Can climate mitigation help the poor?
Measuring impacts of the CDM in rural China

Yimeng Du
Kenji Takeuchi

February 2018
Discussion Paper No.1808

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANCan climate mitigation help the poor?
Measuring impacts of the CDM in rural China
Yimeng Du and Kenji Takeuchi∗
Graduate School of Economics, Kobe University
Kobe 657-8501 Japan

February 21, 2018

Abstract
This study aims to examine whether investment in climate change mitigation plays
a role in poverty alleviation. We investigate impacts of the renewable energy-based
clean development mechanism (RE-CDM) on rural communities in China. The impacts of RE-CDM projects are estimated by combining propensity score matching withthe diﬀerence-in-diﬀerences approach. We found that the promotion of biomass-based
CDM projects significantly contribute to income improvement, employment generation,and industrial transformation in rural communities in China. On the other hand, ourestimation results reveal that large-scale wind and solar energy-based CDM projects
have the potential to increase the labor force in the primary industry in rural areas.

Keywords: CDM; renewable energy; poverty alleviation; rural development; propensity
score matching; diﬀerence-in-diﬀerences∗

E-mail: sissitoh@gmail.com (Du), takeuchi@econ.kobe-u.ac.jp (Takeuchi). Earlier versions of thispaper were presented at the Seventh Congress of the East Asian Association of Environmental and Resource Economics, 2017 annual meeting of the Society for Environmental Economics and Policy Studies, andthe Environmental Economics Seminar at Kyoto University. Authors acknowledge helpful comments from
Yushi Kunugi, Daisuke Ichinose, Makoto Hasegawa, Emiko Inoue and seminar participants. This work was
supported by JSPS KAKENHI Grant Numbers JP16H03006, JP26241033.

1

IntroductionMore than 5.7% of Chinese population live below the poverty line as of 2015, mainly

in remote rural areas with limited energy access and job opportunities (Asian Development
Bank, 2017).1 As one of the policy packages to alleviate poverty in rural area, the Chinese
Government has adopted programs that promote renewable energy in remote areas, including
the Solar Energy for Poverty Alleviation Programme (SEPAP)2 and the 13th Five-year Plan
(FYP) for Rural Bioenergy Development.3 Implementation of the SEPAP, which commenced
in 2014, reveals the government’s aim to alleviate rural poverty through deploying distributed
solar photovoltaic (PV) systems in poor areas. On the other hand, one of ultimate targets of
the 13th FYP for Rural Bioenergy Development is to increase the income of rural residents
and improve living conditions of rural households by promoting utilization of agricultural
wastes. In addition, the Announcement on Accelerating Construction of Energy Projects in
Impoverished Areas for Promoting Poverty Alleviation, published by the National Energy
Administration (NEA) in 2017, emphasizes the contribution of renewable energy to poverty
reduction as well.4
In reality, can renewable energy play a key role in reducing the rural poverty? To explore
the answer to this question, we investigate the past Chinese experiences with clean development mechanism (CDM) projects and examine their impacts on poverty reduction. The
CDM, as a part of the flexible mechanisms defined in the Kyoto Protocol, has opened a hostof possibilities to absorb foreign investment and enhance sustainable development (SD) in
1

The oﬃcial national rural poverty line of China is 2,300 yuan per year at constant 2011 purchasing
power parity.
2
The National Energy Administration and Poverty Alleviation Oﬃce of the State Council decided to implement a poverty alleviation program through installation of solar PV panels in poor households to increase
their incomes. The SEPAP is scheduled to run for a period of six years, starting from 2014. Informationon the SEPAP is available at <http://www.nea.gov.cn/2014-10/17/c 133723326.htm>, last viewed 17 July
2017.
3
Released by the National Development and Reform Commission (NDRC) on 25 January 2017. Moreinformation is available at <http://www.gov.cn/xinwen/2017-02/16/content 5168559.htm#1>, last viewed
16 January 2018.
4
Informations on the announcement is available at <http://zfxxgk.nea.gov.cn/auto82/201711/t2017 1108
3046.htm>, last viewed 16 January 2018.

1

developing countries. According to the definition by the United Nations Framework Convention on Climate Change (UNFCCC), the SD co-benefits of CDM projects can be divided into
three categories: social benefits, economic benefits, and environmental benefits. Examples ofthese benefits include poverty alleviation, employment generation and enhanced education
services (social benefits); new industrial activities, productivity growth, and technology innovation (economic benefits); and improvement of air, water and land quality (environmental
benefits).5
There are many studies that examine how far the CDM will achieve its SD goals. Studies
with positive findings suggest that the CDM could contributes to SD in host countries in
diﬀerent ways. Olsen and Fenhann (2008) conclude, through a text analysis of 744 project
design documents, that small-scale renewable energy projects have comparatively higher
social benefits than large-scale projects. Wang et al. (2013) evaluate the employment impacts
through an input-output approach. Their results show that solar projects have the greatest
potential for indirect job creation, while hydro projects induce job losses. Weitzel et al.
(2015) indicate that larger CDM projects and more advanced technologies are more likely
to involve technology transfer.
However, several researchers provide contrasting results. Sirohi (2007) indicates that thesocio-economic development potential of CDM projects in India is ambiguous, and suggeststhat for CDM to emerge as a “win-win” poverty alleviation strategy, its projects shouldbe implemented at the rural community level. By assessing 16 oﬃcially registered CDM
projects, Sutter and Parreno (2007) conclude that fewer than 1% of the CDM projects
are likely to contribute significantly to SD in the host country. Zhang and Wang (2011)use an econometric approach to estimate the CDM eﬀect on reducing local air pollution
and conclude that the CDM does not have a statistically significant eﬀect in lowering SO2
emissions.
5

The SD tool provided by the UNFCCC enables the project owners to show the value behind the certified
emission reductions their CDM projects oﬀer, by describing the SD co-benefits of projects. Available at:
http://cdmcobenefits.unfccc.int/Pages/SD-Tool.aspx.

