Does a Small Difference Make a Difference?
Impact of Feed-in Tariff on Renewable Power
Generation in China

Yimeng Du
Kenji Takeuchi
November 2018
Discussion Paper No.1828

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANDoes a Small Di↵erence Make a Di↵erence?
Impact of Feed-in Tari↵ on Renewable Power
Generation in China
Yimeng Du and Kenji Takeuchi⇤
Graduate School of Economics, Kobe University

November 16, 2018

Abstract
This study investigates the e↵ectiveness of regionally di↵erentiated feed-in tari↵s(FIT) for the development of renewable energy in China. By using a spatial regression discontinuity design, we estimate the impacts of regionally di↵erentiated FITs on
the outcome indicators of wind and solar power generation, such as utilization rate, installed capacity, power generation, and hours of operation. Our findings show that FIT
implementation plays a role in promoting renewable energy development in resourcepoor regions. A small di↵erence in the tari↵ rate leads to statistically significant
di↵erences in outcome indicators among regions. Our results suggest that regionallydi↵erentiated FITs might help mitigate the overproduction of wind electricity in regionswith abundant wind resources but low electricity demand.
Keywords: Feed-in Tari↵; Renewable Energy; Renewable Curtailment; Spatial
Regression Discontinuity Design
JEL classification: Q42, Q48⇤

E-mail: sissitoh@gmail.com (Du), takeuchi@econ.kobe-u.ac.jp (Takeuchi).

1Introduction
Wind and solar power generation in China have achieved tremendous growth. In 2016, thecumulative wind and solar capacity reached approximately 150 GW and 77 GW, respectively,
which was the largest worldwide (China National Renewable Energy Center, 2017). However,since 2010, renewable energy industries in China have faced the issue of oversupply, leading
to the curtailment of renewable power. The country’s renewable curtailment is the worst in
the world, with a total of 56,200 GWh of renewables curtailed in 2016 – the national averagecurtailment rate was as high as 17% and 10% for wind and solar energies, respectively (China
Electricity Council, 2018). The high curtailment rate is partly due to the dramatic regional
disparity of China’s renewable energy development. Because of the uneven distributionof renewable energy resources, over 70% of China’s large-scale wind and solar farms havebeen built in resource-rich regions where electricity demand and export capacity are low.
Oversupply is particularly significant in Inner Mongolia, with 75 GW of available capacity
versus only 20 GW of peak demand in 2016 (Bloomberg New Energy Finance, 2017). The
imbalance between resource abundance and low electricity demand has led to overcapacity
and high rates of curtailment.
To resolve the overcapacity issue, the regionally differentiated feed-in tariff (FIT) scheme
for on-grid wind power was issued in 2009. Similarly, the policy for on-grid solar energy
was announced in 2013. Several studies explore the weakness of the FIT policy with anational uniform tariff rate and claim that regional differentiation of tariffs can optimize
the investment of renewable energy power plants. For instance, Obermüller (2017) points
out that a uniform FIT policy would incentivize unfavorable wind capacity allocations. By
investigating the discrepancy between economically optimal wind locations under a uniform
wind tariff and system optimal wind locations in Germany, Obermüller (2017) finds that theuniform FIT attains the highest regional revenues in locations with rich wind resources but
independent of electricity demand. Using an empirical optimization model, Schmidt et al.
(2013) compare investment behavior under fixed and premium FITs for the case of Austria.
1

As a result, they find that the premium FIT scheme promotes the location diversification ofwind turbines.
The main objective of this study is to estimate whether the implementation of regional
differentiation of tariffs has a positive impact on mitigating uneven distribution and overproduction of renewable energy in China. Assuming that counties located just south of the FIT
boundary do not differ systematically from those located north of the boundary on relevant
covariates, we estimate the effect of the difference in wind and solar tariffs across the boundary using the spatial regression discontinuity design (RDD). In addition, to investigate theeffects of regionally differentiated FITs on subsequent dynamics, we adopt an approach thatcombines the multiple-period difference-in-differences (DID) model with spatial RDD.
This study makes the following contributions to the literature on the economics of renewable energy policy. First, we examine the impact of regional tariff policy on reduction in
the overcapacity of renewable energy projects through a quasi-experimental design. Existing
empirical studies show inconclusive results regarding the FIT’s impact on the location choiceof renewable energy projects among regions in China. Xia and Song (2017b) empirically investigate the driving factors of the regional disparity of China’s wind power development.
Their findings show that the FITs are most effective in wind resource-rich regions and havelittle impact on other regions. The results indicate that one driving force of the uneven development of wind power in China is the regional differentiation of on-grid wind tariffs. On
the contrary, Zhao et al. (2016) empirically analyze the impacts of regionally differentiated
FITs on the increase in installed wind capacity and conclude that the FIT is more effective
in areas with poor wind resources. Second, while previous studies use installed capacity andpower generation as indicators to capture wind power development, this study uses alternative measures of indicators. For instance, Menz and Vachon (2006) estimate the effectsof the state renewable energy policy on wind power capacity and generation in the UnitedStates. In addition to these indicators, we use the utilization rate and operation hour ofwind turbines in our analysis. These alternative measures allow us to capture the degree2

of effective utilization of the installed wind turbines. Third, while previous studies on theimpact of FIT mainly focus on wind power development, our study investigates the impactof regional differentiation of tariffs on the solar energy industry as well. Our findings on solar
energy deployment are in line with the finding of Wang et al. (2016) that the FIT policy
significantly mitigates the overcapacity of China’s solar power industry.
This study’s results suggest that regionally differentiated FITs have promoted the development of both the wind and solar energy industries in China. Specifically, our findings
show that wind facilities’ utilization rate has improved in regions with relatively poor windresources through adoption of higher tariff rates. To explore this impact of the FIT, we use
the actual amount of power generation and installed power capacity to calculate the utilization rate, which is used as a major production indicator of wind facilities. In addition, we
find that the implementation of regional tariffs relieved the uneven distribution of renewable
power facilities by attracting more projects to resource-poor regions. Interestingly, our findings show that the FIT provided for on-grid solar projects only had a significant impact in theyear the tariff rates were revised. This result indicates an acute impact of the regional tariff
gap, which incentivizes renewable energy developers to locate the projects in resource-poor
regions. Therefore, we conclude that the rapid growth in China’s solar sector still depends
on financial support in the form of higher tariffs paid to renewable power generators.
The remainder of this paper is organized as follows: Section 2 introduces the policy ofregionally differentiated FIT to promote the wind and solar industries in China. Section 3
describes the data. Section 4 follows with an analysis framework, including a description of
the spatial RDD approach and regression discontinuity (RD) polynomial. Estimation results
and discussions are provided in Section 5. Finally, Section 6 presents our conclusions anddiscusses the research implications.

