Can carbon pricing support onshore wind power development in China? An assessment based on a large sample project dataset

Accepted Manuscript Can Carbon Pricing Support Onshore Wind Power Development in China? An Assessment Based on a Large Sample Project Dataset

Qiang Tu, Regina Betz, Jianlei Mo, Ying Fan, Yu Liu PII:

S0959-6526(18)31951-6

DOI:

10.1016/j.jclepro.2018.06.292

Reference:

JCLP 13438

To appear in:

Journal of Cleaner Production

Received Date:

02 March 2018

Accepted Date:

28 June 2018

Please cite this article as: Qiang Tu, Regina Betz, Jianlei Mo, Ying Fan, Yu Liu, Can Carbon Pricing Support Onshore Wind Power Development in China? An Assessment Based on a Large Sample Project Dataset, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.06.292

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Can Carbon Pricing Support Onshore Wind Power Development in China? An Assessment Based on a Large Sample Project Dataset Qiang Tua,b, Regina Betzc, Jianlei Mob,*, Ying Fand, Yu Liub a

College of Management & Economics, Tianjin University, Tianjin, 300072, China b Center for Energy & Environmental Policy Research, Institutes of Science and Development, Chinese Academy of Sciences, Beijing, 100190, China c Center

for Energy and the Environment, School of Management and Law, Zurich University of

Applied Sciences, Winterthur, 8400, Switzerland d School of Economics and Management, Beihang University, Beijing, 100191, China *Corresponding author: [email protected], [email protected]

Abstract

There is much discussion about whether the rapid expansion of onshore wind power in China is sustainable, given the decrease in feed-in tariffs (FIT). It is unclear whether the recently launched nationwide carbon pricing system, which will include 1700 power companies, can compensate for decreasing FITs and possibly provide new incentives for wind power developments. This paper investigates the ability of carbon pricing policies to compensate for declining FITs in support of onshore wind power investment in China. First, we constructed a dataset of 2059 onshore wind power projects from China’s thirty provinces between 2006 and 2015 to estimate the levelized costs of electricity (LCOE). This dataset was used to assess the profitability of each wind project for different carbon prices, varying levels of FITs, curtailment rate, and discount rate. Our findings suggest that the carbon price can compensate partially for the revenue loss caused by declining FITs as well as improving the profitability of projects. However, current carbon prices in China’s carbon emission trading pilots are not sufficiently high to compensate for the revenue losses, especially under the grid parity scenario. Consequently, without FITs, the 1

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sustainable development of onshore wind power in China is uncertain. A sensitivity analysis of the effect of the carbon pricing policy demonstrates that in the case of higher investment risk and more serious curtailment, the effect of carbon pricing policy on promoting the wind power investment seems to be more significant. Keywords: wind power; levelized cost of electricity (LCOE); feed-in tariffs (FITs); carbon pricing; China

1. Introduction At the 2015 Paris Climate Summit, the Chinese State Department (CSD) declared its goal to decrease China's carbon intensity (defined as CO2 emission per unit of GDP) by 60–65% in 2030 compared to 2005 levels. Renewable energy is expected to play an essential role in achieving this target (Esen et al., 2006; Esen et al., 2007; Esen and Yuksel, 2013). For example, the Chinese government has proposed that by 2020 the proportion of non-fossil energy (such as nuclear, wind, solar, etc.) should account for at least 15% in primary energy consumption (CSD, 2016). Due to the constraints on fossil fuels and ambitious emission reduction targets, it is necessary to accelerate the development of renewable energy in China. Wind energy is abundant in China, especially in the northeast, north, northwest, and eastern coastal areas (China Meteorological Administration, 2009). In this paper, we focus on wind power development since this is the most significant renewable energy source in China (other than hydropower), and has been developing rapidly in the last decade.

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Since the introduction of the Renewable Energy Act in 2006, wind power has reached a stage of rapid expansion in China. From 2006–2009, the cumulative installation of wind power doubled for four consecutive years (BP, 2016). In 2009, the newly added capacity in China had reached 13.8 GW, which exceeded the total of China’s capacity installation in the previous twenty years and outpaced the US in the same year. By 2010, cumulative installed capacity had already reached 43.5GW. Since then, wind power in China has moved into the large-scale deployment stage and by the end of 2015 stood at 143.8 GW (BP, 2016). Supportive government policies have underpinned the rapid development of wind power in China (Qi et al., 2014; Wesseh and Lin, 2016; Lacerda and Van der Bergh, 2016). Recent experience from countries around the world suggests that FITs was an effective policy which promoted the rapid and sustained deployment of wind power (European Commission, 2008; Couture and Gagnon, 2010; Thiam, 2011) since it provides long-term financial stability for investors (Lesser and Su, 2008). However, with the increase of renewable energy investment, the necessary subsidies on renewable energy power have been expanding rapidly under the current renewable energy policy in China. The huge funding gap of renewable energy subsidies has become a major obstacle to the sustainable development of renewable energy power industry. To address this problem, the Chinese government has continuously decreased FITs for renewable energy power. Based on NDRC (2009; 2014; 2015a), the on-grid price of onshore wind power in

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China decreased from 0.51, 0.54, 0.58, and 0.61 CNY/kWh in 2009 to 0.47, 0.50, 0.54, and 0.60 CNY/kWh in 2016 for four wind resource categories respectively. Meanwhile, the renewable power price plus increased from 0.002 CNY/kWh in 2006 to 0.019 CNY/kWh in 2016. Even with these changes, the funding gap is growing. By the end of 2016, the cumulative funding gap of renewable energy power subsidies had exceeded 60 billion CNY. Moreover, according to National Energy Agency (NEA) estimates, the cumulative funding gap of renewable energy power subsidies will reach 300 billion CNY by 2020 (NEA, 2016). Consequently, it is likely that the Chinese renewable energy subsidy policy will be adjusted in such a way that the level of subsidies is decreased to the point they are phased out altogether. While a wind project developer can expect guaranteed FITs when the project is approved by the government, this may not be enough to make the project economically viable. To improve viability, project investors have the option of applying to register their project under the Clean Development Mechanism (CDM) and thus be eligible to create Certified Emissions Reductions (CERs). CDM is an international project-based carbon trading mechanism under the Kyoto Protocol that assists developed countries in fulfilling their committed mitigation targets through financing projects that reduce carbon emissions in developing countries. Complementary to FITs provided by Chinese renewable support policies, CDM has also subsidized many wind projects in China since 2002 (Tang and Popp, 2015). Specifically, CDM has two critical roles in the

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development of China’s wind industry. First, it works as a demand-pull policy that subsidizes wind power producers through international carbon trading and increases the demand for wind projects in China. At the same time, it aims to facilitate the transfer of wind technology from developed countries to China (Lewis, 2010; Zhang et al., 2009). According to the UNEP database, 83 GW from the 111 GW of the total capacity of CDM wind projects has been installed in China. Furthermore, wind power generated CERs will account for about one-quarter of total CER issuance potential by 2020, making them one of the most important CDM types (Cames et al., 2016). Despite this, demand for CERs decreased, and the CER price declined significantly after the end of the first Kyoto commitment period in 2012, reaching €1.03/CER (European Energy Exchange market data). Thus, the CER price was less likely to promote wind power development after 2012 (Murata et al., 2016; Koo, 2017; Rahman and Kirkman, 2015; Yang et al., 2010). Following CDM, a Chinese certified emission reduction (CCER) exchange system was established by the National Development and Reform Commission (NDRC) to further promote the deployment of emission reduction projects including wind power projects. However, the new scheme does not provide a transparent and uniform CCER price since it is mainly based on bilateral transactions and differs between provinces and the respective carbon-pilot schemes (Lo and Cong, 2017; Beijing Environment Exchange, 2015; Shanghai Environment Energy

