Journal Search Engine
Search Advanced Search Adode Reader(link)
Download PDF Export Citaion korean bibliography PMC previewer
ISSN : 2233-4165(Print)
ISSN : 2233-5382(Online)
Journal of Industrial Distribution & Business Vol.8 No.7 pp.13-20
DOI : http://dx.doi.org/10.13106/ijidb.2017.vol8.no7.13.

Study on the Influencing Factors of TFP of Low-carbon Tourism Distribution

Xiaoyu Cheng*, Keshen Jiang**
*First Author, Corresponding Author, College of Economics and Management, Nanjing University of Aeronautics and Astronautics, China. Tel: +86-156-5188-5207, E-mail: nuaacxy@163.com
**Second Author, College of Economics and Management, Nanjing University of Aeronautics and Astronautics, China. Tel: +86-136-5517-5909, E-mail: jiangksh@126.com
September 29, 2017. October 15, 2017. December 15, 2017.

Abstract

Purpose - Performance appraisal has a significant influence on the development of low-carbon tourism distribution.
Research design, data, and methodology - Data of this study are collected from 27 provinces (cities) of China. SBM-Malmquist model is used to measure the TFP and its dynamic changes of low-carbon tourism distribution; TOBIT model is used to discuss the factors of TFP of low-carbon tourism distribution.
Results - The results show that, there are obvious differences among regional TFP of low-carbon tourism distribution, the average change tends to grow positively in general, and the western region grows fastest on average due to the improvement of technical efficiency and technical progress, while there are technical efficiency improvement but technical regresses in eastern and central regions. The economic scale, economic strength, structure of energy consumption, location quotient and government regulation have a significant positive effect on the TFP of low-carbon tourism; energy intensity, industrial structure and opening degree have a negative effect; investments in fixed assets, intensity of R&D fund and urbanization rate have no significant influence on the TFP of low-carbon tourism.
Conclusions - Improving the productivity of low-carbon tourism and reducing regional differences are effective ways to develop low-carbon tourism and enhance tourism competitiveness.

JEL Classifications: L83, Q56, R15.

초록


 

Figure

Table

Reference

  1. Bai, Y., Zhang, X., He, Y., & Song, X. (2013). Research on regional environmental performance and its influential factors: Based on SBM-Malmquist-Tobit model. Areal Research and Development, 32(2), 90-95.
  2. Chen, F., & Zhu, D. (2009). Theory of research on low- carbon city: Shanghai empirical analysis. Urban Studies, 16(10), 71-79.
  3. Cao, F., Huang, Z., Xu, M., & Wang, K. (2015). Spatial-temporal pattern and influencing factors of tourism efficiency and the decomposition efficiency in Chinese scenic areas: Based on the Bootstrap-DEA method. Geographical Research, 34(12), 2395-2408.
  4. Choi, I. S., & Lee, S. Y. (2012). A study on the regulatory environment of the French distribution industry and the intermarche's management strategies. International Journal of Industrial Distribution & Business, 3(1), 7-16.
  5. Coelli, T. J., Rao, D. S. P., O’Donnell C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis. Australia: University of Queensland Brisbane.
  6. Fang, Y., Huang, Z., Yu, F., & Tu, W. (2013). Evolution analysis of relative efficiency of provincial tourist resources. Scientia Geographic Sinica, 33(11), 1354-1361.
  7. Gong, Y., Yang, Z., & Tang, C. (2016). A research on the measurement and influential factors of tourism industrial efficiency in the Yangtze river economic zone. East China Economic Management, 30(9), 66-74.
  8. Han, Y., & Wu, P. (2016). The measurement and comparative study of carbon dioxide emissions from tourism industry: Beijing-Tianjin-Hebei, Human Geography, 31(4), 127-134.
  9. Jin, C., & Wang, W. (2014). Calculation and analysis on dynamic efficiency of tourism in China under environmental constraints: Based on three-stage Malmquist index model. Technology Economics, 33(12), 46-53.
  10. Li, J., & Li, M. (1999). Study on tourism industry and the calculation of added value of tourism. Tourism Tribune, 5, 16-19.
  11. Shi, F. (2015). Industrial production efficiency of Chinese provinces and affecting factors: An empirical analysis based on the SBM directional distance function. Journal of Industrial Technological Economics, 6, 137-144.
  12. Wang, K., Huang, Z., Tao, Y., & Fang, Y. (2013). Study on spatial characteristics and spillover effects of urban tourism efficiency: A case of Yangtze River delta. Economic Geography, 33(4), 161-167.
  13. Zhao, J. (2016). The change differences and influence mechanism of TFP in China’s tourism industry under the environmental constraint. Journal of Shanxi University of Finance and Economics, 38(10), 61-74.
  14. Zhao, J. (2016). Study on energy consumption, carbon dioxide emission and low-carbon efficiency. China Population, Resources and Environment, 26(1), 47-54.
  15. Zhang, L. (2014). Research on tourism industry total factor productivity in China-based on stochastic frontier analysis (SFA). Resource Development & Market, 30(2), 221-224.