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Agricultural Efficiency in Different Regions of China: An Empirical Analysis Based on Dynamic SBM-DEA Model

Author

Listed:
  • Shao-Yin Hsu

    (Department of Accounting, Ming-Chuan University, 250, Zhong Shan N. Rd., Sec. 5, Taipei 111, Taiwan)

  • Chih-Yu Yang

    (Department of Economics, Soochow University, 56, Kueiyang St., Sec. 1, Taipei 100, Taiwan)

  • Yueh-Ling Chen

    (Department of Applied Economics, Fo Guang University, No. 160, Linwei Rd., Jiaosi, Yilan County 262, Taiwan)

  • Ching-Cheng Lu

    (Department of Business, National Open University, No. 172, Zhongzheng Road, Luzhou District, New Taipei City 247, Taiwan)

Abstract

This study applies the dynamic slacks-based measure (DSBM) and the total-factor agricultural efficiency (TFAE) to explore the overall agricultural production efficiency of 30 administrative regions and the eastern, central, and western regions of China from 2012 to 2016. The previous literature has mainly focused on China’s economic development and experience, but as the economy continues to grow, more food is needed and agricultural labor is shifting to urban areas. Little attention has been paid to the impact of limited agricultural land on agricultural production efficiency. Therefore, this paper uses the agricultural land area as the carry-over variable and uses agricultural labor, total agricultural machinery power, rural electricity consumption, agricultural fertilizer use, and agricultural GDP as variables to discuss the efficiency of agricultural production in different regions. The empirical results show that from 2012 to 2016, the best administrative region in terms of overall agricultural production efficiency in China was the east. In terms of the overall analysis of the region, the east had the highest overall agricultural production efficiency, while the central region had the lowest. The input variable that needed the most improvement was rural electricity consumption, with the largest adjustment in rural electricity consumption being observed in Hebei and Liaoning provinces of the eastern region. Furthermore, from 2012 to 2016, both overall agricultural production efficiency and agricultural GDP showed upward trends. However, adjustments are still needed for other relevant agricultural input variables to effectively allocate resources and improve the overall agricultural production efficiency.

Suggested Citation

  • Shao-Yin Hsu & Chih-Yu Yang & Yueh-Ling Chen & Ching-Cheng Lu, 2023. "Agricultural Efficiency in Different Regions of China: An Empirical Analysis Based on Dynamic SBM-DEA Model," Sustainability, MDPI, vol. 15(9), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7340-:d:1135431
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    References listed on IDEAS

    as
    1. Singh, Pritpal & Singh, Gurdeep & Sodhi, G.P.S., 2019. "Applying DEA optimization approach for energy auditing in wheat cultivation under rice-wheat and cotton-wheat cropping systems in north-western India," Energy, Elsevier, vol. 181(C), pages 18-28.
    2. Alejandro Nin-Pratt & Bingxin Yu & Shenggen Fan, 2010. "Comparisons of agricultural productivity growth in China and India," Journal of Productivity Analysis, Springer, vol. 33(3), pages 209-223, June.
    3. 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.
    4. Mao, Weining & Koo, Won W., 1996. "Productivity Growth, Technology Progress, And Efficiency Change In Chinese Agricultural Production From 1984 To 1993," Agricultural Economics Reports 23442, North Dakota State University, Department of Agribusiness and Applied Economics.
    5. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    6. Mousavi-Avval, Seyed Hashem & Rafiee, Shahin & Jafari, Ali & Mohammadi, Ali, 2011. "Improving energy use efficiency of canola production using data envelopment analysis (DEA) approach," Energy, Elsevier, vol. 36(5), pages 2765-2772.
    7. Liu, Haomin & Zhang, Zaixu & Zhang, Tao & Wang, Liyang, 2020. "Revisiting China’s provincial energy efficiency and its influencing factors," Energy, Elsevier, vol. 208(C).
    8. Sara Ilahi & Yongchang Wu & Muhammad Ahsan Ali Raza & Wenshan Wei & Muhammad Imran & Lyankhua Bayasgalankhuu, 2019. "Optimization Approach for Improving Energy Efficiency and Evaluation of Greenhouse Gas Emission of Wheat Crop using Data Envelopment Analysis," Sustainability, MDPI, vol. 11(12), pages 1-16, June.
    9. Tone, Kaoru & Tsutsui, Miki, 2010. "Dynamic DEA: A slacks-based measure approach," Omega, Elsevier, vol. 38(3-4), pages 145-156, June.
    10. Yildizhan, Hasan, 2018. "Energy, exergy utilization and CO2 emission of strawberry production in greenhouse and open field," Energy, Elsevier, vol. 143(C), pages 417-423.
    11. 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.
    12. Lilyan E. Fulginiti & Richard K. Perrin, 1998. "Agricultural productivity in developing countries," Agricultural Economics, International Association of Agricultural Economists, vol. 19(1-2), pages 45-51, September.
    13. Nin, Alejandro & Arndt, Channing & Preckel, Paul V., 2003. "Is agricultural productivity in developing countries really shrinking? New evidence using a modified nonparametric approach," Journal of Development Economics, Elsevier, vol. 71(2), pages 395-415, August.
    14. Kiyotaka Masuda, 2018. "Energy Efficiency of Intensive Rice Production in Japan: An Application of Data Envelopment Analysis," Sustainability, MDPI, vol. 10(1), pages 1-11, January.
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