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Exchange, 2015). Given these circumstances, the role of the carbon pricing on promoting the development of wind power is controversial (Cames et al., 2016; Liu, 2015; Spalding-Fecher et al., 2012; Wang and Chen, 2010; Lewis, 2010). In 2014, a nationwide carbon emission trading system was proposed by NDRC and initiated at the end of 2017. This may change the landscape of the carbon pricing in China and a unified carbon price will emerge, providing new opportunities for wind power development in China. In addition to the issues referred to above, curtailment is another significant challenge faced by Chinese wind power development, since only a part of installed wind capacity can be fed into the grid (Yang et al., 2012; Ling and Cai, 2012; Zeng et al., 2013). Based on NEA (2017), the national average utilization hours of wind power equipment (AUHWPE) was 1742 in 2016, an increase of 14 hours compared to 2015, but wind power curtailment reached as much as 49.7 TWh, accounting for about 20% of China's total wind power generation in 2016. Wind power curtailment may be higher in regions and sub-regions with a higher wind capacity. In northeast, northwest, and northern China, which are wind-rich areas, the curtailment rate of wind equipment was about 20%, and the highest was 43% in Gansu province (NEA, 2017). In the future, China needs to improve its exploitation of wind power by upgrading transmission infrastructures in order to reduce wind power curtailment (Caralis et al., 2014; Li et al., 2015).

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Faced with these challenges and new opportunities, it is important to reassess the profitability of wind power projects, as well as the supporting policy system for wind power, based on which the sustainability of the wind power development in China can be determined and the supporting policies optimized. In this paper, we construct datasets of 2059 onshore wind power projects over the 2006–2015 period to calculate the LCOE. Then, by comparing the LCOE with the respective FIT level, the profitability of wind power projects can be determined. If the FITs can cover the LCOE, then the project is profitable; otherwise, the project is unprofitable. Moreover, the role of carbon pricing is explored in terms of the future nationwide carbon trading system in China. By comparing the profitability of the wind power projects with different carbon prices, the role of the carbon pricing can be determined and the critical carbon prices which make wind power projects profitable in China ascertained. The originality and contribution made by this paper is threefold. First, this study estimates the LCOE of onshore wind power in China based on a large sample of project dataset, which includes 2059 wind power projects implemented between 2006 and 2015 and accounting for 84% of the cumulative installed capacity in China in 2015, This is a much larger and more recent database, given that existing studies are based principally on data from one or more representative onshore wind power projects (Ouyang and Lin, 2014; Liu et al., 2015; Mo et al., 2016; He et al., 2015; Li et al., 2018; Yuan et al., 2016). Although there is some research based

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on a large number of projects, such as Lam et al. (2016) and Yao et al. (2015), the projects in question were mainly implemented before 2012 so cannot reflect the most recent development trends of wind power and environmental policy. Our dataset, covering the period 2006-2015, thus provides more recent insights. It is compiled from project design documents (PDDs), investment analysis spreadsheets, and validation reports, as well as monitoring reports up to the end of 2015, which are used for the CDM and CCERE projects registration processes. Secondly, the LCOE of wind power in this study are calculated on the basis of ex-post actual power generation while previous studies such as Timilsina et al. (2013) and IEA (2016), and Davidson et al. (2016), calculated LCOE on the basis of expected generation of wind power projects, which may overestimate power generation and underestimate LCOE. However, using ex-post actual power generation instead of the predicted one, we can calculate LCOE more accurately. To the best of our knowledge, ours is the first study to utilize actual power generation data to calculate LCOE and thereby assess the profitability of onshore wind power projects in China. Thirdly, previous studies such as Lin et al. (2014), Zhang et al. (2009), and Liu et al. (2015) focused mainly on the effect of China’s FIT policy on onshore wind power investment, while the impact of the carbon pricing policy was little explored. We, however, analyze the effect of carbon pricing policy on the profitability of onshore wind power and obtain the threshold level of

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required carbon price in China to support onshore wind power investment, especially in the face of the anticipated future decline in renewable subsidy levels. The remainder of this paper is organized as follows: A literature review is provided in Section 2 while Section 3 explains the methodology used in this paper. Section 4 describes the data of wind power projects, Section 5 reports the results of our analysis, and Section 6 discusses our findings. The paper concludes with policy implications.

2. Literature Review LCOE is a convenient tool for comparing the unit costs of different technologies over the course of their economic life. In the literature, many authors discuss the levelized cost of electricity in the U.S. and European Community member states such as Germany, Spain, and Denmark (Timilsina et al., 2013 and IEA, 2016; Yang et al., 2018). In China, many problems have emerged along with the rapid growth of wind power capacity and generation, which have attracted wide attention in both political and academic circles. In Ouyang and Lin’s (2014) first systemic study of the LCOE for renewable energy in China, they calculated the LCOE for six Chinese onshore wind power plants based on investment cost (Feasible Study Report), O&M cost (Tegen et al., 2012), and other influencing factors. The results showed that the LCOE of Chinese onshore wind power ranged from 0.497-0.624CNY/kWh with a discount rate of 5%. Liu

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et al. (2015) employed an analytical framework for the LCOE to estimate the generation cost of a representative 100 MW wind farm in China using data from previous studies, namely total investment cost (Qiu and Anadon, 2012; Di et al., 2012), O&M expenditure (China Renewable Energy Engineering Institute, 2010), and capacity factors (National Energy Commission, 2012). The findings produced LCOEs between 0.45CNY/kWh and 0.75CNY/kWh in 2009. Yao et al. (2015) empirically estimated the LCOE of Chinese onshore wind power projects, relying on a panel dataset consisting of information from 1207 wind projects in the 30 Chinese provinces over the 2004–2011 period, which ranged from 0.516CNY/kWh to 0.56CNY/kWh. Lam et al. (2016) showed the downward trend of LCOE of Chinese wind power based on data from the CDM project database between 2004 and 2012, and this saw a fall from 0.63CNY/kWh in 2004 to 0.51CNY/kWh in 2012. IEA (2016) estimated the cost generation of Chinese onshore wind power based on 21 onshore wind plants, and the results showed that LCOE would be 0.477CNY/kWh with a discount rate of 7% and 0.576CNY/kWh with a discount rate of 10%. Recent reports from countries around the world suggest that FIT was the most effective policy for promoting the rapid and sustained deployment of renewable energy. Studies also confirmed the effectiveness of FIT policy in China. Wu and Xu (2013) review the FIT policy as well as the subsidy policy for wind power in China and demonstrate that the effects of policy implementation were unsatisfactory. Zhang et al. (2009) analyze opportunities and challenges for

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renewable energy policy in China and point out that government support was the critical driver of wind power development. Schuman and Lin (2012) indicate that China's Renewable Energy Law, which regulates the FIT system and funding mechanisms, leads to rapid growth of wind power in China. Huo and Zhang (2012) point out that there was no predetermined digression of the capital subsidy to push cost reduction; moreover, insufficient R&D in China has impeded the future development of wind power. Timilsina et al. (2013) show that the economic competitiveness of wind power varies at wider ranges across countries or locations and that FIT policy has played a pivotal role in promoting wind power in China. Li et al. (2018) explore in detail 134 China's onshore wind power policies from 2005 to 2015, indicating that the nation’s wind policy is gradually becoming perfected to support the development of wind power. The most recent studies have focused on the identification of barriers of wind power in China, mainly from an institutional analysis perspective (Yang, 2012; Zhao et al., 2012; Wu et al., 2014; Li et al., 2018, among others). Several important barriers have been identified. Zhao et al. (2012) suggest that the inflexibility of FIT and the lack of grid integration are among the key barriers. Wu et al. (2014) argue that the existing incentive structure for generators and grid companies aggravates an imbalance between capacity and utilization. Li et al. (2018) believe that many problems concerning Chinese wind power remain to be solved, including impractical planning, imperfect support measures, immature trading systems, and uncoordinated action by

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stakeholders. Whatever the barriers are, they ultimately contribute to the curtailment of wind power (Li et al., 2015). Based on NEA (2017), wind curtailment reached 49.7 TWh, accounting for about 20% of China's total wind power generation in 2016. Some researchers believe the curtailment problem of wind power is having a negative impact on energy sustainability, climate change, and economic development (Pei et al., 2015; Liu et al., 2015; Lam et al., 2016; Davidson et al., 2016). At the same time, according to the NDRC “13th five-plan” (2017), grid parity is expected to be achieved for the onshore wind power during the 2016-2020 period, and the FIT level will be decreased in the future. Faced with these challenges, it is important to reassess the profitability of wind power projects as well as the supporting policy system for wind power, based on which the sustainability of the wind power development in China can be diagnosed and the supporting policies optimized. In this paper, we analyze the effects of carbon pricing policies in support of Chinese onshore wind power investment, especially with renewable subsidy levels set to decline in the future.

