Spatial characteristics and influencing factors of China's education aid to Africa

doi:10.3969/j.issn.1004-9479.2025.11.20240854

Abstract

Abstract:

Ensuring inclusive and equitable quality education (SDG4) is one of the important goals of the United Nations' 2030 Agenda for Sustainable Development, and educational aid to Africa is an important area of China's aid to Africa, and the study of its spatial pattern and influencing factors can help optimize the spatial allocation of China's educational aid to Africa. Based on AidData and PRIO-GRID databases, we analyze the spatial characteristics of 532 Chinese aid to African education projects and their influencing factors from 2000 to 2014 by comprehensively applying the research methods of nearest neighbor index, kernel density estimation, spatial autocorrelation and geodetector. The results show that: ① China's educational aid programs to Africa show the distribution characteristics of more in the south and less in the north, more in the coast and less in the inland, with significant spatial clustering characteristics, and the degree of clustering gradually increases over time. ② The distribution of the kernel density of China's educational aid programs to Africa vary significantly at different stages. ③ China's educational aid to Africa hot spots are mainly in the southern region of Africa, and the degree of hot and cold imbalance has increased with the passage of time. ④ There are differences in the degree and significance of the influence of different factors on the spatial pattern of China's educational aid to Africa, and the role of geographic factors has become more and more important with the passage of time. China's education aid to Africa should give full play to the radiation-driven role of the core area, pay more attention to the demand orientation of the recipient countries, strengthen the tripartite cooperation in the field of education aid, and take into full consideration the role of multifactorial interactions in the distribution of China's foreign education aid, so as to optimize the spatial pattern of China's education aid to Africa and to promote its high-quality development.

Key words: education aid, spatial pattern, Africa, influencing factors, geodetector

摘要：

确保包容和公平的优质教育（SDG4）是联合国2030年可持续发展议程的重要目标之一，对非教育援助是中国对非援助的重要领域，对其空间格局及影响因素的研究有助于优化中国对非教育援助的空间分配决策。基于AidData和PRIO-GRID数据库，综合运用最近邻指数、核密度估计、空间自相关和地理探测器等研究方法，分析2000—2014年532个中国对非教育援助项目的空间特征及其影响因素。结果表明：①中国对非教育援助项目呈现南多北少、沿海多内陆少的分布特征，空间集聚特征显著，且随时间推移集聚程度逐渐增强；②中国对非教育援助项目核密度分布在不同阶段差异明显；③中国对非教育援助热点区主要在非洲南部地区，且随时间推移其冷热失衡程度有所提高；④不同影响因素对中国对非教育援助空间格局的影响程度和显著性存在差异，且随时间推移地理因素的作用愈发重要。中国对非教育援助应充分发挥核心区的辐射带动作用，重视受援国的需求导向，并加强教育援助领域的三方合作，充分考虑多因素交互在中国对外教育援助分配中的作用，从而优化中国对非教育援助的空间格局，促进其高质量发展。

关键词: 教育援助, 空间格局, 非洲, 影响因素, 地理探测器

Jianmei HU, Fengling LIU. Spatial characteristics and influencing factors of China's education aid to Africa[J]. World Regional Studies, 2025, 34(11): 46-58.

胡建梅, 刘凤玲. 中国对非教育援助的空间格局及其影响因素研究[J]. 世界地理研究, 2025, 34(11): 46-58.

Figures/Tables 4

References 41

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影响因素	2000年	2005年	2010年	平均
距离本国首都的距离	0.000 2	0.005 8^***	0.036 6^***	0.014 2
与边界的距离	0.002 0^***	0.001 2^**	0.001 7^***	0.001 6
到最近城市的平均通行时间	0.000 4	0.001 6^***	0.006 6^***	0.002 9
城乡区域特征	0.000 0	—	0.041 5^***	0.013 8
平均温度	0.000 2	0.000 5	0.000 7	0.000 5
干旱年份占比	0.000 2	0.000 5	0.000 2	0.000 3
夜间灯光强度	0.007 0^***	0.005 1^***	0.027 4^***	0.013 2
总人口	0.000 4	0.001 9^***	0.005 8^***	0.002 7
儿童平均营养不良率	0.000 4	0.000 1	0.001 2^**	0.000 6
婴儿平均死亡率	0.000 2	0.001 4^***	0.001 2^**	0.000 9
水体覆盖占比	0.000 0	—	0.003 5^***	0.001 2
农业用地占比	0.001 2^**	—	0.004 8^***	0.002 0
森林覆盖占比	0.000 0	—	0.001 4^***	0.000 5
被排斥族群数量	0.000 0	0.000 1	0.000 2	0.000 1