2

Previous studies show inconclusive results on whether or not CDM activities actuallycontribute to the SD in host countries. Thus, the primary concern of our study is to evaluate
the SD benefits of the CDM on rural communities of the host country. As for the SD
benefits, our main focus is on social benefits, which include income generation, creationof job opportunities, and changes in the industrial structure. Poverty alleviation throughincome and employment generation is considered as one of the most important indicators
in CDM project evaluation. Moreover, the eradication of poverty is also regarded as an
indispensable requirement for SD (United Nations, 2012).
The contributions of this study can be summarized as follows. Most of above-mentioned
studies on the local impacts of the CDM adopt descriptive or the input-output analysis,
and are not based on rigorous econometric approach. In order to fill this research gap, we
use a fixed eﬀect diﬀerence-in-diﬀerences (DID) model to investigate the social benefits of
the RE-CDM projects at the rural community level. In addition, this research applies thepropensity score matching (PSM) in conjunction with the DID model to adequately deal
with several issues of the simple DID approach, such as selection bias and omitted variablebias. Besides, we check the robustness of our estimation results, obtained through the PSMDID approach by adopting the Mahalanobis distance matching (MDM) method. Finally,
our findings provide policy implications on the possibility of simultaneously achieving thegoal of climate change mitigation and poverty alleviation. It is of critical importance thatcountries achieve their targets of poverty reduction under the Sustainable Development Goals(SDGs)6 , while meeting their commitments of greenhouse gas emission reductions under theParis Agreement.7 With this respect, our study relates to the literature on poverty and theenvironment (Sims, 2010; Sims and Alix-Garcia, 2017), but diﬀers from these studies in that
we examine the eﬀect of projects that require substantial investment and technology.
6

On 1 January 2016, the United Nations SDGs oﬃcially came into force. The first of the seventeen
proposed SDGs is “End poverty in all its forms everywhere.” More information on the SDGs is available at:<http://www.un.org/sustainabledevelopment/>, last viewed 17 January 2018.
7
The Paris Agreement on climate change came into force in 2016 to limit the rise in global temperatures.
More information on the agreement is available at: <http://unfccc.int/paris agreement/items/9485.php>,
last viewed 17 January 2018.

3

The main result of this study is that the RE-CDM contributes significantly to rural development in China. Our findings suggest that biomass-based CDM projects can bring about
income growth, job creation, and industrial transition in rural communities in China. For
example, annual income of rural residents can be increased approximately 15.5% by adopting the biomass CDM projects. In addition, we find that large-scale wind and solar energy
projects can help to increase the labor force in the primary industry in rural communities.
These findings imply that investment in climate change mitigation can play a simultaneousrole in poverty alleviation.
This paper is organized as follows: Section 2 provides the current status of income inequality and promotion of the renewable energy in China. In Section 3 we introduce the data
for estimation and the measures of social benefits. Section 4 follows with an analysis framework, including a description of the empirical model and matching techniques. Estimation
results and discussions are provided in Section 5. Finally, Section 6 presents our conclusions
and discuss the policy implication of this research.

2

Background2.1

Income inequality in China

Beginning in 1978, China’s economic reform has led not only to rapid economic growth
but also to serious income inequality. Figure 1 shows the income trends of rural and urban
residents in China from 1985 to 2015.8 The rural population of China comprised 618 million
in 2014, accounting for about 45.2% of the total populations in 2014 (NBSC, 2014). At
the end of 2015, the net income of urban residents was nearly 3.5 times as much as thatof rural residents. The Gini coeﬃcient, a measure of income inequality, has soared to 0.47
8

Individuals are categorized as either “rural” or “urban” residents by the hukou system, a household
registration system that serves as a domestic passport regime in China. Residents are required to stay
and work within their designated geographic areas. Individuals living in rural areas depend on agriculture
to make a living and are commonly known as rural residents. On the contrary, urban residents usuallydependent on non-agricultural sources of income.

4

from 0.25 in the middle 1980s (China Digital Times, 2013). Xie and Zhou (2014) argue
that China’s current high income inequality is significantly driven by the rural-urban divide
and the regional variation in economic well-being. Diﬀerences in economic structure play a
critical role in creating the overall income inequality between rural and urban residents.
[Figure 1]
Simultaneously, the income structure of the rural population has transformed over the
past two decades. As of 2015, the wage income9 has increased to around 43% of total
income of rural residents, while the proportion of rural residential income from the primarysector has decreased to about 29%.10 The change reflects the fact that the source of incomeof rural residents has been shifted from the primary sector to the secondary and tertiary
sectors. Rural areas tend to have a relatively smaller range of job opportunities, lower
payment, and thus higher unemployment. This has caused a large number of rural laborers
to move out from their registered places of residence and migrate to urban cities in searchof job opportunities. The total stock of rural migrant labor, estimated to be around 282
million as of 2016, constitutes more than one-third of the entire working population of China(Walsh, 2017). The large rural-to-urban migration not only increases the burden on urban
cities but also creates many social problems in rural areas, such as the mental health andeducation of the left-behind children, aging of the rural population, and decline in agricultural
productivity (China Labour Bulletin, 2016). In order to alleviate these issues of rural China,
policy makers focus on the way to improve the employment environment by providing highquality and sustainable job opportunities to the rural community.

2.2

Rural poverty and renewable energy

Recently, the Chinese government has promoted investment in renewable energy in rural
areas. With the formulation of several national promotion policies for renewable energy,
910

The income earned by an individual working as an employee.
Authors’ own calculations. Data collected from the China Statistical Yearbook in 1996 and 2016.

5

such as the SEPAP and the 13th FYP for Rural Bioenergy Development, new energy industries are ready to exploit the wide development space in rural areas. Development of
the renewable energy industry is expected to attract both domestic and foreign investment,
as well as the working-age population, into rural areas. In addition, access to cleaner andaﬀordable energy options can improve the livelihood of rural households by raising theirliving conditions and transforming the production structure of local firms. Moreover, renewable energy industries can focus on retraining the low-skill and low-income workers. For
instance, by the end of December 2014, a total of 16,542 rural residents in Qingxiu County11
had received vocational training related to renewable energy, and 15,308 of them obtained
national vocational qualifications through an examination system.12
By 2020, China’s renewable energy industries are expected to provide employment opportunities for nearly a million people, including research and development, design, production,
construction, operation, service, transportation, management, education, training, consulting, and other related jobs (Worldwatch Institute, 2011).
At the same time, with the aggravation of the energy crisis and increasing importanceof environmental problems, climate policies have been high on the agenda of Chinese government for about a decade. The necessity and urgency of promoting the renewable energy
sector in China have been providing entry points for the RE-CDM. Moreover, because adoption of RE-CDM projects could bring additional foreign investment to the host community,and ultimately drive the development of local renewable energy industries, local governments
encourage local firms to develop renewable energy resources with CDM. Consequently, China
has become the world’s largest host country for CDM projects. Between 2005 and 2012, a
total of 2,983 CDM projects were formally registered in China. Among the registered CDM
projects, renewable energy projects make up the largest share, at about 82.7%. Of these,
40.6% is wind power project while other projects, including bioenergy and solar energy, make11

A county belonging to Nanning city, Guangxi Zhuang Autonomous Region, China.
The Oﬃce of Rural Energy, Guangxi Province. <http://www.gxncny.cn/gxnycms/pxjn/3175.jhtml>,
last viewed 15 February 2018.
126

up about 5.2% and 1.6%, respectively.13
Rural counties14 attract a large part of investment related to RE-CDM deployment because they tend to be sparsely populated, amply endowed with renewable sources of energy,
and spacious enough for land-intensive developments like wind farms. As of 2012, a totalof 461 rural counties had adopted RE-CDM activities in China, which installed capacity
accounts for about 86.8% of total installed capacity of the RE-CDM. Figure 2 depicts thelocational distribution of RE-CDM projects by the cumulative installed capacity at the prefecture level. The RE-CDM projects are not evenly distributed among regions, but mainly
concentrated in regions endowed with large renewable energy resources, the northern, northeastern, and northwestern regions.
[Figure 2]