3. Methodologies First, in Section 3.1, we will briefly introduce the methodologies used to calculate the LCOE of onshore wind power projects with further details in Appendix A. To estimate the actual electricity production (shown in LCOE) for the entire sample, we developed a particular

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approach based on the issuance success rate (see Section 3.2). Finally, by comparing FIT and LCOE for each onshore wind power project, we can define the conditions necessary to differentiate between profitable and unprofitable projects (see Section 3.3).

3.1. The Levelized Cost of Electricity (LCOE) LCOEs are the levelized average lifetime costs or long-run average costs. They represent the net discounted cost to install and operate a wind project divided by power generation over its lifetime. In other words, LCOEs are equivalent to the break-even tariff that wind project developers would require to build and operate a wind farm in a given location. The LCOE method has been widely used in the estimation of power generation costs (Roth and Ambs, 2004; Wiser et al., 2009; NEA/IEA, 2010; Singh and Singh, 2010; IRENA, 2012(a, b); NREL, 2013; Ouyang and Lin, 2014; Liu et al., 2015; IEA, 2016). To calculate the LCOE of a given on-shore wind project in China, we use the following equation (1),  T C  O & M t  LPt  X t  Dt  Pc  ERt   T Et  LCOE    t  /   t (1  r ) (1  r )t   t 0   t 0

(1)

where 𝑟 is the discount rate and 𝑇 is the total lifetime of the project, 𝐸𝑡 is the electricity produced in year 𝑡, 𝐷𝑡 is the debt from the bank in year 𝑡, 𝐶𝑡 is the overnight capital cost of wind project in year 𝑡, 𝐿𝑃𝑡 is the loan payment in year t, 𝑂&𝑀𝑡 signifies the operations and maintenance costs in year 𝑡, and 𝑋𝑡 stands for value added tax, income tax, and surcharge tax for the wind power

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project in year 𝑡. CER revenue in the LCOE calculation equals the carbon price Pc multiplied by the actually issued CERs ERt in year 𝑡. The actually issued CERs ERt can be obtained as follows in Eq. (2), ER j = ER j  j

(2)

where 𝐸𝑅𝑗 is the CERs expected to be produced for the same period 1, which is provided in PDDs, and 𝛾𝑗 is the issuance success rate, which indicates the extent to which the ex-ante expected CER produced by a project differs from the ex-post actually issued CERs and is published in the monitoring reports. Further, the actual electricity production 𝐸𝑗 of project 𝑗 can be calculated based on the actual CERs issued (ERt ) as follows, E j = ER j EF

(3)

where 𝐸𝐹 is the carbon emission factor corresponding to the grid connected with wind project 𝑗.

3.2. Approach for Estimating the Issuance Success Rates for the Entire Sample As referred to above, the issuance success rate 𝛾𝑗 is the basis for calculating the actual issued CERs ERt and the actual electricity production 𝐸𝑗 of project 𝑗. However, the issuance success rates can be obtained from the monitoring reports only for a quarter of the sample (see Section 1

According to the monitoring reports for Chinese wind power projects in the CDM information website (source: http://cdm.unfccc.int/Projects/projsearch.html), the quantity of net electricity generation supplied by the project to the grid in every crediting period has been shown. By multiplying the margin CO2 emission factor for grid connected power generation shown in the project design document (PDD) by the quantity of net electricity generation supplied by the project to the grid, the emission reductions of the project issued by CDM for every crediting period can be ascertained. 14

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3), which we call a reduced sample. This is most likely because when CER prices were low, few project developers were applying for the issuance of CERs, given the costs involved in the monitoring report. Fig. 1 shows our approach for estimating the issuance success rate of the projects where the issuance success rates cannot be obtained from PDDs. First, the location of the project is determined. If the same location is included in the data sample with issuance success rates, we use the average issuance success rate at city level as the issuance success rate for the project. If no information for that city is available, we identify the province of the project and use the average issuance success rate at province or regional level (average issuance success rate for all provinces in this region). For our approach to be reliable, wind availability, electricity demand, conditions of wind power connection to the power grid, and electricity dispatch ability need to be similar within a city or province. ------------------------------------------------------------------------------------------Fig. 1 Approach for estimating issuance success rate of onshore wind power projects ------------------------------------------------------------------------------------------3.3. Profitability of Onshore Wind Power Projects Based on the FIT and LOCE calculated in Section 3.1 for each onshore wind power project, we can determine the profitability of each project and divide them into two groups, i.e., profitable and unprofitable. Specifically, the profitability of a wind power project is determined as follows:

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if the feed-in tariff 𝐹𝐼𝑇𝑗 is higher than the levelized cost of electricity 𝐿𝐶𝑂𝐸𝑗, i.e., 𝐹𝐼𝑇𝑗 ‒ 𝐿𝐶𝑂𝐸𝑗 > 0, the project j is profitable; if not, the project is unprofitable, i.e., 𝐹𝐼𝑇𝑗 ‒ 𝐿𝐶𝑂𝐸𝑗 ≤ 0.

4. Description of the Wind Power Projects Data for the onshore wind power projects used in this study combine information from the CDM project database2 developed by UNDP and the Chinese Certified Emission Reduction Exchange (CCERE) Information Platform3. Data was extracted and compiled from the PDDs, investment analysis spreadsheets, and validation reports, as well as monitoring reports used for CDM and CCERE project registration. The PDDs provide a unique source of detailed financial and technical data for renewable energy at project level. Monitoring reports provide ex-post information on issuance success rates, which is used to calculate actual electricity production (see Section 2.2). Based on these data sources, we can construct a unique dataset of onshore wind power projects implemented over the 2006–2015 period. In total, there are 2059 Chinese wind power projects in the dataset, including 1504 CDM projects and 555 CCERE projects. As shown in Fig. 2, the total capacity of all 2059 wind power projects in the dataset amounts to 120.86 GW, which accounts for 84% of the cumulative installed capacity in China in 2015.

