数智融合对黄河流域城市生态韧性的影响研究

哈梅芳, 隋明强

中国环境科学 ›› 2025, Vol. 45 ›› Issue (8) : 4715-4726.

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中国环境科学 ›› 2025, Vol. 45 ›› Issue (8) : 4715-4726.
环境影响评价与管理

数智融合对黄河流域城市生态韧性的影响研究

  • 哈梅芳, 隋明强
作者信息 +

The impact of digital-intelligence integration on urban ecological resilience in the Yellow River Basin

  • HA Mei-fang, SUI Ming-qiang
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文章历史 +

摘要

通过熵权Topsis、耦合协调模型测算了2011~2022年黄河流域城市的数智融合和生态韧性水平,并分析了数智融合的时空演进.在此基础上利用似不相关回归以及有调节的中介模型等,探究了数智融合影响黄河流域城市生态韧性的效应、机制及机制调节因素.结果表明:研究期内黄河流域数智融合的整体水平由0.144逐渐上升到0.353,截至2022年各城市已基本摆脱极度和严重失调.数智融合存在明显的空间聚集现象,聚集中心主要分布在山东半岛及各省省会,且聚集中心和非中心的差距逐渐增大.数智融合对黄河流域城市生态韧性的总体影响在1%的统计水平显著为正,回归系数为0.197,但对生态韧性抵抗力层面的影响在1%的统计水平显著为负,系数为-0.011,存在一定削弱作用.绿色创新和公众环境关注发挥了机制作用,数智融合对绿色创新和公众环境关注的回归系数分别为0.744、0.345,分别在1%、5%统计水平下显著.金融发展水平能正向调节绿色创新和公众环境关注的机制作用,金融发展水平和数智融合的交互项对绿色创新和公众环境关注的回归系数分别为0.122、0.146,均在1%统计水平下显著.异质性分析表明,仅下游地区的数智融合显著提升了城市生态韧性,系数为0.302,在1%统计水平下显著,上、中游并不明显,非资源型、大数据综合试点城市较其他城市效果更优,系数分别为0.235、0.285,均在1%统计水平下显著.研究结果为黄河流域城市生态韧性在数智时代下的优化提供了科学依据和政策参考.

Abstract

This study measured the levels of Digital-Intelligence Integration (DII) and ecological resilience in cities within the Yellow River Basin from 2011 to 2022 using the entropy weight TOPSIS method and a coupling coordination model. The spatio-temporal evolution of DII was further analyzed. Building on these measurements, seemingly unrelated regression (SUR) and moderated mediation models were employed to investigate the effects, underlying mechanisms, and moderating factors influencing the relationship between DII and urban ecological resilience in the region. The results showed that the overall level of DII in the Yellow River Basin increased from 0.144 to 0.353 during the study period. By 2022, most cities had transitioned out of states of extreme and severe imbalance. A distinct spatial clustering pattern was observed for DII, with cluster centers predominantly located in the Shandong Peninsula and provincial capitals. The gap between these cluster centers and non-central cities was found to be widening. DII exerted a significantly positive overall effect on urban ecological resilience (regression coefficient = 0.197, p<0.01). However, its effect on the resistance dimension of ecological resilience was significantly negative (coefficient = -0.011, p<0.01), suggesting a potential weakening effect. Green innovation and public environmental concern acted as significant mediators. The regression coefficients of DII on green innovation and public environmental concern were 0.744 (p<0.01) and 0.345 (p<0.05), respectively. Financial development positively moderated these mediating roles, as the interaction terms between financial development and DII yielded significant coefficients of 0.122 and 0.146 (both p<0.01) for green innovation and public environmental concern, respectively. Heterogeneity analysis indicated that DII significantly enhanced urban ecological resilience only in downstream regions (coefficient =0.302, p<0.01), with no significant effects observed in upstream or midstream regions. Moreover, non-resource-based cities and those designated as big data comprehensive pilot zones exhibited stronger effects (coefficients = 0.235 and 0.285, respectively, both p<0.01) compared to other cities. These findings provide scientific support and policy references for optimizing urban ecological resilience in the Yellow River Basin in the era of digital intelligence.

