The regional differences and random convergence of urban resilience in China
Abstract
This paper focuses on calculating resilience index of 282 cities in China from 2012 to 2019, to analysis the regional differences and random convergence. We use the entropy method to calculate the urban resilience index, adopt the Dagum Gini coefficient method to analyze the regional differences and the sources, explore the variation coefficients method and beta convergence model to diagnose the convergence mechanism. The conclusions are: (1) The urban resilience in China is at a medium and low level with a stable growth tendency, with a significant regional unbalance of “higher in east, and lower in other regions”. As the sub-resilience, there is a big gap in the regional difference of the resilience structure with good performance in social resilience and economic resilience, poor in ecological resilience and infrastructure resilience. (2) The Gini coefficient of urban resilience continuously decreases with the regional unbalance narrowing accordingly. The Gini coefficients in different regions have a phased convergence tendency, and the hypervariable density contribution and intra-regional differences contribution are the main sources of differences in urban resilience. (3) The urban resilience in China and eastern region has σ convergence, while China and all regions have significant absolute β and conditional β convergence. Therefore, this paper proposes to continuously accelerate the urban resilient construction, make up for the shortcomings, and narrow the regional development gap, to promote the healthy and orderly development of cities.
First published online 19 May 2022
Keyword : urban resilience index, regional differences, random convergence, China
How to Cite
Shi, T., Qiao, Y., Zhou, Q., & Zhang, J. (2022). The regional differences and random convergence of urban resilience in China. Technological and Economic Development of Economy, 28(4), 979–1002. https://doi.org/10.3846/tede.2022.16721
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Motesharrei, S., Rivas, J., Kalnay, E., Asrar G. R., Busalacchi, A. J., Cahalan, R. F., Cane, M. A., Colwell, R. R., Feng, K., Franklin, R. S., Hubacek, K., Miralles-Wilhelm, F., Miyoshi, T., Rut,h M., Sagdeev, R., Shirmohammadi, A., Shukla, J., Srebric, J., Yakovenko, V. M., & Zeng, N. (2016). Modeling sustainability: Population, inequality, consumption, and bidirectional coupling of the Earth and Human Systems. National Science Review, 3(4), 470–494. https://doi.org/10.1093/nsr/nww081
Schlör, H., Venghaus, S., & Hake, J. F. (2018). The FEW-Nexus city index – Measuring urban resilience. Applied Energy, 210, 382–392. https://doi.org/10.1016/j.apenergy.2017.02.026
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UN-Habitat. (1996). An urbanizing world: Global report on human settlements. Oxford University Press.
Vu, K. M. (2013). A note on interpreting the beta-convergence effect. Economics Letters, 118(1), 46–49. https://doi.org/10.1016/j.econlet.2012.09.008
Website of central Government of the People’s Republic of China. (2020). Proposal of the Central Committee of the Communist Party of China on formulating the 14th Five-Year Plan for National Economic and Social Development and the Long-range objective for the year 2035. Retrieved July 2, 2021, from http://www.gov.cn/zhengce/2020-11/03/content_5556991.htm
Xiao, X., & Xie, C. (2021). Rational planning and urban governance based on smart cities and big data. Environmental Technology & Innovation, 21, 101381. https://doi.org/10.1016/j.eti.2021.101381
Zhou, Q., Zhu, M. K., Qiao, Y. R., Zhang, X. L., & Chen, J. (2021). Achieving resilience through smart cities? Evidence from China. Habitat International, 111, 102348. https://doi.org/10.1016/j.habitatint.2021.102348
Angelidou, M., & Psaltoglou, A. (2017). An empirical investigation of social innovation initiatives for sustainable urban development. Sustainable Cities and Society, 33, 113–125. https://doi.org/10.1016/j.scs.2017.05.016
Araral, Edoardo. (2020). Why do cities adopt smart technologies? Contingency theory and evidence from the United States. Cities, 106, 102873. https://doi.org/10.1016/j.cities.2020.102873
Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov A., Bazzani A., Wachowicz M., Ouzounis G., & Portugali, Y. (2012). Smart cities of the future. European Physical Journal-Special Topics, 214, 481–518. https://doi.org/10.1140/epjst/e2012-01703-3
Bibri, S. E. (2018). Backcasting in futures studies: A synthesized scholarly and planning approach to strategic smart sustainable city development. European Journal of Futures Research, 6, 13. https://doi.org/10.1186/s40309-018-0142-z
Bibri, S. E., & Krogstie, J. (2020). Data-driven smart sustainable cities of the future: a novel model of urbanism and its core dimensions, strategies, and solutions. Journal of Futures Studies, 25(2), 77–93.
