IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i13p7746-d846691.html
   My bibliography  Save this article

An Analysis of the Spatiotemporal Characteristics and Diversity of Grain Production Resource Utilization Efficiency under the Constraint of Carbon Emissions: Evidence from Major Grain-Producing Areas in China

Author

Listed:
  • Haokun Wang

    (School of Economics and Management, Northeast Forestry University, Harbin 150040, China)

  • Hong Chen

    (School of Economics and Management, Northeast Forestry University, Harbin 150040, China
    Ecological Civilization Construction and Green Development Think Tank of Heilongjiang Province, Harbin 150040, China)

  • Tuyen Thi Tran

    (School of Economics and Management, Northeast Forestry University, Harbin 150040, China)

  • Shuai Qin

    (School of Economics and Management, Northeast Forestry University, Harbin 150040, China)

Abstract

As the most important driving force for ensuring the effective supply of grain in the country, the production stability of the major grain-producing areas directly concerns the national security of China. In this paper, considering the “water–soil–energy–carbon” correlation, water, soil and energy resource factors, and carbon emission constraints were included in an index system, and the global common frontier boundary three-stage super-efficient EBM–GML model was used to measure the grain production resource utilization efficiency of the major grain-producing areas in China from 2000 to 2019. This paper also analyzed the static and dynamic spatiotemporal characteristics and the restrictions of utilization efficiency. The results showed that, under the measurement of the traditional data envelopment analysis model, the grain production resource utilization efficiency in the major producing areas is relatively high, but there is still room to improve by more than 20%, and grain production still has enormous growth potential. After excluding external environmental and random factors, it was found that the utilization efficiency of grain production resources in the major producing areas decreased, and the efficiency and ranking of provinces changed significantly. External factors inhibit pure technical efficiency and expand the scale efficiency. The utilization efficiency of Northeast China was much higher than that of the Huang-Huai-Hai region and the middle and upper reaches of the Yangtze River region, and its grain production resource allocation management had obvious advantages. The total factor productivity index of food production resources showed an upward trend as a whole, and its change was affected by both technological efficiency and technological progress, of which technological progress had the greater impact. Therefore, reducing the differences in the external environment of different regions while making adjustments in accordance with their own potential is an effective way to further improve the utilization efficiency of food production resources.