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Available at: . Available at: . 16

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Consequently, the dataset is a good representation of China’s installed wind power project population. ------------------------------------------------------------------------------------------Fig. 2 The capacity of onshore wind power projects in our datasets ------------------------------------------------------------------------------------------We also analyzed the distribution of the project locations as shown in Fig. 3. The top five provinces in terms of the number of onshore wind power projects are Inner Mongolia, Shandong, Hebei, Xinjiang, and Ningxia, and the top five provinces in terms of the capacity of onshore wind power projects are Inner Mongolia, Xinjiang, Gansu, Hebei, and Shandong, all of which are located in the “three north areas” (northeast, northwest, and north) of China. ------------------------------------------------------------------------------------------Fig. 3 The distribution of the onshore wind power projects in our sample in different provinces ------------------------------------------------------------------------------------------The summary statistics of wind projects in our dataset are presented in Table 1. They include installed capacity and the issuance success rate, which enables us to determine actual electricity production. The avoided carbon emission intensity equals baseline emissions, which are the CO2 emissions from the fossil-fuel fired power plants (grid emissions factor) displaced by the wind power project. Capital costs consist of equipment costs, land costs, design and construction costs, and other miscellaneous expenses. Operation and maintenance (O&M) costs include insurance costs, maintenance costs, salaries and benefits, and other fixed or semi-fixed O&M costs such as office expenses, transportation, etc. Individual capital ratio indicates the relationship between

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one’s own capital and external loans; for the latter, an interest rate is shown. The internal rate of return (IRR) is calculated without revenues from CERs or CCERs and is used to determine whether a project is viable. Project lifetime is fixed at 20 years and the loan period at 15 years. Based on this data, we can calculate the LCOE for the 2059 onshore wind power projects in Section 4. ------------------------------------------------------------------------------------------Table 1 Summary statistics of the project data ------------------------------------------------------------------------------------------Fig. 4 shows the 97 city level average issuance success rates. The average issuance rates range from 70.95% to 119.48% with the mean and standard deviation being 87.79% and 9.36% (shown in Table 1), respectively. Because of the fluctuation of wind speed as well as limited connection to the grid, expected electricity generation is overestimated when the issuance rate is lower than 100%; otherwise, the expected electricity generation is underestimated. As shown in Fig. 4, the average issuance success rates of 16 city level power grids are higher than 100%, and that of the other 81 cities are lower than 100%. Among these, the city with highest average issuance success rate (119.48%) is Alashan, and the city with lowest average issuance success rate (70.95%) is Shuozhou. In general, the average issuance success rates of northeastern and northern regions of China are lower than other regions, at 84.51% and 91.72% respectively, indicating that grid connection and power dispatching is limited in these regions.

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------------------------------------------------------------------------------------------Fig. 4 The average issuance success rate of 97 city level power grids -------------------------------------------------------------------------------------------

5. Results Based on the methodologies described in Section 3 and data from Section 4, the LCOE of the 2059 onshore wind power projects can be estimated. Then, by comparing the LCOE and FIT of onshore wind power projects, we can calculate the percentage of profitable onshore wind power projects and derive the threshold levels of the required carbon price to compensate for the possible decline of FITs and improved profitability of the projects.

5.1. LCOE of the Wind Power Projects First, we calculate the LCOE without the carbon price and then with the carbon price. In so doing, the effect of the carbon price can be determined.

5.1.1 LCOE Without Carbon Prices Based on actual electricity production, we calculate the LCOE of the 2059 onshore wind power projects with the baseline discount rate of 7%. This is approximately equal to the average onshore wind power projects IRR (shown in Table 1), and the marginal electricity production cost curve can be derived accordingly. As shown in Fig 5, we observe that the total electricity

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production per year from the sample projects is 239.34 TWh and the LCOEs of onshore wind power projects range from 0.316 to 0.884 CNY/kWh, with the mean of the LCOEs given as 0.594 CNY/kWh. This indicates that the overall cost of the wind power electricity is still higher than for the fossil-fuel electricity, although a small proportion, namely 0.44% of the electricity produced from wind power, had a lower average cost than that of coal-fired electricity in 2015 (0.384 CNY/kWh (NEA, 2015)). ------------------------------------------------------------------------------------------Fig. 5 LCOEs of all the wind power projects in our sample ------------------------------------------------------------------------------------------Being a relatively new technology, there is still great learning potential from the proliferation of onshore wind power technology, and this may lead to lower LCOEs for wind projects over time. The evolution of LCOEs from 2006 to 2015 is shown in Fig. 6. As can be seen, although there is still a disparity between different projects each year, mainly owing to the difference in wind resource endowment from region to region, the average LCOE decreases from 0.615 CNY/kWh in 2006 to 0.533 CNY/kWh in 2015, representing a 14% fall4. According to the IEA (2011), the decreases in LCOE was due mainly to investment cost decreases and, in particular, the price of onshore wind turbines, which has fallen significantly since 2005. However, it can also be seen

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By comparing with other similar studies (IEA, 2016; Ouyang and Lin, 2014; Liu et al., 2015), the LCOE estimated in this paper is reliable. Unlike other studies, the LOCE in this paper is based on a large project dataset sample which includes 2059 wind power projects implemented between 2006 and 2015, accounting for 84% of the cumulative installed capacity in China in 2015. Therefore, our findings reflect the downward trend in Chinese onshore wind power generation costs more comprehensively than other studies. 20

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that the LCOE decrease during from 2006-2010 was much greater than from 2011-2015. The average decrease from 2006-2010 was 2.14% but only 0.53% from 2011-2015. ------------------------------------------------------------------------------------------Fig. 6 The evolution of LCOEs of the wind projects from 2006 to 2015 -----------------------------------------------------------------------------------------As the previous study pointed out, investment cost and curtailment play an important role in determining the LCOE (Pei et al., 2015; Li et al., 2015), so changes in the average unit capital cost and curtailment rate were further analyzed. As shown in Fig. 7, the average unit capital cost of wind turbines decreased from 11212.53 CNY/kW in 2006 to 8400.87 CNY/kW in 2010, a 25% decrease, while it fell from 7955.71 CNY/kW in 2011 to 7573.75 CNY/kW in 2015, only 4.8%. In addition, the average curtailment rate decreased from 20.0% in 2006 to 12.4% in 2010, while it remained at around 12% between 2011 and 2015. This indicates that decreases in limited investment cost and curtailment issues combined to slow the rate of decrease of LCOEs during the period of 2011-2015. ------------------------------------------------------------------------------------------Fig. 7 Evolution of the average unit capital cost and curtailment rate of the wind projects in our sample ------------------------------------------------------------------------------------------5.1.2 LCOE with Carbon Prices With the implementation of a carbon pricing policy, wind power plant investors can earn revenue from avoided carbon emissions and part of the electricity production cost can be offset by carbon

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abatement revenues, which in turn decreases the overall LCOE of a project. As shown in Fig. 8 (a), we have plotted the cost curves for three carbon price scenarios, i.e., 20 CNY/t CO2, 50 CNY/t CO2, and 100 CNY/t CO2. As the carbon price increases, the cost curve moves downwards and LCOE becomes lower. Specifically, average LCOEs for the three carbon price levels are 0.576 CNY/kWh, 0.548 CNY/kWh, and 0.502 CNY/kWh, which decrease by 3.11%, 7.75%, and 15.49% respectively, relative to the case with no carbon pricing policy. This indicates that LOCE can currently be reduced by up to approximately 7% with the incorporation of carbon prices in the pilot emission trading system, and this effect may become more significant with an increase in carbon price. Moreover, by comparing the LCOE of onshore wind power projects for three carbon price scenarios (i.e., 20 CNY/t CO2, 50 CNY/t CO2, and 100 CNY/t CO2) with the average Chinese coal-fired power on-grid price of 31 provinces in 2015 i.e., 0.384 CNY/kWh (NDRC, 2015b), we find the number of projects whose LCOE is lower than the average coal-fired power on-grid price is 13, 30 and 85, accounting for 0.63%, 1.46% and 4.12% of the electricity produced from wind power, respectively. The results above indicates that although the overall cost of the wind power electricity is still higher than that of the coalfired power, carbon pricing policy can promote the achievement of grid parity of onshore wind power in future. ------------------------------------------------------------------------------------------Fig. 8 LCOEs of all the wind power projects with different carbon prices 22

