Regarding the characteristics of policy mix and its effectiveness, no complete consensus currently exists. This paper divides the new energy vehicle (NEV) industry policies issued by the Chinese government from 2009 to 2018 into two types: the supply-side and demand-side policy mix. From the three dimensions of comprehensiveness, consistency and balance, we applied the state space method to construct a time-varying parameter model and analyzed the differences in the overall and dynamic impact of the NEV policy mix characteristics on market growth. The results show that the three characteristics differ in their overall impact: comprehensiveness has the largest impact, followed by consistency, and balance has the smallest impact. The dynamic impact of the comprehensiveness and consistency characteristics of the two types of policy mix fluctuates greatly and presents clear differences in the stage effect. The dynamic impact of the balance characteristic is relatively stable, but this characteristic has little dynamic impact on market growth. The NEV industry policy design should fully consider the characteristics of the policy mix and the differences of this policy mix in terms of market development stage and region.
YuanXLiuXZuoJ.The development of new energy vehicles for a sustainable future: a review. Renewable Sustainable Energy Rev2015;
42: 298–305.
2.
LiuZWHaoHChengX, et al.
Critical issues of energy efficient and new energy vehicles development in China. Energy Policy2018;
115: 92–97.
3.
SierzchulaWBakkerSMaatK, et al.
The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy2014;
68: 183–194.
4.
LangbroekJHMFranklinJPSusiloYO.The effect of policy incentives on electric vehicle adoption. Energy Policy2016;
94: 94–103.
5.
LimKKimJJLeeJ.Forecasting the future scale of vehicle to grid technology for electric vehicles and its economic value as future electric energy source: the case of South Korea. Energy Environ. Epub ahead of print 7 January 2020. DOI: 10.1177/0958305X19898283.
6.
XuLSuJ.From government to market and from producer to consumer: transition of policy mix towards clean mobility in China. Energy Policy2016;
96: 328–340.
7.
RoggeKSSchleichJ.Do policy mix characteristics matter for low-carbon innovation? A survey-based exploration for renewable power generation technologies in Germany. Res Policy2018;
47: 1639–1654.
8.
ReichardtKRoggeK.How the policy mix impacts innovation: findings from company case studies on offshore wind in Germany. Environ Innov Soc Trans2016;
18: 62–81.
9.
CostantiniVCrespiFPalmaA.Characterizing the policy mix and its impact on eco-innovation: a patent analysis of energy-efficient technologies. Res Policy2017;
46: 799–819.
10.
GuerzoniMRaiteriE.Demand-side vs. supply-side technology policies: hidden treatment and new empirical evidence on the policy mix. Res Policy2015;
44: 726–747.
11.
XuZLiCY.S&T policy mix characteristics and their impact on industrial innovation in China. Stud Sci Sci2017;
35: 45–53.
12.
KernFRoggeKSHowlettM.Policy mixes for sustainability transitions: new approaches and insights through bridging innovation and policy studies. Res Policy2019;
48: 103832.
13.
FalconePMLopolitoASicaE.Instrument mix for energy transition: a method for policy formulation. Technol Forecast Soc Change2019;
148: 119706.
14.
HowlettMDelRP.The parameters of policy portfolios: verticality and horizontality in design spaces and their consequences for policy mix formulation. Environ Plann C Gov Policy2015;
33: 1233–1245.
15.
WangXZZouHHZhengY, et al.
How will different types of industry policies and their mixes affect the innovation performance of wind power enterprises? Based on dual perspectives of regional innovation environment and enterprise ownership. J Environ Manage2019;
251: 109586.
16.
FlanaganKUyarraELaranjaM.Reconceptualising the ‘policy mix’ for innovation. Res Policy2011;
40: 702–713.
SchaderCLampkinNMulleA, et al.
The role of multi-target policy instruments in Agri-environmental policy mixes. J Environ Manage2014;
145: 180–190.
19.
KivimaaPKernF.Creative destruction or mere niche support? Innovation policy mixes for sustainability transitions. Res Policy2016;
45: 205–217.
20.
TrencherGvan der HeijdenJ.Instrument interactions and relationships in policy mixes: achieving complementarity in building energy efficiency policies in New York, Sydney and Tokyo. Energy Res Soc Sci2019;
54: 34–45.