3

Data

3.1

Measures of the social benefits

There are three dimensions that compose SD in the local community. The first is thesocial dimension, which includes welfare indicators such as household income, employment,
and spending on health and education. The second is the economic dimension, which isoften related to consumption and investment in productive capital. The last one is the environmental dimension, including environmental quality, pollution emissions, and material
consumption (IRENA, 2016). Many previous studies focus on investigating both the economic and environmental benefits of the CDM while its social benefits have received lessattention. In order to estimate the social impacts of increased renewable energy deployment
13

Authors’ own calculations. Data collected from UNFCCC’s Database for Project Activities and Programme of Activities.
14
County-level administrative areas in China consist of the county and county-level city and municipal
districts, where the county is usually considered as the backward region in each prefecture. Considering thatthe objective of this study is to evaluate the impact of the CDM on rural development, we only adopt those
CDM projects located in the county, also known as rural area in our analysis.

7under the CDM, this study employs three indicators: income generation, job creation, andthe transformation of industrial structure.
First, we adopt the per capita net income of rural households to measure the impactof the RE-CDM activities on rural income. Rural communities can diversify, stabilize, orincrease the income of their residents in several ways by adopting RE-CDM activities. For
instance, the income level of rural residents can be increased through subsides from local
governments. In 2009, the Chinese central government’s subsidy standard for rural householdbiogas was improved to 1,500 yuan for each rural household in the northeastern and westernareas, 1,200 yuan in the central region, and 1,000 yuan in the eastern region (Qiu et al.,2013). Besides, RE-CDM projects can reduce the poverty that characterizes rural regions byhelping unskilled laborers in rural areas, such as farmers, unemployed persons, and womenwith low education level in rural area, to serve as assembly line workers, equipment installers,and maintenance or sales staﬀ.
Second, the number of rural laborers is used to capture the working population in a ruralcounty. The working population of rural communities increases with more job opportunities for rural residents. Development and promotion of the renewable energy industry is an
important way to increase employment of local residents. Worldwide, the renewable energy
sector provided about 6.5 million direct and indirect jobs in 2013. Fuel supply from bioenergy feedstock, installations, and equipment manufacturing will generate most jobs in the
renewable energy value chain (IRENA, 2014). Some argue that the decentralized nature of
renewables deployment will raise the overall number of jobs. However, others hold that therelatively higher monetary costs of deploying renewables will reduce purchasing power andconsequently employment. These arguments underscore the need for more country-specificempirical analysis and reliable approaches to estimate the potential social benefits, especiallyemployment creation from renewable energy deployment.
Lastly, we employ the number of rural laborers in the primary sector to capture the impact
of RE-CDM on industrial transformation. Renewable energy related industries can create8valuable job opportunities for people in regions with low employment. It provides both direct
jobs, such as operating and maintaining equipment, and indirect jobs along the supply chain,such as fuel supply, manufacturing, construction, and other related specialized services. For
example, if the presence of renewable energy installations can revive construction activities
related to renewable energy power plants, then the main income sources of farm households
could switch from agricultural activities to the construction industry. According to theConstruction Plan of the National Rural Biogas Project (2006-2010) released by the Ministryof Agriculture of China in 2007, construction and maintenance of every 10,000 biogas pools
can absorb about 800 rural laborers; thus, the whole country can provide about 368,000 jobs
per year for the rural labor (MOA, 2007).

3.2

Data sources

To assess the eﬀect of the RE-CDM on rural development, we collect information on
the construction period and location of RE-CDM projects, rural residential income, numberof laborers in rural area, and other characteristics of each county.15 The panel data used
for analysis cover a total of 1,939 rural counties across China and consist of three typesof variables, namely, social benefits, county characteristics, and characteristics of RE-CDM
projects. The sample period for this study is between 2005 and 2011.
Table 1 contains descriptive statistics on the variables used in our analysis. Counties
that adopted RE-CDM projects between 2005 and 2011 are included in the treatment groupin this study. On the other hand, counties with no RE-CDM activities during the research
period are included in the control group. The average rural household income is about 5,027
yuan in the treatment group, and approximately 5,486 yuan in the control group. The
mean number of rural laborers in a county is around 0.231 million in the treatment group
and about 0.217 million in the control group. The average number of rural laborers in the
primary sector is about 0.122 million in the control group; the corresponding number in the15

A county is an administrative unit ranking below a prefecture and above a township.

9

treatment group is 0.011 million lower. A two-tailed t-test shows that there is a statisticallysignificant diﬀerences in the mean value of social benefits and county characteristics. It
suggests the need to adopt matching techniques in order to avoid selection bias.
[Table 1]
Data related to social benefits are collected from the China Statistical Yearbook for
Regional Economy. Per capita net income of rural households, number of rural laborers, andnumber of rural laborers in the primary sector are used as indicators of social benefits.
The county characteristic variables, including share of gross output of the primary sector,
area of agricultural land, rural population, total government revenue, share of students
accepting compulsory education, production of oil crop, and total capacity of agricultural
machinery, are based on the China Rural Statistical Yearbook. Both geographical and social
characteristics are considered since these factors may aﬀect the existing energy infrastructure
and influence the promotion of renewable energy industries.
CDM data are obtained from the UNFCCC’s Database for Project Activities and Programme of Activities, which includes basic information on every registered project. Only
RE-CDM projects registered between 2005 and 2011 are considered in our study because of
the limitations of the county-level economic data. Hydroelectric projects are excluded fromthe sample because of their potential to generate social benefits and social problems for ruralcommunities at the same time.16 The geographic location of each project is collected fromthe CDM location map provided by the NDRC. ArcGIS 10.1 is used to generate the location
data of RE-CDM projects.

16

The construction of reservoirs can improve water supply, increase farmland irrigation, produce electricity, and produce other social and economic benefits. However, it also has its disadvantages. For example,
the resettlement of residents will lead to changes in the economic structure.

