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Transactions on Machine Design (TMD)

Production efficiency estimation of ChinaÃ¢â‚¬â„¢s construction industry and its influencing factors based on the DEA-Tobit model

Guang Cheng
School of Accounting, Dongbei University of Finance and Economics, 116012Dalian, China School of Accountancy, Henan University of Engineering, 451191Zhengzhou,China

Abstract: The total output value of the construction industry, a pillar industry of China, is continuously growing with the expansion of the production and operation scale of Chinese construction enterprises. The extensive high-input mode has promoted the economic growth of the construction industry. Despite the fruitful and amazing results, the phenomenon of production inefficiency becomes especially prominent. Though developing rapidly, ChinaÃ¢â‚¬â„¢s construction industry possesses less ecological investments, accompanied by low environmental awareness and little importance to environmental protection, always failing to get rid of high energy consumption, high investments, and high emissions. In addition, the contradiction between the economic growth of the construction industry and ecological environment remains evident. In this paper, the production efficiency of ChinaÃ¢â‚¬â„¢s construction industry was transversely and longitudinally measured using the DEA-Malmquist index method, followed by the further analysis of the factors influencing the production efficiency of ChinaÃ¢â‚¬â„¢s construction industry based on the Tobit regression model. The results show that the average production efficiency of ChinaÃ¢â‚¬â„¢s construction industry is obviously the highest in the east, the moderate in the west, and the lowest in the center. The number of construction enterprises, per capita capital, industrial structure, the proportion of state-owned capitals, and GDP are significant at the levels of 1%, 10%, 1%, 1%, and 1%, respectively. The research results can provide theoretical reference for reasonably establishing an ecological efficiency evaluation system for the construction industry to analyze the differences in ecological efficiency among different provinces and regions, further find out the key factors influencing ecological efficiency, propose the corresponding pertinent policy suggestions, and ultimately improve the ecological efficiency of ChinaÃ¢â‚¬â„¢s construction industry.

Keywords: DEA-Malmquist Index, Tobit Model, ChinaÃ¢â‚¬â„¢s Construction Industry, Production Efficiency, Influencing Factor Production efficiency estimation of ChinaÃ¢â‚¬â„¢s construction industry and its influencing factors based on the DEA-Tobit model

DOI:https://doi.org/10.6025/tmd/2024/12/1/20-31

Full_Text PDF 3.09 MB Download: 25 times

References:

[1] Hu, X., Liu, C. (2018). Measuring efficiency, effectiveness and overall performance in the Chinese construction industry. Engineering, Construction and Architectural Management, 25(6), 780-797.
[2] Prabhu, P. G., Ambika, D. (2013). Study on behaviour of workers in construction industry to improve production efficiency. International Journal of Civil, Structural, Environmental and Infrastructure Engineering Research and Development, 3(1), 59-66.
[3] Xue, X., Wu, H., Zhang, X., Dai, J., Su, C. (2015). Measuring energy consumption efficiency of the construction industry: the case of China. Journal of Cleaner Production, 107, 509-515.
[4] Horta, I. M., Camanho, A. S., Johnes, J., Johnes, G. (2013). Performance trends in the construction industry worldwide: an overview of the turn of the century. Journal of Productivity Analysis, 39, 89-99.
[5] Carson, C., Abbott, M. (2012). A review of productivity analysis of the New Zealand construction industry. Australasian Journal of Construction Economics and Building, 12(3), 1- 15.
[6] Zhang, J., Li, H., Xia, B., Skitmore, M. (2018). Impact of environment regulation on th efficiency of regional construction industry: A 3-stage Data Envelopment Analysis (DEA). Journal of Cleaner Production, 200, 770-780.
[7] Du, Q., Deng, Y., Zhou, J., Wu, J., Pang, Q. (2022). Spatial spillover effect of carbon emission efficiency in the construction industry of China. Environmental Science and Pollution Research, 29(2), 2466-2479.
[8] You, T., Zi, H. (2007). The economic crisis and efficiency change: evidence from the Korean construction industry. Applied Economics, 39(14), 1833-1842.
[9] Nazarko, J., Chodakowska, E. (2017). Labour efficiency in construction industry in Europe based on frontier methods: Data envelopment analysis and stochastic frontier analysis. Journal of Civil Engineering and Management, 23(6), 787-795.
[10] Huo, T., Tang, M., Cai, W., Ren, H., Liu, B., Hu, X. (2020). Provincial total-factor energy efficiency considering floor space under construction: An empirical analysis of China’s construction industry. Journal of Cleaner Production, 244, 118749.
[11] Hong, J., Zhong, X., Guo, S., Liu, G., Shen, G. Q., Yu, T. (2019). Water-energy nexus and its efficiency in China’s construction industry: Evidence from province-level data. Sustainable Cities and Society, 48, 101557.
[12] Tang, W., Duffield, C. F., Young, D. M. (2006). Partnering mechanism in construction: An empirical study on the Chinese construction industry. Journal of Construction Engineering and Management, 132(3), 217-229.
[13] Zhang, M., Li, L., Cheng, Z. (2021). Research on carbon emission efficiency in the Chinese construction industry based on a three-stage DEA-Tobit model. Environmental Science and Pollution Research, 28(37), 51120-51136.
[14] Nazarko, J., Chodakowska, E. (2015). Measuring productivity of construction industry in Europe with Data Envelopment Analysis. Procedia Engineering, 122, 204-212.
[15] Chen, Y., Liu, B., Shen, Y., Wang, X. (2016). The energy efficiency of China’s regional construction industry based on the three-stage DEA model and the DEA-DA model. KSCE Journal of Civil Engineering, 20, 34-47.
[16] Chancellor, W., Lu, W. (2016). A regional and provincial productivity analysis of the Chinese construction industry: 1995 to 2012. Journal of Construction Engineering and Management, 142(11), 05016013.
[17] Kapelko, M., Horta, I. M., Camanho, A. S., Lansink, A. O. (2015). Measurement of input-specific productivity growth with an application to the construction industry in Spain and Portugal. International Journal of Production Economics, 166, 64-71.
[18] Jarkas, A. M. (2015). Factors influencing labour productivity in Bahrain’s construction industry. International journal of construction management, 15(1), 94-108.
[19] Hu, X., Liu, C. (2016). Profitability performance assessment in the Australian construction industry: a global relational two-stage DEA method. Construction Management and Economics, 34(3), 147-159.
[20] Charnes, A., Cooper, W. W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
[21] Färe, R., Grosskopf, S., Lindgren, B., Roos, P. (1992). Productivity changes in Swedish pharmacies 1980-1989: A non-parametric Malmquist approach. Journal of Productivity Analysis, 3(1-2), 85-101.

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