32

Regionally differentiated FIT in China
To mitigate the uneven distribution of renewable energy industries, the tariff rate isdifferentiated regionally under the FIT regime in China. The regionally differentiated FIT
policy for on-shore wind power was first introduced by the National Development and Reform
Commission (NDRC) in August 2009. As illustrated in Figure 1, the FIT policy divided theregions of China into four zones, each with a different tariff rate according to onshore windresources and construction conditions. Regions with the richest wind resources in the northand west were given the lowest tariff of 0.51 yuan/kWh, reflecting lower production costs
resulting from resource endowments. Regions with modest wind resources have tariffs of
0.54 yuan/kWh or 0.58 yuan/kWh. Regions with comparatively poor wind resources andconstruction conditions in the central area and coastline of China were given the highest
tariff of 0.61 yuan/kWh.
[Figure 1]
Compared with the rapid growth of the wind power sector, the growth of solar powerindustries in China lagged until the cost of the technology declined sharply since 2009.
In response to the introduction of a national, uniform, on-grid, solar FIT policy in 2011,
installation of solar power plants in China reached a record high of 2.5 GW, accounting for
9.12% of the world total that year (Zhang and He, 2013).1 Because the uniform tariff rate
leads to concentration of solar energy projects in mainly the western regions with rich solar
resources in China, the NDRC issued a new FIT scheme in 2013 that applied different tariff
rates based on the cost of electricity generation. Figure 2 illustrates the division of China
into three resource zones under the regionally differentiated FIT policy. The tariff rates
applied for each resource zone range from 0.90 to 1.00 yuan/kWh.
1

According to the uniform solar FIT, projects approved prior to July 1, 2011, that have completed
construction and achieved commercial operation prior to December 31, 2011, are entitled to a tariff of 1.15
yuan/kWh; projects approved after July 1, 2011, or approved prior to that date but not completed before
the end of 2011 are entitled to a tariff of 1 yuan/kWh.

4

[Figure 2]
As production and construction costs of solar power continue to fall, the NDRC announced that it will cut the FIT offered to solar power to reflect the new market conditionsin 2016 (NDRC, 2015). The tariff rates have reduced by as much as 11%, that is, by 0.02
to 0.1 yuan/kWh for on-grid solar farms. In addition, solar energy developers announced in
December 2016 that the solar tariff will be cut further by as much as 19% in 2017. Therefore, some argue that this series of tariff cut announcements led to a rush in solar power
installation ahead of the start of tariff cuts in June 2016 and January 2017 (Daiwa Capital Markets, 2015). Table 1 represents changes in tariff rates for on-grid wind and solar
projects. It shows that the national uniform tariff rate for solar power was applied in 2011.
The tariff has been regionally differentiated since 2013, creating a gap of 0.1 yuan/kWh at
the largest. Subsequently, the tariff gap between the highest and lowest areas increased to
0.18 yuan/kWh in 2016.
[Table 1]
Figure 3 shows the spatial distribution of the counties selected as the study area of thisresearch. The FIT boundary divides the study area into the south and north. Wind power
developers in counties north of the boundary receive the lowest tariff rate in China. In
contrast, those in the southern counties receive the highest tariff rate for wind power in the
country. The difference in the on-grid wind tariff rates between counties south and north of
the FIT boundary is 0.1 yuan/kWh. We choose this part of the country as the study area
because the regions with highest and lowest wind tariff rates share the same border only inthis area. Similarly, the tariff rate provided for electricity generated by on-grid solar panelsin the south is 0.05 yuan/kWh higher than that in the north.2 Under the RDD, border
cities near the FIT boundary provide good comparison because the observable differencesin renewable resources, land use, and population characteristics tend to be small near the2

Due to the announcement about tariff rate cuts, the difference in solar tariff rate between the southernand northern counties increased to 0.08 yuan/kWh in 2016.

5

boundary line. Likewise, since the RD design’s validity requires all relevant factors besides
treatment to vary smoothly at the cutoff, we can focus exclusively on the counties locatedin these border cities.3[Figure 3]

3

Empirical strategy

3.1

Data

Our data consist of a panel of 64 counties located in Inner Mongolia, Shanxi Province,
and Shaanxi Province. These are unbalanced yearly panel data from 2009 to 2012 for wind
power regression and from 2011 to 2016 for solar energy regression.
Installed power capacity and wind power generation are typically used by previous studies to measure wind power development. In addition to these two indicators, we adopt theutilization rate and operation hour to capture the effectiveness of wind power facilities. The
utilization rate is calculated by the percentage of time a turbine can be used during the
8,760 hours of the year (Welch and Venkateswaran, 2009).4 On the other hand, due to data
availability, only installed capacity is used as the indicator of solar power development. Production indicators of renewable power plants are obtained from the Compilation of Power
Industry Statistics collected by the China Electricity Council. This dataset contains information on the production status of electric power plants of over 6,000 kW, which represent
over 85% of total capacity in China.
As a treatment indicator for the regionally differentiated tariffs, we adopted a dummy
variable that equals one if the county in the study area is located in the south of the FIT
boundary and zero otherwise. During the study period, the tariff applied for wind power3

The counties in our sample are located in border cities of the FIT boundary, including Yulin, Xinzhou,
Shuozhou, Datong, Ordos, Huhhot, and Ulanqab. Yulin is a prefecture-level city located in Shaanxi Province.
Xinzhou, Shuozhou, and Datong are cities in Shanxi Province. These cities border Ordos, Huhhot, andUlanqab in Inner Mongolia to the north.
4
Utilization rate = power generation / capacity × 24 × 365.

