FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Sci Rep / Measurement of eco-efficiency in the horse industry, spatiot...
Scientific reports2025; 15(1); 14729; doi: 10.1038/s41598-025-99073-x

Measurement of eco-efficiency in the horse industry, spatiotemporal evolution and convergence analysis.

Abstract: The horse industry constitutes a vital economic sector in Xinjiang, China. This study quantitatively assesses the sector's sustainable development through eco-efficiency analysis across northern Xinjiang counties from 2001 to 2021. The research employs four analytical methods: the S-SBM model for efficiency measurement, kernel density estimation for distribution analysis, Moran's index for spatial autocorrelation examination, and convergence tests for long-term trend assessment. Results demonstrate a consistent decline in eco-efficiency, decreasing from 0.821 in 2001 to 0.444 in 2021, with an average value of 0.557. Significant regional disparities emerge, with efficiency scores ranging from 0.499 to 1.285 across different prefectures. Spatial analysis reveals pronounced clustering effects, particularly in Yili Prefecture. Convergence tests indicate the presence of β-convergence but the absence of σ-convergence, suggesting narrowing efficiency gaps over time despite persistent regional inequalities. These empirical findings provide substantive evidence for policymakers seeking to enhance Xinjiang's equine economy sustainability and resource efficiency. The study contributes to the limited literature on ecological efficiency measurement in animal husbandry sectors.
© 2025. The Author(s).
Get Full Text
Publication Date: 2025-04-27 PubMed ID: 40289154PubMed Central: PMC12034764DOI: 10.1038/s41598-025-99073-xGoogle Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research paper is a quantified study of the sustainability of the horse industry in Xinjiang, China. The investigation is based on an eco-efficiency analysis over two decades (2001-2021), using diverse analytical methods to explore the developmental trend, regional differences, and patterns.

Methodology

  • The paper employs four primary ways to examine eco-efficiency in the horse industry in Xinjiang. These are the S-SBM model, kernel density estimation, Moran’s index for spatial autocorrelation examination, and convergence tests.
  • The S-SBM model is a non-parametric model that measures efficiency, particularly in situations with multiple outputs and inputs. It was used here to measure the efficiency of the horse industry.
  • Kernel density estimation is a non-parametric technique used for estimating the probability density function of a random variable. The research utilizes this method to analyze the distribution of eco-efficiency.
  • Moran’s index is a measure of spatial autocorrelation – how much one location’s data is similar to nearby locations. This helps to reveal geographical patterns in the distribution of eco-efficiency.
  • Convergence tests assess the long-term trends in eco-efficiency, specifically whether disparities between areas are reducing (β-convergence) or fluctuating (σ-convergence).

Key Findings

  • The research demonstrates a steady decline in eco-efficiency over 20 years from 0.821 in 2001 to 0.444 in 2021, with an average value of 0.557 over the studied period.
  • There are significant regional disparities in eco-efficiency in the horse industry. Individual prefecture’s efficiency scores ranged from a low of 0.499 to a high of 1.285, indicating considerable variations in performance and resource efficiency.
  • Spatial analysis highlights pronounced clustering effects, particularly in Yili Prefecture, suggesting regional similarities in eco-efficiency metrics.
  • Convergence tests ascertain the presence of β-convergence and an absence of σ-convergence. This indicates a gradual reduction in efficiency gaps over time (β-convergence), but continued regional inequalities (lack of σ-convergence).

Conclusion and Applications

  • The findings provide important empirical evidence to aid policymakers in improving the sustainability and resource efficiency of Xinjiang’s horse industry – and by extension, more broadly in China’s animal husbandry sectors.
  • This research also adds to the currently scarce literature on ecological efficiency measurement in livestock sectors.

Cite This Article

APA
Zhang X, Abdusuli N. (2025). Measurement of eco-efficiency in the horse industry, spatiotemporal evolution and convergence analysis. Sci Rep, 15(1), 14729. https://doi.org/10.1038/s41598-025-99073-x

Publication

Scientific reports
ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 15
Issue: 1
Pages: 14729
PII: 14729

Researcher Affiliations

Zhang, Xinyi
  • College of Economics and Management, Xinjiang Agricultural University, Urumqi, 830000, China.
Abdusuli, Nurgul
  • College of Economics and Management, Xinjiang Agricultural University, Urumqi, 830000, China. 453244794@qq.com.