10

4

Empirical analysis

4.1

Model

In order to measure social benefits of RE-CDM in rural communities, we employ a DID
estimator combined with a mix of fixed eﬀects by running a LSDV model. The DID estimator
compares the change in social benefits connected to RE-CDM projects in counties that
adopted the project to the change in social benefits in counties that did not. The fixed eﬀect
estimation allows us to control for time invariant and time varying unobservable county
characteristics that may be correlated with a county’s RE-CDM project adoption decision.
This study uses unbalanced panel data on the social benefit indicators for 1,939 ruralcounties in China from 2005 to 2011. The general form of the model adopted can be written
as follows:
yit = β0 + β1 Dit + β2 Xit + δi + γt + εit ,where yit indicates the social benefits variables, which includes: (a) rural residential income;(b) the number of rural laborers; and (c) the number of rural laborers in the primary sector
in the county i in year t.
Dit is the treatment indicator that takes on the value one in and after the year the
CDM renewable energy power plants have been constructed in county i, and zero otherwise.
We also interact the treatment indicator with diﬀerent scales and types of renewable energy sources to capture their diﬀerences in social benefits. There are two major classes ofrenewable energy-based project scales defined by the CDM Executive Board17 : large utilityscale (>15MW) projects that sell wholesale electricity to energy providers, and small-scale(≤15MW) projects that often include biomass fuel switches, small wind generators for civilian use, farm or rooftop solar implementation, and solar cooker projects (UNFCCC, 2014).
Xit is a set of time-varying county characteristics. δi is the vector of the county dummy
17

The CDM Executive Board supervises the Kyoto Protocol’s clean development mechanism under theauthority and guidance of the Conference of the Parties serving as the Meeting of the Parties to the Kyoto
Protocol.

11

variable, which is used to control for unobserved county characteristics that shape the levelof development across counties. Year dummy γt is included to control for trends that shaperural development over time such as changes in policies and regulations at the national level.
εit is the error term.

4.2

Matching techniques

There is a concern that the DID estimator may suﬀer from two sources of bias. The first
may arise if the levels and trends in social benefit indicators in treatment and control counties
diﬀer before the CDM project adoption. Another bias could arise if the CDM project sites are
not randomly assigned but determined by various geographical, political, and socio-economic
factors. Therefore, in this study, we adopt two matching approaches to mitigate potential
bias by pairing treatment counties with counties that have similar observed attributes fromthe control pool.
We adopt the PSM approach developed by Rosenbaum and Rubin (1983). The objectiveof the PSM is to construct a control group by finding controls that have observed x similar
to those of the treatment group. To match treatment and control units on the basis of x is
equivalent to matching them using a propensity score p(x), which gives the probability ofreceiving treatment given the pretreatment value of x, that is, p(x) = P r(D = 1|x). The
matching method assumes that, in a set of subjects all with the same propensity score, theobserved outcome distribution will be the same between the treatment and control groups. To
check the robustness of the PSM, we also use the simple MDM, which was first discussed by
Cochran and Rubin (1973). For the MDM, the variance-covariance matrix of x is estimatedby the pooled within group sample covariance matrix S. The distance between covariate x1
and x2 is M (x1 ,x2 ) = (x1 -x2 )T S−1 (x1 -x2 ).
In the first step, in order to estimate the propensity score, we use covariates to identify theprobability of a county adopting a RE-CDM project, which include: gross regional productof the primary sector, agricultural land area, amount of oil crop production, and a dummy
12variable for regions that have relatively higher wind power potential.18 We assume eachcovariate aﬀects the county’s decision on RE-CDM adoption and the social benefit outcomesof the treatment and control groups in the pre-treatment period.
In the second step, we use the estimated propensity score to match treatment and control
groups in the baseline year. Here, we use 2005 as the baseline year since most of the RE-CDM
projects were implemented after this year.19 In order to ensure that all the rural counties did
not have RE-CDM activities in the baseline year, counties that adopted RE-CDM activities in2005 were dropped from the sample. A one-to-one matching approach without replacement
was adopted while using the nearest-neighbor PSM and MDM algorithm. It means that
we choose only one county from the counties without RE-CDM activities as a match for atreatment county in terms of their closest propensity score and Mahalanobis distance. An
untreated county cannot be used more than once as a match. The observations decrease
from 11,537 to 2,078 after the PSM, and to 2,031 after the MDM, since the observations outof the common support have been dropped from the sample.
In the final step, in order to ensure that the matching procedure successfully balances thetwo groups, we compare the treatment and control groups after matching. We present thebalancing test results for the PSM in Table 2, Panel A and that of the MDM in Panel B. The
results illustrate that there are statistically significant diﬀerences between the mean valuesof the estimated propensity scores of the treatment and control groups before matching. For
instance, in the first row of Table 2, Panel A, we find that the diﬀerence of primary industry
output between the treatment and control groups is nearly 29.0%. Whereas, the second
row shows that the diﬀerence between these two groups drops to 7.10% when the sample
is matched. In addition, results of the t-test indicate that matched groups do not have
18

High wind potential regions are those regions with on-grid tariﬀs for wind power less than or equal to0.54 CNY per kWh including tax. According to The Notice on Tariﬀ Price of On-shore Wind Power, on-grid
tariﬀs for wind power generators are 0.47–0.60 CNY per kWh, with the lower tariﬀs applying in regions withhigher wind power potential (NDRC, 2016).
19
The first CDM project in China was the Huitengxile wind farm project, which was successfully registered in 2005. <https://cdm.unfccc.int/Projects/DB/TUEV-SUED1113481234.64/view>, last viewed: 21
December 2017.

13

statistically significant diﬀerences in the mean value of covariates. These results illustratethat no statistical diﬀerence emerges after matching the treatment and the control groups.
[Table 2]
The balancing test results are also shown in Figure 3, which depicts the diﬀerences inthe distribution of the propensity scores by treatment and control groups. The figure showsthat selected observations of the control groups have extremely similar kernel density ofpropensity score with observations in the treatment groups. It suggests that diﬀerences inthe distribution of the two groups have been significantly reduced after the PSM is applied
as well.
[Figure 3]

5

Results and discussion

5.1

Impact on rural residential income

The estimation results of the RE-CDM’s eﬀect on rural residential income are reported
in Tables 3 (PSM-DID) and 4 (MDM-DID). The results in Table 3 suggest that a positive
relationship exists between RE-CDM activities and rural residential income. The coeﬃcientof the treatment indicator re cdm is positive and statistically significant at the 10% level, as
shown in column 1. The estimated eﬀects correspond to an increase of approximately 276
yuan in annual income, which is about 5.03% of the average rural income of residents.20[Table 3][Table 4]20This calculation is based on the assumption that annual average income of rural residents is 5,486 yuan.

14

Table 3 also reports the impact of the RE-CDM by diﬀerent energy sources. The largebio
dummy is positive and significant at the 5% level, as shown in column 2. This result indicatesthat the biomass based CDM projects stimulated income growth substantially for rural
residents. Specifically, the adoption of large-scale biomass-CDM projects generated 851
yuan, about a 15.5% increase in annual income for the rural residents. In addition, we finda similar result when utilizing the MDM-DID approach. Our result regarding the impact of
the RE-CDM on income improvement illustrates that only the utility-scale biomass energybased CDM projects were significant in stimulating income generation. According to Faaij
et al. (1998), bioenergy-based electricity production tends to have a greater impact on local
income than power generation using coal because of the use of locally produced feedstocks.
Moreover, Gan and Smith (2007) estimated the co-benefits associated with the utilization of
logging residues for bioenergy production in East Texas, USA. The input-output modelingrevealed that the most noticeable socio-benefits of bioenergy production were income andjob creation.