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------------------------------------------------------------------------------------------According to previous studies, the discount rate plays a key role in affecting LCOE (Ouyang and Lin, 2014), so we have conducted a sensitivity analysis and calculated the LCOEs for all onshore wind power projects using alternative discount rates, as shown in Fig. 9. The lower discount rate we applied was 4%, which is approximately equal to the average Chinese government bond interest, (i.e., the risk-free interest rate), and the higher discount rate was 9%, which broadly corresponds to a high-risk investment environment (IEA, 2015). In the case of the lower discount rate of 4% (Fig. 9 (a)), the average LCOE of the sample projects with the three carbon price levels, i.e., 20 CNY/t CO2, 50 CNY/t CO2, and 100 CNY/t CO2 are 0.530 CNY/kWh, 0.503 CNY/kWh, and 0.457 CNY/kWh, representing falls of 3.46%, 8.38%, and 16.76% respectively, compared to the no-carbon-pricing-policy scenario. In the case of the higher discount rate of 9% (Fig. 9 (b)), the average LCOE of the sample projects with the three carbon price levels are 0.604 CNY/kWh, 0.577 CNY/kWh, and 0.531 CNY/kWh, representing decreases of 3.05%, 7.38%, and 14.77% compared to the no-carbon-pricing-policy scenario. It follows that when comparing the LCOE decrease in the three scenarios, discount rates have a significant effect on the LCOE, and higher discount rates lead to higher LCOEs. Also, the effect of a carbon pricing policy on the discount rate change is strong and seems to be more significant for lower discount rates.

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Fig. 9 LCOEs of all onshore wind power projects with discount rates of 4% (a) and 9% (b) ------------------------------------------------------------------------------------------Moreover, curtailment is a critical issue that currently concerns both government and potential investors (Pei et al., 2015; Li et al., 2015; Li et al., 2016). We conducted a sensitively analysis and calculated the LCOEs for all onshore wind power projects using different curtailment rates, as shown in Fig. 10. The lower curtailment rate is 0%, meaning that all the onshore wind power can be connected to the grid and dispatched (i.e., no curtailment), and the higher curtailment rate is 10%, which corresponds approximately to the recorded curtailment rate for 2017. In the case of the no- curtailment-rate of 0% (Fig. 10 (a)), the average LCOEs of the sample projects with the four carbon price levels, i.e., 0, 20 CNY/t CO2, 50 CNY/t CO2, and 100 CNY/t CO2 are 0.546 CNY/kWh, 0.528 CNY/kWh, 0.500 CNY/kWh, and 0.454 CNY/kWh. In the case of a curtailment rate of 10% (Fig. 10 (b)), the average LCOEs of the sample projects with the four carbon price levels are 0.634 CNY/kWh, 0.615 CNY/kWh, 0.588 CNY/kWh, and 0.542 CNY/kWh respectively, corresponding to increases of 16.1%, 16.5%, 17.6%, and 19.4% relative to the no-curtailment scenario. These results indicate that the curtailment rate has a significant effect on the LCOE and there is still great potential for further decreases to LCOEs simply by resolving the curtailment issue. Interestingly, the LCOE increase becomes more significant with a carbon price increase since the presence of curtailment will lead to more carbon abatement revenue loss in the event of a higher carbon price. Finally, in the case of higher curtailment rate,

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the average LCOE with a carbon price of 100 CNY/t CO2 decreases by about 14.5% relative to the no-carbon-price scenario, and in the no-curtailment scenario, the average LCOE decreases by 16.8%. We can, therefore, deduce that the effect of carbon prices on the decline of LCOE seems to be more significant for a lower curtailment rate. ------------------------------------------------------------------------------------------Fig. 10 LCOEs of all onshore wind power projects with curtailment rates of 0% (a) and 10% (b) ------------------------------------------------------------------------------------------5.2. Profitability of Wind Power Projects In this section, by comparing the LCOE with the corresponding FIT, the profitability of each wind power project can be determined. Using the LCOEs for all the 2059 projects in various scenarios outlined in Section 5.1., the FIT corresponding to each project is calculated on the basis of the national renewable energy subsidy policy. The National Development and Reform Commission (NDRC, 2009) issued a policy in 2009 of regionalized benchmarking pricing for wind power, which divided the whole country into four regions and stipulated four different tariff levels depending on wind resource conditions5. Region I comprises provinces with the highest wind availability and lowest FIT, while region IV includes those with the lowest wind resources and the highest FIT. In the following years, the NDRC updated the wind power tariff 5

Region I: Inner Mongolia Autonomous Region except: Chifeng, Tongliao, Xing’anmeng, Hulunbeier; Xinjiang Uygur Autonomous Region: Urumqi, Yili, Karamay, Shihezi; Region II: Hebei Province: Zhangjiakou, Chengde; Inner Mongolia Autonomous Region: Chifeng, Tongliao, Xing’anmeng, Hulunbeier; Gansu Province: Zhangye, Jiayuguan, Jiuquan; Region III: Jilin Province: Baicheng, Songyuan; Heilongjiang Province: Jixi, Shuangyashan, Qitaihe, Suihua, Yichun, Daxinganling Region, Gansu Province except: Zhangye, Jiayuguan, Jiuquan, Xinjiang Autonomous Region except: Urumqi, Yili, Changji, Karamay, Shihezi, Ningxia Hui Autonomous Region; Region IV: Other areas of China not listed above. 25

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and on-grid prices were reduced gradually (NDRC, 2014; NEA, 2015). FITs of onshore wind power in the four regions are shown in Table 2, and these are expected to decrease further in the future. According to the 13th five-plan by NDRC (2017), grid parity is expected to be achieved for onshore wind power during the 2016-2020 period, so in our analysis, the maximum FIT decrease is assumed to be 0.2 CNY/kWh by comparing the current on-grid electricity price of coal-fired power and FIT levels shown in Table 2. ------------------------------------------------------------------------------------------Table 2 China’s feed-in tariff for onshore wind power in different regions ------------------------------------------------------------------------------------------According to the project location and operation date of the 2059 onshore wind power projects published in PDD, we can calculate the baseline FIT level for each project based on Table 2. By comparing the baseline FIT and LCOE shown in Section 5.1, the profitability of each project in the baseline scenario can be determined. Furthermore, by analyzing the percentage of the profitable projects with different carbon prices incorporated, the effect of carbon pricing policies on the profitability of wind projects can be explored and, accordingly, the threshold levels of carbon price in China required to support wind power investment can be derived. Fig. 11 shows the percentage of profitable wind power projects for different carbon prices and FIT decreases relative to the baseline level. With baseline FIT levels and no carbon price incorporated, the percentage of profitable projects is 41.9%. This ex-post evaluation result indicates that revenues

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from electricity sales cannot compensate for investment and O&M costs for more than half of the projects. With renewable subsidy levels expected to decline in the future, this situation may, in fact, become worse: The percentage of the profitable projects is reduced to 18.46%, 5.29%, 1.46%, and 0.49% with baseline FITs decreasing to 0.05 CNY/kWh, 0.1 CNY/kWh, 0.15 CNY/kWh, and 0.2 CNY/kWh respectively. Previous studies suggest that carbon pricing policies complement FIT policies in support of wind power investment (Cames et al., 2016; Liu, 2015; Spalding-Fecher et al., 2012; Bogner and Schneider, 2011; Wang and Chen, 2010; Lewis, 2010). Fig. 11 shows the threshold level of carbon price required to keep the percentage of profitable wind power projects above a certain level with baseline FITs decreasing by up to 0.2 CNY/kWh from the baseline level. For example, with baseline FIT levels decreasing by 0.06 CNY/kWh, in which case the grid parity of wind power projects in Region I can be achieved, the required carbon prices need to be 114 CNY/t CO2, 138 CNY/t CO2, and 171 CNY/t CO2 respectively to maintain the profitability of 70%, 80%, and 90% of onshore wind power projects. Furthermore, in the most extreme scenario with baseline FIT levels decreasing by 0.2 CNY/kWh, the required carbon price needs to be 264 CNY/t CO2, 288 CNY/t CO2, and 321 CNY/t CO2 respectively to maintain the profitability of 70%, 80%, and 90% of onshore wind power projects. Eventually, based on many relevant studies (Mo and Zhu, 2014, Tu and Mo, 2017), in order to promote low-carbon technology investment

