Key factors to annual investment in public transportation sector: The case of China

In this paper, we establish panel models to examine the relationships between the annual investment in the public transport sector (AIP) and bus network facility scale, and between AIP and the land area for urban roads in different tiers of Chinese cities. The empirical study uses panel data of 160 Chinese cities and a time span over 2000–2015. To capture the characteristics of the panel models, including the individual effects, variable intercepts, and coefficient values, different regression functions are adopted for the relevant analysis. As for these regression functions, seven explanatory variables are selected. These variables are: the length of operated bus transit routes (LR), the total number of annual passengers trips (PT), the land area for urban roads (LA), the density of urban road networks (ND), the resident population (POP), the gross domestic production (GDP), and the value-added tax (TAX). On the other hand, the explained variable in these regression models is the AIP of each city. According to model results, it is found that AIP is positively related to LR and GDP, and negatively to PT, LA, and TAX. However, the effects of ND and POP are negative on the increase of AIPs of mega-cities and medium-size cities. It could also be found that the increase in bus infrastructure investment caused by the expansion of land area for urban roads is not negligible, especially in small cities. When the LR changes greatly, LA should be considered as a more stable factor to predict the AIP in mega-cities. Based on the findings, related implications for the policy-making on annual budget allocations for different tiers of cities are listed.

[1]  Xiaoshu Cao,et al.  Examining the impacts of socio-economic factors, urban form and transportation development on CO2 emissions from transportation in China: A panel data analysis of China's provinces , 2015 .

[2]  P. Nijkamp,et al.  Infrastructure and regional development: A multidimensional policy analysis , 1986 .

[3]  M. V. Geenhuizen,et al.  Port infrastructure investment and regional economic growth in China: Panel evidence in port regions and provinces , 2014 .

[4]  Enrico Capobianco,et al.  Smart cities and urban networks: are smart networks what we need? , 2015 .

[5]  Antonio Aguado,et al.  Sustainability as the key to prioritize investments in public infrastructures , 2016 .

[6]  M. Thériault,et al.  Economic impact of a supply change in mass transit in urban areas: A Canadian example , 2011 .

[7]  Peter C. B. Phillips,et al.  Semiparametric estimation in triangular system equations with nonstationarity , 2013 .

[8]  James H. Lambert,et al.  Prioritizing Infrastructure Investments in Afghanistan with Multiagency Stakeholders and Deep Uncertainty of Emergent Conditions , 2012 .

[9]  J. Hammes Political economics or Keynesian demand-side policies: What determines transport infrastructure investment in Swedish municipalities? , 2015 .

[10]  Jeffrey P. Cohen,et al.  Inter-county spillovers in California’s ports and roads infrastructure: the impact on retail trade , 2008 .

[11]  Cheng Hsiao,et al.  Analysis of Panel Data , 1987 .

[12]  Brian G. Knight,et al.  Parochial Interests and the Centralized Provision of Local Public Goods: Evidence from Congressional Voting on Transportation Projects , 2003 .

[13]  Li Peng,et al.  Factors influencing the efficiency of rural public goods investments in mountainous areas of China —— Based on micro panel data from three periods , 2016 .

[14]  Kari Watkins,et al.  An experiment evaluating the impacts of real-time transit information on bus riders in Tampa, Florida , 2014 .

[15]  Jeffrey M. Wooldridge,et al.  Solutions Manual and Supplementary Materials for Econometric Analysis of Cross Section and Panel Data , 2003 .

[16]  Bilal M. Ayyub,et al.  Strategic Implementation of Infrastructure Priority Projects: Case Study in Palestine , 2002 .

[17]  Qi Li,et al.  SMOOTH VARYING-COEFFICIENT ESTIMATION AND INFERENCE FOR QUALITATIVE AND QUANTITATIVE DATA , 2010, Econometric Theory.

[18]  Shengchuan Zhao,et al.  A study on the determinants of private car ownership in China: Findings from the panel data , 2016 .

[19]  M. Badami,et al.  An analysis of public bus transit performance in Indian cities , 2007 .

[20]  Gwo-Hshiung Tzeng,et al.  A MULTIOBJECTIVE PROGRAMMING APPROACH FOR SELECTING NON-INDEPENDENT TRANSPORTATION INVESTMENT ALTERNATIVES , 1996 .

[21]  Bongsuk Sung Public policy supports and export performance of bioenergy technologies: A dynamic panel approach , 2015 .

[22]  Massimo Florio,et al.  Infrastructure and Growth in a Spatial Framework: Evidence from the EU regions , 2012 .

[23]  Ashim Kumar Debnath,et al.  Sustainable, safe, smart—three key elements of Singapore’s evolving transport policies , 2013 .

[24]  Rakesh Kumar,et al.  Introduction of public bus transit in Indian cities , 2014 .

[25]  M. Tironi,et al.  The publicness of public transport: The changing nature of public transport in Latin American cities , 2016 .

[26]  Andreas Stephan,et al.  The Contribution of Local Public Infrastructure to Private Productivity and its Political Economy: Evidence from a Panel of Large German Cities , 2002 .

[27]  Jianing Mi,et al.  The growth impact of transport infrastructure investment: A regional analysis for China (1978–2008) , 2012 .

[28]  John Nellthorp,et al.  The UK Roads Review--a hedonic model of decision making , 2000 .

[29]  Yi Hu,et al.  Domestic air passenger traffic and economic growth in China: Evidence from heterogeneous panel models , 2015 .

[30]  Gabriel M. Ahlfeldt,et al.  Chicken or egg? The PVAR econometrics of transportation , 2015 .

[31]  Olivier Cadot,et al.  Contribution to Productivity or Pork Barrel? : The Two Faces of Infrastructure Investment , 2002 .

[32]  Qi Li,et al.  Semiparametric Smooth Coefficient Models , 2002 .

[33]  Hiroyuki Iseki Economies of scale in bus transit service in the USA: How does cost efficiency vary by agency size and level of contracting? , 2008 .