关键词

数智融合 / 生态韧性 / 流域治理 / 时空分异

Key words

digital-intelligence integration / ecological resilience / basin governance / spatiotemporal differentiation

引用本文

导出引用
哈梅芳, 隋明强. 数智融合对黄河流域城市生态韧性的影响研究[J]. 中国环境科学. 2025, 45(8): 4715-4726
HA Mei-fang, SUI Ming-qiang. The impact of digital-intelligence integration on urban ecological resilience in the Yellow River Basin[J]. China Environmental Science. 2025, 45(8): 4715-4726
中图分类号: X321    F424   

参考文献

[1] 任保平,巩羽浩.数字经济助推黄河流域高质量发展的路径与政策[J].经济问题, 2023,(2):15-22. Ren B P, Gong Y H. The path and policy of digital economy promoting the high-quality development of the Yellow River Basin [J]. On Economic Problems, 2023,(2):15-22.
[2] 钞小静,沈路.新型数字基础设施对黄河流域城市生态效率的空间溢出效应——基于2013~2020年沿线97个城市数据的分析[J].陕西师范大学学报(哲学社会科学版), 2023,52(6):46-60. Chao X J, Shen L. Patial spillover effect of new digital infrastructure on urban ecological efficiency in the Yellow River Basin——based on data from 97 cities along the line from 2013 to 2020[J]. Journal of Shaanxi Normal University(Philosophy and Social Sciences Edition), 2023,52(6):46-60.
[3] Wang Y, Kang M, Liu Y, et al. Can digital intelligence and cyber- physical-social systems achieve global food security and sustainability? [J]. IEEE/CAA Journal of Automatica Sinica, 2023,10(11):2070-2080.
[4] 张杰,刘亚琪.行之有效还是徒劳无功?数智融合发展对长三角地区减污降碳协同治理的影响研究[J/OL].长江流域资源与环境, 1-17[2025-04-10]. Zhang J, Liu Y Q. Effective or futile? research on the impact of numerical intelligence integration development on collaborative governance of pollution reduction and carbon reduction in the Yangtze River Delta Region [J/OL]. Resources and Environment in the Yangtze Basin, 1-17[2025-04-10].
[5] 熊磊,章琦,严由亮,等.长江中游城市群数智发展水平与碳生产率的耦合协调时空特征及驱动因素[J].经济地理, 2024,44(10): 46-54. Xiong L, Zhang Q, Yan Y L. Spatiotemporal characteristics and driving factors of the coupled coordination between digital intelligence development and carbon productivity in urban agglomerations of the middle reaches of Yangtze River [J]. Economic Geography, 2024,44(10):46-54.
[6] 邝嫦娥,刘江月,李文意.数智融合赋能与制造业企业绿色转型[J].当代财经, 2024,(5):114-127. Kuang C E, Liu J Y, Li W Y. Digital-intelligence integration empowerment and green transformation of manufacturing enterprises [J]. Contemporary Finance & Economics, 2024,(5):114-127.
[7] Shamsipour A, Jahanshahi S, Mousavi S S, et al. Assessing and mapping urban ecological resilience using the loss-gain approach: a case study of Tehran, Iran [J]. Sustainable Cities and Society, 2024, 103:105252.
[8] 王鑫.乡村生态韧性治理的水平测度与融合发展[J].统计与决策, 2023,39(20):45-50. Wang X. Level measurement and integration development of rural ecological resilience governance [J]. Statistics & Decision, 2023, 39(20):45-50.
[9] 刘亮,阮俊杰,庄海涛.数智化赋能长三角城市群绿色发展的效应研究[J].经济地理, 2024,44(9):123-132. Liu L, Ruan J J, Zhuang H T. Impact of digital intelligence on green development in the Yangtze River Delta Urban Agglomeration [J]. Economic Geography, 2024,44(9):123-132.
[10] 赵政楠,茹少峰,薛飞.黄河流域生态韧性的时空格局与分布动态演进——基于能值生态足迹模型的分析[J].中国人口·资源与环境, 2024,34(8):136-147. Zhao Z N, Ru S F, Xue Fei. Spatio-temporal pattern and dynamic evolution of ecological resilience in the Yellow River Basin: based on the analysis of energy ecological footprint model [J]. China Population, Resources and Environment, 2024,34(8):136-147.
[11] 陶洁怡,董平,陆玉麒.长三角地区生态韧性时空变化及影响因素分析[J].长江流域资源与环境, 2022,31(9):1975-1987. Tao J Y, Dong P, Lu Y Q. Spatial-temporal analysis and influencing factors of ecological resilience in Yangtze River Delta [J]. Resources and Environment in the Yangtze Basin, 2022,31(9):1975-1987.
