Abstract
High-quality development is the primary task of comprehensively building a socialist, modern country, as well as the primary task of building urban agglomerations in China. Based on the five development concepts, this paper used the entropy method to measure the High Quality Development Index (HQDI) of the five major urban agglomerations. The results showed that the HQDI of the five major urban agglomerations shows a fluctuating upward trend. First, using the Dagum Gini coefficient to explore the sources of HQDI development differences in urban agglomerations, we found that the main source of HQDI differences in urban agglomerations was interregional differences, while intra-regional differences were not important. Second, kernel density estimation was used to test the dynamic evolution trend of HQDI within urban agglomerations. There was a polarisation phenomenon in the HQDI of urban agglomerations, such as the Pearl River Delta urban agglomeration and the Chengdu-Chongqing urban agglomeration. But overall, the degree of imbalance had decreased. Third, using geographic detectors to examine the driving factors of HQDI in urban agglomerations, we found that the main driving forces for improving HQDI in urban agglomerations were economic growth, artificial intelligence technology and fiscal decentralisation. All the interaction factors had greater explanatory power for the spatial differentiation of HQDI, which can be divided into two types: two-factor improvement and non-linear improvement. This study is conducive to improving and enriching the theoretical system for evaluating the high quality development of urban agglomerations, and provides policy references for promoting the high quality development of urban agglomerations.
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References
Acheampong A O, 2019. Modelling for insight: does financial development improve environmental quality? Energy Economics, 83: 156–179. doi: https://doi.org/10.1016/j.eneco.2019.06.025
Arminen H, Menegaki A N, 2019. Corruption, climate and the energy-environment-growth nexus. Energy Economics, 80: 621–634. doi: https://doi.org/10.1016/j.eneco.2019.02.009
Chen L, Huo C, 2022. The measurement and influencing factors of high-quality economic development in China. Sustainability, 14(15): 9293. doi: https://doi.org/10.3390/su14159293
Chen L, Wang N, Li Q Y et al., 2023. Environmental regulation, foreign direct investment and China’s economic development under the new normal: restrain or promote? Environment, Development and Sustainabilit, 25(5): 4195–1216. doi: https://doi.org/10.1007/s10668-022-02239-0
Chen Y, Miao Q Q, Zhou Q, 2022. Spatiotemporal differentiation and driving force analysis of the high-quality development of urban agglomerations along the Yellow River Basin. International Journal of Environmental Research and Public Health, 19(4): 2484. doi: https://doi.org/10.3390/ijerph19042484
Chen Z, Li X J, Xia X L, 2021. Measurement and spatial convergence analysis of China’s agricultural green development index. Environmental Science and Pollution Research, 28(16): 19694–19709. doi: https://doi.org/10.1007/s11356-020-11953-z
Fang C L, 2022. On integrated urban and rural development. Journal of Geographical Sciences, 32(8): 1411–1426. doi: https://doi.org/10.1007/s11442-022-2003-8
García-Girón J, Tolonen K T, Soininen J et al., 2022. Anthropogenic land-use impacts on the size structure of macroinvertebrate assemblages are jointly modulated by local conditions and spatial processes. Environmental Research, 204: 112055. doi: https://doi.org/10.1016/j.envres.2021.112055
Guo G H, Li K, Zhang D et al., 2022. Quantitative source apportionment and associated driving factor identification for soil potential toxicity elements via combining receptor models, SOM, and geo-detector method. Science of The Total Environment, 830: 154721. doi: https://doi.org/10.1016/j.scitotenv.2022.154721
Han Feng, Xie Rui, 2017. Does the agglomeration of producer services reduce carbon emissions?. The Journal of Quantitative & Technical Economics, 34(3): 40–58. (in Chinese)
