Agric. Econ. - Czech, 2025, 71(8):445-457 | DOI: 10.17221/411/2024-AGRICECON

Efficiency of agricultural and pastoral systems in China considering shared factors and undesirable outputsOriginal Paper

Chunhua Chen1, Chongyu Ma2
1 College of Economics and Management, Inner Mongolia Agricultural University, Hohhot, P.R. China
2 Transportation Institute, Inner Mongolia University, Hohhot, P.R. China

Assessing and optimising the efficiency of agricultural and pastoral systems is crucial for the long-term development of a country. The presence of shared factors and undesirable outputs increases the complexity of evaluating the efficiency of these systems. To address this issue, we first analysed the production possibility sets of the agricultural subsystems, pastoral subsystems, and agricultural and pastoral systems. Then, two bounded adjusted measure (BAM) models considering shared factors and undesirable outputs were proposed to evaluate the divisional efficiency of agricultural and pastoral subsystems. Additionally, a network BAM model in the presence of shared factors and undesirable outputs was developed to assess overall efficiency. Undesirable outputs were handled by slack-based measures in the three novel models. The proposed models were used to evaluate the efficiency of agricultural and pastoral systems across 30 provinces and cities in China. To explore the impact of undesirable outputs, the efficiency of ignoring undesirable outputs was investigated and compared with that obtained from the new method. These results suggest that ignoring undesirable outputs may misestimate efficiency to a certain extent.

Keywords: agriculture; bounded adjusted measure; data envelopment analysis; shared factors; undesirable outputs

Received: October 28, 2024; Revised: April 10, 2025; Accepted: May 20, 2025; Published: August 31, 2025  Show citation

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Chen C, Ma C. Efficiency of agricultural and pastoral systems in China considering shared factors and undesirable outputs. Agric. Econ. - Czech. 2025;71(8):445-457. doi: 10.17221/411/2024-AGRICECON.
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References

  1. Banker R.D., Charnes A., Cooper W.W. (1984): Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30: 1078-1092. Go to original source...
  2. Charnes A., Cooper W.W., Rhodes E. (1978): Measuring the efficiency of decision making units. European Journal of Operational Research, 2: 429-444. Go to original source...
  3. Chen X.D., Wu G., Li D. (2019): Efficiency measure on the truck restriction policy in China: A non-radial data envelopment model. Transportation Research Part A: Policy and Practice, 129: 140-154. Go to original source...
  4. Chen X.D., Miao Z., Wang K.L., Sun C.W. (2020): Assessing eco-performance of transport sector: Approach framework, static efficiency and dynamic evolution. Transportation Research Part D: Transport and Environment, 85: 102414. Go to original source...
  5. Chen X.Q., Liu X.W., Gong Z.W., Xie J.T. (2021a): Three-stage super-efficiency DEA models based on the cooperative game and its application on the R&D green innovation of the Chinese high-tech industry. Computers & Industrial Engineering, 156: 107234. Go to original source...
  6. Chen Y.F., Miao J.F., Zhu Z.T. (2021b): Measuring green total factor productivity of China's agricultural sector: A three-stage SBM-DEA model with non-point source pollution and CO2 emissions. Journal of Cleaner Production, 318: 128543. Go to original source...
  7. Cooper W.W., Pastor J.T., Borras F., Aparicio J., Pastor D. (2011): BAM: A bounded adjusted measure of efficiency for use with bounded additive models. Journal of Productivity Analysis, 35: 85-94. Go to original source...
  8. Färe R., Grosskopf S., Hernandez-Sancho F. (2004): Environmental performance: An index number approach. Resource and Energy Economics, 26: 343-352. Go to original source...
  9. He K., Zhu N. (2023): Efficiency evaluation of Chinese provincial industry systems: A dynamic two-stage slacks-based measure with shared inputs. Journal of Industrial and Management Optimization, 19: 4959-4988. Go to original source...
  10. Izadikhah M., Khoshroo A. (2018): Energy management in crop production using a novel fuzzy data envelopment analysis model. RAlRO-Operations Research, 52: 595-617. Go to original source...
  11. Liu W.B., Meng W., Li X.X., Zhang D.Q. (2010): DEA models with undesirable inputs and outputs. Annals of Operations Research, 173: 177-194. Go to original source...
  12. Manogna R.L., Aswini K.M. (2022): Agricultural production efficiency of Indian states: Evidence from data envelopment analysis. International Journal of Finance & Economics, 27: 4244-4255. Go to original source...
  13. Nandy A., Singh P.K. (2021): Application of fuzzy DEA and machine learning algorithms in efficiency estimation of paddy producers of rural Eastern India. Benchmarking: An International Journal, 28: 229-248. Go to original source...
  14. Scheel H. (2001): Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132: 400-410. Go to original source...
  15. Shabani P., Akbarpour Shirazi M. (2024): Performance evaluation of commercial bank branches in dynamic competitive conditions: A network DEA model with serial and cross-shared resources. Journal of Economic Studies, 51: 1-23. Go to original source...
  16. Tone K. (2001): A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130: 498-509. Go to original source...
  17. Wagan S.A., Memon Q.U., Chunyu D., Jingdong L. (2018): A comparative study on agricultural production efficiency between China and Pakistan using Data Envelopment Analysis (DEA). Custos e Agronegocio Online, 14: 169-190.
  18. Wang D.W., Zhao L.L., Yang F., Chen K.H. (2022): Performance evaluation of the Chinese high-tech industry: A two-stage DEA approach with feedback and shared resource. Journal of Industrial and Management Optimization, 18: 3315-3338. Go to original source...
  19. Zhang Z.W., Wang Z.L., Zhu Y.F. (2021): Optimal path selection of innovation resource allocation in China's regions with shared inputs. Economic Research-Ekonomska Istraživanja, 35: 1457-1480. Go to original source...
  20. Zhao H.H., Liu Y., Li J., Guo X.G., Gui H.J. (2022): Chinese provincial difference in the efficiency of universities scientific and technological activities based on DEA with shared input. Mathematical Problems in Engineering, 2022: 1-19. Go to original source...
  21. Zhou P., Poh K.L., Ang B.W. (2007): A non-radial DEA approach to measuring environmental performance. European Journal of Operational Research, 178: 1-9. Go to original source...
  22. Zhu W., Zhang Q., Wang H. (2019): Fixed costs and shared resources allocation in two-stage network DEA. Annals of Operations Research, 278: 177-194. Go to original source...

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