5.2

Impact on employment generation

In Tables 5 and 6, we assess the impact of the RE-CDM projects on employment generation using the PSM- and MDM-DID method, respectively. The results indicate that theemployment generation impact of the RE-CDM activities in rural areas diﬀer by diﬀerent
renewable energy sources. The coeﬃcients of largebio are positive and significant at the 5%
level, as shown in columns 2 in Tables 5 and 6, respectively. These results suggest that the
number of labor in a rural county can be increased by approximately 13,000 workers, 5.99%of the average number of rural laborers, through the adoption of large-scale biomass-CDM
projects.21[Table 5]
21

The calculation is based on the assumption that the average number of labor is 0.217 million workers
in a rural community.

15

[Table 6]
In line with the arguments of Thornley et al. (2008) and Openshaw (2010), our results
illustrate that biomass energy based projects show remarkable contributions to employment
generation in rural communities. Thornley et al. (2008) quantified the expected employment
impacts of individual bioenergy development and suggested that the larger bioenergy power
plants had a larger employment impact, which confirms our results on the employment
creation impact of large-scale biomass projects. Additionally, Openshaw (2010) highlighted
the importance of bioenergy systems as a means to poverty alleviation. Openshaw foundthat in Malawi, Africa, the equivalent to 93,500 and 133,000 full-time workers were employedin the biomass supply chain in 1996 and 2008, respectively. In contrast, about 3,400 and
4,600 people were employed in the supply chain of other conventional fuels, such as coal andpetroleum, in those years.

5.3

Impact on industrial transformation

In Tables 7 and 8, we report the estimated impact of the RE-CDM adoption on industrial
transformation in rural communities. The coeﬃcient of smallbio, shown in column 3 of Table
7, indicates that the implementation of small-scale bioenergy projects under the CDM is associated negatively with 20,000 workers annually in primary industry, which is about a 16.39%
decrease in the number of rural laborers in the primary sector.22 The results estimated by the
MDM-DID approach in Table 8 confirm the robustness of our PSM-DID estimation. These
results on smallbio suggest that the transformation of the economic structure from primaryindustry to other industries is likely to be achieved by introducing small-scale biomass based
CDM projects into the rural communities. The finding implies that the presence of individual
biomass-CDM projects may promote production activities beyond agricultural productionin those rural areas with large numbers of unskilled laborers. This is based on the thought
22This calculation is based on the assumption that the average number in primary industry rural labor
force is approximately 0.122 million workers.

16

that small-scale projects are often community based, and therefore, it is easier for unskilled
rural laborers to be involved in the production process after skill training. According to thereport released by the Ministry of Agriculture of China, the industrial scale of bioenergy
production has been growing continually, with over 20,000 small-scale biogas projects andmore than 4,700 large and medium-sized biogas projects approved for production as of theend of 2010. More than 300,000 farmers can be transferred to such local jobs each year in
the stages of biogas power plant construction and service providing alone (MOA, 2010).
[Table 7][Table 8]
Interestingly, we found that both large-scale wind and solar energy-based CDM projects
have the potential to induce the reintegration of migrant rural labor. The coeﬃcients oflargewind, shown in column 4 and largesolar in column 5 of Table 7, indicate positive andstatistically significant impacts of large-scale wind- and solar-CDM power plants on thenumber of rural labor in the primary industry sector at the 1% level. Our results illustratethat large-scale wind projects attract approximately 4,000 rural laborers into the primarysector each year, which is about 1.01% of the total population of a county.23 Moreover, due
to the adoption of large-scale solar energy projects, the number of rural work force in the
primary industry can be increased by 19,000 annually, which is about 4.80% of the totalrural population.24 The promotion of photovoltaic agriculture25 is said to have improved
the eﬃciency of agricultural production, encouraged small villages to form an agriculturaltown, and promoted the return of migrant rural workers (China Energy Net, 2011). For
example, this eﬀect is observed in the case of a 20 MW agriculture-photovoltaic power station
23

In this study, mean value of the total population of a rural county is about 0.396 million people incontrol groups, and 0.430 million people in treatment groups.
24
The calculation is based on the assumption that the mean value of the rural population is 0.396 million
people.
25
Photovoltaic agriculture is the combination of photovoltaic power generation and agricultural activities.
There are several main application modes of photovoltaic agriculture such as a photovoltaic agricultural
greenhouse, photovoltaic breeding, and a new type rural solar power station, among others (Xue, 2017).

17

located in Zhengyang county.26 The expected power generation of the Zhengyang ecologicalagricultural farm is 20 GWh per year, with the power generated by solar panels used for
agricultural production inside the farm and the rest of the generated electricity transmitted
to the national grid. More than 120 migrant workers who returned to the county are said tobe employed by this photovoltaic agriculture farm (Farmer Daily, 2017).

6

Conclusions
By focusing on the social benefits brought by the renewable energy projects, we examine

whether the RE-CDM improved Chinese rural communities in terms of rural residential
income, job opportunities, and transforming the industrial structure. In addition, our study
investigates the impact generated by various renewable energy sources in order to understandwhich energy source provides higher social benefits.
Our results indicate that the bioenergy-based CDM projects significantly contribute to
local sustainability of the host counties. The increase in annual income of rural residents byadopting bioenergy CDM projects was calculated at about 851 yuan per year. The growth
in rural residential income caused by biomass-CDM projects was likely due to increased job
opportunities and the transformation of the labor structure. As described in 2009 annual
report by the China Association of Rural Energy Industry, the biogas projects provide a large
number of employment opportunities for the rural surplus labor force and migrant workers,
significantly increasing the income of the farmers, and promoting the social stability in the
project areas (CAAE, 2010). Moreover, we find that not all renewable energy technologies
contribute to the social benefits in the same manner. Small-scale biomass-CDM projects
had the largest potential in improving the labor structure in rural areas since their impact
on transferring primary sector rural labor to other sectors was around 13,000 workers per
year. This result indicates that small-scale bioenergy provides more job opportunities forunskilled laborers than other types of energy sources. In contrast, large-scale wind and solar
26

Zhengyang county is a rural county belonging to Zhumadian city, Henan province, China.

