IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i15p8287-d600689.html
   My bibliography  Save this article

Research on High-Quality Development Efficiency and Total Factor Productivity of Regional Economies in China

Author

Listed:
  • Xiangyu Hua

    (School of Economics, Zhejiang University, Hangzhou 310027, China
    Economic Monitoring and Forecasting Office, Zhejiang Economic Information Center (Zhejiang Price Research Institute), Hangzhou 310006, China)

  • Haiping Lv

    (School of Economics and Management, Zhejiang University of Science and Technology, Hangzhou 310023, China)

  • Xiangrong Jin

    (School of Economics, Zhejiang University, Hangzhou 310027, China
    Business School, Ningbo University, Ningbo 315211, China)

Abstract

Different from the developmental mode of western developed countries, China’s economy has changed from a stage of high-speed growth to a stage of high-quality development, where the people’s growing needs for better lives can be met, embodying this new concept of development. The aim of our study is to evaluate the high-quality development efficiency and total factor productivity (TFP) of regional economies in China, and to explore the characteristics of spatial-temporal pattern evolution and their influencing factors. By using the slacks-based measure of directional distance functions (SBM-DDF) model, based on the undesirable output perspective, the high-quality development efficiency and TFP of regional economies in China, from 2000 to 2018, are evaluated in this paper. The exploratory spatial data analysis (ESDA) and Tobit models are then used to identify the spatial-temporal correlation patterns and influencing factors of high-quality development efficiency and TFP. The key results show the following: (1) from 2001 to 2018, the greatest high-quality development efficiency and TFP belonged to China’s eastern region and the least to its central region. (2) U and inverted-U trend lines show that high-quality development efficiency has significant regional difference in the east–west direction, presenting a significant feature of spatial imbalance. (3) Government, urbanization rate, and marketization level play a positive role in their impact of TFP, whereas financial development, infrastructure, foreign direct investment, and capital labor ratio play a negative one.