Ji X L, Li X Z, He Y Q et al., 2019. A simple method to improve estimates of county-level economics in China using nighttime light data and GDP growth rate. ISPRS International Journal of Geo-Information, 8(9): 419. doi: https://doi.org/10.3390/ijgi8090419
Li Chuntao, Yan Xuwen, Song Min et al., 2020. Fintech and corporate innovation: evidence from Chinese NEEQ-listed companies. China Industrial Economics, (1): 81–98. (in Chinese)
Li D B, Chang Y, Simayi Z et al., 2022. Multi-scenario dynamic simulation of urban agglomeration development on the northern slope of the Tianshan Mountains in Xinjiang, China, with the goal of high-quality urban construction. Sustainability, 14(11): 6862. doi: https://doi.org/10.3390/su14116862
Li X S, Lu Y L, Huang R T, 2021. Whether foreign direct investment can promote high-quality economic development under environmental regulation: evidence from the Yangtze River Economic Belt, China. Environmental Science and Pollution Research, 28(17): 21674–21683. doi: https://doi.org/10.1077/111566-220-12032-z
Liu C G, Sun W, Li P X, 2022. Characteristics of spatiotemporal variations in coupling coordination between integrated carbon emission and sequestration index: a case study of the Yangtze River Delta, China. Ecological Indicators, 135: 108520. doi: https://doi.org/10.1016/j.ecolind.2021.108520
Liu L, Si S S, Li J, 2023. Research on the effect of regional talent allocation on high-quality economic development: based on the perspective of innovation-driven growth. Sustainability, 15(7): 6315. doi: https://doi.org/10.3390/SU15076315
Liu Y Z, Yang R J, Sun M Y et al., 2022. Regional sustainable development strategy based on the coordination between ecology and economy: a case study of Sichuan Province, China. Ecological Indicators, 134: 108445. doi: https://doi.org/10.1016/j.ecolind.2021.108445
Ma T, Liu Y S, Yang M, 2022. Spatial-temporal heterogeneity for commercial building carbon emissions in China: based the Dagum Gini Coefficient. Sustainability, 14(9): 5243. doi: https://doi.org/10.3390/su14095243
Malhi G S, Kaur M, Kaushik P, 2021. Impact of climate change on agriculture and its mitigation strategies: a review. Sustainability, 13(3): 1318. doi: https://doi.org/10.3390/su13031318
Mei Y D, Ma T, Su R, 2021. How marketized is China’s natural gas industry? A bibliometric analysis. Journal of Cleaner Production, 306: 127289. doi: https://doi.org/10.1016/j.jclepro.2021.127289
Mu X F, Fang C L, Yang Z Q, 2022. Spatio-temporal evolution and dynamic simulation of the urban resilience of Beijing-Tianjin-Hebei urban agglomeration. Journal of Geographical Sciences, 32(9): 1766–1790. doi: https://doi.org/10.1007/s11442-022-2022-5
Ren Baoping, Jiang Jie, Guo Han et al., 2017. Beyond Quantity: The Paradigm and Standard of Economics of Quality. Beijing: People’s Publishing House. (in Chinese)
Shen W K, Xia W Q, Li S F, 2022. Dynamic coupling trajectory and spatial-temporal characteristics of high-quality economic development and the digital economy. Sustainability, 14(8): 4543. doi: https://doi.org/10.3390/su14084543
Solow R M, 1957. Technical change and the aggregate production function. The review of Economics and Statistics, 39(3): 312–320. doi: https://doi.org/10.2307/1926047
Song J N, Liu Z R, Fang K et al., 2023. An evolving energy-environmental-economic system towards coordination: spatiotemporal features and key drivers. Journal of Cleaner Production, 384: 135537. doi: https://doi.org/10.1016/j.jclepro.2022.135537
Stoica O, Roman A, Rusu V D, 2020. The nexus between entre-preneurship and economic growth: a comparative analysis on groups of countries. Sustainability, 12(3): 1186. doi: https://doi.org/10.3390/su12031186
Wang Jinfeng, Xu Chengdong, 2017. Geodetector: principle and prospective. Acta Geographica Sinica, 72(1): 116–134. (in Chinese)
Wan J J, Li Y X, Ma C C et al., 2021. Measurement of coupling coordination degree and spatio-temporal characteristics of the social economy and ecological environment in the Chengdu-Chongqing urban agglomeration under high-quality development. International Journal of Environmental Research and Public Health, 18(21): 11629. doi: https://doi.org/10.3390/ijerph182111629