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effectively, Chinese CO2 price in the future should further increase to between 140 CNY/t CO2 and 300 CNY/t CO2. In this situation, the profitable percent of onshore wind power projects would be 10.54% to 85.28% with the baseline FIT levels decreasing by 0.2 CNY/kWh. ------------------------------------------------------------------------------------------Fig. 11 Profitability of wind power projects with different FIT decreases and carbon prices ------------------------------------------------------------------------------------------As discussed in Section 5.1, the discount rate has a significant effect on the LCOE and may, accordingly, affect the critical carbon prices supporting wind power investment. Fig. 12 shows the profitability of the wind power projects at different discount rates, i.e., 4% (Fig 12(a)) and 9% (Fig 12 (b)). Our findings show that the percentage of profitable projects with the lower discount rate is much higher than for those at the higher discount rate. With a discount rate of 4%, the percentages of profitable projects at carbon prices of 0, 50 CNY/t CO2, and 100 CNY/t CO2 are 70.0%, 89.6%, and 96.4%, while at the higher discount rate, the percentages of profitable projects are 27.3%, 55.9%, and 77.5% respectively. By comparing the changes induced by the same carbon prices, it can be inferred that in the case of the higher discount rate, the effect of carbon pricing policy on the wind power profitability is much more significant than in the case of the lower discount rate. As the discount rate can reflect the investment risk perceived by investors, the role of carbon pricing in promoting wind power investment may be more significant if the associated risk of onshore wind power investment increases in the future.

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In addition, the critical carbon prices at the lower discount rate are lower than those at the higher discount rate. With baseline FIT levels, the required carbon prices would need to be 0 CNY/t CO2, 21 CNY/t CO2 , and 51 CNY/t CO2 for the percentage of profitable projects to be 70%, 80%, and 90%, while at the higher discount rate, the critical carbon prices would need to be 82 CNY/t CO2, 105 CNY/t CO2, and 141 CNY/t CO2 respectively. Furthermore, with baseline FITs decreasing to 0.2 CNY/kWh, to achieve grid parity for wind power projects in all four regions, the required carbon prices at the lower discount rate of 4% would need to be 213 CNY/t CO2, 234 CNY/tCO2, and 267 CNY/t CO2 increasing to 297 CNY/t CO2, 321 CNY/t CO2, and 357 CNY/t CO2 at the higher discount rate of 9%, for the percentage of profitable projects to be 70%, 80%, and 90% respectively. --------------------------------------------------------------------------------------Fig. 12 Profitability of the wind power projects with discount rates of 4% (a) and 9% (b) --------------------------------------------------------------------------------------The effect of curtailment rate on critical carbon prices is also explored, as shown in Fig. 13. Fig. 13 (a) shows the results without curtailment and Fig. 13 (b) with a curtailment rate of 10%. The results demonstrate that the proportion of profitable projects with a lower curtailment rate is much higher than with the higher curtailment rate. With a curtailment rate of 10%, the percentages of profitable projects for carbon prices of 0, 50 CNY/t CO2, and 100 CNY/t CO2 are 18.2%, 48.7%, and 75.6%. Without curtailment, the percentages of profitable projects are 78.3%,

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96.5%, and 99.2% respectively. By comparing the percentage changes induced by the same carbon prices, it can be concluded that in the case of higher curtailment rate, the effect of carbon pricing policy on promoting the wind power investment is more significant than when there is no curtailment rate. Moreover, with the higher curtailment rate, the critical carbon prices making the percentages of the profitable projects 70%, 80%, and 90% are 87 CNY/t CO2, 114 CNY/t CO2, and 141 CNY/t CO2. Without curtailment, the required carbon prices are 0 CNY/t CO2, 3 CNY/t CO2, and 24 CNY/t CO2 respectively, which is much lower than with the higher curtailment rate. Furthermore, with the baseline FIT decreasing by 0.2 CNY/kWh, to achieve the grid parity for the wind power projects in all the four regions, the required carbon prices with no curtailment rate are 204 CNY/t CO2, 222 CNY/t CO2, and 243 CNY/t CO2, increasing to 306 CNY/t CO2, 333 CNY/t CO2, and 372 CNY/t CO2 with the higher curtailment rate of 10%. This indicates that without a solution to the curtailment problem, the impact of carbon pricing on onshore wind power profitability and investment may be limited. --------------------------------------------------------------------------------------Fig. 13 Profitability of the wind power projects with curtailment rates of 0% (a) and 10% (b) ---------------------------------------------------------------------------------------

6. Conclusions and Discussion Although China’s onshore wind power industry has experienced rapid development in the past decade, there are still many barriers and challenges, including the high cost of power generation,

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a deficit in the renewable energy fund coupled with quickly diminishing FITs, limited connection to the grid, and a high curtailment rate. At the same time, some new opportunities and positive factors are emerging to promote the development of wind power, such as the national carbon pricing system. Consequently, there are many discussions taking place about whether the Chinese onshore wind power development is sustainable and how its the policy system can be optimized to promote its further development. In this paper, an ex-post assessment evaluated the profitability of existing onshore wind power investment at project level. In particular, we explored the effects of carbon pricing policies on project profitability in the face of various barriers and challenges. We calculated the LCOEs for 2059 onshore wind power projects implemented between 2006 and 2015 and then ascertained the production cost curves of wind power in selected scenarios. By comparing the LCOE with the corresponding FIT levels, the profitability of all the projects was assessed. Moreover, the effect of the carbon pricing on the wind power project profitability for different discount rates and curtailment rates were also assessed. Our principal conclusions are as follows: The average cost of onshore wind power production fell significantly from 0.615 CNY/kWh in 2006 to 0.533 CNY/kWh in 2015, a decrease of 14%. However, these results show that the LCOE of many projects remain higher than the corresponding FIT levels, and consequently, nearly 60% of the sample projects are not

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profitable. Also, our evaluation results show that the overall LCOEs of onshore wind power are higher than those of coal-fired power, indicating that it may still be difficult for onshore wind power to compete with traditional fossil fuel power without additional political support. In summary, there are still enormous challenges ahead for the sustainable development of the onshore wind power industry in China, especially in the face of decreasing FIT levels, a high curtailment rate, the slower investment cost decreases for wind turbines, and the investment risk related to the wind power in the future. With carbon pricing policies being implemented, wind power plant investors can obtain revenue for avoided carbon emissions and part of the cost of electricity production can be offset by this incentive. Our findings show that with a carbon price of 20 CNY/tCO2, which is approximately equal to the average carbon price in seven Chinese pilot carbon emission trading markets in 2017, the average LCOE for all onshore wind power projects in our dataset decreases by about 3.11% relative to no-carbon-pricing-policy scenario and, accordingly, the proportion of profitable projects increases from 41.9% to 56.7%. Furthermore, with the carbon price reaching 50 CNY/kWh, (the highest carbon price from the seven Chinese pilot carbon emission trading markets in 2017), the average LCOE decreases by about 7.75% and the proportion of the profitable projects increases to 70.2%. This indicates that a carbon pricing policy can improve

