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The potential trade-off between environmental protection and employment stability has been a concern in the literature. However, in the case of China, the employment issue has not been adequately addressed despite government’s big push on investing in renewable energy since 2007. This essay addresses the employment issue through estimating the relative employment impacts of renewable energy investments versus spending within the traditional fossil fuel sectors based on input-output modeling with China-specific data of sector and subsector weighting techniques. I find that spending within three segments of the renewable energy sectors – solar, wind and bioenergy, will produce in combination about twice as many jobs per dollar of expenditure than an equal amount of spending on fossil fuels. I also find that, more than 70 percent of jobs from renewable energy sectors are created in the informal economy. This raises questions about the quality of the jobs created through renewable energy investments.
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International Review of Applied Economics
ISSN: 0269-2171 (Print) 1465-3486 (Online) Journal homepage:
Renewable energy investment and employment in
Ying Chen
To cite this article: Ying Chen (2018): Renewable energy investment and employment in China,
International Review of Applied Economics, DOI: 10.1080/02692171.2018.1513458
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Renewable energy investment and employment in China
Ying Chen
Department of Economics, New School for Social Research, New York, USA
The potential trade-obetween environmental protection and
employment stability has been a concern in the literature.
However, in the case of China, the employment issue has not
been adequately addressed despite governments big push on
investing in renewable energy since 2007. This essay addresses
the employment issue through estimating the relative employ-
ment impacts of renewable energy investments versus spending
within the traditional fossil fuel sectors based on input-output
modeling with China-specic data of sector and subsector weight-
ing techniques. I nd that spending within three segments of the
renewable energy sectors solar, wind and bioenergy, will pro-
duce in combination about twice as many jobs per dollar of
expenditure than an equal amount of spending on fossil fuels. I
also nd that, more than 70 percent of jobs from renewable
energy sectors are created in the informal economy. This raises
questions about the quality of the jobs created through renewable
energy investments.
Received 10 January 2018
Accepted 12 January 2018
Green economy; renewable
energy; informal
employment; input-output;
green jobs
Q56; J23; J46
1. Introduction
Chinas big push for investing in renewable energy since 2007 implies a promising
future of a greener economy. However, the potential trade-obetween environmental
protection and employment stability has always been a concern in the literature
(Mehmet 1995; Rose and Wei 2006; Lehr et al. 2008; Moreno and Lopez 2008;
Alvarez et. al 2009; Frondel et al. 2009; Pollin, Heintz, and Garrett-Peltier 2009;
Ragwitz et al. 2009; Mitchell 2011; Pollin et al. 2014). One must ask, is this concern
relevant in the case of China? Specically, will a signicant contraction of production
within the traditional energy sectors (i.e. coal, oil and natural gas) lead to employment
instability in China?
The mainstream media often depicts labor shortage as a nationwide issue in China,
(Das and NDiaye 2013). However, studies show that labor shortage is region-specic
and industry-specic. In fact, it was only observed in some export-oriented manufac-
turing industries in the coastal area (Chan 2010). At the national level, surplus rural
labor exists on a massive scale. A conservative estimation of the 2010 data suggests that
at least 100 million nonfarm jobs need to be created to absorb the surplus labor (Chan
© 2018 Informa UK Limited, trading as Taylor & Francis Group
Moreover, informal employment within the urban sector remains a major concern in
China. Although the percentage of formal employment has been increasing slowly since
the mid-2000s (Chen and Xu 2017), the absolute number of workers in the informal
sector is still rising. In 2012, there are more than 200 million working in the urban
informal sector, compared to 173 million in 2007. Informal employment in China
means low wage, few benets and no labor law protection. These groups of informal
workers also serve as a reserve armypool, which weakens the urban workersbargain-
ing power in general.
Lack of job creation, particularly in the formal sector, therefore, is the major
employment issue facing China today. However, this issue has not been adequately
addressed in the grand plan of renewable energy investment. To date, there are no
reliable employment estimates of renewable energy investments in China.
How to implement this structural change in the fossil fuel industry, while guarantee-
ing a smooth transition in employment, is thus highly relevant in discussing the
feasibility of the renewable energy plan in the case of China.
This article estimates the impact on employment generation through investments in
three renewable energy sectors in China solar, wind and bioenergy. Comparable
estimates are made for Chinas traditional fossil fuel sectors (i.e. coal, oil and natural
gas). These estimates are based on input-output (I-O) modeling with China-specic
data of sectorial and sub-sectorial weighting techniques within Chinas I-O model.
This paper contributes to the literature in several ways. First, it focuses on the unique
labor market structure of China. Second, working with China-specic data, it estimates
job creation in the renewable energy sectors as well as in the traditional fossil fuel
sectors, providing empirical evidence relevant to the feasibility of Chinas transforma-
tion into a clean energy-based economy. Third, this study examines employment
generation in terms of formal and informal jobs within China. This enables us to also
consider the issue of the quality of the jobs being generated by clean energy invest-
ments, as opposed to focusing only on the quantity of those jobs.
The remaining sections of the paper are organized as follows. Section 2 presents a
brief overview of the existing literature regarding estimates of employment generation
through investing in renewable energy sectors in China. Section 3 introduces the I-O
model. I also discuss the advantage and limitation of this methodology by comparing it
with alternative approaches. Section 4 discusses the data sources and data construction
methods used for estimation. Section 5 presents the main results from my estimation
exercises. Section 6 concludes the paper.
2. Literature review
The existing employment estimates on renewable energy investment either lack a
transparent methodological discussion or do not provide a clear denition of employ-
ment as a concept. They are therefore incapable of providing a solid empirical evidence
for policy discussions on building clean energy economies. Most of them, especially in
the Chinese-language studies, appear in the ocial documents or think tanks aliated
to the government, mainly as a justication for the big push on renewable energy
development. But even those have stopped appearing in the literature since 2012,
exactly when the renewable energy investment plan began being implemented.
Table 1. Employment estimates of investing in renewable energy in the existing literature.
Studies Methodology
Specify formal
or informal
direct or
indirect jobs
Specify job crea-
tion relative to
spending level Stock or ow
Total employment generation
for renewable energy invest-
ment (million)
and ITUS
Interview ocials and experts Wind, solar
No No No Stock; by 20070.9
CREIA (2009) NA NA No No No Flow; 2008 1.1
CASS (2010) 2005 I-O table of three aggregated levels Wind and
No Yes 580 billion RMB Stock; 20082011 30
Five Year
Plan (2011)
NA Bioenergy No No 1.8 trillion RMB Stock; 20112015 3.6
CCICED (2011) NA NA No No #909 billion Stock; 20112015 10.6
IRENA (2011) Literature survey Wind, solar
and solar
No No No Stock; by 20100.3
Assuming 14 persons/years of employment
for every new megawatt and 23 billion of
annual investment
Wind No No Annual average of
23 billion
Stock; by 2020 0.3
NDRC (2012) NA Wind No No 1.8 trillion RMB Stock; by 2050 0.7
REN21 (2013) Literature survey Wind, solar
No Yes No Stock; 20092012 1.7
Source: Authors compilation.
Solar thermal technology rst translates the suns light to heat and then to electricity, while solar PV directly converts the sunslight to electricity. Therefore solar PV technology is only
eective during daylight hours as storing electricity is not a particularly ecient process as compared to heat storage.
Table 1 summarizes the employment estimates of renewable energy investments in
China from the recent literature. Specically, I organize the literature as follows.