18

energy-based CDM projects promote rural development by encouraging agricultural reform
and attracting labor force into the primary sector.
Climate change represents a direct and immediate threat to poverty alleviation (World
Bank, 2015). In this study, we assess whether activities for climate change mitigation can
alleviate poverty of rural communities in China. We conclude that the adoption of renewable energy projects under the CDM can oﬀer an eﬀective way to both reducing poverty
and addressing the global externality. By promoting the development of renewable energy,
particularly bioenergy in local communities, it might be possible to reduce poverty in waysthat support low-carbon growth. Providing clean electricity and access to modern energy
services may also contribute to other type of social benefits, by improving health and welfare,access to education and jobs, and drive economic growth while reducing pollution (Climate
Advisers, 2014).
Although our study confirms the role of the RE-CDM in assisting host countries inachieving SD, a further investigation is necessary to understand links between climate change
mitigation and poverty reduction strategies. For example, it is important to compare thesocial benefits of domestic renewable energy projects and RE-CDM projects in order toevaluate the eﬀectiveness of diﬀerent investment channels. Another limitation of our study
is that the long-run eﬀect of the RE-CDM has not been considered. Future research should
be designed to capture the dynamics of the relationship between the RE-CDM and ruraldevelopment in the long-run.

19

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24

Table 1: Descriptive statistics
Unit(1) Control groups(2) Treatment groups

Obs

Obs

Mean

Std. dev.

Mean

Std. dev.

Treatment Indicators
re cdm

dummy

8,719

0.000

0.000

2,818

0.310

0.463

largebio

dummy

8,719

0.000

0.000

2,818

0.057

0.232

smallbio

dummy

8,719

0.000

0.000

2,818

0.004

0.059

largewind

dummy

8,719

0.000

0.000

2,818

0.241

0.428

smallwind

dummy

8,719

0.000

0.000

2,818

0.008

0.086

largesolar

dummy

8,719

0.000

0.000

2,818

0.011

0.104

smallsolar

dummy

8,719

0.000

0.000

2,818

0.011

0.106

Social Benefit Variables
income rural

1,000 yuan

8,247

5.486*

8.748

2,756

5.027

2.837

rural labor

million person

8,719

0.217*

0.169

2,818

0.231

0.176

rural labor primary

million person

8,719

0.122*

0.094

2,818

0.133

0.095

County Characteristics
grp primary%

%

8,719

0.247

0.130

2,818

0.249

0.135

land area agriculture

1,000 km2

6,876

0.387*

0.409

2,178

0.633

0.520

rural population

million person

8,719

0.396*

0.299

2,818

0.430

0.327

government income

billion yuan

8,719

0.416

0.930

2,817

0.434

0.627

student%%

8,719

0.139*

0.035

2,817

0.133

0.039

oil production

million ton

8,450

0.014*

0.022

2,716

0.020

0.034

machinery power

1,000 kw

8,719

0.033*

0.036

2,818

0.045

0.049

Note: 1) * indicates that the means diﬀer with statistical significance in a two-tailed t-test at the 1% level between thetreatment and control groups; 2) re cdm is a dummy variable used to indicate if county i contains RE-CDM projects in year t:
0 = no,1 = yes; income rural is the annual per capita net income of rural households; rural labor is amount of working
population in rural area; rural labor primary is the number of rural laborers in the primary sector; grp primary% is the shareof primary industry product in the gross regional product; government income is the total value of the government budget
revenue; student% is the share of students accepting compulsory education out of total residents; oil production is the amountof oil crop production; machinery power is the total capacity of agriculture machinery.

25

Table 2: Balancing test results
Panel A: Nearest-neighbor propensity score matching (PSM)
Unmatched/
Outcome var: income rural
grp primaryland area agriculture

wind potential

oil production

Meant-testMatchedTreatment

Control

%bias

U

1.232

0.977

29.0

M

1.232

0.170

7.10

U

0.610

0.412

43.6

M

0.610

0.578

7.00

U

0.272

0.166

25.8

M

0.272

0.331

-14.3

U

0.021

0.014

23.7

M

0.021

0.018

9.70

%bias reduction

75.6

83.9

44.5

59.1

t-valuep-value

4.40

0.000

0.82

0.412

6.34

0.000

0.75

0.451

3.84

0.000

-1.50

0.135

3.75

0.000

1.05

0.294

Panel B: Mahalanobis distance matching (MDM)
Unmatched/
Outcome var: income rural
grp primaryland area agriculture

wind potential

oil production

Meant-testMatchedTreatment

Control

%bias

U

1.232

0.977

29.0

M

1.232

1.189

5.00

U

0.610

0.412

43.6

M

0.610

0.581

6.50

U

0.272

0.166

25.8

M

0.272

0.272

0.00

U

0.021

0.014

23.7

M

0.021

0.019

7.60

26

%bias reduction

82.8

85.0

100

67.8

t-valuep-value

4.40

0.000

0.54

0.586

6.34

0.000

0.75

0.453

3.84

0.000

0.00

1.000

3.75

0.000

0.80

0.422

Table 3: Regression results (Explained variable: income rural)
re cdmEstimation method: PSM-DID
(1)
(2)
(3)
0.276∗(0.151)

(4)

(5)

(6)

(7)

0.851∗∗(0.315)largebio

smallbio

1.916(1.279)largewind

0.085(0.147)

smallwind-0.440(0.719)largesolar-1.164(0.879)smallsolar-0.299(0.817)

grp primary%

9.328∗∗∗(3.126)9.274∗∗∗(3.122)

9.091∗∗∗(3.128)9.286∗∗∗(3.132)9.260∗∗∗(3.123)9.191∗∗∗(3.091)

9.258∗∗∗(3.125)land area agri

2.709∗∗∗(0.672)2.793∗∗∗(0.671)2.843∗∗∗(0.676)2.742∗∗∗(0.676)2.775∗∗∗(0.677)2.896∗∗∗(0.717)2.776∗∗∗(0.682)rural population-0.243(1.210)-0.201(1.203)-0.172(1.209)-0.211(1.215)-0.190(1.207)-0.180(1.207)-0.186(1.208)income gov

4.763∗∗∗(1.485)4.769∗∗∗(1.483)4.768∗∗∗(1.484)4.765∗∗∗(1.486)4.767∗∗∗(1.486)4.767∗∗∗(1.486)4.766∗∗∗(1.485)student%

6.920∗(3.966)6.755∗(3.958)6.560∗(3.955)6.650∗(3.969)6.559∗(3.970)6.567∗(3.945)6.533∗(3.968)oil production

15.01∗∗(5.865)14.72∗∗(5.791)14.67∗∗(5.787)15.28∗∗(5.934)15.58∗∗∗(5.884)15.10∗∗(5.965)15.42∗∗∗(5.876)

machinery power

0.626(0.830)0.599(0.812)0.581(0.797)0.634(0.842)0.615(0.831)0.604(0.815)0.624(0.834)

constant-2.629(2.169)
YES
YES
2,078
0.758

-2.646(2.179)
YES
YES
2,078
0.758

1.574
(1.046)
YES
YES
2,078
0.758

1.563
(1.042)
YES
YES
2,078
0.758

1.583
(1.043)
YES
YES
2,078
0.758

1.571
(1.042)
YES
YES
2,078
0.758

1.593
(1.040)
YES
YES
2,078
0.758

Year dummyCounty dummyNadj. R2

Robust standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

27

Table 4: Regression results (explained variable: income rural)
re cdmEstimation method: MDM-DID
(1)
(2)
(3)
0.423
(0.312)

