Abstract
With the development of urbanization, the problem of the disintegrated between industry and city became more prominent, exploring the reasons. The efficiency of new-type industry has been the crucial factor in city-industry integration. This paper constructs the measurement index system of new-type urbanization via DEA-BCC methodology, starting from the quality of urbanization to analyze the efficiency of urbanization. This paper chooses the total energy consumption, general public budget expenditure, and the proportion of employment personnel in the tertiary industry in all urban units as input variables. The total retail sales of consumer goods, urbanization rate, average annual concentration of pm2.5 (popW), and built-up area as output variables. This paper uses DEA method to measure the comprehensive efficiency value, technical efficiency value, and scale efficiency value of new urbanization in Shanghai, and analyzes the influencing factors of urbanization efficiency. The results show the following: (1) The overall level of comprehensive efficiency value, technical efficiency value, and scale efficiency of Shanghai’s new-type urbanization are relatively high, especially the technical efficiency basically stays at a high level. The overall trend of scale efficiency and comprehensive efficiency is consistent, and the comprehensive efficiency is greatly influenced by scale efficiency. (2) The technical efficiency of urbanization in Shanghai is close to the optimal, and there is little space for further increasing technological input to improve the comprehensive efficiency of new-type urbanization. The scale efficiency is slightly lower than the technical efficiency, and there is still some space for optimization. (3) In terms of urbanization input indicators, Shanghai’s total energy consumption and general public budget input were too much in the early years, which led to the reduction of urbanization efficiency, and the situation has been improved in recent years. In terms of the output index of urbanization, increasing the total retail sales of social consumer goods and the output of built-up area can make the urbanization efficiency of Shanghai reach the optimal efficiency.
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References
Ameer W, Ali MS, Farooq F, Ayub B, Waqas M (2023) Renewable energy electricity, environmental taxes, and sustainable development: empirical evidence from E7 economies. Environ Sci Pollut Res 1–16. https://doi.org/10.1007/S11356-023-26930-5/METRICS
Cao H (2022) Entrepreneurship education-infiltrated computer-aided instruction system for college music majors using convolutional neural network. Front Psychol 13. https://doi.org/10.3389/FPSYG.2022.900195
Chen Y, Chen Z, Guo D, Zhao Z, Lin T, Zhang C (2022) Underground space use of urban built-up areas in the central city of Nanjing: nsight based on a dynamic population distribution. Underg Space 7(5):748–766. https://doi.org/10.1016/j.undsp.2021.12.006
Du L, Razzaq A, Waqas M (2022) The impact of COVID-19 on small- and medium-sized enterprises (SMEs): empirical evidence for green economic implications. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-22221-7
Ehigiamusoe KU, Lean HH, Babalola SJ, Poon WC (2022) The roles of financial development and urbanization in degrading environment in Africa: unravelling non-linear and moderating impacts. Energy Rep 8:1665–1677. https://doi.org/10.1016/j.egyr.2021.12.048
Guo Q, Zhong J (2022) The effect of urban innovation performance of smart city construction policies: evaluate by using a multiple period difference-in-differences model. Technol Forecast Soc Chang 184:122003. https://doi.org/10.1016/j.techfore.2022.122003
Gupta M, Saini S, Sahoo M (2022) Determinants of ecological footprint and PM2.5: role of urbanization, natural resources and technological innovation. Environ Challenges 7:100467. https://doi.org/10.1016/j.envc.2022.100467
Hai Ming L, Gang L, Hua H, Waqas M (2022) Modeling the influencing factors of electronic word-of-mouth about CSR on social networking sites. Environ Sci Pollut Res 1–18. https://doi.org/10.1007/s11356-022-20476-8
Hailiang Z, Chau KY, Waqas M (2023) Does green finance and renewable energy promote tourism for sustainable development: empirical evidence from China. Renew Energy 207:660–671. https://doi.org/10.1016/j.renene.2023.03.032
He H, Tuo S, Lei K, Gao A (2023) Assessing quality tourism development in China: an analysis based on the degree of mismatch and its influencing factors. Environ Dev Sustain. https://doi.org/10.1007/s10668-023-03107-1
Jiang S, Zhou J, Qiu S (2022) Digital agriculture and urbanization: mechanism and empirical research. Technol Forecast Soc Change 180. https://doi.org/10.1016/J.TECHFORE.2022.121724
Li M, Yao-Ping Peng M, Nazar R, Ngozi Adeleye B, Shang M, Waqas M (2022a) How does energy efficiency mitigate carbon emissions without reducing economic growth in post COVID-19 era. Front Energy Res 10:1–14. https://doi.org/10.3389/fenrg.2022.832189