Column 1 presents the methodology. Column 2 examines whether the kind of renew-
able energy technologies (i.e. wind, solar, or bioenergy) is specied in their employment
estimates. Column 3 examines whether employment estimates are specied as formal or
informal employment. Here formal employment refers to those jobs with regular
working hours and benets, and that are protected under state labor laws. By contrast,
informal employment refers to jobs outside the formal economy, with low pay, little job
security, few or no benets, and no legal protection. The distinction between formal
and informal employment is crucial because although they can be treated equivalently
in the quantitative sense, one job in the informal economy implies signicantly lower
job quality relative to one created in the formal sector. Column 4 examines whether
employment estimates are identied as direct or indirect jobs. Direct jobs refer to the
core activities in the energy sectors whereas indirect jobs refer to those jobs generated
through the supply chains associated with renewable energy production. Distinguishing
direct and indirect jobs helps to specify the composition of the employment opportu-
nities in the renewable energy sectors. Column 5 presents information on the relative
spending level for the employment estimates available in the examined studies. Column
6 categorizes the employment estimates in terms of stocks or ows and the last column
shows the employment estimates.
A brief overview of the table shows a wide range of estimates over various time spans
with no consistent methodology or denition for any systematic comparison. In terms
of the methodology, almost half the studies do not specify their estimation base (CREIA
2009;Chinas 12th Five Year Plan2011; CCICED 2011; NDRC 2012). Some studies
resort to qualitative methods such as interviews or literature surveys (UNEP et. al 2008;
IRENA 2011; REN21 2013). Only two studies discuss the quantitative methods they use
and the relevant assumptions (CASS 2010; Greenpeace 2012) that are too general for a
specic estimation of the China case.
Most studies in this literature survey have specied the employment generation
among wind, solar and bioenergy sectors. However, it is still impossible to make
comparisons among these estimates, as some studies focus only on one sector of their
interest (Greenpeace 2012; NDRC 2012), yet others produce estimates for a combina-
tion of the three sectors as a whole without fully specifying the employment estimates
for each sector (UNEP, ILO, IOE, and ITUS 2008; REN21 2013).
Regarding the denition of employment, not a single study in this literature survey
discusses its employment estimates for the Chinese case in terms of formal and informal
employment categorizing, although the importance of an inclusive green economyhas
been recognized in the existing literature (Smit and Musango 2015). Only two studies
(CASS 2010; REN21 2013) specify the employment estimates regarding direct or indirect
jobs, despite a slight dierence in the denition used in this paper.
Almost half the studies do not specify the spending level with respect to the employ-
ment estimates, making it dicult to estimate how much employment is generated
relative to certain spending level for each renewable energy sector. Finally, the use of
stocks or ows for employment estimation is also inconsistent among the existing
literature, making it hard to undertake direct comparisons.
In general, the existing studies have not provided any serious estimates for employ-
ment generation through renewable energy investment in China. They do not provide
either a clear methodology for estimation or even a clear concept of the term employ-
ment.It is therefore reasonable to conclude that, to date, there are no reliable estimates
of the employment impacts of renewable energy investments in China, or any relative
employment eects of renewable energy investments versus spending within the tradi-
tional fossil fuel sectors.
3. Methodology: I-O model
This paper mainly builds on the I-O model to estimate the employment impacts of
renewable energy investment in China as is used in many case studies for other
countries (Neuwahl et al. 2008; Pollin, Heintz, and Garrett-Peltier 2009; Simas and
Pacca 2014). A typical I-O model records detailed information on the supply and
demand relationships between various industrial sectors and distinct categories of
nal demand in the economy. A more detailed discussion of the I-O model and its
methodological limitation is presented in the Appendix.
One challenge in using an I-O model is that these renewable energy sector activities
are not as yet specied into distinct industrial sectors, such as solar energy sectoror
wind energy sector.To solve this issue, I use information on the cost components of
such investment from the existing literature and then use the existing sectors within the
I-O model to construct a new renewable energy sector based on the weighting structure
that reects such cost composition.
The rst step in the estimation is to calculate the output-investment ratio (O-I ratio),
meaning how much change in output will be induced by a change in the investment
The second step is to calculate the employment-output ratio (E-O ratio), which
indicates how much employment is required to produce a certain output level of
renewable energy goods. Assuming a linear model, the multiplication result of the
row vector E-O and the matrix O-I implies the employment-investment ratio, suggest-
ing the total employment generation from investment in the renewable energy goods.
4. Data construction and discussion
4.1. I-O data
Starting from 1987, the Chinese Statistics Bureau published the Input-Output Table of
China every 5 years, with the most recent one in 2007.
This 2007 I-O table is the most
detailed table since 1987, providing information on the 135-industry level basis.
more recent I-O table would be preferable for a more accurate prediction of employ-
ment generation, but was not available at the time of writing. A 2010 I-O table is
available at 54-sector, yet I choose the 2007 I-O table for its details that are most critical
for the estimation. Data limitation issue will be addressed and compensated by discus-
sion of productivity change in Section 5.4.
4.2. Importance of separating formal and informal sector employment
There are three main reasons for separating the formal and informal sectors in the discussion
of employment impacts of investing in renewable energy.
First, employment opportunities within the formal economy dier signicantly from those
in the informal economy in terms of job quality, even in the same industrial sector. Table 2
shows the average annual wage comparison between the urban unit employees (or the formal
sector employees) and the rural migrant workers who constitute the majority of the urban
percent of the pay received by regular urban labors, and this pattern has not changed since
2004. In addition to the gap in wage compensation, workers in the informal economy usually
work overtime while receiving little job security.
Second, focus on the formal sector employment will leave out the entire employ-
ment population in the rural sector, which constitutes the preponderance of
employment generated by investing in bioenergy.
Third, it is important to note the possibility that a rise in nal demand might not
increase employment in the informal economy as much as in the formal economy.
This is especially relevant to those self-employed who would work more to address
the rising demand instead of hiring more employees. It could also apply to others
working in the informal economy that are underemployed to some extent and
would be willing to work more hours to receive higher earnings instead of having
their employers hire another worker.
4.3. Employment data: formal sector employment
The 2007 employment data are compiled from Tables 13in the 2008 China Labor
Statistical Yearbook on a 90-industry level basis. The only employment data avail-
able at this level of detail are for the urban unit employment (danwei jiuye renyuan),
aconceptdierent from urban employment (chengzhen jiuye renyuan).
Two major
groups of population are excluded from the statistical denition of urban unit
employment: rst, the entire employment population in the rural sector, which
was about 62 percent of the total national employment in 2007 and 52 percent in
2012; second, those working in the urban sector but in the private enterprises, or as
self-employed or simply unregistered in the national statistics.
This population was
22 percent and 28 percent of the entire employment population for 2007 and 2012,
Table 2. Annual average wage (RMB) comparison between urban unit employees and rural migrant
Urban unit employees Rural migrant workers Rural wages as a share of the urban wages
2008 28,898 16,080 56%
2009 32,244 17,004 53%
2010 36,539 20,280 56%
2011 41,799 24,588 59%
2012 46,769 27,480 59%
Source: Tables 411 China Statistical Yearbook 2013 and National report on rural migrant workers in 2013.
Compared to urban employment data, urban unit employment data are more strictly
dened in the sense that they only include employment in three types of ocially
registered enterprises, or units (danwei). They are the state-owned units (guoyou
danwei), urban collectively owned units (chengzhen jiti), and other ownership units
(i.e. mixed ownership, or enterprises funded by foreign investment, or by investments
from Hong Kong, Macau, and Taiwan).
This type of employment is often associated
with regular wage, normal working hours, standard benets and job security, thus
providing the best available estimates for urban formal sector employment in China.
Table 3 presents the relationships among these statistical concepts for the years
2007 and 2012. As is shown, the urban unit employment (or formal sector employ-
ment), which aggregates employment from the three types of units, represents about
41 percent of total urban employment, and this percentage does not change sig-
nicantly from the 2007 data used in this paper to the latest available employment
data in 2012.