Suggested Citation

  • Xiangyu Hua & Haiping Lv & Xiangrong Jin, 2021. "Research on High-Quality Development Efficiency and Total Factor Productivity of Regional Economies in China," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8287-:d:600689
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/15/8287/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/15/8287/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Elsadig Musa Ahmed, 2012. "Green TFP Intensity Impact on Sustainable East Asian Productivity Growth (Elsadig Musa Ahmed)," Economic Analysis and Policy, Elsevier, vol. 42(1), pages 67-78, March.
    2. Junhao Zhong & Tinghui Li, 2020. "Impact of Financial Development and Its Spatial Spillover Effect on Green Total Factor Productivity: Evidence from 30 Provinces in China," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, January.
    3. Färe, Rolf & Grosskopf, Shawna, 2010. "Directional distance functions and slacks-based measures of efficiency: Some clarifications," European Journal of Operational Research, Elsevier, vol. 206(3), pages 702-702, November.
    4. Kiyotaka Masuda, 2019. "Eco-Efficiency Assessment of Intensive Rice Production in Japan: Joint Application of Life Cycle Assessment and Data Envelopment Analysis," Sustainability, MDPI, vol. 11(19), pages 1-14, September.
    5. Wang, Yun & Sun, Xiaohua & Guo, Xu, 2019. "Environmental regulation and green productivity growth: Empirical evidence on the Porter Hypothesis from OECD industrial sectors," Energy Policy, Elsevier, vol. 132(C), pages 611-619.
    6. Sun, Yifan & Ma, Anbing & Su, Haorui & Su, Shiliang & Chen, Fei & Wang, Wen & Weng, Min, 2020. "Does the establishment of development zones really improve industrial land use efficiency? Implications for China’s high-quality development policy," Land Use Policy, Elsevier, vol. 90(C).
    7. Jingrong Xu & Dechun Huang & Zhengqi He & Yun Zhu, 2020. "Research on the Structural Features and Influential Factors of the Spatial Network of China’s Regional Ecological Efficiency Spillover," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
    8. Lovell, C. A. Knox & Pastor, Jesus T., 1999. "Radial DEA models without inputs or without outputs," European Journal of Operational Research, Elsevier, vol. 118(1), pages 46-51, October.
    9. Shiying Hou & Liangrong Song, 2021. "Market Integration and Regional Green Total Factor Productivity: Evidence from China’s Province-Level Data," Sustainability, MDPI, vol. 13(2), pages 1-19, January.
    10. Wentao Gu & Jiaye Wang & Xiyuan Hua & Zhongdi Liu, 0. "Entrepreneurship and high-quality economic development: based on the triple bottom line of sustainable development," International Entrepreneurship and Management Journal, Springer, vol. 0, pages 1-27.
    11. Ruiyue Lin & Zhiping Chen, 2015. "Super-efficiency measurement under variable return to scale: an approach based on a new directional distance function," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(9), pages 1506-1510, September.
    12. Fukuyama, Hirofumi & Weber, William L., 2009. "A directional slacks-based measure of technical inefficiency," Socio-Economic Planning Sciences, Elsevier, vol. 43(4), pages 274-287, December.
    13. Li, Ye & Chen, Yiyan, 2021. "Development of an SBM-ML model for the measurement of green total factor productivity: The case of pearl river delta urban agglomeration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    14. Zhao Yang & Hong Fang, 2020. "Research on Green Productivity of Chinese Real Estate Companies—Based on SBM-DEA and TOBIT Models," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    15. Xiaowei Song & Yongpei Hao & Xiaodong Zhu, 2015. "Analysis of the Environmental Efficiency of the Chinese Transportation Sector Using an Undesirable Output Slacks-Based Measure Data Envelopment Analysis Model," Sustainability, MDPI, vol. 7(7), pages 1-20, July.
    16. Miao, Zhuang & Chen, Xiaodong & Baležentis, Tomas & Sun, Chuanwang, 2019. "Atmospheric environmental productivity across the provinces of China: Joint decomposition of range adjusted measure and Luenberger productivity indicator," Energy Policy, Elsevier, vol. 132(C), pages 665-677.
    17. Jingjing Hao & Haoming Shi & Victor Shi & Chenchen Yang, 2020. "Adoption of Automatic Warehousing Systems in Logistics Firms: A Technology–Organization–Environment Framework," Sustainability, MDPI, vol. 12(12), pages 1-14, June.
    18. Färe, Rolf & Grosskopf, Shawna, 2010. "Directional distance functions and slacks-based measures of efficiency," European Journal of Operational Research, Elsevier, vol. 200(1), pages 320-322, January.
    19. Tan, Xiujie & Choi, Yongrok & Wang, Banban & Huang, Xiaoqi, 2020. "Does China's carbon regulatory policy improve total factor carbon efficiency? A fixed-effect panel stochastic frontier analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    20. Dong Tian & Fengtao Zhao & Weisong Mu & Radoslava Kanianska & Jianying Feng, 2016. "Environmental Efficiency of Chinese Open-Field Grape Production: An Evaluation Using Data Envelopment Analysis and Spatial Autocorrelation," Sustainability, MDPI, vol. 8(12), pages 1-13, November.
    21. Shen, Zhiyang & Baležentis, Tomas & Ferrier, Gary D., 2019. "Agricultural productivity evolution in China: A generalized decomposition of the Luenberger-Hicks-Moorsteen productivity indicator," China Economic Review, Elsevier, vol. 57(C).
    22. Shiu-Wan Hung & Chao-Liang Chang & Shu Ming Liu, 2019. "Innovation System Assessment Model for Sustainability Planning in Taiwan," Sustainability, MDPI, vol. 11(24), pages 1-24, December.
    23. Qing Yin & Yadong Wang & Kaidi Wan & Delu Wang, 2020. "Evaluation of Green Transformation Efficiency in Chinese Mineral Resource-Based Cities Based on a Three-Stage DEA Method," Sustainability, MDPI, vol. 12(22), pages 1-29, November.
    24. Jin, Peizhen & Peng, Chong & Song, Malin, 2019. "Macroeconomic uncertainty, high-level innovation, and urban green development performance in China," China Economic Review, Elsevier, vol. 55(C), pages 1-18.
