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

被引:11
|
作者
Bi, Chao [1 ]
Jia, Minna [2 ]
Zeng, Jingjing [3 ]
机构
[1] Shaanxi Normal Univ, Int Business Sch, Xian 710119, Shaanxi, Peoples R China
[2] Florida State Univ, Inst Sci & Publ Affairs, Tallahassee, FL 32306 USA
[3] Zhongnan Univ Econ & Law, Sch Publ Adm, Wuhan 430073, Hubei, Peoples R China
来源
SUSTAINABILITY | 2019年 / 11卷 / 03期
基金
中国国家自然科学基金;
关键词
public infrastructure; energy intensity; nonlinear effect; panel smooth transition regression; NUISANCE PARAMETER; STRUCTURAL-CHANGE; ECONOMIC-GROWTH; ELECTRICITY DEMAND; DECOMPOSITION; EFFICIENCY; INVESTMENT; INDUSTRIAL; IMPACTS; ELASTICITIES;
D O I
10.3390/su11030629
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:21
相关论文
共 50 条
  • [1] The effect of government expenditure on energy intensity: a panel smooth transition regression (PSTR) approach
    Movahedi, Mohammad
    Shahbazi, Kiumars
    Farid, Samad Hekmati
    [J]. INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES, 2022, 44 (04) : 292 - 310
  • [2] Nonlinear bilateral trade balance-fundamentals nexus: A panel smooth transition regression approach
    Wu, Po-Chin
    Liu, Shiao-Yen
    Pan, Sheng-Chieh
    [J]. INTERNATIONAL REVIEW OF ECONOMICS & FINANCE, 2013, 27 : 318 - 329
  • [3] The Feldstein-Horioka puzzle: A panel smooth transition regression approach
    Fouquau, Julien
    Hurlin, Christophe
    Rabaud, Isabelle
    [J]. ECONOMIC MODELLING, 2008, 25 (02) : 284 - 299
  • [4] The dynamics of international patents production: A panel smooth transition regression approach
    Trabelsi, Jamel
    Jebeniani, Arbia jihene
    Omri, Sofiene
    [J]. ECONOMICS BULLETIN, 2024, 44 (01): : 466 - 489
  • [5] Spatial agglomeration and productivity in Italy: A panel smooth transition regression approach
    Cainelli, Giulio
    Fracasso, Andrea
    Vittucci Marzetti, Giuseppe
    [J]. PAPERS IN REGIONAL SCIENCE, 2015, 94 : S39 - S67
  • [6] Effects of the Interest Rate and Reserve Requirement Ratio on Bank Risk in China: A Panel Smooth Transition Regression Approach
    Geng, Zhongyuan
    Zhai, Xue
    [J]. DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2015, 2015
  • [7] Non-linear threshold effect of financial development on renewable energy consumption: evidence from panel smooth transition regression approach
    Raza, Syed Ali
    Shah, Nida
    Qureshi, Muhammad Asif
    Qaiser, Shahzad
    Ali, Ramsha
    Ahmed, Farhan
    [J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2020, 27 (25) : 32034 - 32047
  • [8] Non-linear threshold effect of financial development on renewable energy consumption: evidence from panel smooth transition regression approach
    Syed Ali Raza
    Nida Shah
    Muhammad Asif Qureshi
    Shahzad Qaiser
    Ramsha Ali
    Farhan Ahmed
    [J]. Environmental Science and Pollution Research, 2020, 27 : 32034 - 32047
  • [9] Is there an environmental Kuznets curve for water use? A panel smooth transition regression approach
    Duarte, Rosa
    Pinilla, Vicente
    Serrano, Ana
    [J]. ECONOMIC MODELLING, 2013, 31 : 518 - 527
  • [10] The drivers of energy intensity in China: A spatial panel data approach
    Jiang, Lei
    Folmer, Henk
    Ji, Minhe
    [J]. CHINA ECONOMIC REVIEW, 2014, 31 : 351 - 360