[12] 祁小丽,栾博,周文君,等.基于STIRPAT模型的海岸带生态韧性影响因素研究——以深圳市为例[J].中国环境科学, 2025,45(1): 416-429. Qi X L, Luan B Zhou W J, et al. Factors influencing urban coastal ecological resilience based on the STIRPAT model: a case study of Shenzhen City [J]. China Environmental Science, 2025,45(1):416-429.
[13] 郭峰,熊云军,石庆玲,等.数字经济与行政边界地区经济发展再考察——来自卫星灯光数据的证据[J].管理世界, 2023,39(4):16-33. Guo F, Xiong Y J, Shi Q L, et al. The re-estimation of digital economy and economic development in administrative border areas: evidence from nighttime light data [J]. Journal of Management World, 2023, 39(4):16-33.
[14] Argyroudis S A, Mitoulis S A, Chatzi E, et al. Digital technologies can enhance climate resilience of critical infrastructure [J]. Climate Risk Management, 2022,35:100387.
[15] Jiang W, Jiang N. From sustainable development towards resilience: does digital finance matter in enhancing ecological resilience? [J]. Sustainable Development.
[16] Tang Y, Wang Y. Impact of digital economy on ecological resilience of resource-based cities: spatial spillover and mechanism [J]. Environmental Science and Pollution Research, 2023,30(14):41299-41318.
[17] Wu D, Xie Y, Lyu S. Disentangling the complex impacts of urban digital transformation and environmental pollution: evidence from smart city pilots in China [J]. Sustainable Cities and Society, 2023,88:104266.
[18] Li G, Wang L. Study of regional variations and convergence in ecological resilience of Chinese cities [J]. Ecological Indicators, 2023,154:110667.
[19] 黄和平,周桂明,李国民.产业数字化对绿色全要素生产率的影响机制研究——兼议环境规制的门槛效应[J].中国环境科学, 2025, 45(3):1713-1730. Huang H P, Zhou G M, Li G M. The impact of digital economy on industrial carbon emission performance: threshold effect analysis of heterogeneous environmental regulations. [J]. China Environmental Science, 2025,45(3):1713-1730.
[20] 庞瑞芝,王宏鸣.数字经济与城市绿色发展:赋能还是负能? [J].科学学研究, 2024,42(7):1397-1408. Pang R Z, Wang H M. Digital economy and urban green development: empowerment or negative energy? [J]. Studies in Science of Science, 2024,42(7):1397-1408.
[21] 李珒,赵静.环境治理中的数字赋能与监管效率提升——基于人工替代的视角[J].中国人口·资源与环境, 2023,33(8):130-137. Li J, Zhang J. Digital empowerment and regulatory efficiency improvement in environmental governance: from the perspective of labor substitution [J]. China Population, Resources and Environment, 2023,33(8):130-137.
[22] Harahap M A K, Kraugusteeliana K, Pramono S A, et al. The role of information technology in improving urban governance [J]. Jurnal Minfo Polgan, 2023,12(1):371-379.
[23] 黎振强,游肖月.数字经济发展、产业结构升级对长江经济带生态效率的影响效应[J].长江流域资源与环境, 2024,33(8):1624-1636. Li Z Q, You X Y. Effects of the digital economy development and the industrial structure upgrading on the ecological efficiency in the Yangtze River Economic Belt [J]. Resources and Environment in the Yangtze Basin, 2024,33(8):1624-1636.
[24] 辛雅儒,申晨,冯锐,等.机器人应用、CEO绿色经历与企业绿色技术创新[J].经济管理, 2024,46(7):129-145. Xin Y R, Shen C, Feng R, et al. CEO green experience, robot application and green technology innovation [J]. Business and Management Journal, 2024,46(7):129-145.
[25] Li D, Yang W, Huang R. The multidimensional differences and driving forces of ecological environment resilience in China [J]. Environmental Impact Assessment Review, 2023,98:106954.
