Multi-Layered Capital Subsidy Policy for the PV Industry in China Considering Regional Differences

Abstract

Abstract:As a country with huge energy consumption, China has been paying more and more attention to green growth in recent years. Several subsidy policies have been conducted to boost the photovoltaic (PV) industry so far. However, as a matter of fact, there are 31 provinces and municipalities (PM) in mainland China, and the economic condition, environmental resources and energy structure of each PM are all significantly different, which leads to a discrepancy of PV efficiency among regions. This paper proposes that the subsidies for the PV industry should be considered mainly from three aspects: the PM's economic condition, energy efficiency and environmental responsibility. We built 19 evaluation indicators involving economic, environmental and energy factors, allocated weight to each indicator using the entropy weight method and sorted the 31 evaluation objects by TOPSIS (Technique for Order of Preference by Similarity to the Ideal Solution) method. Additionally, through an empirical study, we obtained the subsidy proportion for each PM. The result could make much of a contribution to the green growth of different regions and to the whole country.

Introduction to the Special Issue on the Sustainable Asia Conference 2015

“…A multi-objective mixed-integer programming model is introduced to solve the location problem for sustainable home healthcare centers [15]. Li et al (2016) [10] introduced 19 evaluation indicators involving economic, environmental, and energy factors, allocated weight to each indicator using the entropy weight method, and sorted the 31 evaluation objects by using the TOPSIS method. To test the convergence theory with regard to the economic growth of different regions, Yang et al (2016) [6] utilized the β-convergence method and σ-convergence method based on the fixed effects model.…”

“…Several subsidy policies have been initiated to boost the photovoltaic (PV) industry. However, to ensure sustainable governance of the policies, the subsidies for the PV industry should be considered mainly in terms of the following aspects: the provinces' and municipalities' economic condition, energy efficiency, and environmental responsibility [10]. Using 19 evaluation indicators of economic, environmental, and energy factors, along with the entropy weight method, in the 31 provinces of China, the paper carried out empirical tests to find out the optimal subsidy criteria layer.…”

“…This appealing policy made companies invest large amounts of money into the PV industry, which led to a high idle rate of PV capacity and a huge waste of money and installation resources. Compared to the two-layer capital subsidy policy, the paper proposed a 31-layer policy to differentiate subsidies for regions and controlled the upper limit of capital investment into each province accurately, leading to appropriate capital flow for sustainable PV development [10]. Subsidy modes in the renewable energy industry may affect corporate financial performance as well.…”

Efficiency Measurement and Factor Analysis of China’s Solar Photovoltaic Power Generation Considering Regional Differences Based on a FAHP–DEA Model

“…The environmental responsibility of local government can be assessed in terms of local pollutant discharge and regional environmental investment [28]. A region with strong environmental responsibility and consciousness tends to have less pollutant discharge and higher environmental investment.…”

“…This study deployed the Tobit regression model to test the most important influencing factors of PV power generation efficiency. After a comprehensive consideration, nine indicators were selected, which are: real GDP per capita (X 1 ), general financial revenue (X 2 ), electricity consumption (X 3 ), total energy consumption per unit GDP (X 4 ), financial expenditure for environmental protection (X 5 ) [31], NOx emissions (X 6 ), SO 2 emissions (X 7 ), smoke dust emissions (X 8 ) [30], and R&D in electricity industry (X 9 ) [25][26][27][28]. The Tobit regression model is calculated by Stata 13.1 (StataCorp LLC, College Station, TX, USA).…”