(4)

(5)

(6)

(7)

1.646∗∗∗(0.488)largebio

smallbio

3.720(2.279)largewind-0.013(0.308)smallwind

0.479(1.355)largesolar-0.890(1.413)smallsolar

0.414(1.627)

grp primary%

8.477∗∗(3.817)8.390∗∗(3.809)8.077∗∗(3.842)

8.391∗∗(3.828)8.422∗∗(3.817)8.337∗∗(3.802)

8.398∗∗(3.821)land area agri

4.119∗∗∗(0.935)4.250∗∗∗(0.931)

4.349∗∗∗(0.931)

4.196∗∗∗(0.945)4.190∗∗∗(0.936)4.283∗∗∗(0.976)

4.182∗∗∗(0.937)

rural population

3.063(3.186)

3.131(3.155)

3.183(3.173)

3.154(3.182)

3.133(3.173)

3.166(3.178)

3.143(3.172)income gov

11.33∗∗∗(2.925)

11.34∗∗∗(2.920)11.34∗∗∗(2.923)

11.33∗∗∗(2.927)

11.33∗∗∗(2.925)

11.33∗∗∗(2.926)

11.33∗∗∗(2.926)

student%

13.97∗∗(5.950)13.88∗∗(5.945)13.59∗∗(5.926)

13.56∗∗(5.909)13.58∗∗(5.937)13.62∗∗(5.917)

13.58∗∗(5.938)oil production

30.19∗∗∗(11.38)29.61∗∗∗(11.21)29.56∗∗∗(11.17)30.93∗∗∗(11.49)30.98∗∗∗(11.45)30.81∗∗∗(11.46)30.80∗∗∗(11.41)machinery power-0.410(0.919)-0.464(0.907)-0.495(0.902)-0.404(0.929)-0.424(0.921)-0.423(0.916)-0.394(0.923)

constant-6.005∗∗∗(2.307)
YES
YES
2,031
0.741

-2.613(1.957)
YES
YES
2,031
0.742

-2.587(1.956)
YES
YES
2,031
0.741

-6.085∗∗∗(2.331)
YES
YES
2,031
0.741

-2.597(1.959)
YES
YES
2,031
0.741

-2.621(1.959)
YES
YES
2,031
0.741

-2.593(1.959)
YES
YES
2,031
0.741

Year dummyCounty dummyNadj. R2

Robust standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

28

Table 5: Regression results (explained variable: rural labor)
re cdmEstimation method: PSM-DID
(1)
(2)
(3)
-0.004(0.002)

(4)

(5)

(6)

(7)

0.013∗∗(0.007)largebio

smallbio

0.050(0.036)-0.007∗∗∗(0.002)largewindsmallwind-0.005(0.023)largesolar-0.005(0.007)

smallsolar-0.009(0.014)

grp primary%-0.054∗∗∗(0.014)-0.053∗∗∗(0.014)-0.057∗∗∗(0.014)-0.055∗∗∗(0.014)-0.053∗∗∗(0.014)-0.053∗∗∗(0.014)-0.053∗∗∗(0.014)

landarea agri

-0.019∗∗∗(0.006)-0.020∗∗∗(0.006)-0.018∗∗∗(0.007)-0.018∗∗∗(0.007)-0.020∗∗∗(0.006)-0.020∗∗∗(0.006)-0.020∗∗∗(0.006)rural population

0.066(0.045)0.065(0.045)0.065(0.045)0.067(0.044)0.065(0.045)0.065(0.045)0.065(0.045)income gov-0.001(0.001)-0.001(0.001)-0.001(0.001)-0.001(0.001)-0.001(0.001)-0.001(0.001)-0.001(0.001)student%-0.038(0.036)-0.030(0.035)-0.033(0.035)-0.042(0.036)-0.033(0.036)-0.033(0.036)-0.034(0.036)oil production

0.015(0.098)0.000(0.097)-0.001(0.095)0.017(0.098)0.011(0.098)0.009(0.098)0.012(0.097)

machinery power

0.035(0.027)0.035(0.027)0.034(0.026)0.035(0.027)0.035(0.027)0.035(0.027)0.035(0.027)

constant

0.122∗∗∗(0.013)
YES
YES
2,078
0.988

0.148∗∗∗(0.015)
YES
YES
2,078
0.988

0.149∗∗∗(0.015)
YES
YES
2,078
0.988

0.121∗∗∗(0.013)
YES
YES
2,078
0.988

0.149∗∗∗(0.015)
YES
YES
2,078
0.988

0.148∗∗∗(0.015)
YES
YES
2,078
0.988

0.149∗∗∗(0.015)
YES
YES
2,078
0.988

Year dummyCounty dummyNadj. R2

Robust standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
Note: The values of adjusted R-square are approximately 0.99 since the LSDV estimation including a dummy
variable for each county, namely the county dummy, perfectly explains the between variance.

29

Table 6: Regression results (explained variable: rural labor)[1em] re cdm

Estimation method: MDM-DID
(1)
(2)
(3)
-0.003(0.002)

(4)

(5)

(6)

(7)

0.013∗∗(0.007)largebio

smallbio

0.050(0.037)-0.007∗∗∗(0.002)largewindsmallwind-0.003(0.023)largesolar-0.004(0.007)

smallsolar-0.009(0.014)

grp primary-0.054∗∗∗(0.014)-0.053∗∗∗(0.014)-0.057∗∗∗(0.014)-0.055∗∗∗(0.014)-0.053∗∗∗(0.014)-0.053∗∗∗(0.014)-0.053∗∗∗(0.014)land area agri

-0.021∗∗∗(0.007)-0.021∗∗∗(0.007)-0.019∗∗∗(0.007)-0.020∗∗∗(0.007)-0.021∗∗∗(0.007)-0.021∗∗∗(0.007)-0.021∗∗∗(0.007)

ruralpopulation

0.063(0.043)0.062(0.044)0.063(0.044)0.064(0.043)0.063(0.044)0.063(0.044)0.063(0.044)income gov

0.000(0.001)0.000(0.001)0.000(0.001)0.000(0.001)0.000(0.001)0.000(0.001)0.000(0.001)student-0.031(0.034)-0.030(0.034)-0.030(0.034)-0.034(0.034)-0.028(0.034)-0.028(0.034)-0.028(0.034)oilproduction-0.012(0.097)-0.027(0.096)-0.035(0.094)-0.009(0.097)-0.015(0.094)-0.017(0.097)-0.014(0.095)

machinerypower

0.032(0.025)0.032(0.025)0.031(0.024)0.032(0.025)0.032(0.025)0.032(0.025)0.032(0.025)constant

0.123∗∗∗(0.013)
YES
YES
2,031
0.989

0.148∗∗∗(0.015)
YES
YES
2,031
0.989

0.149∗∗∗(0.015)
YES
YES
2,031
0.989

0.122∗∗∗(0.013)
YES
YES
2,031
0.989

0.149∗∗∗(0.015)
YES
YES
2,031
0.989

0.149∗∗∗(0.015)
YES
YES
2,031
0.989

0.149∗∗∗(0.015)
YES
YES
2,031
0.989

Year dummyCounty dummyNadj. R2

Robust standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
Note: The values of adjusted R-square are approximately 0.99 since the LSDV estimation including adummy variable for each county, namely the county dummy, perfectly explains the between variance.