6

developers in counties south of the FIT boundary is 0.61 yuan/kWh generated electricity,while that for developers in northern counties is 0.51 yuan/kWh. In the case of solar energy,
the tariff provided for on-grid solar energy facilities located in southern counties under theregionally differentiated FIT is 0.95 yuan/kWh, while that for facilities in counties north of
the FIT boundary is 0.90 yuan/kWh.5 Thus, the south dummy captures the higher tariff
rate applied in counties south of the boundary under the FIT regime.
To control for counties’ demographic and geographic characteristics, we use data on population density and agricultural land area of each county from the Statistical Yearbook of
Shanxi Province, Shaanxi Province, and the Inner Mongolia Autonomous Region. Information used to capture the endowment of renewable energy resources, such as annual averagewind speed measured at 70 meters height above the ground level and annual average solar
radiation, are obtained from the China Meteorological Data Service Center. ArcGIS 10.1 isused to calculate the mean area slope and weighted elevation of each county. The elevation
data, namely digital elevation models, are produced by the NASA Shuttle Radar Topography
Mission database.
To capture the impact of conventional energy on the deployment of renewable energy,
we use the installed capacity of thermal power plants provided by the Compilation of Power
Industry Statistics. The database contains thermal power plants whose capacity is larger
than 300 MW. By including information on thermal power plants, we can consider thesubstitution between renewable and traditional energy sources. Although efforts have been
made to diversify the primary sources for power generation, China will continue to rely oncoal for power generation in the near future (Ma, 2011). At present, power grid companies are
obligated to pay a part of the tariff to renewable energy developers, that is, 0.4 yuan/kWh,while the average thermal power price ranges from 0.2 to 0.3 yuan/kWh in China. This
makes the price of renewable power higher than that of coal-fired power. Thus, renewable
5

More precisely, as presented in Table 1, the tariff rate for solar energy projects in southern counties
under the regionally differentiated FIT is cut to 0.88 yuan/kWh, and that for solar projects in northerncounties is 0.80 yuan/kWh in 2016.

7

electricity appears less attractive to power companies (Xia and Song, 2017a). In addition,
subsidies for fossil fuels in China are far larger than those for renewable energy, which may
discourage renewable energy production and investment (Ouyang and Lin, 2014).
The summary statistics for wind power regression are presented in panel A of Table 2,
and those for solar power are presented in panel B. Table 2 shows that there are an averageof 20 MW wind turbines per county in the south of the FIT boundary, and 89 MW in thenorth. Thus, counties in the north seem to have more power capacity. However, as thecomparison does not consider that observations further from the boundary are different inmany respects from those that are closer, we cannot draw any credible causal inferences fromthem (MacDonald et al., 2016).
[Table 2]
The two-tailed t-tests show that there are statistically significant differences in the meanvalues of demographic and geographic characteristics between counties south and north of
the boundary. A visual inspection of the data is more informative. Figures 4 and 5 plot
county characteristics other than renewable energy development at the county level based on
distance to the FIT boundary. Using the ArcGIS 10.1, we calculate the Euclidean distancefrom each county’s government office to the FIT boundary. Counties located south (north)of the boundary are assigned a positive (negative) distance value. We find that there exist
significant discrete changes in county characteristics such as agricultural land area, annual
average wind speed, solar radiation, and elevation at the FIT boundary. Therefore, thesecounty demographic and geographic characteristics are included as covariates in our estimation model. Besides, whereas some counties north of the boundary have relatively highwind speed and solar radiation, this pattern dissipates for counties that are close to the FIT
boundary. Thus, we test our estimate’s robustness by limiting the sample within 80 km fromthe boundary as well. Table 3 represents summary statistics for the sample within 80 km of
the FIT boundary. The t-test results denote that the differences in geographic characteristics

8

such as agricultural land area, elevation, and slope are statistically insignificant between thetreatment and control groups when we restrict the sample to those close to the boundary.6[Figure 4][Figure 5][Table 3]

3.2

Model

Our empirical analysis aims to measure the impact of the regionally differentiated FITs
on the development of renewable energies in China. The spatial RDD approach exploits
the discontinuous changes in tariff rates that drive variations in wind power development
between the south and north of the FIT boundary. The general form of the spatial RDD
model is as follows:

Wit = α + βsouthi + γXit + f (geographic locationi ) + λb + θt + it ,(1)

where Wit refers to the production indicators of wind power generation facilities in county
i and year t. The wind power indicators include utilization rate, installed capacity, power
generation, and operation hour of power plants. southi is a dummy variable for counties southof the resource zone boundary. Our coefficient of interest, β, measures the discontinuouschanges in Wit just south of the policy boundary. The time-varying county characteristics
are captured by Xit , which include the demographic and geographic characteristics suchas population density, agricultural land area, annual average wind speed, annual average
solar radiation, installed capacity of thermal power plants, and mean area weighted slope forcounty i in year t. f (geographic locationi ) denotes the regression discontinuity polynomial,
which controls for smooth functions of the geographic location. Recent studies suggest that
6

Results of two-tailed t-tests are shown in column (4) of Tables 2 and 3.