4.4. Employment data: informal sector employment
In this paper, employment in the informal sector refers to those working in the urban
sector yet either employed by the small and medium private enterprises, or as self-
employed, or not formally counted by the national statistics. Table 3 shows that the
share of urban informal employment as a share of total urban employment (about 59
percent) is relatively stable from 20072012.
The concept of private enterprises here does not imply all nongovernment enterprises as in
theUScontext;theyareinsteaddened as enterprises owned by natural persons(ziranren),
therefore do not include limited liability corporationsor share-holding corporations lim-
itedthat have corporate legal person(fa ren)status(Huang2009). They are mostly small
businesses. In 2012, there were a total of 10.9 million such enterprises registered with a total of
113 million workers (including those registered in both urban and rural areas, thus the
number is greater than 75.6 million in the urban private enterprises presented in Table 1),
making for an average of only 10 workers per enterprise, including the employers of such
enterprises. The average number for 2007 is 13 workers per enterprise, suggesting that the size
of the urban private enterprises has a declining trend.
Workers in such small-scale
enterprises usually enjoy little benets or job security or labor law protection (Huang
Table 3. Urban employment and urban units employment in 2007 and 2012 (in millions).
Category 2007 % 2012 %
Urban employment (million) 293.5 100 371.0 100
Urban units employment 120.2 41.0 152.4 41.1
State-owned units 64.2 21.9 68.4 18.4
Collective-owned units 7.2 2.5 5.9 1.6
Other Ownership units 48.8 16.6 78.1 21.1
Urban informal employment 173.3 59.0 218.6 58.9
Urban private enterprises 45.8 15.6 75.6 20.4
Self-employment 33.1 11.3 56.4 15.2
Not formally counted 94.4 32.2 86.6 23.3
Source: China Labor Statistical Yearbook 2008 and 2012, Table 11. Details of urban private enterprises and self-
employed are retrieved from Table 414 and Table 415 in China Statistical Yearbook (2008); Table 48 and Table 4
9 in China Statistical Yearbook (2013).
2009). Although informal employment usually includes those working in micro-enterprises
according to the Organization for Economic Cooperation and Development (OECD), an
enterprise of average size of 10 people cannot be counted as micro enterprises (which strictly
require number of employees as no more than 10 people, instead of on average) based on the
European Commission standard (Jutting, Parlevliet, and Xenogiani 2008). However, in this
paper, I still include this category based on a consensus in the literature on the Chinese
informal economy.
The self-employed persons (e.g. small shop and stall owners, artisans and apprentices,
proprietors of small eateries and food stalls, repair shop owners etc.) represent about 86.3
million over 41 million entities, making for an average of 2.1 employed persons per entity
usually the person registered together with a relative or friend. This number does not change
Not surprisingly this group of
people does not enjoy benets and job security (Huang 2009).
Finally, there are about 90 million unregistered urban informal employees (recorded as
not formally countedin Table 3), who are working as domestic helps, delivery workers,
street vendors and the like, with even lower levels of job security (Huang 2009).
The three main groups of the urban informal economy (private enterprises, the self-
employed and the unregistered) add up to a composite picture of low pay, little job
security, few or no benets and no protection under state labor laws. These character-
istics are consistent with the features of the informal economy dened by the
International Labor Organization (ILO).
4.5. Constructing E-O ratios for the informal sector
Constructing the employment data for the informal economy mainly rely on three
sources. First, I refer to the employment data on the three strata of the economy (i.e.
primary, secondary and tertiary).
Second, I use the employment data on the urban
private enterprises and self-employment for seven industrial sectors.
Third, I also use
the urban employment composition in Table 3 as a reference to disaggregate employ-
ment for industrial sectors that only have very high level of aggregation.
Constructing informal employment data for agricultural-related sectors is straight-
forward. The three strata data provides the total employment of agricultural-related
activities. When this data is subtracted by the formal employment in agricultural-
related activities we used for calculating the formal E-O ratio, then we will have the
informal employment data for the ve aggregated-related sectors including cropping,
forestry, animal husbandry, shery and service sector related to these four sectors. To
get an average informal E-O ratio for the ve agricultural-related sectors, we divide the
total informal employment by the aggregated gross output for these ve sectors.
Estimating informal employment for nonagricultural sectors is more complicated. In
order to take advantage of all the available information regarding the Chinese informal
economy, I break down the calculation of informal employment into two categories: the
group of people working in private enterprises or as self-employed (P&S), as well as the
group of those not formally counted.
For those working in the P&S, data are available for only seven sectors of high level
of aggregation, amounting to 70 million in total. They are manufacturing; construction;
transport, storage & post; wholesale and retail trades; hotel and catering services; leasing
and business services; services to households and other services. The dierence between
the 70 million and the 78.9 million (see Table 3) of total employment in the P&S is the
P&S employment for the remaining sectors in addition to the seven sectors, or 98
sectors by the details of aggregation as in the I-O model by 135 sectors. Then these 8.9
million workers are allocated to the remaining 22 nonagricultural sectors in the I-O
model according to the formal employment composition in these 22 sectors.
Thus, we
have the informal employment for all the nonagricultural sectors.
The second category of workers who are not formally counted in the national
statistics amounts to 94.4 million in total (Table 3). They are allocated to the 130
nonagricultural sectors according to the composition of P&S employment in those 130
sectors calculated from the previous step.
The two categories of workers constitute the
whole urban informal economy this paper focuses on estimating. Dividing this employ-
ment estimate by the gross output for each of the 130 sectors will give the informal E-O
ratio for these sectors.
4.6. Weighting the energy sectors
This section focuses on estimating the cost components of three kinds of renewable energy,
namely, solar power, wind energy and bioenergy, as well as the fossil fuel energy sectors.
Table 4 presents the aggregated information on their respective weighting structures.
4.6.1. Solar Photovoltaics (PV)
According to the IRENA (2012a)d
enition, the PV module cost is determined by raw
material costs, notably silicon prices, cell processing/manufacturing and module assem-
bly costs. The Balance of System (BOS) cost includes the cost of the structural system
(e.g. structural installation, racks, site preparation and other attachments), the electrical
system costs (e.g. the inverter, transformer, wiring and other electrical installation costs)
and the battery or other storage system cost in the case of o-grid applications (15).
Since the average selling price of solar PV modules has already converged among
nations including China, the cost structure among countries should not vary
The real cost dierences between countries lie in the installation costs.
Therefore I construct the weighting structure for solar energy with China-specic
information on the installation costs.
4.6.2. Wind
Wind energy in China consists of two main categories: onshore and oshore wind
power. According to IRENA (2013), oshore wind power installation usually has a
much higher construction cost share than the onshore wind power installation (25
percent versus 10 percent). However, since oshore wind power constitutes less than 1
percent of the total installed wind power in China, the analysis in this paper will focus
on the onshore wind power case.
I use the world average statistics on wind energy structure from IRENA (2012b) and
IRENA (2013) to work out the Chinas specic cost structure through combining the
information on Chinas total installed costs of on-shore wind energy.
4.6.3. Bioenergy
For the purpose of estimating meaningful employment opportunities, this paper focuses
the estimation for biofuel.
The costs for generating bioenergy power include three critical components (IRENA
2013). The rst part occurs in the process of growing biomass feedstocks. Feedstock cost
usually represents 40 percent to 50 percent of the total cost of electricity produced by biomass
technologies, varying by the transportation costs, labor costs involved and the quality of the
biomass sources (IRENA 2013, 66). Prices for the biomass sources range between USD 10/ton
to USD 160/ton (IRENA 2013, 67).
The second cost component arises when biomass feedstocks are transformed into the
energy form that will be used to generate heat and/or electricity, or in most cases, when
biomass is transformed into biofuel. This includes the cost for the equipment (prime
mover and fuel conversion system), fuel handling and preparation machinery.