Bifulco, F., Tregua, M., Amitrano, C. C., & D’Auria, A. (2016). ICT and sustainability in smart cities management. International Journal of Public Sector Management, 29(2), 132–147. https://doi.org/10.1108/IJPSM-07-2015-0132
Chang, V. (2021). An ethical framework for big data and smart cities. Technological Forecasting and Social Change, 165, 120559. https://doi.org/10.1016/j.techfore.2020.120559
Chmutina, K., Lizarralde, G., Dainty, A., & Bosher, L. (2016). Unpacking resilience policy discourse. Cities, 58, 70–79. https://doi.org/10.1016/j.cities.2016.05.017
Christensen, L., & Krogman, N. (2012). Social thresholds and their translation into social-ecological management practices. Ecology and Society, 17(1), 5. https://doi.org/10.5751/ES-04499-170105
Diaz Dapena, A., Fernandez Vazquez, E., & Rubiera Morollón, F. (2016). The role of spatial scale in regional convergence: The effect of MAUP in the estimation of -convergence equations. Annals of Regional Science, 56, 473–489. https://doi.org/10.1007/s00168-016-0750-0
Domma, F., Condino, F., & Giordano, S. (2018). A new formulation of the Dagum distribution in terms of income inequality and poverty measures. Physica A: Statistical Mechanics and its Applications, 511, 104–126. https://doi.org/10.1016/j.physa.2018.07.027
Feldmeyer, D., Nowak, W., Jamshed, A., & Birkmann, J. (2021). An open resilience index: Crowdsourced indicators empirically developed from natural hazard and climatic event data. Science of the Total Environment, 774, 145734. https://doi.org/10.1016/j.scitotenv.2021.145734
Folke, C. (2006). Resilience: The emergence of a perspective for social-ecological systems analyses. Global Environmental Change, 16(3), 253–267. https://doi.org/10.1016/j.gloenvcha.2006.04.002
Godschalk, D. R. (2003). Urban hazard mitigation: Creating resilient cities. Natural Hazards Review, 4(3), 136–143. https://doi.org/10.1061/(ASCE)1527-6988(2003)4:3(136)
Hill, E., Wial, H., & Wolman, H. (2008). Exploring regional economic resilience (Working Paper). Institute of Urban and Regional Development, UC Berkeley.
Jha, A. K., Miner, T. W., & Stanton-Geddes, Z. (2013). Building urban resilience: Principles, tools, and practice. World Bank Publications. https://doi.org/10.1596/978-0-8213-8865-5
Johnson, P. A., Robinson, P. J., & Philpot, S. (2020). Type, tweet, tap, and pass: How smart city technology is creating a transactional citizen. Government Information Quarterly, 37(1), 101414. https://doi.org/10.1016/j.giq.2019.101414
Kakamu, K. (2016). Simulation studies comparing Dagum and Singh-Maddala income distributions. Computational Economics, 48, 593–605. https://doi.org/10.1007/s10614-015-9538-z
Kong, J., Phillips, P. C. B., & Sul, D. (2019). Weak sigma-convergence: Theory and applications. Journal of Econometrics, 209(2), 185–207. https://doi.org/10.1016/j.jeconom.2018.12.022
Labaka, L., Marana, P., Gimenez, R., & Hermantes, J. (2019). Defining the roadmap towards city resilience. Technological Forecasting and Social Change, 146, 281–296. https://doi.org/10.1016/j.techfore.2019.05.019
Lu, H.-P., Chen, C.-S., & Yu, H. (2019). Technology roadmap for building a smart city: An exploring study on methodology. Future Generation Computer Systems, 97, 727–742. https://doi.org/10.1016/j.future.2019.03.014
Lv, C., Bian, B., Lee, C.-C., & He, Z. (2021). Regional gap and the trend of green finance development in China. Energy Economics, 102, 105476. https://doi.org/10.1016/j.eneco.2021.105476