9the local linear polynomial should be run with kernel weights that assign more weights
on observations near the cutoff (Imbens and Kalyanaraman, 2012; Calonico et al., 2014).
Therefore, our main results are estimated with a local linear regression with triangularkernel weights. We also estimate regressions with quadratic and quartic polynomials forchecking the robustness of the main results. λb represents the boundary segment fixed effects
that denote which of the five equal-length segments of the boundary is the closest to thecounty’s government offices. Finally, the year dummy θt is used to capture external events
that commonly affect the development of the wind and solar industries, such as changes inpolicies and regulations at the national level.
In addition, to investigate the effects of regionally differentiated FITs on the subsequent
dynamics of solar power development, we adopt an approach that combines the multi-period
DID model with the spatial RDD:

Sit = α + β0 southi +3X

βt southi × θt + γXit + f (geographic locationi ) + λb + θt + it , (2)t=−2where Sit denotes the indicator of solar power development. Compared with the wind power
regressions, only the cumulative installed capacity of on-grid solar power generation facilities
in county i in year t has been adopted due to data availability. southi × θt are interaction
terms between the treatment indicator southi and year dummy θt . The excluded timecategory is 2012 (t = −1) such that the effects are measured relative to the year prior to theimplementation of the solar FIT policy in 2013. βt is the coefficient on the tth lead or lagof the policy implementation year. These coefficients of southi × θt capture the effects of adiscontinuous change in the solar tariff rate between the southern and northern counties on
the installation of solar power generation facilities in each year during the research period.

10

4

Results and discussions

4.1

Impact on wind power industries

We estimate the effects of regionally differentiated tariffs on renewable energy development using the spatial RDD model. Table 4 reports the regression results regarding the FIT’s
impact on the production indicators of wind power facilities, including the annual utilization
rate, installed capacity, power generation, and operation hours. Panels A and B in Table 4report the specification that includes a single-dimensional RD approach. Particularly, thelinear polynomial in distance from the county government to the FIT boundary with kernel
weights in panel A allows us to assign more weights on observations near the boundary.
We also report alternative specifications that use multiple dimensional discontinuities in thelongitude-latitude space in panels C and D of the table. It provides useful checks on the
regression results estimated by the model with the single-dimensional RD polynomial. All
regressions include controls for boundary segment fixed effects and year fixed effects. Controls for demographic and geographic conditions as well as conventional energy sources are
adopted in all regressions as well.
[Table 4]
Our estimates imply that regional differentiation of tariffs has positively affected thedevelopment of the wind power industry in China. According to the results in column (1)in panel A of Table 4, a 0.1 yuan difference in the tariff rate will result in approximately an
8.66% increase in annual utilization rates of wind facilities. This implies that the adoption ofregionally differentiated FIT increases the utilization rate by 1.53 times of the total utilization
rate per year.7 In column (2), the coefficient of South is positive and statistically significant.
The result suggests that the regionally differentiated tariff encourages the installation ofwind power plants of nearly 82.93 MW in regions with higher tariff rates. This implies that
7

According to the summary statistics in Table 2, the average utilization rate of wind facilities in thecontrol group is around 5.69%.

11the regional FIT has attracted more plants to resource-poor regions. In addition, according
to the results in columns (3) and (4), implementation of the regionally differentiated tariffs isrelated positively to the annual total power generation and operation hours of wind facilities.
The annual increase in power generation of wind turbines caused by the difference in tariff
rate is approximately 163.4 GWh. Moreover, due to the FIT, the annual operation hours
have increased to 157,900 hours, which is about 1.51 times the annual average.8 These results
indicate that the implementation of regional FITs might help mitigate the overproductionof wind electricity in regions with rich wind resources but lower electricity demand. Panels
B, C, and D in Table 4 examine the robustness of the main results through two alternative
specifications of the RD polynomial. The effects of regionally differentiated FIT on wind
deployment are statistically significant across all specifications.
[Table 5]
Table 5 limits the sample to counties located within 80 km of the FIT boundary. The
specification reported in panel A of Table 5 suggests a statistically significant and positiveeffect of the tariff at around 12.65%, as compared with the mean utilization rate of 2.41%
throughout the north counties located within 80 km of the FIT boundary, which again isstatistically significant in panels B, C, and D. We find that the regression results are broadly
robust to the choice of average distances to the boundary, that is, for counties located within
80 km from the boundary, counties located within 50 km from the boundary (see Table A1
in Appendix), or all counties. Our estimation results are consistent with the findings of Zhao
et al. (2016), showing that the FIT policy had a strong impact on the promotion of wind
power in areas with fewer wind resources, namely the southern counties, than in areas withrich wind resources in China.

8

According to the summary statistics in Table 2, the average operation hours of wind facilities in thecontrol group are around 104,800 hours.

12

4.2

Impact on solar power industries

This section investigates the effect of the regionally differentiated FIT on the developmentof solar energy, with a focus on location choices of the solar industries. The installed power
capacity of solar power is adopted as the dependent variable in the regression model. The
sample period of the solar power-related regression can be divided into two sub-periods:
pre-FIT period from 2011 to 2012 and post-FIT period from 2013 to 2016. The approach of
spatial RDD combined with the multiple time-period DID model allows us to estimate theevolution of the coefficients of South over time.
[Table 6]
Table 6 illustrates the regression results on the impact of the regional differentiationof tariffs on the installed capacity of solar energy facilities. Similar to the wind power
regression, specifications that include a single-dimensional location polynomial are reported
in columns (1) and (2) of Table 6. Particularly, the location polynomial used in column (1)is a linear polynomial in the distance to the FIT boundary with kernel weights. In addition,
specifications that use multiple dimensional location polynomials are reported in columns (3)and (4). As represented by the coefficients of South×2011 in Table 6, solar capacity additions
caused by the implementation of the FIT is insignificant in the pre-treatment period. The
result indicates that the observed FIT effect is not driven by the fact that counties just south
and north of the FIT boundary are affected differently based on geographic and demographic
conditions. On the other hand, we find that the estimated coefficients are negative andsignificant in 2013, the year in which regionally differentiated FIT had been adopted. This
result may be because the announcement about the implementation of the on-grid solar FIT
was made in the last quarter of the year and investments from developers were suspended
until then. After that, the FIT’s impact was insignificant until 2016, which is when thetariff cut of on-grid solar power was announced. This result indicates that the difference intariff rate between resource regions when the on-grid solar FIT was first adopted in 2013 was
13not enough to incentivize developers to locate the power plants in resource-poor regions.9
When the tariff rate cuts for solar power were announced in 2016, the difference in tariff
rates between resource-poor and rich regions became larger. A large gap in tariffs helps toincentivize solar energy developers to invest in regions with relatively poor resources andlocation conditions.10 The coefficient of South×2016 in column (1) of Table 6 suggests thatthe solar installed capacity increased to 99.6 MW due to the 0.08-yuan/kWh difference insolar tariff. This result shows that the annual capacity addition of solar facilities caused by
the FIT in 2016 is approximately 1.88 times the average solar power installed capacity ineach county.11 Our results suggest that the regionally differentiated FIT was not effective
until new tariffs with higher differences in tariff rates among regions were announced.
[Figure 6]
Figure 6 illustrates the same results as Table 6 but in a more intuitive way. The interaction coefficient was positive and statistically significant in 2016. This result suggests
that, only in the year when the new tariff rates of on-grid solar power were announced, theinstalled capacity of solar power plants increased in the southern counties compared withcounties located in the north of the FIT boundary.
[Table 7]
Similar to the wind power regression, Table 7 limits the sample to counties located within
80 km of the FIT boundary. In addition, the regression results estimated by the sample
limited to the counties located within 50 km of the FIT boundary are reported in Table A2
9