The last cost component is generated during the use of power generation technol-
ogies, including engineering, construction and planning costs. It also includes grid
connection, roads and new infrastructure required for the project (IRENA 2013, 68).
For non-OECD countries, such costs are estimated to be in the range of USD 600 to
USD 1400/kW (IRENA 2013, 68).
Based on the statistics for some non-OECD countries, I use an average of the
costs to represent the cost breakdown for China.
4.7. Traditional fossil fuel sectors
4.7.1. Coal
UNEP et. al (2008)found that modern coal-red power plants are becoming much less
labor-intensive than a decade ago. However, developing countries are still lagging
behind advanced nations in applying the technology to reduce the labor-intensiveness
in the coal sector.
The coal sector in China distinguishes itself in its heavily weighted component of
transportation costs. Since most coal mining activities are conducted in the western and
northern part of China while major coal demand occurs in the eastern and southern
part of China, coal transportation cost usually makes up about 55 percent to 60 percent
of the consumer-end electricity costs (Mao et. al 2008). In addition to the transporta-
tion costs, coal-red energy production costs include production costs on the coal-
mining sites as well as the actual electricity generation costs occurred in the coal-red
power plant. Xie et al. (2011) show that 88 percent of the coal-red power generation
costs come from the coal products (including the mining, processing and transporta-
tion) and the rest (i.e. electricity distribution and other related services) constitute only
12 percent.
4.7.2. Oil/natural gas
The reason to combine oil and natural gas in the same industry, although they have a
slight dierence in their cost structure to produce energy, is that they are also combined
in the I-O table. The process of using oil/natural gas to produce energy mainly depends
on activities in mining, transportation, rening and chemical product manufacturing,
10 Y. CHEN
as well as management-level activities (Cui, Zhang, and Li 2010). The weighting
structure presented in Table 4 is based on two case studies from Bing et al. (2008).
5. Results and discussion
5.1. Employment generation per million of USD
Tables 5 and 6present the results in terms of job created per US#1 million spent in both
renewable energy and fossil fuel sectors.
As we can see (Table 5), the bioenergy sector generates the highest number of jobs
with a given amount of spending level, with 224 direct jobs per $1 million. This
contrasts with a range of about 2729 for solar and wind energy due to the signicant
amount of agricultural-related activities involved in bioenergy production. It is also
signicantly higher than the direct jobs generation in the fossil fuel sectors, which range
between 30 to 70 jobs per $1 million. In terms of indirect jobs those jobs generated
through the supply chains associated with renewable energy production the three
kinds of renewable energy sectors show relatively consistent estimates, about 6070 jobs
per $1 million. This is substantially higher than the fossil fuel sectors which range
between 4050 jobs per $1 million.
Table 4. Industries and weights for renewable and fossil fuel energy in the I-O models.
Energy Source I-O Industry Weight (%)
Solar Energy Mining of Non-Ferrous Metal Ores 17.1
Smelting of Non-Ferrous Metals and Manufacture of Alloys 8.5
Manufacture of Equipments for Power Transmission and Distribution and Control 11.1
Manufacture of Other Electronic Equipment 12.7
Manufacture of Special Purpose Machinery for Mining, Metallurgy and Construction 15.2
Research and Experimental Development 12.7
Construction 22.7
Wind Energy Research and Experimental Development 13
Construction 22
Manufacture of Synthetic Materials 10
Manufacture of Boiler and Prime Mover 7
Manufacture of Metal Products 30
Manufacture of Equipments for Power Transmission and Distribution and Control 10
Production and Supply of Electric Power and Heat Power 8
Bioenergy Agriculture 25
Forestry 20
Manufacture of Lifters 36
Construction 15
Manufacture of Equipments for Power Transmission and Distribution and Control 3
Research and Experimental Development 1
Coal Mining and Washing of Coal 28
Manufacture of Special Purpose Machinery for Mining, Metallurgy and Construction 27
Transport Via Railway 23
Other Services 22
Oil and Gas Extraction of Petroleum and Natural Gas 50
Processing of Petroleum and Nuclear Fuel 20
Transport Via Pipeline 5
Other Services 25
Source: The weighting structures for all energy sectors are calculated by the author based on information from existing
literature. These studies include Li et al. (2007), IRENA 2012a) Figure 4.2 and 4.5, IRENA (2013), 5255; IRENA (2012b),
19&p24; IRENA (2013); Mao (2008) and Xie et al. (2011); Bing et al. (2008). See more details regarding the
weighting construction in the authors dissertation.
If equal weights are assigned across the three renewable energy sectors and the two fossil
fuel sectors, respectively, then results suggest that spending $1 million on renewable energy
generates about 162.3 jobs, including 93.1 direct and 69.2 indirect jobs, on average. This
contrasts with only 96.7 jobs, including 49.5 direct and 47.1 indirect jobs, generated from $1
million overall spending on both coal and oil/gas.
The results suggest that, for China, spending on the clean energy economy (with
a combination of the three kinds of renewable energy focused in this paper) will
produce nearly 70 percent more jobs per dollar of expenditure than an equal
amount of spending on fossil fuels. Thus, a clean energy investment strategy will
not destabilize the overall employment level in China relative to the investment
strategy biased towards the fossilfuelenergysectors.
5.2. Composition of employment
Table 6 presents the composition of employment in terms of formal and informal
employment. As we can see, the bioenergy sector is constituted mostly (261 jobs or
90 percent of jobs) by employment in the informal economy. Those are the people
growing or logging for bioenergy feedstock, as well as workers engaged in manu-
facturing equipment and machinery to transform feedstocks into the energy form
that will be used to generate heat and/or electricity.
On the other hand, three quarters of employment (or about 75 jobs per #1 million) created
by solar and wind energy sectors are within the informal economy. Workers in the construc-
tion sector constitute a signicant portion. Specic manufacturing sectors also contribute to
the informal component of jobs in these two sectors. They include manufacturing of power
transmission equipment and mining machinery for the solar energy sector (i.e. for the mining
Table 5. Total employment generation in renewable energy and fossil fuel energy sectors (unit: jobs
per $1 million).
Direct Indirect Direct + indirect
Renewables (average) 93.1 69.2 162.3
Solar PV 28.1 72.0 100.1
Wind 27.1 73.0 100.1
Bioenergy 224.0 62.5 286.4
Fossil fuels (average) 49.5 47.1 96.7
Coal 68.0 43.6 111.6
Oil/natural gas 31.0 50.7 81.7
Source: Authors own calculation
Table 6. Formal and informal employment share in total employment in renewable energy and fossil
fuel energy sectors.
Total employment (jobs per #1 million) Formal employment share Informal employment share
Solar PV 100.1 26% 74%
Wind 100.1 25% 75%
Bioenergy 286.4 9% 91%
Fossil fuels    
coal 111.6 30% 70%
Oil/natural gas 81.7 19% 81%
Source: Authors calculation.
12 Y. CHEN
of polycrystalline silicon materials crucial for building solar panels), as well as manufacturing
of metal products and other sectors related with building wind turbines.
The fossil fuel energy sectors also have a high level of informal employment
composition. In the oil and natural gas sector, 20.9 jobs (or 81 percent of the jobs)
generated from #1 million spending are within the informal economy. These are mostly
workers on the eld extracting oil and natural gas, corresponding to the informalization
of the state-owned oil and natural gas enterprises in the recent decade. With respect to
the coal energy sector, 78.6 jobs (or 70 percent of the jobs) generated from #1 million
spending are within the informal economy. These are mostly workers in the railway
transportation, mining and coking sectors.
It is important to note that the amount of informal employment generation might be
overestimated either due to the inclusion of small- and medium-scale private enter-
prises, or the fact that the nal demand is only raising earnings instead of generating
new employment especially for the self-employed population.
5.3. O-I ratio (output multipliers)
In this section, I compare the output multipliers over time to show their relative
stability for all energy sectors in the past decade.