30

Table 7: Regression results (explained variable: rural labor primary)re cdmEstimation method: PSM-DID
(1)
(2)
(3)
0.002
(0.002)largebio

(4)

(5)

(6)

(7)

-0.005(0.003)-0.020∗∗(0.009)smallbio

0.004∗∗∗(0.002)largewindsmallwind

0.002(0.011)0.019∗∗∗(0.005)largesolar

smallsolar

0.012(0.009)grp primary%-0.056∗∗∗(0.017)-0.056∗∗∗(0.017)-0.054∗∗∗(0.017)-0.055∗∗∗(0.017)-0.056∗∗∗(0.017)-0.055∗∗∗(0.017)-0.056∗∗∗(0.017)

landarea agri

0.000
(0.004)0.000(0.004)-0.000(0.004)-0.001(0.004)0.000(0.004)-0.002(0.004)-0.000(0.004)rural population

0.037∗∗(0.017)0.037∗∗(0.017)0.037∗∗(0.017)

0.036∗∗(0.017)0.037∗∗(0.017)0.037∗∗(0.017)0.037∗∗(0.017)

income gov-0.001(0.001)-0.001(0.001)-0.001(0.001)-0.001(0.001)-0.001(0.001)-0.001(0.001)-0.001(0.001)student%-0.008(0.030)-0.010(0.030)-0.010(0.030)-0.004(0.030)-0.010(0.030)-0.010(0.029)-0.010(0.030)oil production

0.101(0.080)0.106(0.080)0.110(0.081)

0.0989(0.080)0.102(0.081)0.107(0.081)0.100(0.081)machinery power-0.032∗∗(0.014)-0.032∗∗(0.014)-0.031∗∗(0.014)-0.032∗∗(0.014)-0.032∗∗(0.014)-0.031∗∗(0.014)-0.031∗∗(0.014)

constant

0.100∗∗∗(0.014)
YES
YES
2,078
0.981

0.078∗∗∗(0.007)
YES
YES
2,078
0.981

0.078∗∗∗(0.007)
YES
YES
2,078
0.981

0.100∗∗∗(0.014)
YES
YES
2,078
0.981

0.078∗∗∗(0.007)
YES
YES
2,078
0.981

0.079∗∗∗(0.007)
YES
YES
2,078
0.981

0.078∗∗∗(0.007)
YES
YES
2,078
0.981

Year dummyCounty dummyNadj. R2

Robust standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
Note: The values of adjusted R-square are approximately 0.98 since the LSDV estimation including a dummy
variable for each county, namely the county dummy, perfectly explains the between variance.

31

Table 8: Regression results (explained variable: rural labor primary)re cdmEstimation method: MDM-DID
(1)
(2)
(3)
0.001
(0.002)largebio

(4)

(5)

(6)

(7)

-0.005(0.003)-0.019∗∗(0.009)smallbio

0.004∗∗∗(0.002)largewindsmallwind-0.000(0.011)0.020∗∗∗(0.005)largesolar

smallsolar

0.013(0.009)grp primary%-0.071∗∗∗(0.015)-0.071∗∗∗(0.015)-0.070∗∗∗(0.015)-0.070∗∗∗(0.015)-0.071∗∗∗(0.015)-0.070∗∗∗(0.014)-0.070∗∗∗(0.015)land area agri

0.000
(0.004)0.000(0.004)-0.000(0.004)-0.001(0.004)0.001(0.004)-0.000(0.004)0.000(0.004)ruralpopulation

0.038∗∗(0.017)0.039∗∗(0.017)0.039∗∗(0.017)0.038∗∗(0.018)0.039∗∗(0.017)0.038∗∗(0.017)0.038∗∗(0.017)

income gov-0.002(0.002)-0.002(0.002)-0.002(0.002)-0.002(0.002)-0.002(0.002)-0.002(0.002)-0.002(0.002)student-0.002(0.028)-0.004(0.028)-0.003(0.028)0.001(0.028)-0.003(0.028)-0.004(0.028)-0.003(0.028)oilproduction

0.071(0.079)0.076(0.079)0.080(0.079)0.070(0.079)0.073(0.079)0.075(0.079)0.069(0.079)machinerypower-0.034∗∗(0.015)-0.033∗∗(0.015)-0.033∗∗(0.015)-0.033∗∗(0.015)-0.033∗∗(0.015)-0.033∗∗(0.015)-0.033∗∗(0.015)constant

0.105∗∗∗(0.012)
YES
YES
2,031
0.981

0.079∗∗∗(0.007)
YES
YES
2,031
0.981

0.079∗∗∗(0.007)
YES
YES
2,031
0.981

0.106∗∗∗(0.012)
YES
YES
2,031
0.982

0.079∗∗∗(0.007)
YES
YES
2,031
0.981

0.079∗∗∗(0.007)
YES
YES
2,031
0.982

0.079∗∗∗(0.007)
YES
YES
2,031
0.981

Year dummyCounty dummyNadj. R2

Robust standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
Note: The values of adjusted R-square are approximately 0.98 since the LSDV estimation including adummy variable for each county, namely the county dummy, explains the between variance perfectly.

32

30000
25000
20000
15000
10000
5000
0

Household per capita income in yuan

1985

1988

1991

1994

1997

2000

Urban

2003

2006

2009

2012

2015

Rural

Figure 1: Trends in growth of per capita income of urban and rural households in
China. Source: China Statistical Yearbook.

33

Figure 2: Locational distributions of RE-CDM projects by the cumulative installedcapacity (MW) of power plants in 201234

kernel density of propensity score
2
4
6
0

0

.2

.4

.6

.8

propensity scores BEFORE matching
control0kernel density of propensity score
1
2
3

4

treatment

0

.2

.4
.6
propensity scores AFTER matchingtreatment.8

control

Figure 3: Distribution of propensity scores by treatment and control groups: before andafter the nearest-neighbor PSM

35