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the profitability of the wind power projects although the effect seems negligible based on current low carbon prices. Given the expectation of declining FITs and grid parity for onshore wind power in 2020, the carbon pricing policy can complement FITs in promoting the wind power investment in the future. The Chinese government is promoting regional pilot carbon emissions trading systems to smooth their transition into a nationwide carbon emissions trading market, and carbon emission regulations may become more stringent (CSD, 2016), leading to higher carbon prices and giving a longer-term certainty that the carbon price will improve the profitability of wind power projects. Our evaluation shows that with baseline FIT levels decreasing by 0.06 CNY/kWh (in which case the grid parity of wind power projects in Region I can be achieved), the carbon price needs to be 114 CNY/t CO2, 138 CNY/t CO2, and 171 CNY/t CO2 respectively to maintain the profitability of 70%, 80%, and 90% onshore wind power projects. Furthermore, in the most extreme scenario with baseline FIT levels decreasing by 0.2 CNY/kWh, grid parity can be achieved in all the four regions if carbon prices are 264 CNY/t CO2, 288 CNY/t CO2, and 321 CNY/t CO2 respectively. Sensitive analysis of the effect of carbon pricing policy on the promotion of wind power investment reveals that in the case of a higher discount rate and higher curtailment rate, the effect of carbon pricing policies seems to be more significant. The usual discount rate for onshore wind

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power investment is somewhat inflated due to the high risks perceived by wind power investors. Political and legislative instability also negatively affect the discount rate for onshore wind power projects since the economic feasibility of an investment usually depends on the related support mechanisms. Consequently, in the face of a possible future decline in FIT levels, the discount rate for Chinese onshore wind power investment may remain high. Meanwhile, according to NEA (2018), the curtailment rate of Chinese wind power in 2017 was about 12%. Although the Chinese government has implemented various policies to decrease the curtailment rate for wind power, such as improving the power source structure and power source control capability and constructing new transmission corridors for wind power delivery, it may be difficult to solve the curtailment problem entirely in the short-term. In this situation, the carbon pricing policy may play an important role in complementing the declining FIT and promoting the development of wind power sector in China. Accordingly, this paper proposes to improve the CER system to provide a reliable guarantee for gauging the profitability of wind projects and wind power industry development, especially with a national level carbon trading system being implemented. Firstly, the conditions of tradable Chinese CERs participating in the Chinese carbon market, including the number of CCERE projects, project area, starting date, and type need to be determined. Currently, Chinese CER is a complementary factor in the carbon emission trading

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market. However, if excessive tradable Chinese CERs enter the Chinese carbon market, these may impact the price of carbon permits and affect carbon emission reduction. At the same time, excessive tradable Chinese CERs will have a negative impact on the price of CCER and the returns for renewable energy project investors. If the supply of tradable Chinese CER is too small, it may increase the performance costs for carbon reduction companies, which would have a direct negative impact on the carbon market. Secondly, the pricing mechanism for Chinese CER needs to be improved. Based on “Measures for the Administration of Carbon Emissions Trading” published by NDRC, Chinese CER and permits for the carbon emission trading market should be equal but, in reality, the difference between the prices of tradable Chinese CER and the carbon emission permits is still sizeable. The essential reason for this phenomenon is that the pricing mechanism of tradable Chinese CER is based on bargaining between two agents, thereby evading market rules, which has a negative impact on the abatement effectiveness of carbon emission trading market. To promote the positive role of CER revenue on grid parity for wind power, the Chinese government needs to create a more viable pricing mechanism for tradable Chinese CER. Some limitations of our study should also be mentioned. Our stylized modeling approach may not adequately reflect the system costs of onshore wind power, such as additional balancing costs, additional cycling costs, and transaction costs. A more comprehensive cost-benefit

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analysis using systematic approach might be a useful avenue for further research. It should also be pointed out that the ex-post analysis of onshore wind power in China was reached by making some relevant assumptions. One example is the relation between issuance success rate and actual generation production, which may affect the LCOE and profitability of all onshore wind power projects. A higher issuance success rate means more wind power being fed into the grid, leading to a lower LCOE for onshore wind power projects. Accordingly, the carbon price may need to be lower for the project to be profitable.

Acknowledgments Support from the National Natural Science Foundation of China under grant nos. 71403263, 71774153, 71690245, 71503115 and the National Key Research and Development Program of China (Grant No.2016YFA0602500) are gratefully acknowledged. In addition, it is part of the activities of SCCER CREST, which is financially supported by Innosuisse, the Suisse Innovation Agency.

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Report commissioned by the High Level Panel on the CDM Policy Dialogue. Bonn. Available: http://www.cdmpolicydialogue.org/research/1030_impact.pdf. Tang, T., Popp, D., 2016. The learning process and technological change in wind power: evidence from China's CDM wind projects. Journal of Policy Analysis and Management, 35(1), 195-222. Tegen, S., Hand, M., Maples, B., Lantz, E., Schwabe, P., Smith, A., 2012. 2010 Cost of Wind Energy Review. National Renewable Energy Laboratory, Colorado, USA Thiam, D.R., 2011. An energy pricing scheme for the diffusion of decentralized renewable technology investment in developing countries. Energy Policy, 39(7), 4284-4297. Timilsina, G. R., Kooten, G. C. V., Narbel, P. A., 2013. Global wind power development: Economics and policies. Energy Policy, 61(3), 642-652. Tu, Q., Mo, J., 2017. Coordinating carbon pricing policy and renewable energy policy with a case study in China. Computers & Industrial Engineering, 113, 294–304. Wang, Q., Chen, Y., 2010. Barriers and opportunities of using the clean development mechanism to advance renewable energy development in China. Renewable and Sustainable Energy Reviews, 14(7), 1989-1998.

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Appendix A: Detailed Methodology for LCOE Calculation The detailed calculation methodology for LCOE is described by the following equations. The threshold price is determined when the total NPV, including prepaid investment and debt, capital

46

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cost, O&M cost, as well as loan payment and tax, is equal to zero. To simplify the variability of wind resource, the capacity factor for each grid cell is assumed to be constant during the lifetime of the wind turbine, T

NPV =  t 0

CFt =0 (1  r )t ,

(A.1)

where NPV is the net present value, CFt is the cash flow in year t, r is the discount rate, and T is the total lifetime of the project, which is 20 years based on the project’s PDD.

CFt  Rt  Dt  Ct  O & M t  LPt  X t +Pc  ERt

(A.2)

Rt is the wind power sale revenue of projects, which is the wind power actual generation (Et) in year t multiplied by the sale price of wind power (P), with the assumption of a constant price over the project lifetime, that is 𝑅𝑡 = 𝐸𝑡 ∙ 𝑃, and the constant price is LCOE. Dt is the debt from the bank in year t, which can be shown as, Dt  (1   )  Ct

(A.3)

in which α is the own capital ratio of the wind project. Ct is the overnight capital cost of the wind project in year t. LPt is the loan payment in year t, which can be shown as, Dt  i  (1  i )15 LPt  , t  3  17 (1  i )15  1

(A.4)

where i is the interest rate of debt Dt. In this paper, we assume the annual loan payment LPt is constant (Davidson et al., 2016) and the payment period is 15 years based on the project’s PDD; O&Mt signifies the operations and maintenance costs in year t which can be shown in the

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project’s PDD. Xt stands for value-added tax, income tax, and surcharge tax for wind power project in year t, which can be shown as

X t  VATt  EITt  Surtaxt (A.5) where VATt is the value-added tax in year t with the tax rate Surtaxt is the education, and urban construction accessory component of value-added tax with a rate of EITt is the enterprise income tax which can be shown as EITt    IBT

(A.6)

in which  is the enterprise income tax rate and IBTt is the income before tax in year t calculated as follows,

IBTt  Rt  IPt  Ot & M t .

(A.7)

IPt is the interest payment, and prt-1 is the principal in year t which can be shown as

IPt  i  prt 1 , t  3  17 ,

(A.8)

 D2 , t  3 prt 1   (1  i )  prt  2  LPt 1 , t  4  17

(A.9)

where the depreciation period is 15 years. Thus, the wind power LCOE can be shown as  T C  Ot & M t  LPt  X t  Dt  Pc  ERt LCOE    t (1  r )t  t 0

  T Et   /  t    t 0 (1  r ) 

48

(A.10)

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City of on-shore wind power project

Yes

City is included in reduced sample?