The output multipliers give the
amount of output increase as a result of an increase in nal demand, thus providing
information on the production relationships between sectors in the I-O table. The data
were taken from the World Input-Output Database (WIOD), a project of the European
Commission, which produces annual national I-O tables for selected countries. For
China, the WIOD tables are more aggregated (i.e. 35 sectors) than the one I used to
produce the employment estimates in this paper (i.e. 135 sectors). I use the same
weighting schemes that were applied to produce employment estimates to estimate
output multipliers for synthetic sectors of both renewable and fossil fuels the energy
sectors that are not readily available in the original I-O tables. Table 7 presents the
As we can see, for all the energy sectors, the annual average percentage changes in
the output multipliers from 19952007 are negligible.
This concludes that the pro-
duction relationships between the domestic sectors in China did not change signi-
cantly over the 12-year period between 1995 and 2007. This conclusion addresses the
concerns for not incorporating dynamic elements in the model. It is now reasonable to
assume that output multipliers would change only at a modest pace over the next two
Table 7. Output multipliers and percentage changes in energy sectors in China, 19952011.
1995 2007 2011 19952007 20072011
Renewables     Annual average % increase
Solar 2.41 2.56 2.64 0.5% 0.7%
Wind 2.40 2.56 2.58 0.6% 0.2%
Bioenergy 2.17 2.31 2.41 0.5% 1.1%
Fossil fuels        
Coal 2.05 2.15 2.14 0.4% 0.1%
Oil and Natural Gas 2.18 2.17 2.06 0.1% 1.3%
Source: Authors calculation based on World Input-Output Database.
decades for which employment projection will be made. It also implies that it is
reasonable to think that the employment estimates will not be improved much by
using the 2011 I-O table with only information on 35 aggregated sectors than to use the
2007 I-O table (with a detailed breakdown of 135 sectors) as this paper does.
5.4. Productivity and declining E-O ratio
Since an I-O table also provides information on gross output by sectors, lack of a more
updated I-O table with a comparable level of detail as in the 2007 I-O table means that
even though more recent data are available for employment by sector, they are not
compatible with the 2007 industry data and therefore cannot be utilized to calculate a
more updated E-O ratio at a desired level of detail. To compensate for the lost
information, I calculate the E-O ratio for the period 20072011 to show its general
pattern in the years post-2007, although it is dened more loosely and at a more
aggregated level with a dierent unit compared with the E-O ratio I use for the
major estimation.
Note that the E-O ratio is simply the inverse of labor productivity,
so an increase in labor productivity will reduce the E-O ratio. I use the same weighting
scheme to aggregate relevant sectors to synthetic energy sectors of interest to this
The results are presented in Table 8.
As we can see, the coal sector has the most signicant productivity gain among all
energy sectors, almost twice the productivity gain for all renewable energy sectors. This
increasing productivity suggests that for the same spending level, the employment
generation in the coal sector will be much smaller now than 5 years ago. Although
we observe that the coal sector shows comparable employment generation per #1
million relative to the renewable energy sectors in terms of both formal and informal
employment, this result suggests that the coal sector will very quickly lose its advan-
tagein terms of labor intensiveness. The oil and gas sector does not show productivity
gains as dramatic as the other energy sectors, yet it is already the least labor-intensive
energy sector. Thus, it is reasonable to conclude that renewable energy sectors, com-
pared with fossil fuel energy sectors, have the advantage over fossil fuel energy in terms
of job creation in the long run.
Table 8. Productivity changes in energy sectors in China. Measured as the inverse of productivity:
Number of persons 1 million of RMB.
2007 2011 2007 to 2011
Renewables   Annual Average Percentage change 
Solar 3.19 2.03 9.1%
Wind 3.19 2.02 9.2%
Bioenergy 3.23 2.14 8.4%
Fossil fuels     
Coal 2.67 1.22 13.6%
Oil and Natural Gas 0.91 0.78 3.6%
Source: Authors calculation based on Table 132or142 Main Indicators of Industrial Enterprises above Designated
Size by Industrial Sector from China Statistical Yearbook 20082012. Price index based on Table 91, China Statistical
Yearbook 2012. Data for productivity for construction sector is calculated based on Table 1534 China Statistical
Yearbook 2012 and Table 1436 in China Statistical Yearbook 2008.
14 Y. CHEN
6. Conclusion
This paper addresses the impacts of a transformative renewable energy investment
program for China. It focuses on estimating the relative employment impacts of
investments in three renewable energy sectors in China solar, wind and bioe-
nergy as compared to spending within Chinas traditional fossil fuel sectors (i.e.
coal, oil and natural gas).
Ind that the bioenergy sector generates the highest number of jobs from a given
level of spending. That is, I estimate that Chinas bioenergy sector generates 224 direct
jobs per #1 million of spending. This is in contrast with the generation of a range of
2729 direct jobs in the solar or wind energy sector. The large dierence here is the
result of the signicant amount of agricultural-related activities involved in bioenergy
production. Such job generation due to bioenergy is also signicantly higher than the
direct jobs generated through spending within Chinas fossil fuel sectors, which gen-
erate about 30 to 70 jobs per $1 million of spending.
In terms of indirect jobs, the three kinds of renewable energy sectors show relatively
consistent estimates about 6070 jobs per $1 million, and are much higher than the
fossil fuel sectors, with a range between 4050 jobs per $1 million. If equal weights are
assigned across the three renewable energy sectors, then results suggest that spending
$1 million on renewable energy generates about 162.3 jobs. This is in contrast with the
mere 96.7 jobs generated from $1 million overall spending on both coal and oil/gas. I
also show that the coal industry is likely to lose its advantageover some renewable
energy sources in terms of job creation as productivity increases in the near future.
Like many deindustrializing countries, employmentlossasaresultofphasingoutfossilfuel
energy in China is also clustered in specic regions. In 2017, the China Academy of Social
Sciences, a government-aliated research institute, reported that employment in the coal
industry is concentrated in three regions: the coal-mining provinces in the Northeastern rust-
belt region (i.e. Liaoning, Jilin and Heilongjiang), Shanxi province, and the Inner-Mongolia
Autonomous Region. Job loss has been fast: total employment in the coal mining sector was
around 3.96 million in 2016, almost a one quarter decline from the peak level of 5.3 million in
2013. However, the report barely mentioned any plans for investing in infrastructure or
human capital that will help re-employ the workers who lost their jobs. The only suggestion
for the local government was to rely on market and private capital.Encouraging ride-sharing
industries (i.e. the Chinese Uber and Lyft) was mentioned as one example for reemployment.
The region-specic feature of job loss, therefore, needs to be taken into consideration for job
In terms of formal and informal employment, with Chinas bioenergy sector, to
begin with, job creation is heavily weighted toward informal jobs specically, about 90
percent, or 261 jobs per #1 million, will be informal jobs. The proportions of informal
jobs are somewhat lower at about 75 percent across the solar and wind energy
sectors. Within Chinas fossil fuel energy sectors, informal job creation is about 81
percent in the oil and gas sector and 70 percent in the coal sector.
For rural workers, job creation in the agricultural sector as a result of investment in
bioenergy might be welcomed as it implies that they do not need to migrate to the
urban sector to earn extra income to support the family. Yet, whether these jobs created
in or close to their hometown are formal or informal remains an important question.
Formal agricultural employment with stable pay and standard benets will attract
workers to stay in the rural sector. The very reason that agricultural work is generally
considered informal in the existing literature is due to the nature of its seasonality. If
income is still unstable and insucient for the agricultural jobs created in the bioenergy
industry, then workers will still have to migrate to the urban sector to earn more
income. The employment issue thus remains unsolved.