MeSH Terms

  • Animals
  • Horses
  • China
  • Spatio-Temporal Analysis
  • Animal Husbandry / economics
  • Conservation of Natural Resources
  • Sustainable Development

Grant Funding

  • 2022A0213-4 / YaoJuan
  • 2022A0213-4 / YaoJuan
  • 22BMZ123 / Nurgulu00b7Abdusuli
  • 22BMZ123 / Nurgulu00b7Abdusuli

Conflict of Interest Statement

Declarations. Competing interests: The authors declare no competing interests.

References

This article includes 49 references
  1. Chang J. Research on green development efficiency of pig industry based on DEA-Tobit model. Feed Res 47, 176–181 (2024).
    doi: 10.13557/j.cnki.issn1002-2813.2024.03.033google scholar: lookup
  2. Wang Q, Zou Y. China’s equine industries in a transitional economy: development, trends, challenges, and opportunities. Sustainability 12, 5135 (2020).
    doi: 10.3390/sሒ5135google scholar: lookup
  3. Senggli, Yao Y, Sun L. Research on the measurement and influencing factors of the integration development of the horse industry chain in Xinjiang–Taking farmers and herdsmen in Zhaosu County as an example. China Market 8, 32–34 (2022).
    doi: 10.13939/j.cnki.zgsc.2022.08.032google scholar: lookup
  4. Zhao X, Yao J, Ma J. Research on spatio-temporal characteristics of geographic agglomeration of animal husbandry in Xinjiang. Heilongjiang Anim. Sci. Vet. Med. 10, 38–40 (2016).
    doi: 10.13881/j.cnki.hljxmsy.2016.0846google scholar: lookup
  5. Roberts J, Johnson T. Problem gambling: how Japan could actually become the next Las Vegas. UNLV Gaming Law J. 6, 3 (2016).
  6. Koveshnikov, V. S., Slotina, E. V., Wang, X. & Wang, Y. Optimization of herd structure as an important reserve for increasing the efficiency of horse breeding. In Current Problems and Ways of Industry Development: Equipment and Technologies 890–896 (Springer, 2021) Cao,H. Countermeasures for the development of China’s horse industry based on SWOT analysis. Heilongjiang Anim. Sci. Vet. Med. 10, 23–28: (2020). 10.13881/j.cnki.hljxmsy.2019.10.0071
  7. Cao X, Wang H, Wang Y. Countermeasures for the development of China’s horse industry based on SWOT analysis. Heilongjiang Anim. Sci. Vet. Med. 10, 23-28 (2020).
    doi: 10.13881/j.cnki.hljxmsy.2019.10.0071google scholar: lookup
  8. Huang D, Li H. Analysis of the development of horse industry in Zhaosu County and its possibility of promoting the growth of trade between China and Kazakhstan under the strategy of ‘belt and road’. Heilongjiang Anim. Sci. Vet. Med. 6, 35–37 (2017).
    doi: 10.13881/j.cnki.hljxmsy.2017.0472google scholar: lookup
  9. Wang H. Research on the development strategy of revitalization of horse industry in inner Mongolia. North. Econ. 11, 31–34 (2019).
  10. Li X, Zhu X, Liu R. Development path analysis of inner Mongolia horse industry. Mod. Anim. Husb. Sci. Technol. 12, 126–128 (2022).
    doi: 10.19369/j.cnki.2095-9737.2022.12.039google scholar: lookup
  11. Schaltegger S, Sturm A. Ökologische rationalität. Die Unternehmung 4, 273–290 (1990).
  12. Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444 (1978).
  13. Yan Z et al. The role of green production process innovation in green manufacturing: empirical evidence from OECD countries. Appl. Econ. 6, 1–13 (2022).
    doi: 10.1080/00036846.2022.2083569google scholar: lookup
  14. Luo H, Wang F, Chen X. Study on the efficiency of livestock economic development under environmental constraints–based on three-stage directional distance function. China Agric. Resour. Reg. Plan. 43, 115–124 (2022).
  15. Jiang H. Spatial-temporal differences of industrial land use efficiency and its influencing factors for China’s central region: analyzed by SBM model. Environ. Technol. Innov. 22, 101489 (2021).
    doi: 10.1016/j.eti.2021.101489google scholar: lookup
