Abstract
Recently , the investment efficiency analysis of agricultural PPP projects is a controversial topic in agricultural research area, and the identification of influencing factors is an important basic premise of the investment efficiency analysis. This paper is based on the input and output index of investment efficiency, presents a method to determine the impact of the investment efficiency of the agricultural PPP project based on the Data Envelop model (\(\varepsilon \)-BC\(^2\)) and the Truncated regression model (Tobit). This paper firstly build the turn \(\varepsilon \)-BC\(^2\) model to measure the investment efficiency of agricultural PPP projects, then build the Tobit regression model with the Investment efficiency value as the dependent variable and the alternative influencing factors as the independent variable; the next part is identifying the main influencing factors of agricultural PPP projects by using the Tobit regression model. The research refers to related projects of 24 provinces in China as an example, from analyzing those projects, it shows that the investment efficiency fluctuation of China’s agricultural PPP projects ranged from 0.65–0.89 from 2008 to 2015 with higher average value and less fluctuation, and the result shows that investment efficiency of east and central China was significantly better than that of western China. The development degree of the intermediary market and external environmental factors, such as the development level of regional economy, the legal system, play an important role in the investment efficiency of China’s agricultural PPP projects. This paper aims to make an attribution to researching and improving the management level of agricultural PPP projects.
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Acknowledgement
The work is partially supported by the Shaanxi Social Science Fund (Grant No. 2018S48).
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Xiong, G., Chai, Y., Cao, Y., Wang, X. (2020). Influence Factors on Investment Efficiency of the Agriculture PPP Project based on DEA-Tobit Method. In: Xu, J., Ahmed, S., Cooke, F., Duca, G. (eds) Proceedings of the Thirteenth International Conference on Management Science and Engineering Management. ICMSEM 2019. Advances in Intelligent Systems and Computing, vol 1001. Springer, Cham. https://doi.org/10.1007/978-3-030-21248-3_3
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DOI: https://doi.org/10.1007/978-3-030-21248-3_3
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