Suggested Citation

  • Haokun Wang & Hong Chen & Tuyen Thi Tran & Shuai Qin, 2022. "An Analysis of the Spatiotemporal Characteristics and Diversity of Grain Production Resource Utilization Efficiency under the Constraint of Carbon Emissions: Evidence from Major Grain-Producing Areas ," IJERPH, MDPI, vol. 19(13), pages 1-25, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:7746-:d:846691
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/13/7746/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/13/7746/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jean-Joseph Minviel & Timo Sipiläinen, 2021. "A dynamic stochastic frontier approach with persistent and transient inefficiency and unobserved heterogeneity," Post-Print hal-03236127, HAL.
    2. Helfand, Steven M. & Levine, Edward S., 2004. "Farm size and the determinants of productive efficiency in the Brazilian Center-West," Agricultural Economics, Blackwell, vol. 31(2-3), pages 241-249, December.
    3. Tone, Kaoru & Tsutsui, Miki, 2010. "An epsilon-based measure of efficiency in DEA - A third pole of technical efficiency," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1554-1563, December.
    4. Eliav Shtull-Trauring & Asher Azenkot & Nirit Bernstein, 2022. "Translational Platform for Increasing Water Use Efficiency in Agriculture: Comparative Analysis of Plantation Crops," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 571-587, January.
    5. Zhiwei Pan & Decai Tang & Haojia Kong & Junxia He, 2022. "An Analysis of Agricultural Production Efficiency of Yangtze River Economic Belt Based on a Three-Stage DEA Malmquist Model," IJERPH, MDPI, vol. 19(2), pages 1-15, January.
    6. Boris Bravo-Ureta & Daniel Solís & Víctor Moreira López & José Maripani & Abdourahmane Thiam & Teodoro Rivas, 2007. "Technical efficiency in farming: a meta-regression analysis," Journal of Productivity Analysis, Springer, vol. 27(1), pages 57-72, February.
    7. Wang, Xue-Chao & Klemeš, Jiří Jaromír & Wang, Yutao & Dong, Xiaobin & Wei, Hejie & Xu, Zihan & Varbanov, Petar Sabev, 2020. "Water-Energy-Carbon Emissions nexus analysis of China: An environmental input-output model-based approach," Applied Energy, Elsevier, vol. 261(C).
    8. Atici, Kazim Baris & Podinovski, Victor V., 2015. "Using data envelopment analysis for the assessment of technical efficiency of units with different specialisations: An application to agriculture," Omega, Elsevier, vol. 54(C), pages 72-83.
    9. Xin Yang & Guangyin Shang, 2020. "Smallholders’ Agricultural Production Efficiency of Conservation Tillage in Jianghan Plain, China—Based on a Three-Stage DEA Model," IJERPH, MDPI, vol. 17(20), pages 1-12, October.
    10. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    11. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
    12. Kuang, Bing & Lu, Xinhai & Zhou, Min & Chen, Danling, 2020. "Provincial cultivated land use efficiency in China: Empirical analysis based on the SBM-DEA model with carbon emissions considered," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    13. Jean Joseph Minviel & Timo Sipiläinen, 2021. "A dynamic stochastic frontier approach with persistent and transient inefficiency and unobserved heterogeneity," Agricultural Economics, International Association of Agricultural Economists, vol. 52(4), pages 575-589, July.
    14. Meenakshi Sharma & Rajesh Kaushal & Prashant Kaushik & Seeram Ramakrishna, 2021. "Carbon Farming: Prospects and Challenges," Sustainability, MDPI, vol. 13(19), pages 1-15, October.
    15. Harold Fried & Shelton Schmidt & Suthathip Yaisawarng, 1999. "Incorporating the Operating Environment Into a Nonparametric Measure of Technical Efficiency," Journal of Productivity Analysis, Springer, vol. 12(3), pages 249-267, November.
    16. Qiu, Tongwei & Shi, Xinjie & He, Qinying & Luo, Biliang, 2021. "The paradox of developing agricultural mechanization services in China: Supporting or kicking out smallholder farmers?," China Economic Review, Elsevier, vol. 69(C).
    17. Zhao, Rongqin & Liu, Ying & Tian, Mengmeng & Ding, Minglei & Cao, Lianhai & Zhang, Zhanping & Chuai, Xiaowei & Xiao, Liangang & Yao, Lunguang, 2018. "Impacts of water and land resources exploitation on agricultural carbon emissions: The water-land-energy-carbon nexus," Land Use Policy, Elsevier, vol. 72(C), pages 480-492.
    18. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    19. M. Dinesh Kumar & Nitin Bassi & O. P. Singh, 2020. "Rethinking on the methodology for assessing global water and food challenges," International Journal of Water Resources Development, Taylor & Francis Journals, vol. 36(2-3), pages 547-564, March.
    20. Tone, Kaoru & Tsutsui, Miki, 2010. "Dynamic DEA: A slacks-based measure approach," Omega, Elsevier, vol. 38(3-4), pages 145-156, June.
    21. C A K Lovell & A P B Rouse, 2003. "Equivalent standard DEA models to provide super-efficiency scores," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(1), pages 101-108, January.
    22. Mosbah, Ezzeddine & Zaibet, Lokman & Dharmapala, P. Sunil, 2020. "A new methodology to measure efficiencies of inputs (outputs) of decision making units in Data Envelopment Analysis with application to agriculture," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    23. Chen, Po-Chi & Yu, Ming-Miin & Chang, Ching-Cheng & Hsu, Shih-Hsun, 2008. "Total factor productivity growth in China's agricultural sector," China Economic Review, Elsevier, vol. 19(4), pages 580-593, December.
    24. Ferreira, Marcelo Dias Paes & Féres, José Gustavo, 2020. "Farm size and Land use efficiency in the Brazilian Amazon," Land Use Policy, Elsevier, vol. 99(C).
    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. Bin Fan & Mingyang Li, 2022. "The Effect of Heterogeneous Environmental Regulations on Carbon Emission Efficiency of the Grain Production Industry: Evidence from China’s Inter-Provincial Panel Data," Sustainability, MDPI, vol. 14(21), pages 1-27, November.
    2. Hexiong Zhang & Yun Qin & Jinlong Xu & Wenqin Ren, 2023. "Analysis of the Evolution Characteristics and Impact Factors of Green Production Efficiency of Grain in China," Land, MDPI, vol. 12(4), pages 1-14, April.
    3. Bing Jiang & Wenjie Tang & Meijia Li & Guangchao Yang & Xiaoshang Deng & Lihang Cui, 2023. "Assessing Land Resource Carrying Capacity in China’s Main Grain-Producing Areas: Spatial–Temporal Evolution, Coupling Coordination, and Obstacle Factors," Sustainability, MDPI, vol. 15(24), pages 1-26, December.

