Next Article in Journal
Study on Formation Mechanism of Pre-stressed Anchor Pressure Arch Based on Safe Co-Mining of Deep Coal and Gas
Next Article in Special Issue
Review of Energy Portfolio Optimization in Energy Markets Considering Flexibility of Power-to-X
Previous Article in Journal
HBIM-Based Decision-Making Approach for Sustainable Diagnosis and Conservation of Historical Timber Structures
Previous Article in Special Issue
Study on Critical Factors Affecting Tidal Current Energy Exploitation in the Guishan Channel Area of Zhoushan
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 4
10.3390/su15043005
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Policy Recommendations for Distributed Solar PV Aiming for a Carbon-Neutral Future

by
Jiehui Yuan
1,2,*,
Wenli Yuan
1,
Juan Yuan
1,
Zhihong Liu
1,
Jia Liao
1 and
Xunmin Ou
1,3
1
Institute of Energy & Resource, Environment and Carbon Neutrality, Yichun University, Yichun 336000, China
2
Center for Western Jiangxi Regional Economic and Social Development, Yichun University, Yichun 336000, China
3
Institute of Energy, Environment and Economy, Tsinghua University, Beiijng 100081, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(4), 3005; https://doi.org/10.3390/su15043005
Submission received: 8 December 2022 / Revised: 3 February 2023 / Accepted: 5 February 2023 / Published: 7 February 2023
(This article belongs to the Special Issue Sustainable Energy Management: Research in Technology, Economics, and Policy)
Browse Figures
Versions Notes

Abstract

:
Distributed-solar-photovoltaic (PV) generation is a key component of a new energy system aimed at carbon peaking and carbon neutrality. This paper establishes a policy-analysis framework for distributed-solar-PV generation based on a technical- and economic-evaluation model. Given that the resource endowment is becoming lower and the raw material costs are becoming higher, the profitability of the deployment of distributed-solar-PV-generation projects in China is generally becoming much worse. Some distributed-PV-generation projects are even becoming unprofitable. This will not be helpful for the sustainable development of distributed-PV generation, which will play a vital role in attaining the goal of carbon neutrality. Based on the established model for techno-economic evaluation, a systematic policy analysis is performed to identify the effect of possible policy instruments such as financial policies on improving the economic profitability of distributed-PV-development in China. The results indicate that policy instruments related to preferential financing, green certificate, tax incentives and combinations thereof are available for priority measures aimed at optimizing incentive policies for enhancing the economic viability of distributed-PV deployment in China. Based on these findings, recommendations are proposed to optimize the currently available policy instruments for accelerating the sustainable development of the distributed-PV industry towards a carbon-neutral future.

1. Introduction

As an important contributor to carbon dioxide (CO2) emissions in the world, China is putting more efforts into playing an increasing role in global climate governance. To demonstrate its role as a responsible major country, China pledged that it would peak its CO2 emissions before 2030 and that it would achieve carbon neutrality before 2060. Increasing the share of renewable-energy use for establishing a new energy system is a key basis of achieving the goal of carbon peaking and carbon neutrality in China [1,2]. Renewable energy, including distributed-solar-PV-power generation is a key component of the future energy systems aiming at carbon peaking and carbon neutrality. Many countries like China are increasing their efforts to develop distributed solar PV [3].
Apart from the low resource endowment, several factors, including high raw-material cost and companies’ inefficiencies explain why the profitability of developing distributed solar PV is currently not well understood [4,5,6]. This will not be helpful for the investment decision and the sustainable development of distributed-PV generation which contributes to playing a vital part in attaining the goal of carbon neutrality. Given its importance in establishing the future energy system, the deployment of distributed-PV-generation projects should be consistently stimulated by government departments [7,8]. In recent years, the government departments in many countries such as China usually provide policy incentives aiming at improving the profitability of the development of distributed-solar-PV generation, which contributes to the sustainable development of the distributed-solar-PV industry.
In view of the short history of developing distributed solar PV in China, very few studies focused on the comprehensive analysis with the combination of a techno-economic evaluation and a policy analysis aiming at optimizing currently available policies to promote the industry development of distributed PV in China towards carbon neutrality. Some studies such as Zhang (2016) [9], Garlet et al. (2019) [10] and Li et al. (2020) [11] present policy suggestions for supporting the development of distributed solar PV based on a qualitative analysis of the shortcomings of the existing policy system. Some studies focus on policy implications for promoting the development of distributed PV after conducting a simple economic analysis of distributed-PV projects [12,13,14]. For example, in their work, Zhang et al. (2015) [12] and Alhammami et al. (2021) [13] recommended policy options such as increasing education and training activities for distributed solar PV aiming at increasing public acceptance after a simple economic analysis. There are some studies highlighting an analysis of the economic viability of one type of rooftop-distributed-PV projects such as residential distributed-PV projects without considering the impact of the electricity-production-decline law, which proposed policy suggestions on the economic-evaluation model [15,16,17]. For instance, Zhang et al. (2021) [15] evaluated the economic benefits of residential distributed PV in rural areas of China without incorporating the effect of the law of annual electricity-production decline. In the work of Zhang et al. (2020) [16], a grid-parity analysis was conducted from the demand-side including industrial and commercial consumers and residential consumers, and from the supply-side of distributed-PV generation at the province level. They presented policy recommendations for the government and the distributed-PV industry without considering the impact of proposed policy suggestions on the economic evaluation. Moreover, these aforementioned studies usually performed an analysis on traditional incentive policies without focusing on new incentive policies such as carbon trading and green finance in the context of carbon neutrality [18,19,20]. In addition, there are some studies focused on the policy analysis of distributed-PV development adopting other methods including game theory, system dynamics and econometric models [21,22,23].
To address the questions mentioned above, this article aims at filling the gap in existing studies by (1) determining the profitability of developing distributed-solar-PV generation projects, considering the project type and the electricity-production-decline law, (2) providing more details explaining why the economic viability of the deployment of distributed-solar-PV-generation projects is generally becoming much worse, due to the new situation of carbon neutrality, and (3) conducting a policy analysis for identifying which policy instruments may have a more significant effect on enhancing the economic feasibility of deploying distributed-solar PV-generation projects. The proposed policy recommendations, which are supported by a techno-economic evaluation, may help policymakers in China to achieve carbon neutrality to address the practical obstacles to distributed-PV adoption, which may also be applicable to other countries attempting to develop distributed solar PV to achieve carbon neutrality.
The rest of this article is designed as follows. Section 2 describes a framework for policy analysis and the methods for evaluating the profitability of the deployment of distributed-PV-generation projects. In Section 3, the practical data obtained and key assumptions for this study are presented. Section 4 provides the result of the techno-economic evaluation for the typical cases. In Section 5, the effect of currently available policy instruments on enhancing the economic feasibility of deploying distributed solar PV is discussed. Finally, Section 6 concludes the article and presents policy recommendations.

