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Nonlinear Effect of Public Infrastructure on Energy Intensity in China: A Panel Smooth Transition Regression Approach

Chao Bi, Minna Jia and Jingjing Zeng
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Chao Bi: International Business School, Shaanxi Normal University, Xi’an 710119, China
Minna Jia: Institute of Science and Public Affairs, Florida State University, Tallahassee, FL 32306, USA
Jingjing Zeng: School of Public Administration, Zhongnan University of Economics and Law, Wuhan 430073, China

Sustainability, 2019, vol. 11, issue 3, 1-21

Abstract: Public infrastructure not only promotes economic growth, but also influences energy intensity, which plays an important role in the strategies related to energy. Therefore, infrastructure policy can be used as an important instrument to reconcile the dilemma of energy, economy, and environment in China. However, few studies have been made to assess the effect of public infrastructure on energy intensity in China. This paper presents an analysis of how three typical types of public infrastructure (i.e., transportation, energy, and information infrastructure) affect energy intensity for 30 Chinese provinces, from 2001 to 2016. To account for nonlinearities, we adopt the panel smooth transition regression (PSTR) approach. The results show that transportation infrastructure has a significantly negative effect on energy intensity, and this negative effect gradually strengthens when the transportation infrastructure stock exceeds the threshold value. Adversely, energy infrastructure has a significantly positive effect on energy intensity, and this positive effect gradually strengthens with the development of energy infrastructure. Our results also suggest that the development of information infrastructure could not only strengthen its own significantly negative effect on energy intensity, but also could promote the negative effect of transportation infrastructure on energy intensity. Moreover, the positive impact of energy infrastructure on energy intensity gradually decreases when the stock of information infrastructure surpasses the larger threshold value. Our findings suggest that policy makers could reduce energy intensity by accelerating the development of transportation and information infrastructure. Furthermore, they could strengthen the negative effects of transportation and information infrastructure on energy intensity and weaken energy infrastructure’s positive effect on energy intensity by increasing their information infrastructure investment.

Keywords: public infrastructure; energy intensity; nonlinear effect; panel smooth transition regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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