Li Q, Miao Y, Zeng X, Tarimo CS, Wu C, Wu J (2020) Prevalence and factors for anxiety during the coronavirus disease 2019 (COVID-19) epidemic among the teachers in China. J Affect Disord 277:153–158. https://doi.org/10.1016/J.JAD.2020.08.017
Li T, Li Y, Hoque MA, Xia T, Tarkoma S, Hui P (2022b) To what extent we repeat ourselves? Discovering daily activity patterns across mobile app usage. IEEE Trans Mob Comput 21:1492–1507. https://doi.org/10.1109/TMC.2020.3021987
Li X, Sun Y (2021) Application of RBF neural network optimal segmentation algorithm in credit rating. Neural Comput Appl 33:8227–8235. https://doi.org/10.1007/S00521-020-04958-9/METRICS
Li L (2023) An empirical analysis of rural labor transfer and household income growth in China. J Chin Hum Resour Manag 14(1):106–116. https://doi.org/10.47297/wspchrmWSP2040-800505.20231401
Liu X, Kong M, Tong D, Zeng X, Lai Y (2022) Property rights and adjustment for sustainable development during post-productivist transitions in China. Land Use Policy 122:106379. https://doi.org/10.1016/j.landusepol.2022.106379
Ma J, Wang J, Szmedra P (2019) Economic efficiency and its influencing factors on urban agglomeration—an analysis based on China’s top 10 urban agglomerations. Sustain 11:5380. https://doi.org/10.3390/SU11195380
Ren Y, Li H, Shen L, Zhang Y, Chen Y, Wang J (2018) What is the efficiency of fast urbanization? A China study. Sustain 10:3180. https://doi.org/10.3390/SU10093180
Safdar S, Khan A, Andlib Z (2022) Impact of good governance and natural resource rent on economic and environmental sustainability: an empirical analysis for South Asian economies. Environ Sci Pollut Res Int 29:82948–82965. https://doi.org/10.1007/S11356-022-21401-9
Tang YM, Chau KY, Fatima A, Waqas M (2022) Industry 4.0 technology and circular economy practices: business management strategies for environmental sustainability. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-19081-6
Wei H, Su H, Han Z (2018) Evaluation and analysis on the efficiency of urbanization in China: from the perspective of environment and resource efficiency. Chinese J Urban Environ Stud 06:1–14. https://doi.org/10.1142/S2345748118500082
Xing Z, Huang J, Wang J (2023) Unleashing the potential: exploring the nexus between low-carbon digital economy and regional economic-social development in China. J Clean Prod 413. https://doi.org/10.1016/j.jclepro.2023.137552
Xiong Z, Weng X, Wei Y (2022) SandplayAR: evaluation of psychometric game for people with generalized anxiety disorder. Arts Psychother 80. https://doi.org/10.1016/J.AIP.2022.101934
Yang B, Zhang Z, Wu H (2022) Detection and attribution of changes in agricultural eco-efficiency within rapid urbanized areas: a case study in the urban agglomeration in the middle reaches of Yangtze River. China Ecol Indic 144:109533. https://doi.org/10.1016/J.ECOLIND.2022.109533
Yang C, Zhang C, Li Q, Liu H, Gao W, Shi T, Liu X, Wu G (2020) Rapid urbanization and policy variation greatly drive ecological quality evolution in Guangdong-Hong Kong-Macau Greater Bay Area of China: a remote sensing perspective. Ecol Indic 115. https://doi.org/10.1016/j.ecolind.2020.106373
Yang T, Guan X, Qian Y, Xing W, Wu H (2019a) Efficiency evaluation of urban road transport and land use in Hunan Province of China based on hybrid data envelopment analysis (DEA) models. Sustain 11:3826. https://doi.org/10.3390/SU11143826
Yang Y, Liu J, Lin Y, Li Q (2019b) The impact of urbanization on China’s residential energy consumption. Struct Chang Econ Dyn 49:170–182. https://doi.org/10.1016/j.strueco.2018.09.002
Yu L, Gao X, Lyu J, Feng Y, Zhang S, Andlib Z (2023) Green growth and environmental sustainability in China: the role of environmental taxes. Environ Sci Pollut Res Int 30:22702–22711. https://doi.org/10.1007/S11356-022-23355-4
Zhang L, Huang L, Xia J, Duan K (2022) Spatial-temporal evolution and its influencing factors on urban land use efficiency in China’s Yangtze River economic belt. Land 12:76. https://doi.org/10.3390/LAND12010076
Zhang W, Zhou L, Liu L, -Preface Chaohui Ye, al, Lin Wang, Z., Zhou, B., Wei, W., Li, M., Jiao, Y., (2020) Evaluation and strategy of new-type urbanization policy based on S-CAD method - a case study of Wuhan, China. IOP Conf Ser Earth Environ Sci 514:032014. https://doi.org/10.1088/1755-1315/514/3/032014
Acknowledgements
This work is supported by the Applied Economics of Shanghai Dian Ji University (no. 16YSXK03) and the Youth Fund for Humanities and Social Sciences of the Ministry of Education (no. 20YJCZH027).
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Lin Su: conceptualization, data curation, methodology, writing—original draft, data curation. Jingjing Jia: visualization, supervision, editing, writing—review and editing, and software.
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Su, L., Jia, J. New-type urbanization efficiency measurement in Shanghai under the background of industry city integration. Environ Sci Pollut Res 30, 80224–80233 (2023). https://doi.org/10.1007/s11356-023-27933-y
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DOI: https://doi.org/10.1007/s11356-023-27933-y