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Assessment and Decomposition of Regional Land Use Efficiency of the Service Sector in China

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  • Mingzhi Zhang

    (School of Economics, Institute of Population and Economic Development, Shandong University of Finance and Economics, Jinan 250014, China)

  • Hongyu Liu

    (School of Economics, Institute of Population and Economic Development, Shandong University of Finance and Economics, Jinan 250014, China)

  • Yangyue Su

    (School of Management Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Xiangyu Zhou

    (School of Economics, Institute of Population and Economic Development, Shandong University of Finance and Economics, Jinan 250014, China)

  • Zhaocheng Li

    (School of Economics, Institute of Population and Economic Development, Shandong University of Finance and Economics, Jinan 250014, China)

  • Chao Chen

    (School of Economics, Institute of Population and Economic Development, Shandong University of Finance and Economics, Jinan 250014, China)

Abstract

High land use efficiency is the key to improving total factor productivity, and also an important force behind achieving sustained economic growth. Existing studies have mainly focused on the land use efficiency of the industry sector. Yet, the issue of land use efficiency of the service sector (SLUE) has been largely overlooked. This study examines regional differences and efficiency decomposition by using a slack based model (SBM) of undesirable output, and the Malmquist productivity index (MPI) under a data envelopment analysis framework. The results reveal that: (1) In China, the land use efficiency of the service sector is unbalanced, showing an inverted growth law of “low in developed areas and high in backward areas”. (2) The land use efficiency of the service sector can be decomposed into technical progress, pure technical efficiency, and scale efficiency. From the decomposition results, the growth rate of pure technical efficiency presents a trend of “low in the east and high in the west”; the scale efficiency also falls into the situation of weak group growth. Technological progress has maintained steady improvement. (3) The coordinated improvement of land use efficiency of the service sector needs to focus on resolving the “beggar-thy-neighbor” issue caused by existing large regional differences. In this article, the puzzle of land use efficiency differences in the service industry is well solved, and thus provides valuable enlightenment for the benign growth of service industries in countries and regions around the world.

