Innovative Application of the Public–Private Partnership Model to the Electric Vehicle Charging Infrastructure in China

Yang, Tong; Long, Ruyin; Li, Wenbo; Rehman, Saif Ur

doi:10.3390/su8080738

Cited by 56 publications

(33 citation statements)

References 21 publications

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“…The Chinese government is now making great efforts to maintain its economic growth in a so-called “New Normal” state, which refers to expectations of 6%~7% GDP growth rates in China for the foreseeable future [1]. As emphasized by Prime Minister Li, the key solution is to cultivate a new economic momentum and promote innovation.…”

Section: Introductionmentioning

confidence: 99%

How Does the Concentration of Determinants Affect Industrial Innovation Performance? – An Empirical Analysis of 23 Chinese Industrial Sectors

2017

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The agglomeration of innovation determinants has a significant influence on the innovation performance of industries and enterprises. Such an effect has received less attention in empirical research studies. This study involves a survey of the agglomeration effect of two important innovation determinants, R&D investment and R&D personnel, and its influence on innovation performance from the perspective of the industrial level. We analysed the agglomeration features based on the panel data of 23 Chinese industrial sectors from 2001~2013. An interpretation model is proposed to examine the agglomeration effect on innovation performance for 4 industrial groups: state-owned enterprises, individual enterprises, foreign-owned enterprises and enterprises as a whole. We found two main results. First, the agglomeration of determinants has a clear positive effect on the innovation performance of all 4 groups but affects individual enterprises more significantly, followed by state-owned and foreign-owned enterprises. Second, the state-owned enterprises show a much higher concentration of R&D investment and R&D personnel than other groups. However, the induced innovation efficiency in the state-owned enterprises is worse than in the individual enterprises. The advantage of resources and capital does not translate into corresponding innovation output. The privately owned small and medium-sized enterprises (SMEs) show a high capability of technological innovation and mercerization but have limited innovation resources.

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Section: Introductionmentioning

confidence: 99%

How Does the Concentration of Determinants Affect Industrial Innovation Performance? – An Empirical Analysis of 23 Chinese Industrial Sectors

2017

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“…The authors classify these attributes demonstrating their positive effect on EV adoption. According to Yang et al [36] and Li et al [30], inefficient supporting facilities can negatively affect the intention to buy an EV. David [37] and Rezvani et al [4] suggest that the EV market shows several technological differences when compared to the traditional market of fuel vehicles and the level of consumer knowledge on EV characteristics and performance is quite low.…”

Section: Electric Car Attributesmentioning

confidence: 99%

Does Value Co-Creation Really Matter? An Investigation of Italian Millennials Intention to Buy Electric Cars

et al. 2017

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Abstract:The present research aims to explore the determinants of (full) electric vehicle (EV) buying intention of Italian millennials focusing on the role that value co-creation initiatives might play in the buying decision-making process. Value co-creation initiatives in the EV domain are studied employing an enhanced version of the Theory of Reasoned Action which, in addition to the traditional variables of the model, also includes perceived importance of cars' attributes. Structural Equation Modeling (SEM) is used to analyze the data collected though an online survey on 523 Italians aged 18-35. The outcomes provide recommendations to tailor proper initiatives to encourage millennials' buying intention of electric vehicles supporting private companies in favoring the adoption of wide-spread pro-environmental behaviors among Italian youngsters.

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“…Moreover, references found with the above-mentioned keyword search, dealing with economic aspects such as financing schemes (Frade et al, 2011;Sierzchula, Bakker, Maat, & Van Wee, 2014;Yang, Long, Li, & Rehman, 2016), business models for CS (Kang, Feinberg, & Papalambros, 2015), market integration (Khoo & Gallagher, 2012;Yu, Li, & Tong, 2014;Yu, Li, & Tong, 2016), pricing strategies for CS (Bernardo, Borrell, & Perdiguero, 2016), and public incentives for the adoption of EV (Mersky, Sprei, Samaras, & Qian, 2016;Silvia & Krause, 2016;Taefi, Kreutzfeldt, Held, & Fink, 2016;Zhang, Xie, Rao, & Liang, 2014) are also not considered for further analysis. Market penetration scenarios for CS and EV (Foley & Gallach oir, 2015;Nemry & Brons, 2010;Pl€ otz, Gnann, K€ uhn, & Wietschel, 2013) are investigated issues in the EV sector as well, but are also discarded due to the specific review focus without a CS locating approach.…”

Section: Literature Without Location Modelingmentioning

confidence: 99%

A review of spatial localization methodologies for the electric vehicle charging infrastructure

Pagany

Camargo

Dorner

2018

International Journal of Sustainable Transportation

119

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With view to the high share of the transport sector in total energy consumption, e-mobility should play an important role within the transition of the energy systems. Policymakers in several countries consider electric vehicles (EV) as an alternative to fossil-fueled vehicles. In order to allow for the development of EV, the charging infrastructure has to be set up at locations with high charging potential, identified by means of various criteria such as demand density or trip length. Many methodologies for locating charging stations (CS) have been developed in the last few years. First, this paper presents a broad overview of publications in the domain of CS localization. A classification scheme is proposed regarding modeling theory and empirical application; further on, models are analyzed, distinguishing between users, route or destination centricity of the approaches and outcomes. In a second step, studies in the field of explicit spatial location planning are reviewed in more detail, that is, in terms of their target criteria and the specialization of underlying analytical processes. One divergence of these approaches lies in the varying level of spatial planning, which could be crucial depending on the planning requirements. It is striking that almost all CS locating concepts are proposed for urban areas. Other constraints, such as the lack of extensive empirical EV traffic data for a better understanding of the driving behavior, are identified. This paper provides an overview of the CS models, a classification approach especially considering the problem's spatial dimension, and derives perspectives for further research. ARTICLE HISTORY

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Innovative Application of the Public–Private Partnership Model to the Electric Vehicle Charging Infrastructure in China

Cited by 56 publications

References 21 publications

How Does the Concentration of Determinants Affect Industrial Innovation Performance? – An Empirical Analysis of 23 Chinese Industrial Sectors

How Does the Concentration of Determinants Affect Industrial Innovation Performance? – An Empirical Analysis of 23 Chinese Industrial Sectors

Does Value Co-Creation Really Matter? An Investigation of Italian Millennials Intention to Buy Electric Cars

A review of spatial localization methodologies for the electric vehicle charging infrastructure

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