The overall implication of this study is that a renewable energy investment strategy does
not necessarily destabilize the overall employment level in China relative to the investment
strategy biased towards the fossil fuel energy sectors. The challenges of raising job quality
standards in Chinas energy economy will nevertheless remain substantial.
1. CASS (2010) uses the Input-Output model (I-O) at a very aggregated level. Multipliers are
calculated for the interactions among only three sectors: agricultural, industrial and service
sector (8687). The calculations by Greenpeace (2012) are based on the assumptions that
for every new megawatt of capacity installed in a country in a given year, 14 persons/years
of employment is created through manufacturing, component supply, wind farm devel-
opment, construction, and transportationand 0.33 person/yearsnecessary for opera-
tions and maintenance work at existing wind farms. Although this might be useful as a
rst approximation for a global estimate, they do not estimate the employment-output
ratios for individual renewable energy sectors, and for specic countries.
2. Note that denition of direct and indirect jobs in REN21 (2013) is slightly dierent from
this paper, whereas the former does not include R&D jobs as direct jobs.
3. This O-I ratio corresponds to the Leontief inverse coecient, generated through matrix
manipulation on the raw I-O data.
4. The 2012 I-O table had not been published at the time of writing.
5. The 135 sectors include ve agricultural sectors; ve mining sectors; 81 manufacturing
sectors; three utilities sectors; one construction sector; nine transportation, storage and
postal services sectors; three communication sectors, one retail sector, two hotel and
restaurant sector, two nance sectors, one housing sector, and 22 other services sectors.
6. Lindner et. al (2012,2013)developed a rigid method to disaggregate the electricity
production, heat and water distribution and supply sector (EPHWD) in the I-O table.
This enables them to expand a 42 by 42 table from the World Input-Output Database
(WIOD) to a 50 by 50 table. However, in this paper, I focus on the employment impacts in
the initial stage of building a green economy: the research & development as well as
production phases. Therefore I do not include this EPHWD supply in the weighting
structure. Also the table I start with has 135 sectors, a more detailed breakdown compared
with the tables from WIOD. The results will not be aected if I disaggregate the sector in
my calculation.
7. See National report on rural migrant workers in 2013, published on May 12th, 2013 and
retrieved on 5 October 2014. See
8. Note that according to the statistical denition available from the China Bureau of
Statistics, those who work in the Township and Village Enterprises (xiangzhen qiye) are
counted as rural employment, therefore not included in urban employment from Table 3
1. The urban and rural division here is, in the administrative sense, unrelated to the
household registration status of the worker.
9. The percentage estimation is calculated based on Table 11from the 2008 and 2013 China
Labor Statistical Yearbook.
10. Ibid.
16 Y. CHEN
11. State-holding enterprises refer to those mixed-ownership enterprises where the govern-
ment has a larger share of the equity capital than any other shareholder. See Explanatory
Notes on Main Statistical Indicatorsin the China Labor Statistical Yearbook.
12. China Statistical Yearbook (2008)and (2013).
13. Ibid.
14. See
15. See Table 44from China Statistical Yearbook (2008). The primary industry includes
agriculture, forestry, animal husbandry and shery; Secondary industry includes mining,
manufacturing, power sector and construction sector; Tertiary industry includes every-
thing else.
16. See Tables 413 from China Statistical Yearbook (2008). The seven industrial sectors are
manufacturing; construction; transport, storage & post; wholesale and retail trades; hotel
and catering services; leasing and business services; services to households; and other
17. This allocation method assumes relatively similar formal and informal employment ratios
in dierent sectors. Although the assumption might not hold for certain sectors, this is the
best available method given the data limitation in the Chinese informal economy. 22 is the
result of subtracting 135 sectors by the 5 agricultural-related sectors and the 98 sectors
with data available on the private enterprises.
18. This allocation method assumes a relatively stable ratio between those not formally
counted in the national statistics and those counted as working for private enterprises or
as self-employment in all the nonagricultural sectors. Although this assumption might still
not hold for certain sectors, it is a more realistic assumption than the one I use for
allocating the employment group of private enterprises and the self-employed. And again,
this is the best available method given the limited information on the Chinese informal
19. This paper focuses on the employment eects of solar PV, on-shore wind and low-
emission bioenergy. They are chosen based on their relatively signicant employment
impacts. See more details in the authors dissertation.
20. Note that the use of BOS is slightly dierent in IRENA (2013, 51), where BOS are used to
refer all costs excluding both the module costs and the installation costs. Here we still use
BOS as including the installation costs for convenience.
21. Solarbuzz 30 November 2012: Installed PV system continue to exhibit strong global
22. See more about the calculation of weighting structure in the authorsdissertation.
23. See China unable to achieve 5GW oshore wind goal by 2015 (http://www.windpower
2015) & China National Renewable Energy Center.
24. See details in the authorsdissertation.
25. Ibid.
26. Ibid.
27. Ibid.
28. Ibid.
29. Output multipliers are calculated from the Leontief inverse for each of the four
countries. The Leontief inverse matrix is given by L = (I-A)
in which L is the
Leontief inverse matrix, I is the identify matrix, and A is the matrix of I-O
coecients derived from the WIOD tables.
30. I intentionally choose 2007 as the end point to avoid cyclical complication by the 2008
economic crisis.
31. Note that the latest China Statistical Yearbook 2013 did not publish estimates of gross
output value or the annual average persons by industrial sector consistent with those
published in previous yearbooks. Thus, I exclude the 2012 data for comparison.
32. Note that gross output data are not available for all the sectors relevant for the energy
sectors (such as R&D and Transportation). Under such circumstances, it is assumed that
these sectors with missing information will experience the same productivity changes as
the weighted average of productivity changes in other relevant sectors for producing the
same kind of energy.
Disclosure statement
No potential conict of interest was reported by the author.
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The Input-Output model is as follows:
Xi¼ai1X1þai2X2þ...þaij XjþDi(1)
X¼AX þD(2)
In the rst equation, Xiindicates the output produced by the ith sector; a1iindicates the
required input from the ith sector to produce output for the jth sector. Equation (2)
contains the same information as in Equation (2), only is written in the vector form, where
notation Aindicates an iby jmatrix containing all elements of ai1through aij.Equation
(3) is a re-writing of Equation (2) where notation Iindicates an identity matrix. Equation
(4) expresses Xin terms of all the other components in the equation with the assumption
that (I-A) is invertible.
The assumption of a linear I-O model creates some limitations that need to be
addressed. To put it into the context of this paper, linearity here suggests that the
employment impacts of a #1 billion renewable energy investment project will be exactly
1000 times greater than a #1 million spending on the same project. However, this
assumption might not be accurate.
First, the basic linear I-O model does not incorporate any supply constraints that might occur
from investing, for example, 1,000 times more in the same project. Yet within the current context
of the Chinese economy, which is operating with substantial overcapacity especially after the
2008 global economic recession, it is reasonable to assume that supply constraints are less
binding than demand constraints in the short and medium term.
Second, the basic linear I-O model assumes that relative prices are xed regardless of any
changes in demand. For example, if demand for solar panels declines due to economic recession,
then prices of the panel will fall. This could provide incentives for purchasing more solar panels,
raising demand. This issue could be addressed in a more fully specied model such as a
Computable Generable Equilibrium (CGE) model, but with its own limitation as discussed later.
Third, when applying a basic linear I-O model, productive relationships are assumed to
be stable over the period of analysis. This assumption would seem especially relevant in
employment estimation of the renewable energy investment. However, when put into
context, it only implies that productive relationships such as those between the manufac-
turing sector and construction sector in building solar energy are fairly stable, which is
realistic to a certain extent. In Section 5.3, I compare the O-I ratio I-O tables from 1995 to
2011 to show that productive relationship among sectors could be realistically assumed to
be fairly stable in the context of China.