In our case, the difference in on-grid solar tariff rates between the southern and northern counties was
0.05 yuan/kWh before the tariff cut had been announced in 2016.
10
Because the tariff cut occurred in early 2016, the difference in solar tariff rates between the treatment
and control groups in our study increased from 0.05 yuan/kWh to 0.08 yuan/kWh. In other words, after thetariff cut, tariff provided for per kWh electricity generated by on-grid solar projects located in the southerncounties is 0.08 yuan higher than that for solar projects located in the north. More details can be found in
Table 1.
11
According to the summary statistics in Table 2, the mean solar capacity in the control group is 24.3
MW.

14

in Appendix. We find that the regression results are robust to the choice of average distances
to the boundary. The coefficients of South×2016 in the first column of Table 7 suggest astatistically significant and positive effect of the solar tariff at around 146.7 MW in 2016.

5

Conclusions
By focusing on the wind and solar power industry in China, this study estimates whether

the implementation of regionally differentiated FIT mitigated the uneven development of renewable energy. The spatial RDD approach allows us to examine the impact of differentiated
FIT across the resource zone boundary through a quasi-experimental design. In addition,
the multiple time-period model helps us to consider how the estimated impact changes overtime.
According to the estimation results, the adoption of regional differentiation of tariffs
effectively enhanced location diversification of renewable projects, at least for a limited
distance around the FIT boundary. In the case of wind power industry, we find that a higher
tariff rate leads to an increase in the utilization rate of wind turbines in counties locatedin resource-poor regions by approximately 8.66%, as compared with the mean wind facility
utilization rate of 5.69% throughout the northern counties. In addition, when considering the
FIT’s effect on the installation of wind power plants, we find that the annual wind capacity
additions caused by FIT adoption are about 82.9 MW. The effect of regionally differentiated
FIT is also found in the case of solar energy. The annual increase in cumulative installedcapacity of solar power plants through adoption of the FIT is estimated at about 99.6 MW in
2016, the year when the significant cut in solar tariff was proposed. Before that, the FIT for
on-grid solar power did not have a significant effect on promoting the location diversificationof the solar power industry.
Our results suggest that the regional differentiation of tariffs has mitigated the unevenregional distribution of both the wind and solar industries in China. This finding is in line15

with those in the existing literature, which indicate that cost-based tariffs can incentivize
renewable energy developers to diversify the locations of wind turbines (Schmidt et al., 2013;
Zhao et al., 2016). In addition, we also find that the regionally differentiated FIT mitigates
overproduction in wind-rich yet remote regions, by improving the utilization rate of windturbines in resource-poor regions. Lastly, our results indicate that the tariffs provided foron-grid solar projects significantly encouraged installations of solar panels in the year thatnew tariff rates with a higher regional gap were enforced. This result indicates that even asmall increase in the tariff rate can provide a strong incentive for the development of solar
power.
The endowment of renewable energy is regionally diverse. Therefore, the renewablecurtailment issue arising from geographical concentration of the renewable project and thelimited transmission grid is a challenge for many countries in the world. For instance, Kyushu
Electric Power Co. in Japan restricted third-party solar power supplies four times during
October 2018. With rich endowment of solar radiation, there has been massive investment
in solar power in Kyushu area, particularly after the FIT policy’s introduction in 2012. The
capacity of solar power in Kyushu area is 8.07GW, which accounts for more than 80% of
the electricity demand when demand is low.12 Moreover, in the case of Germany, wind
power projects are concentrated in the northern area with abundant wind resources, whilemost solar projects are located in the southern areas rich in solar radiation (Obermüller,2017). In these countries, the unified nationwide tariff policy for on-grid renewable electricity
has been implemented, instead of the regionally differentiated one. The findings from thisstudy provide a policy implication for countries throughout the world facing the challenge ofoverproduction of renewable energy, which is caused by the increasing capacity installationand shortage of the transmission grid.

12

The Nikkei newspaper, 13 October 2018.

16

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TariffPolicyfor

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18

Acknowledgements
This work was supported by JSPS KAKENHI Grant Number JP16H03006. We thankhelpful comments from Daisuke Ishinose and session participants at the 2018 Annual Meetingof the Society for Environmental Economics and Policy Studies. All errors are our own.

19

Table 1: Tariff Rates for On-grid Wind and Solar Projects in China (yuan/kWh)

Wind
2009

2013

2016

Zone I

0.51

0.49

0.47

Zone II

0.54

0.52

0.50

Zone III

0.58

0.56

0.54

Zone IV

0.61

0.61

0.60

2011

2013

2016

Zone I

1.15/1.00

0.90

0.80

Zone II

1.15/1.00

0.95

0.88

Zone III

1.15/1.00

1.00

0.98

Solar

Source: The National Development and Reform Commission.

