IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i15p9177-d873193.html
   My bibliography  Save this article

Does Optimization of Industrial Structure Improve Green Efficiency of Industrial Land Use in China?

Author

Listed:
  • Bingqing Li

    (School of Public Administration, China University of Geosciences, Wuhan 430074, China)

  • Zhanqi Wang

    (School of Public Administration, China University of Geosciences, Wuhan 430074, China)

  • Feng Xu

    (School of Public Administration, China University of Geosciences, Wuhan 430074, China)

Abstract

Improving the green efficiency of industrial land use (GEILU) is essential to promoting low-pollution and highly efficient development, and industrial structure is a key factor in this dynamic. This paper aims to reveal how the optimization of industrial structure (OIS) affects GEILU in China. First, an analytical framework was proposed to understand the effect mechanisms from the perspective of rationalization, upgrading, and ecologization of industrial structure. Second, the panel data of 31 provincial units collected from 2006 to 2020 were taken as cases for empirical study. The super-SBM model was adopted to measure GEILU, and some variables were used to evaluate OIS. Finally, the spatial effects of OIS on GEILU were analyzed based on the spatial Durbin model. The results show that the GEILU presented a wave change and kept increasing after 2016. From a global perspective, the rationalization of industrial structure helped improve GEILU; however, the upgrading and ecologization of industrial structure generated inhibiting effects. When integrating the three perspectives, optimization of industrial structure was considered to have negative effects on GEILU. The negative effects stemmed from an inefficient expansion of industrial land and pollution from heavy chemical industries. From a phased perspective, in the early period of this study, the outdated technology in traditional industries brought about the negative effects; however, with high-knowledge and high-tech industries forming a market scale, optimization of industrial structure gradually became conducive to the improvement of GEILU. This study suggests that eliminating the market segmentation between provinces and accelerating the development of high-knowledge and high-tech industries can help promote low-pollution and highly efficient industrial land use in China.