Average issuance success rate of city level grid

Yes

No

Province is included in reduced sample?

Average issuance success rate of province level grid

No

Average issuance success rate of region level grid

Fig. 1 Approach for estimating issuance success rate of onshore wind power projects

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160000 Annual installed capacity

140000

Annual capacity supported by CDM and CCERE

Capacity (MW)

120000

Cumulative installed capacity

100000

Cumulative capacity supported by CDM and CCERE

80000 60000 40000 20000 0 2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Year

Fig. 2 The capacity of onshore wind power projects in our datasets

2

Inner Mongolia Shandong Hebei Xinjiang Ningxia Liaoning Heilongjiang Jilin Gansu Yunnan Shanxi Shaanxi Guangdong Fujian Guizhou Hunan Jiangsu Hubei Zhejiang Sichuan Anhui Henan Guangxi Jiangxi Shanghai Qinghai Hainan Tianjin Chongqing Beijing

Number of projects 400

350

300

Cumulative capacity

Number of projects

250

150

100

0

3

20000

200

15000

10000

5000

Cumulative capacity (MW)

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25000

50 0

Fig. 3 The distribution of the onshore wind power projects in our sample in different provinces

Alashan Beijing Dali Chenzhou Qixia Wenzhou Jiangmen Yangjiang Zhongwei Datong Nantong Weihai Weifang Lingwu Zhoushan Wenchang Chongqing Xinzhou Guyuan Shanghai Qingyang Ningbo Jiamusi Baotou Zhangjiakou Dalian Taizhou Xingan Meng Chuzhou Cangzhou Tianjin Yulin Zhuhai Qiqihaer Turfan Xilinguole Meng Bijie Eerduosi Yancheng Songyuan Tangshan Xianning Baicheng Chaoyang Jinhua Shenyang Kaiyuan Jiuquan Shuozhou

Issuance success rate (%)

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130

120

110

100

90

80

70

Fig. 4 The average issuance success rate of 97 city-level power grids

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1.0

LCOE (CNY/kWh)

.8

.6

.4

.2

0.0 0

50

100

150

200

Cumulative generation (TWh)

Fig. 5 LCOEs of all the wind power projects in our sample

5

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0.90

LCOE (CNY/kWh)

0.76

0.62

0.48

0.34 Mean

= Median

Outliers

0.20 2006

2007

2008

2009

2010

2011

Year

2012

2013

2014

2015

Fig. 6 The evolution of the LCOEs of the wind projects from 2006 to 2015

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Unit capital cost Curtailment rate

Curtailment rate

20%

12000 11000 10000

15% 9000 10% 8000 5%

7000

0%

Unit capital cost (CNY/kW)

25%

6000 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Year

Fig. 7 Evolution of the average unit capital cost and curtailment rate of the wind projects in our sample

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1.0 0 CNY/t CO2 20 CNY/t CO2 50 CNY/t CO2 100 CNY/t CO2

LCOE (CNY/kWh)

.8

.6

.4

.2 0

50

100

150

200

Cumulative generation (TWh)

Fig. 8 LCOEs of all the wind power projects with different carbon prices

8

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(a)

(b)

1.0 0 CNY/t CO2 20 CNY/t CO2 50 CNY/t CO2 100 CNY/t CO2

0 CNY/t CO2 20 CNY/t CO2 50 CNY/t CO2 100 CNY/t CO2

.8

LCOE (CNY/kWh)

LCOE (CNY/kWh)

.8

1.0

.6

.6

.4

.4

.2

.2 0

50

100

150

0

200

50

100

150

Cumulative generation (TWh)

Cumulative generation (TWh)

Fig. 9 LCOEs of all onshore wind power projects with discount rates of 4% (a) and 9% (b)

9

200

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(a)

(b)

1.0

0 CNY/t CO2 20 CNY/t CO2 50 CNY/t CO2 100 CNY/t CO2

0 CNY/t CO2 20 CNY/t CO2 50 CNY/t CO2 100 CNY/t CO2

.8

LCOE (CNY/kWh)

LCOE (CNY/kWh)

.8

1.0

.6

.6

.4

.4

.2

.2 0

50

100

150

0

200

50

100

150

Cumulative generation (TWh)

Cumulative generation (TWh)

Fig. 10 LCOEs of all onshore wind power projects with curtailment rates of 0% (a) and 10% (b)

10

200

100% 80% 60% 40% 20%

288

252

216

180

144

Carbon price (CNY/t CO2)

108

72

36

0

0.05

324

0.1

360

0.15

396

0

0%

0.2

Percentage of the profitable project

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FIT decrease (CNY/kWh)

Fig. 11 Profitability of the wind power projects with different FIT decreases and carbon prices

11

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Percentage of the profitable project

(b) 100%

80% 60% 40% 20% 390 360

330 300

270 240

210 180

150 120

Carbon price (CNY/t CO2)

0

0%

90 60 30 0 FIT decrease (CNY/kWh)

80% 60% 40% 20% 0% 432 396

360 324 288 252 216 180 144 108 72 Carbon price (CNY/t CO2)

Fig. 12 Profitability of the wind power projects with discount rates of 4% (a) and 9% (b)

12

0.2 0.15 0.1 0.05 0

100%

0.2 0.15 0.1 0.05

Percentage of the profitable project

(a)

36 0 FIT decrease (CNY/kWh)

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80% 60% 40% 20%

330 300

270 240

210 180

150 120

Carbon price (CNY/t CO2)

0.2 0.15 0.1 0.05

390 360

0

0%

90 60 30 0 FIT decrease (CNY/kWh)

(b) 100% 80% 60% 40% 20% 0% 390 360

330 300

270 240

210 180

150 120

Carbon price (CNY/t CO2)

0

100%

90 60 30 0 FIT decrease (CNY/kWh)

Fig. 13 Profitability of the wind power projects with curtailment rates of 0% (a) and 10% (b)

13

0.2 0.15 0.1 0.05

Percentage of the profitable project

Percentage of the profitable project

(a)

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Highlights 

A dataset of 2059 onshore wind power projects in China is constructed.



The LCOEs are estimated and the profitability is assessed with carbon pricing.



The carbon pricing policy can improve the profitability of the wind power projects.



The effect of current carbon price is limited under the grid parity scenario.



The effect becomes more significant in case of higher risk and curtailment rate.

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Table 1 Summary statistics of the project data Parameters

Unit

Mean

Std.Dev.

Min

Max

Number of observations

Capacity

MW

58.70

47.20

6.00

600.00

2059

Issuance success rate

%

87.79

9.60

66.78

170.31

558

GWh

125.24

92.51

16.05

1567.24

2059

t CO2/MWh

0.92

0.09

0.60

1.65

2059

Unit capital cost

CNY/kW

8897.40

810.72

2947.80

11514.58

2059

Unit O&M cost

10000CNY/kW

25.54

4.99

8.67

71.67

2059

Own capital ratio

%

20.14

1.64

15

30

2059

IRR without CER

%

6.50

0.01

4.31

8.00

1959

Project lifetime

yr

20

0

20

20

2059

Loan interest rate

%

6.00

0.01

4.90

8.06

1902

Loan period

yr

15

0

15

15

2059

Expected electricity generation Avoided carbon emission intensity

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Table 2 China’s feed-in tariff for the onshore wind power in different regions Year

On-grid price (CNY/kWh) IV

Effective time

I

II

III

2009

0.51

0.54

0.58

0.61

From 1 Aug. 2009

2014

0.49

0.52

0.56

0.61

From 1 Jan. 2015

2016

0.47

0.50

0.54

0.60

From 1 Jan. 2016

2018

0.44

0.47

0.51

0.58

From 1 Jan 2018

2