20

Table 2: Descriptive Statistics (Full Sample)
Table 2-1: Summary Statistics (Wind)
Control groups (south=0)

Treatment groups (south=1)

Unit

Obs

Mean

Std.dev.

Obs

Mean

Std.dev.

%

112

5.690

8.946

172

3.574

7.912

Wind capacity

MW

112

89.25

185.4

172

20.17

50.44

Power generation

GWh

112

149.5

345.1

172

33.76

98.02

Operation hour

1,000 hour

112

104.8

287.1

172

47.97

155.6

Population density

1,000 person/km2

1120.192 **

0.315

171

0.668

2.527

Secondary industry output

billion yuan

112

9.760***

11.71

172

5.255

9.506

Agricultural land area

105 ha112

0.371*

0.241

171

0.336

0.187

Wind speed

m/s

112

5.862***

1.027

172

5.456

0.773

Weighted average elevation

100 m

112

13.53***

1.719

172

12.75

2.047

Slope

degree112

4.098***

2.728

168

8.438

3.456

Thermal capacity

GW

112

0.763***

1.379

172

0.398

0.934

Wind facility production indicators
Utilization rate

County characteristics

Table 2-2: Summary Statistics (Solar)
Control groups (south=0)

Treatment groups (south=1)

Unit

Obs

Mean

Std.dev.

Obs

Mean

Std.dev.

MW

168

24.30

55.41

258

27.16

90.51

Secondary industry output

billion yuan

168

12.58***

14.80

258

7.010

12.18

Weighted average elevation

100 m

168

13.53***

1.716

258

12.75

2.045

Solar facility production indicators
Solar capacity
County characteristics

Slopedegree168

4.098***

2.724

258

8.242

3.642

Solar radiation

100 kWh/m2

168

16.26***

0.258

258

15.30

0.448

Note: Mean value of variables differ with statistical significance in a two-tailed t-test between the treatment and control
groups, and they are denoted by * p < 0.1, ** p < 0.05, and *** p < 0.01.

21

Table 3: Descriptive Statistics(Sample Falling within ≤ 80 km of the Boundary)
Table 3-1: Summary Statistics (Wind)
Control groups (south=0)

Treatment groups (south=1)

Unit

Obs

Mean

Std.dev.

Obs

Mean

Std.dev.

Utilization rate

%

64

2.410

5.956

108

4.593

8.860

Wind capacity

MW

64

23.46

63.42

108

26.62

59.16

Power generation

GWh

64

28.10

83.14

108

46.66

118.4

Operation hour

1,000 hour

64

23.30

81.49

108

63.28

185.8

Population density

1,000 person/km2640.207*

0.306

107

0.979

3.159

Secondary industry output

billion yuan

6412.15**

13.40

108

7.533

11.37

Agricultural land area

105 ha

64

0.301

0.184

107

0.345

0.188

Wind speed

m/s

64

5.646

0.748

108

5.665

0.757

Elevation

100 m

64

13.41

1.489

108

13.08

1.846

Slope

degree643.890***

2.571

104

6.701

2.901

Thermal capacity

GW

64

0.928*

1.503

108

0.621

1.115

Wind facility production indicators

County characteristics

Table 3-2: Summary Statistics (Solar)
Control groups (south=0)

Treatment groups (south=1)

Unit

Obs

Mean

Std.dev.

Obs

Mean

Std.dev.

MW

96

14.17

30.77

162

41.72

110.93

Secondary industry output

billion yuan

96

15.72**

16.90

162

10.12

14.46

Elevation

100 m

96

1341

148.5

162

1308

184.4

Slope

degree963.890***

2.564

162

6.453

3.112

Radiation

100 kWh/m2

9616.12***

0.179

162

15.52

0.296

Solar facility production indicators
Solar capacity
County characteristics

Note: Mean value of variables differ with statistical significance in a two-tailed t test between the treatment and control
groups, and they are denoted by * p < 0.1, ** p < 0.05, and *** p < 0.01.

22

Table 4. Effect of FIT on Wind Power Development (Full Sample)
Utilization rate
Wind capacity(1)(2)
Panel A: Linear Polynomial in Distance with Kernel Weights
82.93∗∗∗
South
8.656∗∗∗(1.825)(17.58)Adj.R20.407
0.492
Panel B: Quadratic Polynomial in DistanceSouth
8.240∗∗∗
69.41∗∗∗(1.748)(20.13)
Adj.R20.455
0.522
Panel C: Linear Polynomial in Longitude and Latitude
south
6.633∗∗∗
10.84(1.730)(21.15)
Adj.R20.482
0.511
Panel D: Quadratic Polynomial in Longitude and LatitudeSouth
8.437∗∗∗
53.79∗∗(1.800)(22.02)
Adj.R20.499
0.585
Geographic location polynomial
yes
yes
Controlyes
yes
Segment fixed effects
yes
yesYear fixed effects
yes
yes
Observations
279
279

Power generation(3)

Operation hour(4)163.4∗∗∗(31.63)0.466

157.9∗∗∗(36.58)0.349

135.9∗∗∗(35.87)0.493

152.0∗∗∗(37.64)
0.371

32.02
(39.14)0.473

95.46∗∗(39.28)0.362

110.7∗∗∗(39.46)
0.535
yes
yes
yes
yes
279

149.6∗∗∗(40.66)
0.384
yes
yes
yes
yes
279

Note: Robust standard errors, adjusted for clustering by county, are in parentheses. If z denotes the geometricdistance to the tariff zone boundary, the linear polynomial in distance is z + z × south and the quadratic
polynomial in distance is z + z 2 . If x denotes the longitude and y denotes the latitude of each county, thelinear polynomial in longitude and latitude is x + y and the quadratic polynomial in longitude and latitude
is x + y + x2 + y 2 + xy. Coefficients that are significantly different from zero are denoted by * p < 0.1, **p < 0.05, and *** p < 0.01.