  16. Cui J, Wang ML, Shi ZZ. Analysis of total factor productivity of grass-fed animal husbandry in China based on greenhouse gas emission constraint. Agric. Technol. Econ. 3, 66–78 (2018).
    doi: 10.13246/j.cnki.jae.20180116.001google scholar: lookup
  17. Liu C, Zhou Y. Spatial correlation and spillover effects of environmental efficiency of large-scale pig farming in China. World Agric. 8, 105–111 (2020).
    doi: 10.13856/j.cn11-1097/s.2020.08.013google scholar: lookup
  18. Qianrong W, Lanzhuang X, Xianhui G. Ecological efficiency of hog scale production under environmental regulation in China:based on an optimal super efficiency SBM-Malmquist-Tobit model. Environ. Sci. Pollut Res. 29, 53088–53106 (2022).
    pubmed: 35279751doi: 10.1007/s11356-022-19576-2google scholar: lookup
  19. Qi H et al. Analysis of carbon source/sink time-series changes in farmland ecosystems in Heihe City, Heilongjiang Province based on carbon footprint. Geol. Bull. 1–19 (2025).
  20. Wang J, Zou X, He L. Total factor productivity growth rate measurement and decomposition in Guangdong Province - Based on stochastic frontier analysis model. Pract. Underst. Math. 1–8 (2025).
  21. Shen Z et al. Research on the green total factor productivity of laying hens in China. J. Clean. Prod. .
    pubmed: 0doi: 10.1016/j.jclepro.2021.128001google scholar: lookup
  22. Qin CY et al. Benefit evaluation of a double-season rice-fish farming model based on energy value analysis. J. Agric. Resour. Environ. 1–12 (2025).
    doi: 10.13254/j.jare.2023.0718google scholar: lookup
  23. Liang YW et al. Research on Spatial and Temporal characteristics and spillover effects of eco-efficiency of animal husbandry in China. China Agric. Resour. Reg. Plan. 44, 189–201 (2023).
  24. Ning G et al. Measurement of green total factor productivity and its Spatial convergence test on the Pig-Breeding industry in China. Sustainability 14, 211390 (2022).
    doi: 10.3390/su142113902google scholar: lookup
  25. He N, Yao C, Zhang C, Northwest J. Dynamic evolution and convergence characteristics of agro-ecological efficiency in the Yellow River Basin under the goal of ‘double carbon’. A&F Univ. (Soc Sci. Ed) 24, 149–160 (2024).
    doi: 10.13968/j.cnki.1009-9107.2024.03.16google scholar: lookup
  26. Linwei W, Qian C, Rong K. Regional differences and convergence of green total factor productivity in pig breeding: evidence from China. Front. Environ. Sci. 11 (2023).
    doi: 10.3389/fenvs.2023.1162502google scholar: lookup
  27. Wang XJ, Xiao HF. Total factor productivity growth of meat sheep production in China and its convergence analysis. J. Agric. Econ. Manag. 17, 185–193 (2018).
    doi: 10.16195/j.cnki.cn36-1328/f.2018.02.22google scholar: lookup
  28. Yang C, Wang ML. Analysis of productivity growth and convergence of beef cattle breeding in pastoral areas under the grassland ecological protection subsidy policy. Agric. Technol. Econ. 3, 96–105 (2019).
    doi: 10.13246/j.cnki.jae.2019.03.007google scholar: lookup
  29. Zhang JX, Wang HL. Regional differences, dynamic evolution and convergence analysis of carbon emissions from China’s animal husbandry industry–Based on the data of animal husbandry industry in 31 provinces (cities) of China from 1997 to 2017. Jianghan Forum 9, 41–48 (2020).
  30. Ji YF, He ZJ, Xu T. High-quality development of animal husbandry: level measurement, regional differences and convergence analysis. China Agric. Resour. Reg. Plan. 45, 190–203 (2024).
  31. Zhao LL, Li XD, Xia Y. Evaluation of eco-efficiency of grassland animal husbandry and factors affecting it under the perspective of rural revitalization–an analysis based on the cases of pure pasture and semi-agricultural and semi-pasture areas in Northern Xinjiang. J. Grassland Sci. 31, 3074–3084 (2023).
  32. Tone K. A slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 130, 498–509 (2001).