2. Methods

2.1. Modelling Framework

For establishing a new energy system dominated by renewable energy towards carbon neutrality, more distributed-solar-PV projects have to be deployed, even with lower resource endowment. In light of this new situation, a more accurate evaluation of the profitability of distributed-PV-generation projects becomes more important for the investment decision. Considering current policy incentives, a techno-economic evaluation model based on a discounted-cash-flow (DCF) method is established to determine the profitability of deploying distributed-PV-generation projects. This analysis highlights the impact of the project type and the electricity-production-decline law on the profitability of developing distributed solar PV in China. The evaluation will be a key basis of the subsequent analysis for policy issues. These findings will help investigate the effect of currently available policy instruments on enhancing the economic feasibility of distributed-solar-PV projects, and provides guidance to improve current incentive policies. After a techno-economic evaluation combined with a subsequent policy analysis, possible useful ideas and suggestions aimed at optimizing policy incentives for stimulating the deployment of distributed-PV generation projects are proposed. Figure 1 illustrates the schematic diagram for the modelling framework of this paper.

2.2. Evaluation Methods

DCF analysis [24,25,26], a dynamic financial-analysis method, is usually adopted for the techno-economic evaluation of energy development such as distributed-PV development. DCF analysis aims to determine the time value of an investment. This method is typically adopted to investigate a potential investment opportunity [27,28]. A financial-analysis model is established based on the DCF method and is embedded in a techno-economic evaluation, aiming to examine the economic viability of a distributed-PV project in China. This study develops a software tool with the Visual Basic language to calculate the results and to display them.
The purpose of the DCF analysis is to predict annual cash flows (CF) of investment projects for distributed-PV generation over its production life and to discount them in order to examine the current value of the development project. It offers available measures that allow us to evaluate the economic performance of deploying distributed-PV-generation projects. Several classical economic indicators, including the net present value (NPV), internal rate of return (IRR), and discounted profitability index (DPI), are typically adopted to help make the decision for investment projects [29,30].
In any operational year, the annual undiscounted net CF related to the investment in distributed-PV-generation projects is developed by the follow formula:
N C F t = R D P V t − I I 0 − O C t − I D t − T D t
where R D P V t is the total annual revenue of developing distributed-PV-generation projects in year t; I I 0 denotes initial investments such as installation costs; O C t is the annual operating and maintenance costs; I D t denotes annual insurance dues; T D t denotes annual taxes and dues. The classical economic indicators for calculating are NPV, IRR and DPI. The NPV is computed using the following equations:
NPV = ∑ t = 0 n N C F t ( 1 + i 0 ) t
IRR = i ,   i f   NPV = 0
DPI = NPV ∑ t = 0 k I C t × ( 1 + i 0 ) − t + 1
where i 0 is the benchmark discount rate; i is the discount factor; n represents the operating time of deploying distributed-PV-generation projects; I C t is the investment capital; k is the number of years of the capital spending. If t ≥ k , no new capital costs are generated in year t.

3. Key Data and Assumptions

As mentioned previously, more lower-resource-endowment distributed-PV projects are necessary to be deployed, aiming at establishing a new energy dominated by renewable energy moving towards carbon neutrality. Two types of distributed-PV projects [31,32,33] in areas such as Jiangxi province in China, which are the third type of resource areas, are selected as the case project for the analysis. The rationality of this assessment will be ensured via the adoption of two typical distributed-PV-generation projects in lower-resource-endowment areas such as Jiangxi province in China. One is an industrial and commercial distributed-PV project, with a typical installment capacity of 3 MWp [27,29], and the other is a residential distributed-PV project with a classical installment capacity of 5 kWp [16,30]. The primary data used for the analysis are drawn from practical distributed-PV projects by conducting a detailed field investigation, and from recent studies [16,24,28], as presented in Table 1.
The following assumptions are obtained from field investigations and recent studies on distributed PV. Due the decay of the distributed-PV-generation system, the annual degradation of the electricity yield is assigned to 2% of the initial annual generation in the first year and 0.9% annually thereafter, in this study [34,35]. Given the escalation of the electricity-payment price and the FIF price in future, the annual growth rate is assumed to be 3% in this paper [36,37]. In addition, current incentive policies such as county-wide pilot programs for distributed PV create a favorable condition for the development of this industry [38]. Moreover, to facilitate green and sustainable development, China introduced a set of guidelines on preferential tax and fee policies for stimulating the development of distributed PV [39]. For instance, regarding the industrial and commercial distributed PV project in China, the IT rate during the first three years is 0% and during the second three years it is 12.5%, and subsequently becomes 25%.