Fourth, the static I-O model does not incorporate the treatment of time. It is certainly
realistic to think that investment and employment generation occurs over a reasonable
amount of time, rather than happening at one xed point in time. A dynamic model would
addressthisconcernmoreaccurately,bitisnot necessary for this paper since the estimates
cover an intermediate term rather than a specicyear.
The advantage of a relatively simple and transparent I-O approach is seen more clearly by
comparing it with the CGE model, which is a relatively more complex modeling framework. In
the CGE models, price dynamics, supply constraints and technological change are incorporated
into the basic I-O structure through assumptions on a variety of price elasticities and equilibrium
conditions. Critically, most CGE models operate with an assumption of full employment. Despite
the crucial roles these assumptions play in the model, they are almost impossible to be identied.
In addition, these models are usually proprietary. This proprietary nature generally prevents
independent verication of the logic of the model. Also, the assumption that the economy
20 Y. CHEN
operates at full employment at all times is unrealistic and inherently contrary to the purpose of
using the model, which is to estimate job creation through investments. Compared with the CGE
model, the I-O model has critical benets in terms of its relative simplicity, clarity, minimum
number of behavioral assumptions and ability to handle details more fully as a result.
In general, a basic linear I-O model is still the most eective available tool for estimating the
employment eects of a large-scale renewable energy investment project in a national economy.
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... 69 Per dollar of expenditure, spending on renewable energy will produce nearly 70 per cent more jobs than spending on fossil fuels. 70 As a result, net employment gains in renewable-based electricity are estimated at some 2.5 million jobs, offsetting employment losses of around 400,000 jobs in fossil fuel-based electricity generation. 71 Similarly, measures to improve energy efficiency have an important job-creation potential: each US$ 1 million spent in improved energy efficiency supports 7.72 jobs, while similar investments in the renewable and fossil fuel sectors create 7.49 and 2.65 jobs, respectively. ...
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As the world faces an unprecedented economic crisis, governments have adopted various stimulus plans to speed up the recovery. This article examines how the fight against poverty can be combined with the search for a development model that mitigates climate change and halts the erosion of biodiversity. The transformation also needs to strengthen the rights of people living in poverty. In specific areas, such as energy, buildings, food or mobility, 'triple-dividend' actions can be taken that would reduce the ecological footprint while simultaneously creating employment opportunities for people with low levels of qualification and facilitating access to goods and services essential to the enjoyment of human rights. Such actions should be underpinned by a different development model that places the fight against inequalities above the exclusive focus on economic growth. 'Building back better' entails taking public action towards the eradication of poverty within planetary boundaries.
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High economic and tourism growth demand extensive energy production that needs the energy creation industry’s attention and demands the researchers’ and policymakers’ emphasis. Hence, the present study examines the impact of economic and tourism growth on renewable energy production (REP) in Vietnam. The present research has taken the gross domestic product (GDP), exports, human capital, and employment rate to measure the economic growth, while international tourism receipts and expenditures have been used to measure the tourism growth. The secondary data have been extracted from 1983 to 2020 using World Development Indicators (WDI) database. The Nonlinear Autoregressive Distributed Lagged (NARDL) model has been applied to investigate the linkage between the constructs. The findings indicated that GDP, exports, human capital, employment rate, international tourism receipts, and expenditures have a significant and positive relationship with REP in Vietnam. These results guide the regulators while making regulations related to the extensive energy production in return for high economic and tourism growth.
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Dans un contexte marqué par la modération de la croissance, l’augmentation des taux de chômage, notamment depuis la crise financière de 2008 et la crise sanitaire de 2020, et la fluctuation des prix énergétiques sur les marchés à cause de l’invasion Russe de l’Ukraine, et par une déficience des ressources énergétiques fossiles au niveau national, la question du développement des énergies renouvelables pour une transition énergétique « verte » se pose avec plus d’acuité aujourd’hui. A cet effet, la question principale de la recherche est : Quel est l’impact du développement des technologies des énergies renouvelables sur la création des emplois au Maroc ?.
Conference Paper
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این قرن پایان سوخت‌های فسیلی است، از این رو این مطالعه، انقلاب انرژی تجدیدپذیر باد را تا نیمه قرن حاضر نشان می‌دهد. ازآنجاییکه انتشار کربن، نگرانی اصلی این قرن است، این مقاله با هدف اصلی کاهش انتشار گازهای گلخانه‌ای به منظور رهایی محیط زیست از بند این بحران و کاهش دمای کره‌ی زمین انجام شده است. در ادامه پیشرفت امنیت انرژی، افزایش سود اقتصادی، افت هزینه‌ها و اشتغال‌زایی به عنوان اهداف تکمیلی درنظرگرفته شده است. این مقاله به بررسی آینده نیروگاه‌های ساحلی و فراساحلی در پنج شاخص ظرفیت، پیشرفت، هزینه، سرمایه‌گذاری، وزن و اندازه پرداخته است و نوآوری، سیاست ها و موانع نیروگاه‌های بادی را معرفی می‌کند.
The American Geological Association has made some estimates for reserves in the Eastern Mediterranean region. According to these estimates, it is predicted that there are very high amounts of natural gas and oil resources in the deltas of Nile, Levant and Aphrodite. These figures are large enough to meet Europe’s 10-year natural gas need. On the other hand, another important point in this process is that these reserves are the amount determined in the studies conducted so far. In other words, there is a possibility that the actual reserve amount in this region is well above the estimated. Investigations in the relevant region are ongoing. As a result of these investigations, there is a possibility that new reserves will be identified. Because of this situation, there are many countries engaged in energy exploration in the Eastern Mediterranean. Firstly, the countries, which have a border to this region like Turkey, Israel, Egypt, the Turkish Republic of Northern Cyprus, Cyprus, Greece, Lebanon, Syria and Libya, are very active in this region. In addition, although there is no border to the region, countries such as the USA, Russia, England, France and Italy want to maintain their weight in the energy equation in the Eastern Mediterranean. It is obvious that the size of the energy reserves in the region lies at the heart of the energy struggle in the Eastern Mediterranean. This situation causes an increase in political tensions between some countries operating in the region. No country wants to give up this economic size. Therefore, some countries like Turkey-Greece and Libya-Egypt have problems with each other. The aim of this study is to develop strategies to reduce the political tension between countries due to energy reserves in the Eastern Mediterranean region. In this context, an analysis has been carried out by considering data mining and AHP methods in a hybrid way. The analysis process in question consists of 2 different stages. In the first process of the analysis, the most common words in academic studies are categorized using the LDA model. In this framework, 4 different categories are created. In the second phase of the analysis, more important factors are determined with the help of AHP method. The findings indicate that political relationship is the most significant factor to solve the conflict in Eastern Mediterranean region. Furthermore, transportation also plays a key role in this framework. Nevertheless, energy production and reserve amount have lower important roles to minimize this problem. Considering these results, ways of establishing positive relations between all countries in the region should be sought. Searching for a reconciliation with the energy companies making exploration, as well as with the countries, may contribute to solving the problems in the region.
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Most proposed solutions in the Global Green New Deal literature involve finance and technology transfers to address the imbalance between the Global North and Global South, while providing little discussion of the internal socioeconomic structures within countries in the Global South. This article uses China as a case study to show that without addressing the issue of domestic informality, the potential benefits of a Global Green New Deal are less likely to be fully realised in the Global South. We use the Input-Output method and our originally constructed data on formal and informal employment to calculate the informal employment share in two exemplary renewable energy sectors: solar and wind. We find that more than half of the jobs created in the solar and wind energy sectors, with a given level of spending, will be in the informal economy, and hence are associated with low wages and little social welfare protection. The results imply that, without addressing informality, both renewable energy sectors perpetuate the informal structure in the broader economy. We also question the capitalist nature of ‘green jobs’ created by the Green New Deal. Based on the results, we call for a more organic integration of a Global South perspective in the studies of a Global Green New Deal. JEL Codes: Q01, E26, N55
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We measure the size of informal employment using both labor market as well as energy consumption approaches. We find that the share of informal employment in total employment was increasingly most of the time between the mid-1990s and mid-2000s, and started steadily decreasing after that until now. This has two contradicting implications for the Chinese economy. On the one hand, it is good news for China’s progress in human development as the process of formalization often brings better income and welfare. On the other hand, for China’s capitalism this could be bad news since the labor costs are likely to continue increasing with a decline in the reserve army of labor.