  33. Tone K. A slacks-based measure of super-efficiency in data envelopment analysis. Eur. J. Oper. Res. 143, 32–41 (2002).
  34. Chi M et al. Spatial distribution of agricultural eco-efficiency and agriculture high-quality development in China. Land 11, 722 (2022).
    doi: 10.3390/land11050722google scholar: lookup
  35. Jin G et al. An analysis of Spatiotemporal patterns in Chinese agricultural productivity between 2004 and 2014. Ecol. Indic. 105, 591–600 (2019).
  36. Guo L et al. Global and local indicators of Spatial association between points and polygons: A study of land use change. Int. J. Appl. Earth Obs Geoinf. 21, 384–396 (2013).
  37. Li X et al. A study on the Spatial and Temporal characteristics of green total factor productivity in dairy farming under the dual-carbon objective - an example from 15 provinces in Northern China. J. Livest. Ecol. 44, 60–68 (2023).
  38. Zhu H, Xu G. Research on meat sheep production efficiency and its influencing factors-analysis of meat sheep productivity in Xinjiang based on DEA-SFA method. Price Theory Pract. 9, 63–66 (2019).
    doi: 10.19851/j.cnki.cn11-1010/f.2019.09.016google scholar: lookup
  39. Sun H, Fan X, Guo J. Analysis of meat sheep industry efficiency and influencing factors in 10 Southern provinces based on DEA-Tobit model. Chin. J. Anim. Husb. 59, 312–317 (2023).
    doi: 10.19556/j.0258-7033.20220427-06google scholar: lookup
  40. Zhao Z et al. Evaluation of eco-efficiency of grass and pasture industry in Hulunbuir region based on super-efficiency DEA and analysis of influencing factors. J. Ecol. 38, 7968–7978 (2018).
  41. Han Z, Han C, Yang C. Spatial econometric analysis of environmental total factor productivity of animal husbandry and its influencing factors in China during 2001–2017. Sci. Total Environ. 723, 137726 (2020).
    pubmed: 32213419doi: 10.1016/j.scitotenv.2020.137726google scholar: lookup
  42. Tang H et al. Study on the carbon emission pattern and equity of animal husbandry in Xinjiang. Arid Zone Geogr. 40, 1338–1345 (2017).
  43. Zhang Y, Yan J. Peak-attainment artefacts of carbon emissions in China’s beef cattle industry–an empirical analysis and test based on EKC theory. Issues Agric. Econ. 6, 93–105 (2022).
    doi: 10.13246/j.cnki.iae.20220509.001google scholar: lookup
  44. Shen Z et al. Green total factor productivity of dairy cow in China: key facts from scale and regional sector. Technol. Forecast. Soc. Change. .
    doi: 10.13826/j.cnki.cn65-1103/x.2017.06.025google scholar: lookup
  45. Ning F et al. Spatial and Temporal variability of agroecological efficiency and its influencing factors in Yunnan Province. China Agric. Resour. Reg. Plan. 45, 49–61 (2024).
  46. Wu L et al. Analysis of livestock production efficiency and influencing factors of herdsmen based on DEA-Tobit model–Taking Xilingol league as an example. China Grassl J. 43, 97–105 (2021).
    doi: 10.16742/j.zgcdxb.20200323google scholar: lookup
  47. Li H. Measurement of eco-efficiency of grassland animal husbandry in China and research on influencing factors. Feed Res. 1–9 (2024).
    doi: 10.13557/j.cnki.issn1002-2813.2024.20.033google scholar: lookup
  48. Lin Z, Zhang Y. Research on the Spatial and Temporal differences in green total factor productivity of animal husbandry in China. J. Ecol. Rural Environ. 39, 1144–1157 (2023).
    doi: 10.19741/j.issn.1673-4831.2022.0907google scholar: lookup
  49. Zhao L, Li X, Xia Y. Measurement of eco-efficiency and influencing factors of grassland animal husbandry in Ili river Valley. Heilongjiang Anim. Sci. Vet. Med. 24, 9–17 (2023).
    doi: 10.13881/j.cnki.hljxmsy.2023.03.0004google scholar: lookup

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 99,344 horse owners like you who receive our email updates on horse health and nutrition.