21.
OECD.The Innovation policy mix.OECD Science, Technology and Industry Outlook 2010. Paris: OECD, 2010.
22.
RoggeKSReichardtK.Policy mixes for sustainability transitions: an extended concept and framework for analysis. Res Policy2016;
45: 1620–1635.
23.
ScordatoLKlitkouATartiuVE, et al.
Policy mixes for the sustainability transition of the pulp and paper industry in Sweden. J Clean Prod2018;
183: 1216–1227.
24.
GawelEStrunzSLehmannP.A public choice view on the climate and energy policy mix in the EU – how do the emissions trading scheme and support for renewable energies interact?Energy Policy2014;
64: 175–182.
25.
CantnerUGrafHHerrmannJ, et al.
Inventor networks in renewable energies: the influence of the policy mix in Germany. Res Policy2016;
45: 1165–1184.
26.
SovacoolBK.The importance of comprehensiveness in renewable electricity and energy-efficiency policy. Energy Policy2009;
37: 1529–1541.
27.
UyarraEShapiraPHardingA.Low carbon innovation and enterprise growth in the UK: challenges of a place-blind policy mix. Technol Forecast Soc Change2016;
103: 264–272.
28.
MaSCFanYFengLY.An evaluation of government incentives for new energy vehicles in China focusing on vehicle purchasing restrictions. Energy Policy2017;
110: 609–618.
29.
XiongYQChenML.Study of the new energy vehicles demand market’s cultivated policy orientation: supply side or demand side. China population. Resour Environ2016;
26: 129–137.
30.
SchmidtTSSewerinS.Measuring the temporal dynamics of policy mixes – an empirical analysis of renewable energy policy mixes’ balance and design features in nine countries. Res Policy2019;
48: 103557.
31.
GongHWangMQWangH.New energy vehicles in China: policies, demonstration, and progress. Mitig Adapt Strateg Glob Change2013;
18: 207–228.
32.
ZhangLQinQ.China’s new energy vehicle policies: evolution, comparison and recommendation. Transp Res Part A Policy Pract2018;
110: 57–72.
33.
ZhangLLiuY.Analysis of new energy vehicles industry policy in china’s cities from the perspective of policy instruments. Energy Procedia2016;
104: 437–442.
34.
ZhangXBaiX.Incentive policies from 2006 to 2016 and new energy vehicle adoption in 2010-2020 in China. Renewable Sustainable Energy Rev2017;
70: 24–43.
35.
WangNTangLHZhangWJ, et al.
How to face the challenges caused by the abolishment of subsidies for electric vehicles in China?Energy2019;
166: 359–372.
36.
CorradiniMCostantiniVMarkandyaA, et al.
A dynamic assessment of instrument interaction and timing alternatives in the EU low-carbon policy mix design. Energy Policy2018;
120: 73–84.
37.
XiongYQWangLY.Policy cognition of potential consumers of new energy vehicles and its sensitivity to purchase willingness. J Clean Prod2020;
261: 121032.
38.
QiuYQZhouPSunHC.Assessing the effectiveness of city-level electric vehicle policies in China. Energy Policy2019;
130: 22–31.
39.
LibecapGD.Economic variables and the development of the law: the case of Western mineral rights. J Econ Hist1978;
38: 338–362.
40.
CoolsMBrijsKTormansH, et al.
Optimizing the implementation of policy measures through social acceptance segmentation. Transp Policy2012;
22: 80–87.
41.
PengJSZhongWGSunWX.Policy measurement, policy co-evolution and economic performance: an empirical study based on innovation policy. Manag World2008;
9: 25–36.
42.
ZhaoRYangLLiangX, et al.
Last-mile travel mode choice: data-mining hybrid with multiple attribute decision making. Sustainability2019;
11: 6733.
43.
GuoWTaoKLiZG.An analysis of the impact of policy mix on leading markets by taking the new energy vehicle industry as an example. Sci Res Manage2018;
39: 30–36.
44.
TanRTangDLinB.Policy impact of new energy vehicles promotion on air quality in Chinese cities. Energy Policy2018;
118: 33–40.
45.
HarveyAC.Forecasting, structural time series models and the Kalman filter.
Cambridge:
Cambridge University Press, 1990.