4. Results

4.1. Economic Performance of Industrial and Commercial Distributed-PV Projects

Given the difference between the electricity-payment price and the fixed-FIF price, the self-consumption ratio of electricity production will generate a distinct impact on the economics of industrial and commercial distributed-PV projects. Five typical cases are designed to explore the impact. The self-consumption ratio (SCR) is set at various rates, of 50%, 70%, 80%, 90% and 100%. According to the framework presented previously, a financial analysis is conducted to investigate the economic performance of industrial and commercial distributed-PV projects in lower-resource-endowment areas such as Jiangxi province in China. The main economic indicators of the target project at various SCR rates are elaborated in Table 2.
In view of the resource endowment, the development of industrial and commercial distributed-PV projects in Jiangxi province in China is currently not economically viable. For example, the NPV of Case 4 with the most widely used SCR [40,41,42] is CNY −1,182,029.5, with an IRR of 7.2% and an DPI of 0.93. These values are slightly lower than the reference values. To exclude the impact of taxes, including corporate IT and AT, pre-tax economic metrics are determined. The pre-tax NPV is CNY 2,470,953.4, the pre-tax IRR is 9.6% and the pre-tax DPI is 1.16. These values are distinctly higher than the reference values. These results suggest that if there are no taxes including corporate IT and AT, the profitability of the deployment of industrial and commercial distributed-PV projects will become better.
Table 3 presents a comparison of the economic-indicator results of similar studies focused on industrial and commercial distributed PV in China and other countries. This study presents results with economic metrics similar to that of other studies in China and other countries such as Chile and Bangladesh [18,29,40,41]. However, these studies present a result that is relatively optimistic compared to the results presented in this study because of different resource endowments [29], policy conditions (such as financial subsidies) [40,41] and calculation methods [18,40].
The net cash flow (CF), discounted cash flow (DCF) and accumulated DCF of the industrial and commercial distributed-PV project is specified in Figure 2. We can also see that this industrial and commercial distributed-PV project is not profitable. Increasing the SCR can help improve the profitability of industrial and commercial distributed-PV projects. For instance, an increase in the SCR will generate a significant improvement in profitability; i.e., the DPI increases from 0.76 to 0.97. However, the DPI of 0.97 is lower than 1, and the effect is small.
To explore the effect of key factors, such as the initial annual electricity generation (IAEG), initial electricity price (IEP), feed-in tariff price (FIFP), initial investment costs (IIC), operating and maintenance costs (OMC), and insurance costs (IC), a sensitivity analysis is conducted. After carrying out calculations, the NPV of the industrial and commercial distributed-PV project in this study in relation to the changes in the key factors such as IAEG and IIC, is illustrated in Figure 3. We can see that the three most important influential factors are IIC, IAEG and IEP. Among them, IIC is the single most influential factor in the NPV. When the IIC is reduced by 10%, the NPV will increase by approximately 1430%, with an IRR of 8.4% and with a DPI of 1.04. Therefore, the measure that can decrease the IIC of the residential distributed-PV project should be adopted as a priority option.

4.2. Economic Performance of Residential Distributed-PV Projects

Similarly to the evaluation of the development of industrial and commercial distributed-PV projects in Jiangxi province in China, this study creates five typical cases to explore the impact of SCR on the economic performance of residential distributed-PV projects. The economic indicators for the deployment of distributed-PV generation in this study is calculated for SCR rates of 20%, 30%, 50%, 90% and 100%. Table 4 presents the three economic indicators for residential distributed-PV projects for various SCR cases.
For an increase in the SCR of residential distributed-PV projects, the IRR increases from 6.4% to 10%, which indicates that the SCR plays a vital part in enhancing the profitability. However, given the electricity-absorption capacity of the resident, the SCR of residential distributed-PV projects cannot be too high. Usually, the SCR of most residential distributed-PV projects is lower than 30% [43,44,45]. Therefore, the economic viability of residential distributed-PV projects in lower-resource-endowment areas such as Jiangxi province in China is poor, under current conditions. To exclude the impact of taxes, including corporate IT and AT, pre-tax economic indicators are calculated. The pre-tax NPV is CNY 178, the pre-tax IRR is 7.1% and the pre-tax DPI is 1.01. These values are significantly higher than the reference values. These results suggest that if there are no taxes, including corporate IT and AT, the profitability of the deployment of residential distributed-PV projects will improve. However, the profitability of industrial and commercial distributed-PV projects is higher than that of residential distributed-PV projects without the impact of taxes.
A comparison of the economic-metric results of similar studies focusing on residential distributed PV in China and other countries is listed in Table 5. This study presents results with economic indicators similar to those of other studies in China and other countries such as Chile and Bangladesh [16,41,42,44]. However, these studies present results that are relatively optimistic compared to the results presented in this study, due to the differences in resource endowment [16,42], and policy conditions [41,42,44].
To investigate the effects of the key factors, including IAEG, IEP, FIFP, IIC, OMC and IC, on the NPV of the residential distributed-PV project, a sensitive analysis is performed. The effects of key factors such as IAEG, IEP and IIC on the economic value of the residential distributed-PV project in this study is demonstrated in Figure 4. We can see that the four factors with the most influence on the NPV of the residential distributed-PV project are IAEG, IIC, FIFP and IEP. Among them, IAEG is the most influential factor that can attribute to affecting the NPV. Therefore, those measures that can increase the IAEG of the residential distributed-PV project should be the target of more effort which aims to improve the economic profitability of residential distributed-PV projects in China.