    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. Yu, Ming-Miin, 2010. "Assessment of airport performance using the SBM-NDEA model," Omega, Elsevier, vol. 38(6), pages 440-452, December.
    2. Pointon, Charlotte & Matthews, Kent, 2016. "Dynamic efficiency in the English and Welsh water and sewerage industry," Omega, Elsevier, vol. 58(C), pages 86-96.
    3. Chiu, Yung-Ho & Chen, Yu-Chuan, 2009. "The analysis of Taiwanese bank efficiency: Incorporating both external environment risk and internal risk," Economic Modelling, Elsevier, vol. 26(2), pages 456-463, March.
    4. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    5. Pointon, Charlotte & Matthews, Kent, 2016. "Reprint of: Dynamic efficiency in the English and Welsh water and sewerage industry," Omega, Elsevier, vol. 60(C), pages 98-108.
    6. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, April.
    7. Qin, Quande & Li, Xin & Li, Li & Zhen, Wei & Wei, Yi-Ming, 2017. "Air emissions perspective on energy efficiency: An empirical analysis of China’s coastal areas," Applied Energy, Elsevier, vol. 185(P1), pages 604-614.
    8. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    9. Zebin Zheng & Wenjun Xiao & Ziye Cheng, 2023. "China’s Green Total Factor Energy Efficiency Assessment Based on Coordinated Reduction in Pollution and Carbon Emission: From the 11th to the 13th Five-Year Plan," Sustainability, MDPI, vol. 15(9), pages 1-20, April.
    10. Ren, Siyu & Hao, Yu & Wu, Haitao, 2022. "The role of outward foreign direct investment (OFDI) on green total factor energy efficiency: Does institutional quality matters? Evidence from China," Resources Policy, Elsevier, vol. 76(C).
    11. Angela Stefania Bergantino & Enrico Musso, 2011. "A Multi-step Approach to Model the Relative Efficiency of European Ports: The Role of Regulation and Other Non-discretionary Factors," Chapters, in: Kevin Cullinane (ed.), International Handbook of Maritime Economics, chapter 18, Edward Elgar Publishing.
    12. Zhong, Meirui & Huang, Gangli & He, Ruifang, 2022. "The technological innovation efficiency of China's lithium-ion battery listed enterprises: Evidence from a three-stage DEA model and micro-data," Energy, Elsevier, vol. 246(C).
    13. Nolwenn Roudaut & Anne Vanhems, 2012. "Explaining firms efficiency in the Ivorian manufacturing sector: a robust nonparametric approach," Journal of Productivity Analysis, Springer, vol. 37(2), pages 155-169, April.
    14. 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.
    15. Morais, G. & Braga, J.M., 2018. "Irrigation and farm efficiency in Brazil," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 275987, International Association of Agricultural Economists.
    16. Miki Tsutsui & Kaoru Tone, 2007. "Separation of uncontrollable factors and time shift effects from DEA scores," GRIPS Discussion Papers 07-09, National Graduate Institute for Policy Studies.
    17. McDonald, John, 2009. "Using least squares and tobit in second stage DEA efficiency analyses," European Journal of Operational Research, Elsevier, vol. 197(2), pages 792-798, September.
    18. Yuanying Chi & Wenbing Zhou & Zhenyu Wang & Yu Hu & Xiao Han, 2021. "The Influence Paths of Agricultural Mechanization on Green Agricultural Development," Sustainability, MDPI, vol. 13(23), pages 1-16, November.
    19. Avkiran, Necmi K. & Rowlands, Terry, 2008. "How to better identify the true managerial performance: State of the art using DEA," Omega, Elsevier, vol. 36(2), pages 317-324, April.
    20. Zhicheng Lai & Lei Li & Zhuomin Tao & Tao Li & Xiaoting Shi & Jialing Li & Xin Li, 2023. "Spatio-Temporal Evolution and Influencing Factors of Ecological Well-Being Performance from the Perspective of Strong Sustainability: A Case Study of the Three Gorges Reservoir Area, China," IJERPH, MDPI, vol. 20(3), pages 1-25, January.

    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:13:p:7746-:d:846691. 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.