Suggested Citation

  • Mingzhi Zhang & Hongyu Liu & Yangyue Su & Xiangyu Zhou & Zhaocheng Li & Chao Chen, 2022. "Assessment and Decomposition of Regional Land Use Efficiency of the Service Sector in China," Land, MDPI, vol. 11(11), pages 1-19, October.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:11:p:1911-:d:955046
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    1. Frank A. Cowell, 1980. "On the Structure of Additive Inequality Measures," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(3), pages 521-531.
    2. Yue Zhou & Yi Chen & Yi Hu, 2021. "Assessing Efficiency of Urban Land Utilisation under Environmental Constraints in Yangtze River Delta, China," IJERPH, MDPI, vol. 18(23), pages 1-18, November.
    3. Koroso, Nesru H. & Zevenbergen, Jaap A. & Lengoiboni, Monica, 2020. "Urban land use efficiency in Ethiopia: An assessment of urban land use sustainability in Addis Ababa," Land Use Policy, Elsevier, vol. 99(C).
    4. Zehui Chen & Qianxi Zhang & Fei Li & Jinli Shi, 2021. "Comprehensive Evaluation of Land Use Benefit in the Yellow River Basin from 1995 to 2018," Land, MDPI, vol. 10(6), pages 1-22, June.
    5. Aiping Wang & Weifen Lin & Bei Liu & Hui Wang & Hong Xu, 2021. "Does Smart City Construction Improve the Green Utilization Efficiency of Urban Land?," Land, MDPI, vol. 10(6), pages 1-18, June.
    6. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    7. Yu, Junqing & Zhou, Kaile & Yang, Shanlin, 2019. "Land use efficiency and influencing factors of urban agglomerations in China," Land Use Policy, Elsevier, vol. 88(C).
    8. Guanglong Dong & Yibing Ge & Haiwei Jia & Chuanzhun Sun & Senyuan Pan, 2021. "Land Use Multi-Suitability, Land Resource Scarcity and Diversity of Human Needs: A New Framework for Land Use Conflict Identification," Land, MDPI, vol. 10(10), pages 1-14, September.
    9. Xue, Dan & Yue, Li & Ahmad, Fayyaz & Draz, Muhammad Umar & Chandio, Abbas Ali & Ahmad, Munir & Amin, Waqas, 2022. "Empirical investigation of urban land use efficiency and influencing factors of the Yellow River basin Chinese cities," Land Use Policy, Elsevier, vol. 117(C).
    10. Bourguignon, Francois, 1979. "Decomposable Income Inequality Measures," Econometrica, Econometric Society, vol. 47(4), pages 901-920, July.
    11. Hengji Li & Jiansheng Qu & Dai Wang & Peng Meng & Chenyu Lu & Jingjing Zeng, 2021. "Spatial-Temporal Integrated Measurement of the Efficiency of Urban Land Use in Yellow River Basin," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    12. Muhammad, Sulaman & Pan, Yanchun & Agha, Mujtaba Hassan & Umar, Muhammad & Chen, Siyuan, 2022. "Industrial structure, energy intensity and environmental efficiency across developed and developing economies: The intermediary role of primary, secondary and tertiary industry," Energy, Elsevier, vol. 247(C).
    13. Zheng, Xiao-Ping, 2001. "Determinants of agglomeration economies and diseconomies: : empirical evidence from Tokyo," Socio-Economic Planning Sciences, Elsevier, vol. 35(2), pages 131-144.
    14. Kun Ge & Shan Zou & Danling Chen & Xinhai Lu & Shangan Ke, 2021. "Research on the Spatial Differences and Convergence Mechanism of Urban Land Use Efficiency under the Background of Regional Integration: A Case Study of the Yangtze River Economic Zone, China," Land, MDPI, vol. 10(10), pages 1-20, October.
    15. Kemeny, Tom & Osman, Taner, 2018. "The wider impacts of high-technology employment: Evidence from U.S. cities," Research Policy, Elsevier, vol. 47(9), pages 1729-1740.
    16. Shorrocks, A F, 1980. "The Class of Additively Decomposable Inequality Measures," Econometrica, Econometric Society, vol. 48(3), pages 613-625, April.
    17. Yang Bai & Wei Zhou & Yanjun Guan & Xue Li & Baohua Huang & Fengchun Lei & Hong Yang & Wenmin Huo, 2020. "Evolution of Policy Concerning the Readjustment of Inefficient Urban Land Use in China Based on a Content Analysis Method," Sustainability, MDPI, vol. 12(3), pages 1-21, January.
    18. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
    19. Lin, Shoufu & Lin, Ruoyun & Sun, Ji & Wang, Fei & Wu, Weixiang, 2021. "Dynamically evaluating technological innovation efficiency of high-tech industry in China: Provincial, regional and industrial perspective," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    20. Lili Yang & Jian Wang & Yuhao Feng & Qun Wu, 2022. "The Impact of the Regional Differentiation of Land Supply on Total Factor Productivity in China: From the Perspective of Total Factor Productivity Decomposition," Land, MDPI, vol. 11(10), pages 1-17, October.
    21. Gao, Xin & Zhang, Anlu & Sun, Zhanli, 2020. "How regional economic integration influence on urban land use efficiency？ A case study of Wuhan metropolitan area, China," Land Use Policy, Elsevier, vol. 90(C).
    22. Liu Yang & Bingyang Han & Zhili Ma & Ting Wang & Yingchao Lin, 2022. "Analysis of the Urban Land Use Efficiency in the New-Type Urbanization Process of China’s Yangtze River Economic Belt," IJERPH, MDPI, vol. 19(13), pages 1-22, July.
    23. Mei Gai & Xiuqi Wang & Changli Qi, 2020. "Spatiotemporal Evolution and Influencing Factors of Ecological Civilization Construction in China," Complexity, Hindawi, vol. 2020, pages 1-14, October.
    24. Rao, P.M. & Balasubrahmanya, M.H., 2017. "The rise of IT services clusters in India: A case of growth by replication," Telecommunications Policy, Elsevier, vol. 41(2), pages 90-105.
    25. Yingkai Tang & Kun Wang & Xuanming Ji & He Xu & Yangqing Xiao, 2021. "Assessment and Spatial-Temporal Evolution Analysis of Urban Land Use Efficiency under Green Development Orientation: Case of the Yangtze River Delta Urban Agglomerations," Land, MDPI, vol. 10(7), pages 1-19, July.
    26. Yayuan Pang & Xinjun Wang, 2020. "Land-Use Efficiency in Shandong (China): Empirical Analysis Based on a Super-SBM Model," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    27. Fare, Rolf & Grosskopf, Shawna, 1992. "Malmquist Productivity Indexes and Fisher Ideal Indexes," Economic Journal, Royal Economic Society, vol. 102(410), pages 158-160, January.
    28. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    29. Shangui Peng & Jian Wang & Hao Sun & Zhengning Guo, 2022. "How Does the Spatial Misallocation of Land Resources Affect Urban Industrial Transformation and Upgrading? Evidence from China," Land, MDPI, vol. 11(10), pages 1-22, September.
    30. Zhou, Di & Huang, Qing & Chong, Zhaohui, 2022. "Analysis on the effect and mechanism of land misallocation on carbon emissions efficiency: Evidence from China," Land Use Policy, Elsevier, vol. 121(C).
    31. Kottas, Angelos T. & Bozoudis, Michail N. & Madas, Michael A., 2020. "Turbofan aero-engine efficiency evaluation: An integrated approach using VSBM two-stage network DEA," Omega, Elsevier, vol. 92(C).
    32. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, December.
    33. Xu, Weiyi & Jin, Xiaobin & Liu, Jing & Zhou, Yinkang, 2021. "Analysis of influencing factors of cultivated land fragmentation based on hierarchical linear model: A case study of Jiangsu Province, China," Land Use Policy, Elsevier, vol. 101(C).
    34. Xin Janet Ge & Xiaoxia Liu, 2021. "Urban Land Use Efficiency under Resource-Based Economic Transformation—A Case Study of Shanxi Province," Land, MDPI, vol. 10(8), pages 1-20, August.
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