    25. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    26. Simona Bigerna, Maria Chiara DErrico, and Paolo Polinori, 2019. "Environmental and Energy Efficiency of EU Electricity Industry: An Almost Spatial Two Stages DEA Approach," The Energy Journal, International Association for Energy Economics, vol. 0(The New E).
    27. Jian Hou & Heng Chen & Jianzhong Xu, 2017. "External Knowledge Sourcing and Green Innovation Growth with Environmental and Energy Regulations: Evidence from Manufacturing in China," Sustainability, MDPI, vol. 9(3), pages 1-17, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wenhan Ren & Yu Chen, 2022. "Realizing the Improvement of Green Total Factor Productivity of the Marine Economy—New Evidence from China’s Coastal Areas," IJERPH, MDPI, vol. 19(14), pages 1-22, July.
    2. Jing Deng & Tiantian Chen & Yun Zhang, 2023. "Effect of Collaborative Innovation on High-Quality Economic Development in Beijing–Tianjin–Hebei Urban Agglomeration—An Empirical Analysis Based on the Spatial Durbin Model," Mathematics, MDPI, vol. 11(8), pages 1-22, April.
    3. Wanping Yang & Ruoying Huang & Dong Li, 2024. "China’s high-quality economic development: a study of regional variations and spatial evolution," Economic Change and Restructuring, Springer, vol. 57(2), pages 1-30, April.
    4. Xiaoyan Li & Yaxin Tan & Kang Tian, 2022. "The Impact of Environmental Regulation, Industrial Structure, and Interaction on the High-Quality Development Efficiency of the Yellow River Basin in China from the Perspective of the Threshold Effect," IJERPH, MDPI, vol. 19(22), pages 1-15, November.
    5. Lingming Chen & Congjia Huo, 2022. "The Measurement and Influencing Factors of High-Quality Economic Development in China," Sustainability, MDPI, vol. 14(15), pages 1-24, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gang Tian & Jian Shi & Licheng Sun & Xingle Long & Benhai Guo, 2017. "Dynamic changes in the energy–carbon performance of Chinese transportation sector: a meta-frontier non-radial directional distance function approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(2), pages 585-607, November.
    2. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    3. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    4. Zhang, Bin & Lu, Danting & He, Yan & Chiu, Yung-ho, 2018. "The efficiencies of resource-saving and environment: A case study based on Chinese cities," Energy, Elsevier, vol. 150(C), pages 493-507.
    5. Kounetas, Konstantinos & Zervopoulos, Panagiotis D., 2019. "A cross-country evaluation of environmental performance: Is there a convergence-divergence pattern in technology gaps?," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1136-1148.
    6. Chen, Po-Chi & Yu, Ming-Miin & Chang, Ching-Cheng & Hsu, Shih-Hsun & Managi, Shunsuke, 2015. "The enhanced Russell-based directional distance measure with undesirable outputs: Numerical example considering CO2 emissions," Omega, Elsevier, vol. 53(C), pages 30-40.
    7. Lv, Chengchao & Song, Jie & Lee, Chien-Chiang, 2022. "Can digital finance narrow the regional disparities in the quality of economic growth? Evidence from China," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 502-521.
    8. Adler, Nicole & Volta, Nicola, 2016. "Accounting for externalities and disposability: A directional economic environmental distance function," European Journal of Operational Research, Elsevier, vol. 250(1), pages 314-327.
    9. Magdalena Kapelko, 2018. "Measuring inefficiency for specific inputs using data envelopment analysis: evidence from construction industry in Spain and Portugal," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 43-66, March.
    10. Kapelko, Magdalena & Oude Lansink, Alfons, 2017. "Dynamic multi-directional inefficiency analysis of European dairy manufacturing firms," European Journal of Operational Research, Elsevier, vol. 257(1), pages 338-344.
    11. Chia-Hsuan Wu & Ching-Cheng Chang & Po-Chi Chen & Ken-Nan Kuo, 2013. "Efficiency and productivity change in Taiwan’s hospitals: a non-radial quality-adjusted measurement," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 21(2), pages 431-453, March.
    12. Siying Hu & Shangkun Lu & Huiqiu Zhou, 2023. "Public Investment, Environmental Regulation, and the Sustainable Development of Agriculture in China Based on the Decomposition of Green Total Factor Productivity," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    13. Chen, Po-Chi & Yu, Ming-Miin & Chang, Ching-Cheng & Managi, Shunsuke, 2014. "Non-Radial Directional Performance Measurement with Undesirable Outputs," MPRA Paper 57189, University Library of Munich, Germany.
    14. Wang, Zhaohua & Feng, Chao, 2015. "A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: An application of global data envelopment analysis," Applied Energy, Elsevier, vol. 147(C), pages 617-626.
    15. Liu, Guangtian & Wang, Bing & Zhang, Ning, 2016. "A coin has two sides: Which one is driving China’s green TFP growth?," Economic Systems, Elsevier, vol. 40(3), pages 481-498.
    16. Can Cheng & Xiuwen Yu & Heng Hu & Zitian Su & Shangfeng Zhang, 2022. "Measurement of China’s Green Total Factor Productivity Introducing Human Capital Composition," IJERPH, MDPI, vol. 19(20), pages 1-19, October.
    17. Song, Malin & Wang, Jianlin, 2018. "Environmental efficiency evaluation of thermal power generation in China based on a slack-based endogenous directional distance function model," Energy, Elsevier, vol. 161(C), pages 325-336.
    18. Barros, Carlos Pestana & Managi, Shunsuke & Matousek, Roman, 2012. "The technical efficiency of the Japanese banks: Non-radial directional performance measurement with undesirable output," Omega, Elsevier, vol. 40(1), pages 1-8, January.
    19. Manh D. Pham & Valentin Zelenyuk, 2018. "Slack-based directional distance function in the presence of bad outputs: theory and application to Vietnamese banking," Empirical Economics, Springer, vol. 54(1), pages 153-187, February.
    20. Zelenyuk, Valentin & Zhao, Shirong, 2024. "Russell and slack-based measures of efficiency: A unifying framework," European Journal of Operational Research, Elsevier, vol. 318(3), pages 867-876.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8287-:d:600689. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.