach. FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 2022. [DOI: 10.3389/fsufs.2022.997549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
International food chains and trade usually support many vulnerable and food-insecure developing countries to ensure stable access to food and sufficient resources to maintain and enhance economic growth. However, the smooth transition of uninterrupted food trade and supply is one of the major concerns for almost every country. Based on the economic threshold model grouping tactics, the study evaluates the influencing factors of global food imports and how different countries foster food imports in different conditions. The empirical data has been comprised of panel data from 91 countries along the “Belt and Road” for the last 21 years. The results show that: (i) Population size and arable land endowment have single and double threshold effects on food imports. (ii) Economic development has different effects on the food imports of each group of countries. It has an inverted U-shaped relationship with countries with large populations and high arable land endowments and a “U-shaped relationship” with countries with low arable land endowments. There is a linear relationship between the food imports of countries with small populations and medium arable land endowments, and there is no significant impact on food imports of countries with large populations and medium arable land endowments. (iii) The impact of infrastructure, technological progress, food stocks, and industrial structure on food imports varies from country to country, but tariff policies have no significant impact on food imports. All member countries should utilize the platform of “Belt and Road Initiatives” to capture the resource endowment and exchange associated science and technology of food production, processing, transport, and storage. Food productivity and self-dependency on food should also be increased.
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Spatiotemporal Evolution of Cultivated Land Non-Agriculturalization and Its Drivers in Typical Areas of Southwest China from 2000 to 2020. REMOTE SENSING 2022. [DOI: 10.3390/rs14133211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Cultivated land resources are crucial to food security and economic development. Exploring the spatiotemporal pattern of cultivated land non-agriculturalization and its drivers is a prerequisite for cultivated land conservation. This paper used GlobeLand30 data to reveal the spatial and temporal pattern, the shift of the gravity center and the drivers of cultivated land non-agriculturalization by employing spatial analysis, gravity center model and the geographical detector model. The results show a dramatic increase in the non-agriculturalization of cultivated land in the period of 2010–2020 compared to 2000–2010. Spatially, the cultivated land non-agriculturalization mainly occurred in areas with high urbanization levels, such as eastern Sichuan Province and western Chongqing Municipality, while the cultivated land non-agriculturalization in other areas was small-scale and spatially scattered. Furthermore, the speed of cultivated land non-agriculturalization showed spatial unevenness, and the gravity center of cultivated land non-agriculturalization shifted towards the northeast at a distance of 123.21 km. The cultivated land non-agriculturalization was affected by GDP per capita, population density, GDP per unit of land and total retail sales of social consumer goods. The key drivers for the cultivated land non-agriculturalization in the study area were the continuous expansion of urban space and the large-scale cultivation of economic fruit trees. The government should promote small-scale machinery suitable for agricultural cultivation in the mountainous and hilly areas of Southwest China, and appropriately develop economic fruit groves and livestock farming to reduce the phenomenon of cultivated land non-foodization.
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