Majeed, U., Khan, L. U., Yaqoob, I., Ahsan Kazmi, S. M., Salah, K., & Hong, C. S. (2021). Blockchain for IoT-based smart cities: Recent advances, requirements, and future challenges. Journal of Network and Computer Applications, 181, 103007. https://doi.org/10.1016/j.jnca.2021.103007
Malakar, K., Mishra, T., & Patwardhan, A. (2018). Inequality in water supply in India: An assessment using the Gini and Theil indices. Environment Development and Sustainability, 20, 841–864. https://doi.org/10.1007/s10668-017-9913-0
Matos, P. V., & Faustino, H. C. (2012). Beta-convergence and sigma-convergence in corporate governance in Europe. Economic Modelling, 29(6), 2198–2204. https://doi.org/10.1016/j.econmod.2012.07.004
Miao, Z., Chen, X. D., & Baležentis, T. (2021). Improving energy use and mitigating pollutant emissions across “Three Regions and Ten Urban Agglomerations”: A city-level productivity growth decomposition. Applied Energy, 283, 116296. https://doi.org/10.1016/j.apenergy.2020.116296
Motesharrei, S., Rivas, J., Kalnay, E., Asrar G. R., Busalacchi, A. J., Cahalan, R. F., Cane, M. A., Colwell, R. R., Feng, K., Franklin, R. S., Hubacek, K., Miralles-Wilhelm, F., Miyoshi, T., Rut,h M., Sagdeev, R., Shirmohammadi, A., Shukla, J., Srebric, J., Yakovenko, V. M., & Zeng, N. (2016). Modeling sustainability: Population, inequality, consumption, and bidirectional coupling of the Earth and Human Systems. National Science Review, 3(4), 470–494. https://doi.org/10.1093/nsr/nww081
Schlör, H., Venghaus, S., & Hake, J. F. (2018). The FEW-Nexus city index – Measuring urban resilience. Applied Energy, 210, 382–392. https://doi.org/10.1016/j.apenergy.2017.02.026
Shi, T., Qiao, Y., & Zhou, Q. (2021). Spatiotemporal evolution and spatial relevance of urban resilience: Evidence from cities of China. Growth and Change: A Journal of Urban and Regional Policy, 52(4), 2364–2390. https://doi.org/10.1111/grow.12554
Shi, T., Yang, S., Zhang, W., & Zhou, Q. (2020). Coupling coordination degree measurement and spatiotemporal heterogeneity between economic development and ecological environment – Empirical evidence from tropical and subtropical regions of China. Journal of Cleaner Production, 244, 118739. https://doi.org/10.1016/j.jclepro.2019.118739
Smart Mature Resilience. (2016). Revised resilience maturity mode report. Retrieved May 12, 2021, from https://smr-project.eu/deliverables/revised-resilience-maturity-model
The World Bank. (2008). World development report 2009. World Bank. https://doi.org/10.1596/978-0-8213-7607-2
UN-Habitat. (1996). An urbanizing world: Global report on human settlements. Oxford University Press.
Vu, K. M. (2013). A note on interpreting the beta-convergence effect. Economics Letters, 118(1), 46–49. https://doi.org/10.1016/j.econlet.2012.09.008
Website of central Government of the People’s Republic of China. (2020). Proposal of the Central Committee of the Communist Party of China on formulating the 14th Five-Year Plan for National Economic and Social Development and the Long-range objective for the year 2035. Retrieved July 2, 2021, from http://www.gov.cn/zhengce/2020-11/03/content_5556991.htm
Xiao, X., & Xie, C. (2021). Rational planning and urban governance based on smart cities and big data. Environmental Technology & Innovation, 21, 101381. https://doi.org/10.1016/j.eti.2021.101381
Zhou, Q., Zhu, M. K., Qiao, Y. R., Zhang, X. L., & Chen, J. (2021). Achieving resilience through smart cities? Evidence from China. Habitat International, 111, 102348. https://doi.org/10.1016/j.habitatint.2021.102348