5. Discussion

In view of the low profitability of distributed-PV-generation projects in China, a systematic analysis is performed to investigate the effects of the currently available policy incentives, aiming at improving the profitability. To accurately determine the validity of these possible incentive instruments, two different policy scenarios are designed, with a single policy and two policies. Considering the typicality of the SCR of distributed-PV projects, the industrial and commercial project with an SCR of 90% and the residential project with an SCR of 30% are assigned as the benchmark project for exploring the impact of policy incentives. Based on these findings, appropriate measures will be provided to give directions for optimizing current policy instruments, aiming at enhancing the economic value of distributed-PV generation projects in China.

5.1. The Impact of a Single Policy

For a benchmark industrial and commercial distributed-PV project, the impact of a single policy such as preferential AT and green finance has been investigated. For example, the DPI for the deployment of industrial and commercial distributed-PV projects in China is calculated for the AT rates of 11%, 9%, 7%, 5%, 3%, and 0, and for loan interest rates of 8%, 7%, 6%, 4%, 2%, and 0. The results are illustrated in Figure 5a,b. A preferential-AT policy measure will be helpful to increase the economic feasibility of the benchmark industrial and commercial project, as the effect is quite small. However, a green-finance policy highlighting a lower loan interest rate can render industrial and commercial distributed PV economically viable in the target area.
For the preferential-VAT policy and the preferential-corporate-IT policy, the DPI for the deployment of industrial and commercial distributed-PV projects in China is calculated for VAT rates of 17%, 8.5%, 7%, 5%, 3%, and 0 and for corporate IT rates of 25%, 20%, 15%, 10%, 5%, and 0. The results show that both of them can contribute to enhancing the DPI and to making it become a profitable industrial and commercial distributed PV. If the carbon-trading policy is applied to industrial and commercial distributed-PV projects in China, profitability will be improved significantly, as shown in Figure 6. In addition, a rising initial-electricity-price policy and a rising initial-FIF-price policy can also play an important part in enhancing the economic viability of industrial and commercial distributed-PV projects.
For a benchmark residential distributed-PV project, the impact of a single policy such as preferential VAT and corporate IT has been determined. For instance, the DPI for the deployment of residential distributed-PV projects is calculated for the VAT rates of 3%, 2.5%, 2%, 1.5%, 1%, and 0 and for corporate-IT rates of 1.5%, 1.25%, 1%, 0.75%, 0.5%, and 0. The results are shown in Figure 7a,b. As a preferential-corporate-VAT policy is introduced, the DPI becomes higher, indicating improved economic value for residential distributed-PV projects. A smaller VAT rate thus leads to a bigger DPI. Therefore, preferential-VAT policies will play an important part in enhancing profitability. Although a preferential-corporate-IT policy measure can also contribute to making residential distributed-PV profitable, the effect is lower than the preferential-VAT policy.
For the green-finance policy and the preferential-AT policy, the DPI for the deployment of residential distributed-PV projects in China is computed for various loan rates of 7%, 6%, 5%, 4%, 2%, and 0 and for AT rates of 5%, 4%, 3%, 2%, 1%, and 0. The results show that the green-finance policy will be helpful for improving the DPI and for creating a profitable residential distributed-PV project. The reduction in the AT rate can somewhat improve the economic viability of deploying residential distributed PV, but this policy instrument alone cannot make residential distributed PV become profitable. Preferential-AT policies will contribute to improving the economic feasibility of residential distributed PV. However, this policy instrument has a small effect. If the carbon-trading policy is applied to residential distributed-PV projects in China, the profitability will be improved significantly, as showed in Figure 8. In addition, a rising initial-electricity-price policy and a rising initial-FIF-price policy can also play a vital role in enhancing the profitability of residential distributed-PV projects in China.

5.2. The Impact of Two Policies

After the single-policy analysis, we found in this study that not all available policy instruments can help increase the economic viability of distributed PV projects. For example, preferential-AT policies have a limited effect on improving the economic viability, which cannot render distributed PV economically viable. Profitability for distributed PV is thus poor if only one of the available policy instruments is introduced. Moreover, most of the other preferential-policy instruments, such as a preferential-corporate-IT policy, need a drastic reduction to play an important part in enhancing the economic viability of the distributed PV. Hence, the subsequent policy analysis will attempt to explore the effects of a combination of two policy instruments on the economic feasibility of distributed-PV projects.
For a benchmark industrial and commercial distributed-PV project containing a combination of two policy instruments such as the AT rate with the corporate-IT rate, the VAT rate, the green finance, the rising initial electricity price, the rising initial FIF and the DPI for distributed PV, is computed. We can see that the DPI of many policy combinations is bigger than 1. These policy instruments will have a significant impact on helping improve the profitability of industrial and commercial distributed-PV projects. For example, the effect of the combination of the AT rate and corporate-IT rate on improving economic viability is significant, as demonstrated in Table 6. However, the DPI of some combinations is lower than 1. This means that the combination of two policy incentives such as preferential-AT polices has a relatively low effect, and cannot help enhance the economic performance of industrial and commercial distributed-PV projects in the target area.
The calculation of the DPI for industrial and commercial distributed-PV projects containing a combination of two policy instruments including the green finance and the corporate-IT rate, the green finance and the VAT rate, the green finance and the initial electricity price, and the green finance and the initial FIF, is conducted. These results suggest that the DPI value of most preferential combinations of available policy instruments is larger than 1. These preferential policy combinations play a vital role in increasing the economic profitability of distributed-PV deployment. For instance, the combination of green finance and the corporate-IT rate play a distinct role in increasing the DPI and improving economic viability, as displayed in Figure 9. Moreover, it is obvious that the effect of these policy combinations is more powerful than that of the combinations including the preferential-AT policy.
For a benchmark residential-distributed-PV project, the DPI of the combination of two policy incentives such as the VAT rate with the AT rate, the corporate-IT rate, the green finance, the rising initial electricity price and the rising initial FIF is evaluated. We can see that the DPI of some policy combinations of the available policy instruments is higher than 1. The preferential-policy combinations play a significant part in enhancing the profitability of residential distributed-PV projects. For example, the impact of the combinations of the preferential-VAT rate and AT rate on improving the economic performance is significant, as illustrated in Figure 10. However, the DPI of another combinations is slightly lower than 1, which means that the combinations of two policies which include the preferential-VAT rate have a smaller effect, and do not effectively enhance the profitability of residential distributed-PV projects in this study.
The DPI for residential distributed-PV projects containing a combination of two policy instruments including the green finance with the VAT rate, the corporate-IT rate, the initial electricity price and the initial FIF, is determined. We obtained results showing that the DPI of most preferential-policy combinations is bigger than 1. The combination of the available policy instruments plays a vital part in enhancing the economic profitability of residential distributed-PV projects. For instance, the combination of green finance and the VAT rate on improving the economic viability is significant, as summarized in Table 7. Moreover, we can obtain results showing that the incentive combination has a more pronounced effect than the combinations which include the preferential-AT policy, and will help increase the DPI and render residential distributed-PV economically feasible.