Suggested Citation

  • Bingqing Li & Zhanqi Wang & Feng Xu, 2022. "Does Optimization of Industrial Structure Improve Green Efficiency of Industrial Land Use in China?," IJERPH, MDPI, vol. 19(15), pages 1-18, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9177-:d:873193
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/15/9177/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/15/9177/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ahmad, Munir & Wu, Yiyun, 2022. "Natural resources, technological progress, and ecological efficiency: Does financial deepening matter for G-20 economies?," Resources Policy, Elsevier, vol. 77(C).
    2. Xie, Hualin & Chen, Qianru & Lu, Fucai & Wu, Qing & Wang, Wei, 2018. "Spatial-temporal disparities, saving potential and influential factors of industrial land use efficiency: A case study in urban agglomeration in the middle reaches of the Yangtze River," Land Use Policy, Elsevier, vol. 75(C), pages 518-529.
    3. Khezrimotlagh, Dariush & Zhu, Joe & Cook, Wade D. & Toloo, Mehdi, 2019. "Data envelopment analysis and big data," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1047-1054.
    4. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    5. Troy D. Abel & Jonah White & Stacy Clauson, 2015. "Risky Business: Sustainability and Industrial Land Use across Seattle’s Gentrifying Riskscape," Sustainability, MDPI, vol. 7(11), pages 1-36, November.
    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. Weidong Sun & Zhigang Chen & Danyang Wang, 2019. "Can Land Marketization Help Reduce Industrial Pollution?," IJERPH, MDPI, vol. 16(12), pages 1-16, June.
    8. Groenwold, Nicolaas & Lee, Guoping & Chen, Anping, 2008. "Inter-regional spillovers in China: The importance of common shocks and the definition of the regions," China Economic Review, Elsevier, vol. 19(1), pages 32-52, March.
    9. Zhao, Zhe & Bai, Yuping & Wang, Guofeng & Chen, Jiancheng & Yu, Jiangli & Liu, Wei, 2018. "Land eco-efficiency for new-type urbanization in the Beijing-Tianjin-Hebei Region," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 19-26.
    10. 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.
    11. Deng, Xiangzheng & Gibson, John, 2019. "Improving eco-efficiency for the sustainable agricultural production: A case study in Shandong, China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 394-400.
    12. Yan, Siqi & Peng, Jianchao & Wu, Qun, 2020. "Exploring the non-linear effects of city size on urban industrial land use efficiency: A spatial econometric analysis of cities in eastern China," Land Use Policy, Elsevier, vol. 99(C).
    13. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    14. Liu, Jingming & Hou, Xianhui & Wang, Zhanqi & Shen, Yue, 2021. "Study the effect of industrial structure optimization on urban land-use efficiency in China," Land Use Policy, Elsevier, vol. 105(C).
    15. 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.
    16. Xinqi Zheng & Bing Geng & Xiang Wu & Lina Lv & Yecui Hu, 2014. "Performance Evaluation of Industrial Land Policy in China," Sustainability, MDPI, vol. 6(8), pages 1-16, July.
    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. Mengtian Zhang & Huiling Wang, 2023. "Evolution of Industrial Ecology and Analysis of Influencing Factors: The Yellow River Basin in China," Land, MDPI, vol. 12(7), pages 1-21, June.
    2. Huilan Wei & Yanlong Wang & Chendan Yang & Peiyao Yu, 2024. "Empirical Analysis of the Role of Digital Agriculture in Enabling Coordinated Development of Ecosystem Services and Human Well-Being: Evidence from Provincial Panel Data in China," Sustainability, MDPI, vol. 16(23), pages 1-34, November.
    3. Chunhua Luo & Dianlong Wei & Wunhong Su & Jinjing Lu, 2023. "Association between Regional Digitalization and High-Quality Economic Development," Sustainability, MDPI, vol. 15(3), pages 1-19, January.
    4. Zhang, Han & Zheng, Jinhui & Hunjra, Ahmed Imran & Zhao, Shikuan & Bouri, Elie, 2024. "How does urban land use efficiency improve resource and environment carrying capacity?," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    5. Yuling Wu & Min Luo, 2025. "Study on Spatial-Temporal Evolution Law of Green Land Use Efficiency in Resource-Based Cities," Land, MDPI, vol. 14(2), pages 1-23, February.
    6. Guihong Yang & Xuxi Wang & Li Peng & Xinyue Zhang, 2024. "Dynamic Interactions of Urban Land Use Efficiency, Industrial Structure, and Carbon Emissions Intensity in Chinese Cities: A Panel Vector Autoregression (PVAR) Approach," Land, MDPI, vol. 14(1), pages 1-22, December.
    7. Ping Han & Ziyu Zhou, 2023. "The Harmonious Relationship between Energy Utilization Efficiency and Industrial Structure Development under Carbon Emission Constraints: Measurement, Quantification, and Identification," Sustainability, MDPI, vol. 15(14), pages 1-21, 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. 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.
    2. Zhang, Han & Zheng, Jinhui & Hunjra, Ahmed Imran & Zhao, Shikuan & Bouri, Elie, 2024. "How does urban land use efficiency improve resource and environment carrying capacity?," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    3. 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.
    4. 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.
    5. Hangsheng Yang & Min Tang & Xiangrui Chao & Pu Li, 2024. "How environmental regulation influences the green economy efficiency of resource-based cities: an empirical study from China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(11), pages 27249-27276, November.
    6. Yin Ma & Minrui Zheng & Xinqi Zheng & Yi Huang & Feng Xu & Xiaoli Wang & Jiantao Liu & Yongqiang Lv & Wenchao Liu, 2023. "Land Use Efficiency Assessment under Sustainable Development Goals: A Systematic Review," Land, MDPI, vol. 12(4), pages 1-21, April.
    7. Yu, Binbin & Zhou, Xinru, 2023. "Urban administrative hierarchy and urban land use efficiency: Evidence from Chinese cities," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 178-195.
    8. Qiao Wang & Meixian Wei & Nan Wang & Qiuhua Chen, 2024. "The Impact of Human Capital and Tourism Industry Agglomeration on China’s Tourism Eco-Efficiency: An Analysis Based on the Undesirable Super-SBM-ML Model," Sustainability, MDPI, vol. 16(16), pages 1-24, August.
    9. Hangang Hu & Lisha Pan & Xin Jing & Guan Li & Yuefei Zhuo & Zhongguo Xu & Yang Chen & Xueqi Wang, 2022. "The Spatiotemporal Non-Stationary Effect of Industrial Agglomeration on Urban Land Use Efficiency: A Case Study of Yangtze River Delta, China," Land, MDPI, vol. 11(5), pages 1-27, May.
    10. Yahong Liu & Hailian Sun & Lei Shi & Huimin Wang & Zhai Xiu & Xiao Qiu & Hong Chang & Yu Xie & Yang Wang & Chengjie Wang, 2021. "Spatial-Temporal Changes and Driving Factors of Land-Use Eco-Efficiency Incorporating Ecosystem Services in China," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    11. Chengzhen Song & Qingfang Liu & Jinping Song & Zhengyun Jiang & Zhilin Lu & Yueying Chen, 2022. "Land Use Efficiency in the Yellow River Basin in the Background of China’s Economic Transformation: Spatial-Temporal Characteristics and Influencing Factors," Land, MDPI, vol. 11(12), pages 1-22, December.
    12. Jie Zhang & Yajing Wang & Jiangfeng Li, 2023. "Maximize Eco-Economic Benefits with Minimum Land Resources Input: Evaluation and Evolution of Land Use Eco-Efficiency of Agglomerations in Middle Reaches of Yangtze River, China," IJERPH, MDPI, vol. 20(3), pages 1-19, January.
    13. Tone, Kaoru & Toloo, Mehdi & Izadikhah, Mohammad, 2020. "A modified slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 287(2), pages 560-571.
    14. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    15. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    16. Ningyi Liu & Yongyu Wang, 2022. "Urban Agglomeration Ecological Welfare Performance and Spatial Convergence Research in the Yellow River Basin," Land, MDPI, vol. 11(11), pages 1-18, November.
    17. Vicente J. Bolós & Rafael Benítez & Vicente Coll-Serrano, 2023. "Continuous models combining slacks-based measures of efficiency and super-efficiency," 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. 31(2), pages 363-391, June.
    18. Liu, Fuh-Hwa Franklin & Wang, Peng-hsiang, 2008. "DEA Malmquist productivity measure: Taiwanese semiconductor companies," International Journal of Production Economics, Elsevier, vol. 112(1), pages 367-379, March.
    19. Xiaoxue Wei & Rui Zhao & Ranran Li & Ke Liu, 2025. "High-quality development efficiency in Yangtze River Delta urban agglomeration: analysis of spatiotemporal evaluation and influencing factors," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(3), pages 7297-7323, March.
    20. Yung-ho Chiu & Chin-wei Huang & Chung-te Ting, 2012. "A non-radial measure of different systems for Taiwanese tourist hotels’ efficiency assessment," 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. 20(1), pages 45-63, March.

    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:jijerp:v:19:y:2022:i:15:p:9177-:d:873193. 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.