23

Table 5. Effect of FIT on Wind Power Development(Sample Falling within ≤ 80 km of the Boundary)
Utilization rate
Wind capacity(1)(2)
Panel A: Linear Polynomial in Distance with Kernel Weights
South
12.65∗∗∗
82.05∗∗∗(2.440)(19.60)Adj.R20.395
0.337
Panel B: Quadratic Polynomial in DistanceSouth
12.73∗∗∗
80.64∗∗∗(2.463)(19.62)
Adj.R20.400
0.349
Panel C: Linear Polynomial in Longitude and Latitude
south
12.45∗∗∗
74.70∗∗∗(2.543)(21.93)
Adj.R20.378
0.349
Panel D: Quadratic Polynomial in Longitude and Latitude
South
10.10∗∗∗
64.05∗∗∗(2.567)(21.80)Adj.R20.419
0.363
Geographic location polynomial
yes
yes
Controlyes
yes
Segment fixed effects
yes
yesYear fixed effects
yes
yes
Observations
167
167

Power generation(3)

Operation hour(4)153.9∗∗∗(34.03)0.318

174.1∗∗∗(52.65)0.282

153.1∗∗∗(33.82)0.327

176.7∗∗∗(53.00)0.283

149.1∗∗∗(43.51)0.320

175.7∗∗∗(63.16)0.277

132.6∗∗∗(41.42)
0.332
yes
yes
yes
yes
167

157.7∗∗(61.58)
0.277
yes
yes
yes
yes
167

Note: Robust standard errors, adjusted for clustering by county, are in parentheses. If z denotes geometricdistance to the tariff zone boundary, the linear polynomial in distance is z + z × south and the quadratic
polynomial in distance is z + z 2 . If x denotes the longitude and y denotes the latitude of each county, thelinear polynomial in longitude and latitude is x + y and the quadratic polynomial in longitude and latitude
is x + y + x2 + y 2 + xy. Coefficients that are significantly different from zero are denoted by * p < 0.1, **p < 0.05, and *** p < 0.01.

24

Table 6. Effect of FIT on Solar Power Development (Full Sample)

South
South×2011
South×2013
South×2014
South×2015
South×2016
Cons.
Geographic location polynomial
Control
Segment fixed effects
Year fixed effects
Observations
Adj.R2

Explanatory variable: solar capacity (MW)Single-dimensional RDD
Multi-dimensional RDD
LinearQuadratic
LinearQuadratic
(1)
(2)
(3)
(4)
45.67∗∗∗
35.69∗∗∗
59.24∗∗∗
67.82∗∗∗(14.75)(12.78)(16.10)(19.79)
0.278
1.605
1.688
1.580
(1.253)(2.276)(2.269)(2.290)-7.284∗-9.950∗∗-10.12∗∗-9.897∗∗(4.071)(4.282)(4.289)(4.313)-11.01-18.38-18.67-18.29(13.30)(14.11)(14.12)(14.19)1.865-8.648-9.151-8.494(15.47)(16.48)(16.51)(16.56)99.62∗∗∗
68.75∗∗
68.16∗∗
68.92∗∗(37.14)(33.43)(33.33)(33.50)-16.90-401.91778-16451(349.1)(274.9)(1250)(25012)
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
426
426
426
426
0.234
0.185
0.200
0.207

Note: Robust standard errors, adjusted for clustering by county, are in parentheses. If z denotes geometricdistance to the tariff zone boundary, the linear polynomial in distance is z + z × south and the quadratic
polynomial in distance is z + z 2 . If x denotes the longitude and y denotes the latitude of each county, thelinear polynomial in longitude and latitude is x + y and the quadratic polynomial in longitude and latitude
is x + y + x2 + y 2 + xy. Coefficients that are significantly different from zero are denoted by * p < 0.1, **p < 0.05, and *** p < 0.01.

25

Table 7. Effect of FIT on Solar Power Development(Sample Falling within ≤ 80 km of the Boundary)

South
South×2011
South×2013
South×2014
South×2015
South×2016
Cons.
Geographic location polynomial
Control
Segment fixed effects
Year fixed effects
Observations
Adj.R2

Explanatory variable: solar capacity (MW)Single-dimensional RDD
Multi-dimensional RDD
LinearQuadratic
LinearQuadratic
(1)
(2)
(3)
(4)
31.89
32.46
30.30
19.15
(22.46)(20.33)(22.90)(24.51)-0.693-0.460-0.434-0.401(1.662)(1.870)(1.699)(1.520)-2.093-1.447-1.639-1.882(4.759)(4.440)(4.347)(4.274)
2.086
1.796
1.524
1.180
(14.93)(15.86)(15.85)(15.66)
20.65
20.01
19.47
18.78
(17.12)(17.78)(17.79)(17.38)146.7∗∗∗
143.3∗∗∗
142.7∗∗∗
141.9∗∗∗(45.84)(46.59)(46.55)(46.25)-468.6-651.5-7.192-19659(562.5)(498.6)(1557)(50567)
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
258
258
258
258
0.292
0.282
0.278
0.281

Note: Robust standard errors, adjusted for clustering by county, are in parentheses. If z denotes geometricdistance to the tariff zone boundary, the linear polynomial in distance is z + z × south and the quadratic
polynomial in distance is z + z 2 . If x denotes the longitude and y denotes the latitude of each county, thelinear polynomial in longitude and latitude is x + y and the quadratic polynomial in longitude and latitude
is x + y + x2 + y 2 + xy. Coefficients that are significantly different from zero are denoted by * p < 0.1, **p < 0.05, and *** p < 0.01.

26

Wind tariff zone
(yuan/kWh)

Zone IV 0.61
Zone III 0.58
Zone II 0.54
Zone I 0.51

Figure 1: Distribution of wind resource zones and regionally differentiated on-grid wind tariffs in China.

27

Solar tariff zone
(yuan/kWh)

Zone III 1.00
Zone II 0.95
Zone I 0.90
Tibet

Figure 2: Distribution of solar resource zones and regionally differentiated on-grid solar tariffs in China.