6. Conclusions

Aiming at achieving their carbon neutrality goals, many countries such as China, moving towards carbon neutrality, need to establish renewable-dominated energy systems, including distributed-PV generation. With the continuous development of distributed solar PV, increasing lower-resource-endowment distributed-PV projects are necessary. In addition to the permanently changing market environment, such as the rising raw-material cost, the economic feasibility of the distributed-solar-PV generation project becomes less certain. It will be not helpful for the high-quality development of distributed-PV generation, which will play an important part in attaining the goal of carbon neutrality.
This paper establishes a policy-analysis framework for distributed solar PV, based on a techno-economic-evaluation model and considering the impact of the project type and the electricity-production-decline law on the profitability of developing distributed solar PV. The results show that the profitability of the deployment of two types of distributed-solar-PV projects in low-resource-endowment areas in China is generally becoming much worse. Some distributed-PV projects are even becoming unprofitable under current policy conditions. Based on the techno-economic evaluation, a policy analysis is performed to determine the effects of the currently available policy incentives on improving the profitability of distributed-solar-PV projects. The findings in this study indicate that not all available policy instruments can help enhance the economic viability of distributed-PV projects. The combination of two policy instruments should be considered for policy formulation to better optimize the profitability of distributed-PV projects.
Based on these findings, some policy recommendations are proposed for distributed solar PV, aiming at carbon neutrality. First, from the perspective of a single policy: (a) for industrial and commercial distributed-PV projects, an incentive policy instrument such as preferential-corporate-IT rate, rising initial electricity price and carbon-trading participation can be adopted to improve economic feasibility; (b) for a residential distributed-PV project, an incentive policy instrument such as green finance, rising initial-FIF price and carbon-trading participation can be adopted for enhancing profitability. Second, from the perspective of two policies: (a) for industrial and commercial distributed-PV projects, a combination of two available policy incentives such as the AT rate with the corporate-IT rate, the VAT rate and the green finance can be adopted as policy instruments to improve economic viability; (b) for a residential distributed-PV project, a combination of two available incentive policies such as the VAT rate with the corporate-IT rate and the green finance can be adopted to increase the economic performance. Considering the specific characteristics of distributed-PV projects in different regions, a dynamic evaluation and analysis is the base for the selection of suitable policy incentives aimed at promoting the deployment of the distributed-PV industry.
The systematic analysis in this article will be helpful for decision-makers to determine the issues of current incentive policies applied to distributed PV. It is anticipated that these findings of the paper are a valuable resource for decision-makers in many countries such as China who are working towards carbon neutrality. The policy recommendations proposed in this paper will also be helpful for decision-makers to implement suitable approaches aimed at optimizing current policy incentives in their nations. Lastly, we anticipate that these findings in the article will provide a useful reference for the researchers aiming at further optimizing policy incentives for stimulating the sustainable development of distributed solar PV, aiming for a carbon-neutral future.