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The informal economy is a vibrant and growing phenomenon, offering both opportunities and lessons on resilience and innovation. When considering global social, economic and environmental challenges, resilience and innovation are valuable response strategies. The notion of a green economy has similarly inspired a number of ideological, geopolitical and institutional responses, yet a review of the dominant approach indicates the propensity to undervalue or ignore the informal economy. Within the context of sustainable development and poverty eradication, connecting the informal economy with the green economy is imperative. This paper explores possible connections between the green economy and the informal economy in South Africa and argues that by engaging the informal economy in discussions on the green economy, a more informed policy and planning environment may ensue, resulting in more socially equitable and environmentally sustainable development.
The positive impacts of an increasing share of renewable energy on the mitigation of climate change as well as on the decrease of the dependency of energy imports are indisputable. However, one persistent problem for the German economy has been its high level of unemployment in the recent past. Therefore, any policy strategy will be measured also by its net impact on the labor market. The paper describes the results of a study that models this impact and is novel within three respects: firstly, an Input–Output-Vector for the renewable energy sector was developed based on the results of more than 1000 interviews with an extensive questionnaire, secondly gross and net effect of two different policy scenarios for Germany until 2030 were calculated and thirdly the approach varies from earlier studies by its explicit modeling of export and foreign trade effects.
Oil is a curse, it is often said, that condemns the countries producing it to an existence defined by war, corruption and enormous inequality. Carbon Democracy tells a more complex story, arguing that no nation escapes the political consequences of our collective dependence on oil. It shapes the body politic both in regions such as the Middle East, which rely upon revenues from oil production, and in the places that have the greatest demand for energy. Timothy Mitchell begins with the history of coal power to tell a radical new story about the rise of democracy. Coal was a source of energy so open to disruption that oligarchies in the West became vulnerable for the first time to mass demands for democracy. In the mid-twentieth century, however, the development of cheap and abundant energy from oil, most notably from the Middle East, offered a means to reduce this vulnerability to democratic pressures. The abundance of oil made it possible for the first time in history to reorganize political life around the management of something now called "the economy" and the promise of its infinite growth. The politics of the West became dependent on an undemocratic Middle East. In the twenty-first century, the oil-based forms of modern democratic politics have become unsustainable. Foreign intervention and military rule are faltering in the Middle East, while governments everywhere appear incapable of addressing the crises that threaten to end the age of carbon democracy-- the disappearance of cheap energy and the carbon-fuelled collapse of the ecological order. -- Book jacket.
Energy saving and emission reduction make new requests for power generation enterprises. Coal-fired power generation enterprises must report their own energy consumption and pollutant emission to trading center, meanwhile the energy consumption cost, environmental cost and other cost of these enterprises should be analyzed and on this basis their electricity prices to be reported are decided. By means of analyzing dynamic cost of coal-fired power plant, the constitution elements of generating cost are determined and the analysis model and calculation model of cost elements are built. Through the calculation of actual cases, the relation between generating cost and unit loads, which is available for the reference of coal-fired power plants to control generating cost, coal consumption and pollutant emission, is obtained.
Missing process detail of sectors in Input–Output (I–O) tables has been pointed out as a limitation of I–O analysis in environmental-economic life cycle assessment. Aggregation of resource-intensive sectors decreases the accuracy of the results. Often, economic sectors are compiled in a more aggregated form than environmental satellite accounts, and as [Lenzen, M. (2011) Aggregation Versus Disaggregation in Input–Output Analysis of the Environment. Economic Systems Research, 23, 73–89] asserts, it is superior for environmental analysis to disaggregate the I–O table, even if only partial information exists for the disaggregation. In this paper we present a methodology to disaggregate the electricity sector of the Chinese national I–O table by using regional information and cost data for operation and maintenance of power plants. The electricity sector is disaggregated into a transmission and distribution sector as well as eight sub-sectors representing different types of technology in power plants (subcritical coal, hydro, etc.). The electricity consumption mix of each industry is determined by using regional industry presence and regional electricity power mixes. The disaggregated I–O table offers refined results for calculating emissions embodied in international exports from China, a valuable contribution for estimating national greenhouse gases emissions inventories under the consumption-based approach for countries that rely heavily on imports of goods from China.
Missing process detail of sectors in Input-Output tables has been pointed out to be a limitation to using Input-Output Analysis (IOA) as an environmental-economic Life Cycle Analysis (EE-LCA) tool as it increases uncertainty of results. Often, economic sectors are compiled in a more aggregated form then environmental satellite accounts, and as Lenzen (2011) has pointed out it is superior for determining environmental multipliers to disaggregate economic data as opposed to aggregating the environmental datasets. In this paper we present data and methodology to disaggregate the electricity sector of China's national IO table, using as much external information as possible, into a transmission and distribution sector as well as 8 sub-sectors representing power plants. We determine sector specific electricity consumption mixes based on regional industry clusters and local electricity generation mixes. By multiplication with a CO 2 emissions satellite account we show the total embodied emissions in sectors' final demand in the disaggregated table. These results are compared with emissions intensity from a second disaggregation run in which the disaggregation criterion based on the national average electricity generation mix.
Environmental concerns and the search for climate change mitigation have led to the deployment of renewable energy technologies (RET) in several countries. The adoption of incentive policies, especially those based on heavy subsides, has motivated the discussion of social and economic benefits brought about by these technologies, mainly on the impact on employment rates. In this context, several studies have been conducted to quantify job creation by RET, concluding that the latter are more labor intensive than traditional fossil fueled technologies. However, results for different assessments vary largely due to distinct methodological approaches, and are frequently highly aggregated. Thus, results are not comparable or applicable to other contexts. Previous studies have failed to quantify the effects of imports and exports of RET equipment in total employment, usually associating employment and installed capacity in the year studied. This study has aimed to address these issues, creating an index for employment quantification based on production, instead of installed, capacity. We have estimated both direct jobs in manufacture, construction, and operation and management, and indirect jobs both in the upstream supply chains of materials and inputs to manufacture of wind turbines and construction of wind farms. We have also performed an assessment of jobs created in wind energy projects which are expected to begin operation in Brazil until 2017. The resulting job potential in Brazil corresponds to13.5 persons-year equivalent for each MW installed between manufacture and first year of operation of a wind power plant, and 24.5 persons-year equivalent over the wind farm lifetime. Results show that major contribution from wind power for job creation are in the construction stage and, despite of the low amount of jobs created in operation and maintenance relative to new installed capacity, those stable jobs stand out as they persist over the entire wind farm's life time.
Disaggregating a sector within the Leontief input–output (IO) framework is not a straightforward task since there is more than one possibility for the unknown technical coefficients of the disaggregated IO table, and more information than what is embodied in the aggregated IO table is thus required. This paper presents a methodology for disaggregating sectors into an arbitrary number of new sectors when the only available information about the newly formed sectors is their output weights. A random walk algorithm is used to explore the polytope containing the admissible combinations for the unknown technical coefficients of the disaggregated IO table. These combinations are then used to construct the probability distribution of the coefficients of the inverse Leontief matrix. The methodology is illustrated by disaggregating the electricity production sector of China's 2007 IO table and by looking at the probability distribution of the CO2 emission intensity factors of the sectors of the economy.