Wang M J, Yu D H, Chen H Q et al., 2022. Comprehensive measurement, spatiotemporal evolution, and spatial correlation analysis of high-quality development in the manufacturing industry. Sustainability, 14(10): 5807. doi: https://doi.org/10.3390/su14105807
Wang S Q, Zheng X Q, 2023. Dominant transition probability: combining CA-Markov model to simulate land use change. Environment, Development and Sustainability, 25(7): 6829–6847. doi: https://doi.org/10.1007/s10668-022-02337-z
Wang Y P, Shen Z H, 2021. Comparing Luojia 1-01 and VIIRS nighttime light data in detecting urban spatial structure using a threshold-based kernel density estimation. Remote Sensing, 13(8): 1574. doi: https://doi.org/10.3390/rs13081574
Wei Jianfei, Ding Zhiwei, Meng Yiwei et al., 2020. Regional sustainable assessment at city level based on CSDIS (China Sustainable Development Indicator System) concept in the New Era, China. Chinese Geographical Science, 30(6): 976–992. doi: https://doi.org/10.1007/s11769-020-1158-4
Wu H T, Hao Y, Geng C Z et al., 2023. Ways to improve cross-regional resource allocation: does the development of digitalization matter? Journal of Economic Analysi, 2(4): 7. doi: https://doi.org/10.58567/jea02040001
Xu M X, 2022. Research on the relationship between fiscal decentralization and environmental management efficiency under competitive pressure: evidence from China. Environmental Science and Pollution Research, 29(16): 23392–23406. doi: https://doi.org/10.1007/s11356-021-17426-1
Yang Y, Zhang Y Y, Yang H et al., 2022. Horizontal ecological compensation as a tool for sustainable development of urban agglomerations: exploration of the realization mechanism of Guanzhong Plain urban agglomeration in China. Environmental Science & Policy, 137: 301–313. doi: https://doi.org/10.1016/J.ENVSCI.2022.09.004
Yu W L, Zhang L P, Yang C, 2023. The impact of the digital economy on enterprise innovation behavior: based on CiteSpace knowledge graph analysis. Frontiers in Psychology, 14: 1031294. doi: https://doi.org/10.3389/fpsyg.2023.1031294
Zhang F T, Tan H M, Zhao P et al., 2022. What was the spatiotemporal evolution characteristics of high-quality development in China? A case study of the Yangtze River economic belt based on the ICGOS-SBM model. Ecological Indicators, 145: 109593. doi: https://doi.org/10.1016/j.ecolind.2022.109593
Zhao J Q, Xiao Y, Sun S Q et al., 2022. Does China’s increasing coupling of ‘urban population’ and ‘urban area’ growth indicators reflect a growing social and economic sustainability? Journal of Environmental Management, 301: 113932. doi: https://doi.org/10.1016/j.jenvman.2021.113932
Zhou C, Li X, Lin X et al., 2022. Influencing factors of the high-quality economic development in China based on LASSO model. Energy Reports, 8: 1055–1065. doi: https://doi.org/10.1016/j.egyr.2022.10.167
Zou W Y, Xiong Y J, 2023. Does artificial intelligence promote industrial upgrading? Evidence from China. Economic Research-Ekonomska Istraživanja, 36(1): 1666–1687. doi: https://doi.org/10.1080/1331677X.2022.2092168
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ZOU Weiyong: conceptualization, methodology, writing-original draft, data collection and data curation, software, writing-review and editing, validation; XU Lingli: supervision, funding acquisition.
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Foundation item: Under the auspices of National Natural Science Foundation of China (No. 72373094, 72303149), Scientific Research Start-up Funds of Guangdong Ocean University (No. 060302082319)
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Dynamic Development Characteristics and Driving Factors of High Quality Development Level in China’s Five Major Urban Agglomerations
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Zou, W., Xu, L. Dynamic Development Characteristics and Driving Factors of High Quality Development Level in China’s Five Major Urban Agglomerations. Chin. Geogr. Sci. (2024). https://doi.org/10.1007/s11769-024-1425-x
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DOI: https://doi.org/10.1007/s11769-024-1425-x