影响因素	2000年	2005年	2010年	平均
距离本国首都的距离	0.000 2	0.005 8^***	0.036 6^***	0.014 2
与边界的距离	0.002 0^***	0.001 2^**	0.001 7^***	0.001 6
到最近城市的平均通行时间	0.000 4	0.001 6^***	0.006 6^***	0.002 9
城乡区域特征	0.000 0	—	0.041 5^***	0.013 8
平均温度	0.000 2	0.000 5	0.000 7	0.000 5
干旱年份占比	0.000 2	0.000 5	0.000 2	0.000 3
夜间灯光强度	0.007 0^***	0.005 1^***	0.027 4^***	0.013 2
总人口	0.000 4	0.001 9^***	0.005 8^***	0.002 7
儿童平均营养不良率	0.000 4	0.000 1	0.001 2^**	0.000 6
婴儿平均死亡率	0.000 2	0.001 4^***	0.001 2^**	0.000 9
水体覆盖占比	0.000 0	—	0.003 5^***	0.001 2
农业用地占比	0.001 2^**	—	0.004 8^***	0.002 0
森林覆盖占比	0.000 0	—	0.001 4^***	0.000 5
被排斥族群数量	0.000 0	0.000 1	0.000 2	0.000 1

影响因素	2000年	2005年	2010年	平均
距离本国首都的距离	0.000 2	0.019 4^***	0.048 9^***	0.022 8
与边界的距离	0.002 0^***	0.002 2^***	0.002 0^***	0.002 1
到最近城市的平均通行时间	0.000 4	0.006 4^***	0.024 6^***	0.010 5
城乡区域特征	0.000 0	—	0.049 4^***	0.016 5
平均温度	0.000 2	0.000 4	0.003 3^***	0.001 3
干旱年份占比	0.000 2	0.000 2	0.000 2	0.000 2
夜间灯光强度	0.007 0^***	0.011 6^***	0.038 7^***	0.019 1
总人口	0.000 4	0.007 0^***	0.024 6^***	0.010 7
儿童平均营养不良率	0.000 4	0.001 8^***	0.002 7^***	0.001 6
婴儿平均死亡率	0.000 2	0.000 8^*	0.004 7^***	0.001 9
水体覆盖占比	0.000 0	—	0.005 5^***	0.001 8
农业用地占比	0.001 2^**	—	0.021 6^***	0.007 6
森林覆盖占比	0.000 0	—	0.004 9^***	0.001 6
被排斥族群数量	0.000 0	0.000 2	0.001 3^**	0.000 5

影响因素	2000年	2005年	2010年	平均
距离本国首都的距离	0.000 2	0.019 4^***	0.048 9^***	0.022 8
与边界的距离	0.002 0^***	0.002 2^***	0.002 0^***	0.002 1
到最近城市的平均通行时间	0.000 4	0.006 4^***	0.024 6^***	0.010 5
城乡区域特征	0.000 0	—	0.049 4^***	0.016 5
平均温度	0.000 2	0.000 4	0.003 3^***	0.001 3
干旱年份占比	0.000 2	0.000 2	0.000 2	0.000 2
夜间灯光强度	0.007 0^***	0.011 6^***	0.038 7^***	0.019 1
总人口	0.000 4	0.007 0^***	0.024 6^***	0.010 7
儿童平均营养不良率	0.000 4	0.001 8^***	0.002 7^***	0.001 6
婴儿平均死亡率	0.000 2	0.000 8^*	0.004 7^***	0.001 9
水体覆盖占比	0.000 0	—	0.005 5^***	0.001 8
农业用地占比	0.001 2^**	—	0.021 6^***	0.007 6
森林覆盖占比	0.000 0	—	0.004 9^***	0.001 6
被排斥族群数量	0.000 0	0.000 2	0.001 3^**	0.000 5