Author Contributions

Conceptualization, W.Y.; methodology, J.Y. (Jiehui Yuan) and Z.L.; investigation, J.L.; resources, J.Y. (Jiehui Yuan); data curation, W.Y.; writing—original draft preparation, J.Y. (Juan Yuan) and W.Y.; writing—review and editing, J.Y. (Jiehui Yuan) and X.O. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by grants from the Humanities and Social Sciences Research Youth Foundation of the Ministry of Education of China (No. 20YJCZH220), Jiangxi Provincial Social Science Foundation (No. 21GL53) and Jiangxi Province Double Thousand Plan (No. 3350200018).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhuo, Z.; Du, E.; Zhang, N.; Nielsen, C.P.; Lu, X.; Xiao, J.; Wu, J.; Kang, C. Cost increase in the electricity supply to achieve carbon neutrality in China. Nat. Commun. 2022, 13, 3172. [Google Scholar] [CrossRef] [PubMed]
  2. Zhang, A.H.; Sirin, S.M.; Fan, C.; Bu, M. An analysis of the factors driving utility-scale solar PV investments in China: How effective was the feed-in tariff policy? Energy Policy 2022, 167, 113044. [Google Scholar] [CrossRef]
  3. Yan, J.; Yang, Y.; Campana, P.E.; He, J. City-level analysis of subsidy-free solar photovoltaic electricity price, profits and grid parity in China. Nat. Energy 2019, 4, 709–717. [Google Scholar] [CrossRef]
  4. Barbose, G.; Satchwell, A.J. Benefits and costs of a utility-ownership business model for residential rooftop solar photovoltaics. Nat. Energy 2020, 5, 750–758. [Google Scholar] [CrossRef]
  5. Timilsina, G.R. The economics of deploying distributed solar photovoltaics in developing countries: Some insights from an analysis for Bangladesh. Energy Sustain. Dev. 2022, 70, 247–258. [Google Scholar] [CrossRef]
  6. Samper, M.; Coria, G.; Facchini, M. Grid parity analysis of distributed PV generation considering tariff policies in Argentina. Energy Policy 2021, 157, 112519. [Google Scholar] [CrossRef]
  7. Wilkinson, S.; John, M.; Morrison, G.M. Rooftop PV and the renewable energy transition; a review of driving forces and analytical frameworks. Sustainability 2021, 13, 5613. [Google Scholar] [CrossRef]
  8. Cai, X.; Xie, M.; Zhang, H.; Xu, Z.; Cheng, F. Business models of distributed solar photovoltaic power of China: The business model canvas perspective. Sustainability 2019, 11, 4322. [Google Scholar] [CrossRef]
  9. Zhang, S. Analysis of DSPV (distributed solar PV) power policy in China. Energy 2016, 98, 92–100. [Google Scholar] [CrossRef]
  10. Garlet, T.B.; Ribeiro, J.L.D.; de Souza Savian, A.; Siluk, J.C.M. Paths and barriers to the diffusion of distributed generation of photovoltaic energy in southern Brazil. Renew. Sust. Energy Rev. 2019, 111, 157–169. [Google Scholar] [CrossRef]
  11. Li, H.; Lin, H.; Tan, Q.; Wu, P.; Wang, C.; De, G.; Huang, L. Research on the policy route of China’s distributed photovoltaic power generation. Energy Rep. 2020, 6, 254–263. [Google Scholar]
  12. Zhang, F.; Deng, H.; Margolis, R.; Su, J. Analysis of distributed-generation photovoltaic deployment, installation time and cost, market barriers, and policies in China. Energy Policy 2015, 81, 43–55. [Google Scholar] [CrossRef]
  13. Jia, X.; Du, H.; Zou, H.; He, G. Assessing the effectiveness of China’s net-metering subsidies for household distributed photovoltaic systems. J. Clean. Prod. 2020, 262, 121161. [Google Scholar] [CrossRef]
  14. Alhammami, H.; An, H. Techno-economic analysis and policy implications for promoting residential rooftop solar photovoltaics in Abu Dhabi, UAE. Renew Energy 2021, 167, 359–368. [Google Scholar] [CrossRef]
  15. Zhang, W.; Zhao, Y.; Huang, F.; Huang, Y.; Zhou, J. Forecasting the energy and economic benefits of photovoltaic technology in China’s rural areas. Sustainability 2021, 13, 8408. [Google Scholar] [CrossRef]
  16. Qiu, S.; Wang, K.; Lin, B.; Lin, P. Economic analysis of residential solar photovoltaic systems in China. J. Clean. Prod. 2021, 282, 125297. [Google Scholar] [CrossRef]
  17. Zhang, M.; Zhang, Q. Grid parity analysis of distributed photovoltaic power generation in China. Energy 2020, 206, 118165. [Google Scholar] [CrossRef]
  18. Kabir, M.A.; Farjana, F.; Choudhury, R.; Kayes, A.I.; Ali, M.S.; Farrok, O. Net-metering and Feed-in-Tariff policies for the optimum billing scheme for future industrial PV systems in Bangladesh. Alex. Eng. J. 2023, 63, 157–174. [Google Scholar] [CrossRef]
  19. Zhao, X.; Zeng, Y.; Zeng, D. Distributed solar photovoltaics in China: Policies and economic performance. Energy 2015, 88, 572–583. [Google Scholar] [CrossRef]
  20. Suparwoko, W.; Qamar, F.A. Techno-economic analysis of rooftop solar power plant implementation and policy on mosques: An Indonesian case study. Sci. Rep. 2022, 12, 4823. [Google Scholar] [CrossRef]
  21. Zhu, X.; Liao, B.; Yang, S. An optimal incentive policy for residential prosumers in Chinese distributed photovoltaic market: A Stackelberg game approach. J. Clean. Prod. 2021, 308, 127325. [Google Scholar] [CrossRef]
  22. Wang, W.; Zhao, X. Can the incentives polices promote the diffusion of distributed photovoltaic power in China. Environ. Sci. Pollut. Res. 2021, 29, 30394–30409. [Google Scholar]
  23. Sun, B.; Sankar, A. The changing effectiveness of financial incentives: Theory and evidence from residential solar rebate programs in California. Energy Policy 2022, 162, 112804. [Google Scholar] [CrossRef]
  24. Yang, Y.; Campana, P.E.; Yan, J. Potential of unsubsidized distributed solar PV to replace coal-fired power plants, and profits classification in Chinese cities. Renew. Sust. Energy Rev. 2020, 131, 109967. [Google Scholar] [CrossRef]
