Public-Private Partnership (PPP) has been adopted extensively in public infrastructures in China. Numerous studies have explored development patterns for future PPP projects success based on either literature review or case studies. This paper aims to probe into the spatiotemporal dynamics of China’s PPP and clarify regional differentiation by frequency analysis, coefficient of variation and cluster analysis. The result shows that the implementation rate has a slight increase along with time series although PPP projects gradually return to rational development. The fuzzy set is utilized to handle with ambiguity in some linguistic risk allocation criteria to lower the fuzziness and bias in knowledge expert which is qualitative that handles real time decision making. The municipal sector has been occupied the largest share of PPP projects while state-owned enterprises still dominate the biggest PPP marketed components. Local governments prefer viability gap subsidies although central government encourages user payment. Spatial difference coefficient is so high reflecting obvious regional differentiation of provincial PPP development. Recommendations are ultimately put forward to enhance China’s future PPP promotion, which will provide a valuable direction for government to make PPP customized policies.
AlbertA. and ZhangL.X., A novel definition of the multivariate coefficient of variation, Biometrical Journal52 (2010), 667–675.
2.
BennaouiA., SaadiS. and AmeurA., Invasive weed optimization algorithm for tuning transitioning from Type-1 to interval Type-2 fuzzy logic controller for boost DC-DC converters, Journal Europ’e,en des Systèmes Automatisés53 (2020), 195–202.
3.
ChanA.P.C., LamP.T.I., ChanD.W.M., CheungE. and KeY.J., Potential obstacles to successful implementation of public-private partnerships in Beijing and the Hong Kong special administrative region, Journal of Management in Engineering26 (2010), 30–40.
4.
LoutfiB., SamirZ., AliD. and ZinelaabidineG.M., Real time implementation of type-2 fuzzy backstepping sliding mode controller for twin rotor MIMO system (TRMS), Traitement du Signal36 (2019), 1–11.
5.
ShaoB., HuZ., LiuQ., ChenS. and HeW., Fatal accident patterns of building construction activities in China, Safety Science111 (2019), 253–263.
6.
TamC. and LeungA., Risk management of BOT projects in Southeast Asian countries, Profitable Partnering in Construction Procurement (1999), 499–507.
7.
AdamsJ., YoungA. and ZhihongW., Public private partnerships in China, International Journal of Public Sector Management19 (2006), 384–396.
8.
YuanJ.F., SkibniewskiM.J., LiQ.M. and ZhengL., Performance objectives selection model in public-private partnership projects based on the perspective of stakeholders, Journal of Management in Engineering26 (2010), 89–104.
9.
SongJ.H., XieH. and ShiL.P., Design of improved algorithm and model for multi-constrained fuzzy predictive analysis, Revue d’Intelligence Artificiell33 (2019), 467–473.
10.
ChouJ.S. and PramudawardhaniD., Cross-country comparisons of key drivers, critical success factors and risk allocation for public-private partnership projects, International Journal of Project Management33 (2015), 1136–1150.
11.
SaadaouiK., BouderahB., AssasO. and KhodjaM.A., Type-1 and type-2 fuzzy sets to control a nonlinear dynamic system, Revue d’Intelligence Artificielle33 (2019), 1–7.
12.
NgK.F., LoH.K. and HuaiY., Robust public-private partnerships for joint railway and property development, Frontiers of Engineering Management4 (2017), 437–450.
13.
LagosL., PicosJ. and ValeroE., Temporal pattern of wild ungulate-related traffic accidents in northwest Spain, European Journal of Wildlife Research58 (2012), 661–668.
14.
ZhongL.J., MolA.P.J. and FuT., Public-private partnerships in China’s urban water sector, Environmental Management41 (2008), 863–877.
15.
WangL.P., ZhangP., LiR.B., ZhangY.K. and WuY.Q., A systematic approach for public-private partnership projects CSFs evaluation, Engineering Construction and Architectural Management25 (2018), 1127–1145.
16.
TangL.Y., ShenQ.P., SkitmoreM. and ChengE.W.L., Ranked critical factors in PPP briefings, Journal of Management in Engineering29 (2013), 164–171.
17.
LiM., HeS.H., YouL.N. and HuangZ.C., Dynamic intuitionistic fuzzy multiple attributes decision making method based on prospect theory and VIKOR, Journal Europ’een des Systèmes Automatisés53 (2020), 243–248.
18.
CarbonaraN., CostantinoN. and PellegrinoR., Revenue guarantee in public-private partnerships: a fair risk allocation model, Construction Management and Economics32 (2014), 403–415.
19.
JonesR. and NobleG., Managing the implementation of public-private partnerships, Public Money & Management28 (2008), 109–114.
20.
MuR., de JongM. and KoppenjanJ., The rise and fall of Public-Private Partnerships in China: A path-dependent approach, Journal of Transport Geography19 (2011), 794–806.
21.
Osei-KyeiR. and ChanA.P.C., Review of studies on the Critical Success Factors for Public-Private Partnership (PPP) projects from 1990 to 2013, International Journal of Project Management33 (2015), 1335–1346.
22.
KikeriS., NellisJ.R. and ShirleyM.M. Privatization: The Lessons of Experience, World Bank (1992).
23.
ZhangS., ChanA.P.C., FengY., DuanH. and KeY., Critical review on PPP research - A search from the Chinese and International Journals, International Journal of Project Management34 (2016), 597–612.
24.
ZhangS., GaoY., FengZ. and SunW., PPP application in infrastructure development in China: Institutional analysis and implications, International Journal of Project Management33 (2015), 497–509.
25.
MurphyT.J. and The case for public-private partnerships in infrastructure, Canadian Public Administration-Administration Publique Du Canada51 (2008), 99–126.
26.
LiuT.T., WangY. and WilkinsonS., Identifying critical factors affecting the effectiveness and efficiency of tendering processes in Public-Private Partnerships (PPPs): A comparative analysis of Australia and China, International Journal of Project Management34 (2016), 701–716.
27.
ReddyU.J., ReddyB.R.V.R. and ReddyB.E., Recognition of lung cancer using machine learning mechanisms with fuzzy neural networks, Traitement du Signal36 (2019), 87–91.
28.
LiuW., LiW.J. and XuX. The analysis of the PPP financing model application in ports of China, Journal of Coastal Research: (2015), 4-8.
29.
TangX., ZengT., TanY. and DingB.X., Conflict analysis based on three-way decision theoretic fuzzy rough set over two universes, Ing’enierie des Systèmesd’ Information25 (2020), 75–82.
30.
HeX.C., Coefficient of variation and its application to strength prediction of clinched joints, Advanced Science Letters4 (2011), 1757–1760.
31.
ZhangY., From state to market: Private participation in china’s urban infrastructure sectors, 1992–2008 . World Development64 (2014), 473–486.
32.
ChengZ., KeY., LinJ., YangZ. and CaiJ., Spatio-temporal dynamics of public private partnership projects in China, International Journal of Project Management34 (2016), 1242–1251.
33.
LiZ.L., ZhouY. and BaoR., An image classification method based on optimized fuzzy bag-of-words model, Traitement du Signal36 (2019), 239–244.