28

Study Area

Tariff Zone Boundary
Treatment groups (higher tariff)
Control groups (lower tariff)
0

112.5

225

450 Kilometers

Figure 3: Distribution of the feed-in tariff (FIT) boundary and counties in the study area.
Counties located in the south of the FIT boundary contributed to the treatment group
and are colored in dark grey (south=1).

29

20
15
10
0

5

Secondary industry output

25

500 1000 1500 2000 2500
0

Population density

-500

5

6

7

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4Degree South to the Boundary

5

6

7

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4Degree South to the Boundary

5

6

7

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4Degree South to the Boundary

5

6

7

4.5

5

Wind speed
5.5
6

6.5

7

Agricultural land area
.15 .2 .25 .3 .35 .4 .45 .5 .55-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4Degree South to the Boundary

Figure 4(a): Local polynomial smoothing of characteristics by county relative tothe distance from the feed-in tariff boundary (wind).

30

12
10
8
Slope
6
4
2

Elevation

11 11.5 12 12.5 13 13.5 14 14.5

5

6

7

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4Degree South to the Boundary

5

6

7

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4Degree South to the Boundary

1000
500
0

Thermal power

1500

2000

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4Degree South to the Boundary

Figure 4(b): Local polynomial smoothing of characteristics by county relative tothe distance from the feed-in tariff boundary (wind) - Continued

31

5

6

7

Elevation

6

7-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4Degree South to the Boundary

5

6

7

11 11.5 12 12.5 13 13.5 14 14.5

35
30
25
20
15
10
5

5

6

7

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4Degree South to the Boundary

5

6

7

2

1450

4

1500

Slope
6
8

Radiation

1600

10

1650

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4Degree South to the Boundary

1550

Secondary industry output

0

5

12

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4Degree South to the Boundary

Figure 5: Local polynomial smoothing of characteristics by county relative tothe distance from the feed-in tariff boundary (solar).

32

150
100
50
0
-50

Effects on installed capacity of solar power facilities (MW)

2011

2012

2013

2014

2015

2016

Note: Dashed line indicates 90% confidence intervals.

Figure 6: Annual effect of on-grid solar feed-in tariffs from the regression discontinuity design and multidifference-in-differences model.

33

Appendix
Table A1: Effect of FIT on Wind Power Development(Sample Falling within ≤ 50 km of the Boundary)
Utilization rate
Wind capacity(1)(2)
Panel A: Linear Polynomial in Distance with Kernel Weights
south
10.29∗∗∗
50.15∗∗(3.623)(21.93)
Adj.R20.379
0.299
Panel B. Quadratic Polynomial in DistanceSouth9.020∗∗
41.68∗(3.193)(23.85)
Adj.R20.383
0.293
Panel C. Linear Polynomial in Longitude and LatitudeSouth
9.306∗∗
35.33(3.763)(23.19)Adj.R20.387
0.296
Panel D. Quadratic Polynomial in Longitude and Latitude
South
9.863∗∗∗
39.08∗∗(3.463)(19.61)
Adj.R20.510
0.379
Geographic location polynomial
yes
yes
Controls
yes
yes
Segment fixed effects
yes
yesYear fixed effects
yes
yes
Observations
112
112

Power generation(3)

Operation hour
(4)84.10∗∗(32.77)0.271

82.41∗(47.85)0.256

68.92∗∗(32.85)
0.268

68.23
(48.00)0.252

76.55∗(41.57)
0.264

95.24
(58.80)0.256

66.79∗(35.20)
0.325
yes
yes
yes
yes
112

80.81
(54.71)
0.253
yes
yes
yes
yes
112

Note: Robust standard errors, adjusted for clustering by county, are in parentheses. If z denotes geometricdistance from the county’s government office to the tariff zone boundary, x denotes the longitude, and y denotes
the latitude of the county, the linear polynomial in distance is z + z × south, the quadratic polynomial in distanceis z + z 2 , the linear polynomial in longitude and latitude is x + y, and the quadratic polynomial in longitude andlatitude is x + y + x2 + y 2 + xy. Coefficients that are significantly different from zero are denoted by * p < 0.1,** p < 0.05, and *** p < 0.01.

34

Table A2: Effect of FIT on Solar Power Development(Sample Falling within ≤ 50 km of the Boundary)southSouth×2011
South×2013
South×2014
South×2015
South×2016
Cons.
Geographic location polynomial
Control
Segment fixed effects
Year fixed effects
Observations
Adj.R2

Explanatory variable: solar capacity (MW)Single-dimensional RDD
Multi-dimensional RDD
LinearQuadratic
LinearQuadratic
(1)
(2)
(3)
(4)
38.19∗
17.50
23.32
20.12
(21.42)(18.86)(27.22)(23.98)-1.797-1.668-1.745-1.685(1.738)(1.669)(1.781)(1.804)-3.378-3.127-3.077-3.116(6.133)(5.858)(5.882)(5.940)
2.280
1.688
1.782
1.709
(14.28)(14.03)(14.10)(14.22)
21.87
20.67
20.88
20.72
(17.38)(17.00)(17.10)(17.27)146.4∗∗∗
145.1∗∗
145.4∗∗
145.2∗∗(52.65)(53.55)(53.85)(54.28)-782.1-897.2-1709-22978(556.8)(577.3)(1901)(44733)
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
174
174
174
174
0.339
0.332
0.328
0.320

Note: Robust standard errors, adjusted for clustering by county, are in parentheses. If z denotes geometricdistance from the county’s government office to the tariff zone boundary, x denotes the longitude, and ydenotes the latitude of the county, the linear polynomial in distance is z + z × south, the quadratic polynomial
in distance is z + z 2 , the linear polynomial in longitude and latitude is x + y, and the quadratic polynomial
in longitude and latitude is x + y + x2 + y 2 + xy. Coefficients that are significantly different from zero aredenoted by * p < 0.1, ** p < 0.05, and *** p < 0.0135