[26] Fu S, Lou J, Wang J, et al. Enhancing urban ecological resilience through integrated green technology progress: evidence from Chinese cities [J]. Environmental Science and Pollution Research, 2024,31(25): 36349-36366.
[27] 王薇,谢雄辉.互联网使用对公众环境治理效能感的影响研究[J].统计与信息论坛, 2022,37(3):108-117. Wang W, Xie X H. Research on the impact of internet use on public environmental governance sense of efficacy [J]. Journal of Statistics and Information, 2022,37(3):108-117.
[28] 邱成梅,苏健,周丽娟.中国公众环境意识及影响因素变化——基于CSS2013和CSS2019的实证研究[J].干旱区资源与环境, 2023, 37(5):37-44. Qiu C M, Su J, Zhou L J. Empirical research on the changes of public environmental awareness and influencing factors in China [J]. Journal of Arid Land Resources and Environment, 2023,37(5):37-44.
[29] 吴力波,杨眉敏,孙可哿.公众环境关注度对企业和政府环境治理的影响[J].中国人口·资源与环境, 2022,32(2):1-14. Wu L B, Yang M M, Sun K. Impact of public environmental attention on environmental governance of enterprises and local governments [J]. China Population, Resources and Environment, 2022,32(2):1-14.
[30] 许金花,叶妃三,商丽霞.公众环境关注度对企业碳绩效水平的影响研究[J].管理学报, 2024,21(6):865-875. Xu J H, Ye F S, Shang L X. The impact of public environmental concern on the level of corporate carbon performance [J]. Chinese Journal of Management, 2024,21(6):865-875.
[31] 葛鹏飞,黄秀路,徐璋勇.金融发展、创新异质性与绿色全要素生产率提升——来自“一带一路”的经验证据[J].财经科学, 2018,(1):1-14. Ge P F, Huang X L, Xu Z Y. Financial Development, innovation heterogeneity and promotion of green TFP: evidence from “The Belt and Road” [J]. Finance & Economics, 2018,(1):1-14.
[32] 汤龙,陈享光.数字金融能缓解短贷长投对企业持续绿色创新的不利影响吗? [J].中国人口·资源与环境, 2024,34(9):123-131. Tang L, Chen X G. Can digital finance alleviate the adverse effects of short-term loans for long-term investments on corporate sustainable green innovation? [J]. China Population, Resources and Environment, 2024,34(9):123-131.
[33] 江艇.因果推断经验研究中的中介效应与调节效应[J].中国工业经济, 2022,(5):100-120. Jiang T. Mediating effects and moderating effects in causal inference [J]. China Industrial Economics, 2022,(5):100-120.
[34] 彭文斌,曹笑天.城市更新作用下生态韧性时空分异及其影响效应——以环长株潭城市群为例[J].经济地理, 2023,43(10):44-52. Peng W B, Cao T X. Spatiotemporal differentiation of ecological resilience under urban renewal and its influencing mechanisms around the Changsha-Zhuzhou-Xiangtan Urban Agglomeration [J]. Economic Geography, 2023,43(10):44-52.
[35] 王淑佳,孔伟,任亮,等.国内耦合协调度模型的误区及修正[J].自然资源学报, 2021,36(3):793-810. Wang G J, Kong W, Ren L, et al. Research on misuses and modification of coupling coordination degree model in China [J]. Journal of Natural Resources, 2021,36(3):793-810.
[36] 郭蔚,潘红玉.金融发展促进了区域科技创新吗?——基于教育人力资本调节效应的分析[J].财经理论与实践, 2024,45(2):33-39. Guo W, Pan H Y. Does financial development promote regional science and technology innovation?: an analysis based on the moderating effect of educational human capital [J]. The Theory and Practice of Finance and Economics, 2024,45(2):33-39.
[37] 王海,闫卓毓,郭冠宇,等.数字基础设施政策与企业数字化转型:“赋能”还是“负能”? [J].数量经济技术经济研究, 2023,40(5):5-23. Wang H, Yan Z M, Guo G Y, et al. Digital infrastructure policy and digital transformation of enterprises: “empowerment” or “negative energy”? [J]. Journal of Quantitative & Technological Economics, 2023,40(5):5-23.
[38] 郑世林,熊丽.中国培育经济发展新动能的成效研究[J].技术经济, 2021,40(1):1-11. Zheng S L, Xiong L. Assessment and policy suggestions for new kinetic energy of China's economic development [J]. Journal of Technology Economics, 2021,40(1):1-11.

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国家自然科学基金项目(72364029)

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