Can the incentives polices promote the diffusion of distributed photovoltaic power in China?

被引:30
作者
Wang Wei [1 ,2 ]
Zhao Xin-gang [1 ,2 ]
机构
[1] North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
[2] North China Elect Power Univ, Beijing Key Lab New Energy & Low Carbon Dev, Beijing 102206, Peoples R China
关键词
Incentives policy; Distributed photovoltaic power plants; Technology diffusion; System dynamics; Cost reduction; RENEWABLE ENERGY TECHNOLOGY; FEED-IN TARIFF; WIND POWER; SOLAR PV; ANALYTICAL FRAMEWORK; INNOVATION EVIDENCE; RESIDENTIAL SOLAR; SYSTEM DYNAMICS; MODEL; ADOPTION;
D O I
10.1007/s11356-021-17753-3
中图分类号
X [环境科学、安全科学];
学科分类号
083001 [环境科学];
摘要
Government incentive policies play an important role in the promotion of distributed photovoltaic power. However, which policy is more effective for the diffusion of distributed photovoltaic power? This is a question that needs to be answered. Based on this, we combined the two-factor learning curve and system dynamics model to study the dynamic diffusion process of China's distributed photovoltaic power (DSP). The results show that (1) the coefficients of learning by doing and learning by researching for DSP are 0.0435 and 0.2971 respectively. This indicates that technological innovation caused by R&D expenditures in the DSP is the driving force for cost reduction. (2) Both demand-pull and technology-push policies contribute to the diffusion of DSP; (3) the effect of FIT policy on the diffusion of distributed photovoltaic technology is more significant than that of R&D policy; and the reduction of production cost of photovoltaic power industry by R&D policy is more significant than FIT policy.
引用
收藏
页码:30394 / 30409
页数:16
相关论文
共 62 条
[1]
Green certificates as an instrument to support renewable energy in Poland-strengths and weaknesses [J].
Adamczyk, Janusz ;
Graczyk, Magdalena .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2020, 27 (06) :6577-6588
[2]
Causal loop modelling of residential solar and battery adoption dynamics: A case study of Queensland, Australia [J].
Agnew, Scott ;
Smith, Carl ;
Dargusch, Paul .
JOURNAL OF CLEANER PRODUCTION, 2018, 172 :2363-2373
[3]
Understanding dynamics and policy for renewable energy diffusion in Colombia [J].
Arias-Gaviria, Jessica ;
Ximena Carvajal-Quintero, Sandra ;
Arango-Aramburo, Santiago .
RENEWABLE ENERGY, 2019, 139 :1111-1119
[4]
NEW PRODUCT GROWTH FOR MODEL CONSUMER DURABLES [J].
BASS, FM .
MANAGEMENT SCIENCE SERIES A-THEORY, 1969, 15 (05) :215-227
[5]
Diffusion of photovoltaic technology in Germany: A sustainable success or an illusion driven by guaranteed feed-in tariffs? [J].
Baur, Lucia ;
Uriona M, Mauricio .
ENERGY, 2018, 150 :289-298
[6]
The impact of the German feed-in tariff scheme on innovation: Evidence based on patent filings in renewable energy technologies [J].
Boehringer, Christoph ;
Cuntz, Alexander ;
Harhoff, Dietmar ;
Asane-Otoo, Emmanuel .
ENERGY ECONOMICS, 2017, 67 :545-553
[7]
The long-term effects of cautious feed-in tariff reductions on photovoltaic generation in the UK residential sector [J].
Castaneda, Monica ;
Zapata, Sebastian ;
Cherni, Judith ;
Aristizabal, Andres J. ;
Dyner, Isaac .
RENEWABLE ENERGY, 2020, 155 :1432-1443
[9]
Slow diffusion of renewable energy technologies in China: An empirical analysis from the perspective of innovation system [J].
Chen, Yufang ;
Lin, Boqiang .
JOURNAL OF CLEANER PRODUCTION, 2020, 261
[10]
Market diffusion of household PV systems: Insights using the Bass model and solar water heaters market data [J].
da Silva, Hendrigo Batista ;
Uturbey, Wadaed ;
Lopes, Bruno M. .
ENERGY FOR SUSTAINABLE DEVELOPMENT, 2020, 55 (55) :210-220