  25. Burns, J.E.; Kang, J. Comparative economic analysis of supporting policies for residential solar PV in the United States-Solar Renewable Energy Credit (SREC) potential. Energy Policy 2012, 44, 217–225. [Google Scholar] [CrossRef]
  26. Zeraatpisheh, M.; Arababadi, R.; Pour, M.S. Economic analysis for residential solar PV systems based on different demand charge tariffs. Energies 2018, 11, 3271. [Google Scholar] [CrossRef]
  27. Qiu, F.; Chen, Q. Research on the economic effect of the subsidy policy on the distributed photovoltaic power generation. Price Theory Pract. 2016, 6, 93–96. [Google Scholar]
  28. Yuan, J.; Liao, J.; Yuan, W.; Zhao, X.; Peng, T. Policy recommendations to distributed roof PV based on economic feasibility analysis: A case study in China. Energy Proc. 2021, 17, 8873. [Google Scholar]
  29. Wu, Y.; Wang, J.; Ji, S.; Song, Z.; Ke, Y. Optimal investment selection of industrial and commercial rooftop distributed PV project based on combination weights and cloud-TODIM model from SMEs’ perspectives. J. Clean. Prod. 2019, 234, 534–548. [Google Scholar] [CrossRef]
  30. Chen, X.; Peng, X. Analysis of China’s distributed PV power generation investment risk. Sol. Energy 2019, 10, 5–10, 39. [Google Scholar]
  31. Wang, Y.; He, J.; Chen, W. Distributed solar photovoltaic development potential and a roadmap at the city level in China. Renew. Sustain. Energy Rev. 2021, 141, 110772. [Google Scholar] [CrossRef]
  32. International Energy Agency (IEA). Prospects for Distributed Energy Systems in China; IEA: Paris, France, 2017. [Google Scholar]
  33. He, G.; Kammen, D.M. Where, when and how much solar is available? A provincial-scale solar resource assessment for China. Renew. Energy 2016, 85, 74–82. [Google Scholar] [CrossRef]
  34. Yuan, J.; Sun, S.; Zhang, W.; Xiong, M. The economy of distributed PV in China. Energy 2014, 78, 939–949. [Google Scholar] [CrossRef]
  35. Lee, M.; Hong, T.; Koo, C. An economic impact analysis of state solar incentives for improving financial performance of residential solar photovoltaic systems in the United States. Renew. Sustain. Energy Rev. 2016, 58, 590–617. [Google Scholar] [CrossRef]
  36. Sow, A.; Mehrtash, M.; Rousse, D.R.; Haillot, D. Economic analysis of residential solar photovoltaic electricity production in Canada. Sustain. Energy Techn. Assess. 2019, 33, 83–94. [Google Scholar] [CrossRef]
  37. Zhang, M.; Zhou, D.; Zhou, P. Optimal feed-in tariff for solar photovoltaic power generation in China: A real options analysis. Energy Policy 2016, 97, 181–192. [Google Scholar] [CrossRef]
  38. National Energy Administration (NEA) of China. The Notice on Submitting the Pilot Scheme for Roof Distributed Photovoltaic Development in the Whole County (City, District); NEA: Beijing, China, 2021. [Google Scholar]
  39. State Taxation Administration (STA) of China. Guidelines on Preferential Tax and Fee Policies for Supporting Green Development; STA: Beijing, China, 2022. [Google Scholar]
  40. Zhao, X.; Xie, Y. The economic performance of industrial and commercial rooftop photovoltaic in China. Energy 2019, 187, 119561. [Google Scholar]
  41. Ramírez-Sagner, G.; Mata-Torres, C.; Pino, A.; Escobarb, R.A. Economic feasibility of residential and commercial PV technology: The Chilean case. Renew. Energy 2017, 111, 332–343. [Google Scholar] [CrossRef]
  42. Shao, H.; Zhang, J.; Zhang, W. Economy and policy analysis of distributed photovoltaic generation. Electr. Power Constr. 2014, 35, 51–57. [Google Scholar]
  43. Sheikhhoseini, M.; Rashidinejad, M.; Ameri, M.; Abdollahi, A. Economic analysis of support policies for residential photovoltaic systems in Iran. Energy 2018, 165, 3853–3866. [Google Scholar] [CrossRef]
  44. De Boeck, L.; Van Asch, S.; De Bruecker, P.; Audenaert, A. Comparison of support policies for residential photovoltaic systems in the major EU markets through investment profitability. Renew. Energy 2016, 87, 42–53. [Google Scholar] [CrossRef]
  45. Cucchiella, F.; D’Adamo, I.; Gastaldi, M. A profitability assessment of small-scale photovoltaic systems in an electricity market without subsidies. Energy Convers. Manag. 2016, 129, 62–74. [Google Scholar] [CrossRef]
Sustainability 15 03005 g001 550
Figure 1. The model framework of policy analysis for distributed PV.
Figure 1. The model framework of policy analysis for distributed PV.
Sustainability 15 03005 g001
Sustainability 15 03005 g002 550
Figure 2. The annual cash flow of the industrial and commercial distributed-PV project.
Figure 2. The annual cash flow of the industrial and commercial distributed-PV project.
Sustainability 15 03005 g002
Sustainability 15 03005 g003 550
Figure 3. The sensitivity of the economic feasibility of the industrial and commercial distributed-PV project to the changes in key factors such as IAEG and IIC. Note. The effect of OMC on the NPV is almost the same as that of IC.
Figure 3. The sensitivity of the economic feasibility of the industrial and commercial distributed-PV project to the changes in key factors such as IAEG and IIC. Note. The effect of OMC on the NPV is almost the same as that of IC.
Sustainability 15 03005 g003
Sustainability 15 03005 g004 550
Figure 4. Key factors affecting the economic viability of the residential distributed-PV project.
Figure 4. Key factors affecting the economic viability of the residential distributed-PV project.
Sustainability 15 03005 g004
Sustainability 15 03005 g005 550
Figure 5. (a) The impact of preferential-AT rate on the DPI; (b) The impact of green finance on the DPI.
Figure 5. (a) The impact of preferential-AT rate on the DPI; (b) The impact of green finance on the DPI.
Sustainability 15 03005 g005
Sustainability 15 03005 g006 550
Figure 6. Effects of carbon-trading policy on the DPI of industrial and commercial distributed-PV projects.
Figure 6. Effects of carbon-trading policy on the DPI of industrial and commercial distributed-PV projects.
Sustainability 15 03005 g006
Sustainability 15 03005 g007 550
Figure 7. (a) Effect of preferential-VAT policies on the DPI; (b) Effect of preferential-corporate-IT policies on the DPI.
Figure 7. (a) Effect of preferential-VAT policies on the DPI; (b) Effect of preferential-corporate-IT policies on the DPI.
Sustainability 15 03005 g007
Sustainability 15 03005 g008 550
Figure 8. Effects of carbon-trading policy on the DPI of residential distributed-PV projects.
Figure 8. Effects of carbon-trading policy on the DPI of residential distributed-PV projects.
Sustainability 15 03005 g008
Sustainability 15 03005 g009 550
Figure 9. Effects of combinations of green finance and corporate-IT rates on the DPI of industrial and commercial distributed-PV projects.
Figure 9. Effects of combinations of green finance and corporate-IT rates on the DPI of industrial and commercial distributed-PV projects.
Sustainability 15 03005 g009
Sustainability 15 03005 g010 550
Figure 10. Effect of combining a preferential-VAT and AT rate on the DPI of residential distributed-PV projects.
Figure 10. Effect of combining a preferential-VAT and AT rate on the DPI of residential distributed-PV projects.
Sustainability 15 03005 g010
Table
Table 1. Key parameters used for the evaluation.
Table 1. Key parameters used for the evaluation.
TypeIndustrial and CommercialResidentialUnit
Initial annual generation3,220,0005370kWh
Initial electricity price0.6610.600CNY/kWh
Initial feed-in-tariff (FIF) price0.41430.4143CNY/kWh
Escalation rate of the price33%
Initial investment15,840,00026,400CNY
Operating and maintenance costs80,000400CNY/year
Insurance costs125,000625CNY
Value-added-tax (VAT) rate173%
Additional-tax (AT) rate115%
Income-tax (IT) rate251.5%
Bank-loan ratio5030%
Loan interest rate87%
Loan period55year
Electricity-producing life2525year
Table
Table 2. Economic performance of industrial and commercial distributed-PV projects for various cases.
Table 2. Economic performance of industrial and commercial distributed-PV projects for various cases.
CasesSCR (%)NPV (CNY)IRR (%)DPI
Case 150−3,729,706.85.30.76
Case 270−2,455,868.16.20.84
Case 380−1,818,948.86.70.89
Case 490−1,182,029.57.20.93
Case 5100−545,110.27.60.97
Table
Table 3. Results of economic metrics of industrial and commercial distributed-PV projects presented in different studies.
Table 3. Results of economic metrics of industrial and commercial distributed-PV projects presented in different studies.
RegionNPVIRR (%)DPIYearSource
Jiangxi (III type)<07.20.932022This study
Shandong (II type)>014.7–19.6N/A2019[29]
China>013.8–27.5N/A2019[40]
ChileN/A1–13N/A2017[41]
Bangladesh>0N/A1.722023[18]
Table
Table 4. Economic indicators for residential distributed-PV projects for various cases.
Table 4. Economic indicators for residential distributed-PV projects for various cases.
Cases SCR (%)NPV (CNY)IRR (%)DPI
Case 120−1495.26.40.94
Case 230−268.206.90.99
Case 3502185.97.81.08
Case 4907094.09.61.27
Case 51008321.1101.32
Table
Table 5. Results of economic indicators of residential distributed-PV projects presented in different studies.
Table 5. Results of economic indicators of residential distributed-PV projects presented in different studies.
RegionNPVIRR (%)DPIYearSource
Jiangxi (III type)<06.90.992022This study
ChinaN/A5.2–14.3N/A2014[42]
ChinaN/A6–18N/A2021[16]
ChileN/A0–8N/A2017[41]
EU>00–19.6N/A2016[44]
Table
Table 6. Effects of combining AT and corporate-IT rates on the DPI of industrial and commercial distributed-PV projects.
Table 6. Effects of combining AT and corporate-IT rates on the DPI of industrial and commercial distributed-PV projects.
AT RatesCorporate-IT Rates
25%20%15%10%5%0
11%0.9250.9671.0081.0491.0911.132
9%0.9290.9701.0121.0531.0951.136
7%0.9330.9741.0161.0571.0991.141
5%0.9360.9781.0201.0621.1031.145
3%0.9400.9821.0241.0661.1081.149
00.9450.9871.0291.0721.1141.156
Note. The number with a red color is lower than 1, which demonstrates that this project is not profitable.
Table
Table 7. Effects of the combination of green finance and a preferential-VAT rate on the economic indicator.
Table 7. Effects of the combination of green finance and a preferential-VAT rate on the economic indicator.
VAT RatesLoan Interest Rates
7%6%5%4%2%0
3%0.9901.0011.0121.0241.0461.069
2.5%0.9961.0071.0191.0301.0521.075
2%1.0021.0131.0251.0361.0591.081
1.5%1.0081.0191.0311.0421.0651.087
1%1.0141.0261.0371.0481.0711.094
01.0271.0381.0501.0611.0841.106
Note. The value with a red color is lower than 1, which demonstrates that this project is not profitable.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yuan, J.; Yuan, W.; Yuan, J.; Liu, Z.; Liao, J.; Ou, X. Policy Recommendations for Distributed Solar PV Aiming for a Carbon-Neutral Future. Sustainability 2023, 15, 3005. https://doi.org/10.3390/su15043005

AMA Style

Yuan J, Yuan W, Yuan J, Liu Z, Liao J, Ou X. Policy Recommendations for Distributed Solar PV Aiming for a Carbon-Neutral Future. Sustainability. 2023; 15(4):3005. https://doi.org/10.3390/su15043005

Chicago/Turabian Style

Yuan, Jiehui, Wenli Yuan, Juan Yuan, Zhihong Liu, Jia Liao, and Xunmin Ou. 2023. "Policy Recommendations for Distributed Solar PV Aiming for a Carbon-Neutral Future" Sustainability 15, no. 4: 3005. https://doi.org/10.3390/su15043005

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop