Structural change in Chinese economy: Impacts on energy use and ...
Transcript of Structural change in Chinese economy: Impacts on energy use and ...
Structural change in Chinese economy: Impacts onenergy use and CO2 emissions in the period 2013–2030
J. Luukkanena,∗∗, J. Panula-Onttoa,∗, J. Vehmasa, Liu Liyongb, J. Kaivo-ojaa,L. Hayhaa, B. Auffermanna
aFinland Futures Research Centre, University of Turku, Yliopistonkatu 58 D, FI-33100Tampere, Finland
bGraduate School of Chinese Academy of Social Sciences, Fangshan District, 102488,Beijing, China
Abstract
This study investigates the effect of the change of economic structure on the
emissions and energy use in China. The study uses the ChinaLINDA model, a
calculation framework especially well suited for analysing structural change and
sectoral shifts in Chinese economy and industry. We illustrate the implications
of the different economic development paths up to year 2030 regarding emissions,
energy use and employment with a set of three scenarios.
In comparison with the reference scenario, the reduction in CO2 emission
level in policy scenario is less than 13 by 2030 even with huge investment in
renewable energy capacity and a clear turn away from carbon-intensive growth
pattern. Cumulatively the difference of emissions between the reference sce-
nario and policy scenario within 2013–2030 is less than 20%. The Chinese CO2
intensity and energy intensity targets are rather stringent. Reaching the CO2
intensity target is difficult even with the optimistic assumption of fast expan-
sion of renewable energy capacity and fast structural change. However, while
the renewable energy capacity grows fast, targets set for it are not equally ambi-
tious. The set CO2 intensity and energy intensity targets call for concretization
∗Corresponding author∗∗Secondary corresponding author
Email addresses: [email protected] (J. Luukkanen ), [email protected](J. Panula-Ontto ), [email protected] (J. Vehmas), [email protected] (Liu Liyong),[email protected] (J. Kaivo-oja), [email protected] (L. Hayha),[email protected] (B. Auffermann)
Preprint submitted to Technological Forecasting and Social Change September 28, 2014
of energy policy.
Keywords: China, Structural change, Economic modeling, CO2 emissions,
Energy consumption
Acknowledgements
The authors acknowledge that this article is based on research work sup-
ported by the Academy of Finland under the project ”China and EU in the
context of global climate change–Analysis of changing economic structures and
related policies” (CHEC).
1. Introduction
1.1. Background
The Chinese economy is in a transitional state: Chinese leadership hopes
to steer the economic development away from exports and investment and to-
wards serving the Chinese domestic market, increasing the living standard of the
Chinese citizenry as well as reducing the environmental impact that the long-
enduring high economic growth has caused. In addition to the general vision
laid out in the 12th five year plan and related state planning documents, there
are several factors that also drive the change towards a more service-oriented
economy and higher-value-added industrial profile that will also be discussed in
this paper.
Among the national economies, China is the largest emitter of CO2 emissions
and the development of its economic system in the next 15 years will have great
weight in determining cumulative global emissions. An important question is
what kind of impact these structural changes will have on emissions and energy
use and how do these contrast with the emission-related targets set by the
Chinese leadership for the next decades.
2
1.2. Research task
This study investigates the effect of the change of economic structure on
the emissions and energy use in China. We present three scenarios that illus-
trate the different development paths for China’s economy up to year 2030 and
their implications regarding emissions, energy use and employment. The study
uses the ChinaLINDA model, a calculation framework especially well suited for
analysing structural change and sectoral shifts in Chinese economy and industry.
Scenarios can be useful in handling uncertainty. They also provide a possi-
bility for exploration of the future. In contrast to forecasts, that aim to play out
the consequences of most probable developments, a set of scenarios can illustrate
the possible developments even when they are not the most likely. The scenario
set in this study opens up the alternative developments for Chinese economy
with recent trends and political will directed to a more service-oriented economy
in mind.
We attempt to answer the following two questions:
1. How much can we expect the reorientation of the Chinese economy and
the policy initiatives of the Chinese leadership to lower the energy use and
emissions in the time period 2013–2030?
2. How important is the profile of the industry for the amount of the emis-
sions?
The Chinese targets concerning economic growth, emissions and energy ex-
pressed in planning documents such as the 12th five-year plan [18] and the 2011
white paper on climate change [14, Ch.VII] can already be seen as quite am-
bitious, and reaching these targets is by no means certain. The ChinaLINDA
approach can give us an idea of the potential of the proposed policy actions
and the likely bottlenecks and conflicts in reaching the targets. We present an
answer to the research questions in the form of three scenarios that illustrate
what can be achieved with the successful implementation of the stated policies
concerning structural change and how much the industrial structure influences
the energy use and emissions.
3
This study does not include calculations of the cost implications of different
scenarios, even though extremely relevant for policy making, they are, however,
outside the scope of this paper. Furthermore, the exploration of possible fu-
tures concerning economic structure could be expanded in future research with
more variation to key drivers of economic development, as this study focuses on
exploration of a similar economic activity level with different sectoral emphasis
configurations in the Chinese economy.
1.3. Originality
There are several papers presenting scenarios about emissions and energy
use in China (see for example [36], [38], [30], [10], [12]). However, these studies
differ from our study both in methodology and aims. Some studies ([10],[12]) use
input-output approach for scenario creation. The strength of the ChinaLINDA
approach in comparison to these studies is that it allows for taking technological
change into account clearly better. Input-output approaches usually assume a
constant technology factor, which is an unrealistic assumption when examining
energy systems with a time span of twenty to thirty years. Van Vuuren et al.[36]
present a set of long-range energy and emission scenarios, but the parameters
vary on a general, highly aggregated level and do not focus on the effect of
economic structure on emissions. In comparison, our study takes the different
energy use and emission impacts of economic activity in different sectors clearly
better into account. The scenarios presented by Wang and Watson [38] look
into the impact of economic structure on the economy among other things, but
it is not the primary focus of the scenarios and the impact of economic structure
cannot be isolated and analysed as many other things beside economic structure
and industrial profile vary in the scenarios. Our scenario study focuses specif-
ically on answering the research questions about the impact of the economic
structure and in the scenarios we present only a select number of variables vary
outside economic structure.
4
2. Drivers of structural change in China
Steenhof and Fulton have presented a high-level framework[31] of the drivers
of the future development of the Chinese electricity sector. Figure 1 presents
this framework. The identification of drivers of structural change in our study
is based mostly on this framework.
Decomposition analysis has also been used for evaluating the contribution
of different driving forces on China’s carbon emissions and energy use. Two
fairly recent examples of this include studies by Kaivo-oja et al.(2014) [17] and
Minx (2011) [20]. These studies underline the contribution of energy efficiency
improvements in lowering the emissions and the opposite contribution of growing
affluence in growth of the emissions.
Development of nuclear power
base
Building of natural gas
infrastructure
Electricity demand
Energy intensity
Economic growth
State development
initiatives
Ren
ewab
le energy
law
Emissio
n lim
its
State environmental
goals
Fuel m
arkets
Techn
olo
gical ch
ange
Changes in • Industrial
structure • Energy
efficiency • Urbanization • Per capita
income
Emissions
Electricity
Fuels
Decision-making in the supply
sector
Domestic to global
environmental and economic
impacts
Figure 1: Conceptual framework of the drivers and processes effecting the future development
of China’s electricity sector [31]
2.1. Goals and policies
Hopes are expressed in the 12th five-year plan that China’s economy should
be transformed from an investment- and export-driven economy to an economy
5
driven mostly by domestic consumption. This is hoped to help in reducing
the unequality of income, decrease imprudent fixed asset investment and lessen
China’s reliance on exports, making it less vulnerable to global trends. Smaller
reliance on exporting should decrease the trade surplus and reduce the need to
artificially devalue Yuan. As the construction sector and China’s current export
industries are very energy- and emission-intensive, the policies aimed at shifting
the focus from investment and exports to consumption are expected to lower
the emissions and energy use.
China has not committed to absolute reductions to its emissions. Its po-
sition, until 2007, has been that developing economies should not be expected
to commit to emission reductions. This position has since been changed. Be-
fore the climate conference of 2009 in Copenhagen China’s premier Wen Jiabao
stated that China would reduce its carbon dioxide emissions per unit of GDP
by 40% – 45% by 2020 compared to the 2005 level. While some commenters [27]
see this target as unambitious to the point of being almost meaningless, even
this target should mean a clear shift away from high-polluting industries such
as steel, cement and aluminum industries.
China has set a target for renewable energy in terms of share of consumption:
by 2020, 15% of all energy should come from renewable sources [30] [32]. Cur-
rently, about 8% of China’s total primary energy come from non-fossil sources.
By 2020, installed capacity should reach 420 GW for hydropower [6], 200 GW
for wind power (with 170 GW onshore and 30 offshore) [6], 100 GW for solar
power [11] and 30 GW for biomass [6]. Vehicle fuels should also have at least
15% renewable energy content by 2020 . [32] The target for nuclear capacity is
86 GW by 2020 [30]. With the currently planned additional reactors, capacity
in 2020 will increase to at least 58 GW.
2.2. Economic growth and economic reform
The last five-year plans have set GDP growth targets which, perhaps sur-
prisingly, are not lower bounds in their nature but rather exact targets that, at
least in theory, should not be exceeded. The 11th five year plan set the growth
6
target at 7.5%, while the actual growth turned out to be about 11% [18]. The
12th five-year plan sets the growth target to 7%, which may be exceeded again.
It looks, however, that the GDP growth might be slowing down slightly. The
component of fixed investment in Chinese GDP remains quite high and the on-
going urbanization process partially explains the abnormally high GDP growth
rates.
The general trend of reforms in the Chinese economy has been an increas-
ing market-economy orientation from 1980 onwards, but some observers argue
that the reforms have stopped and even reversed since 2005 [26]. The policy of
the the state forcing mergers and consolidation in many fields and on the other
hand actively grooming certain big corporations for becoming future ”national
champions” as expressed in the 12th five-year plan [18] can be seen as an indi-
cation of a partial reversal of market-oriented reforms. This is likely to slow the
economic growth and along that, the energy use.
2.3. Demographic change
China is in the final stages of demographic transition, with the most sig-
nificant mortality declines having taken place in the period 1950–1975 and the
fertility level declined from 2.74 in 1979 to well below the replacement level of
2.1 to about 1.6 in 2010[39]. The government-enforced one-child policy has been
central to making the Chinese demographic transition exceptionally fast [13],
but also causing problems such as sex imbalance and a looming dependency
ratio crisis in the future [37], causing experts to recommend hasty modifications
to the policy [23].
The size of Chinese work force is peaking in the next few years and generally
the population aging is fast in comparison to the US and even to Europe[33].
The rapid increase of the number of elderly is a major challenge for the under-
developed pension system [37]. The workforce will still remain big in compar-
ison to EU-27 and USA, but China’s advantage of being easily able to supply
masses of low-paid laborers for simple manufacturing jobs is coming to a close.
China remains a demographic giant well into the future, but its relative share of
7
the global population will shrink considerably over the next decades. In 2030,
China is estimated to be home to less than 17% of world’s population [33]. In
summary, the demographic factors, that have been favourable to growth in the
period 1980–2010, can be argued to be unfavourable to growth for the next
30-year period: the growth of the Chinese economy will slow down, reducing
energy use.
2.4. Urbanization
The urbanization level of China is comparatively low, about 50%. It is ris-
ing rapidly and expected to continue rising over the next decades. The urban
population is estimated to number over 1 billion in 2030. [19]. The urbaniza-
tion and the huge migration involved are seen among the most motive forces of
China’s economic growth and development [23]. The fixed investment required
by the urbanization drives the strong growth of the Chinese economy. McKinsey
Global Institute estimates that during the period 1999-2009 as much as 50% of
the GDP has come from urban fixed investment [19], and its share of GDP is
likely to remain high in the future. The construction sector is also very energy-
intensive and will contribute greatly to energy use during the period 2013–2030.
Urbanization of the next 20 years is also estimated to move about 300 million
people from rural to urban areas, with many of these people being employed
in jobs with much higher productivity than before. This process will also have
major implications for the lifestyle of the new Chinese urbanites, changing con-
sumption patterns of much of the Chinese population significantly: urbanization
will also contribute to the environmental impact through this transformation.
2.5. Energy technology
Chinese energy policy has emphasised the use of non-fossil energy sources in
the future energy production. The possibilities for a major increase of non-fossil
energy depend largely technology development. The fast diffusion of Chinese
wind power has relied on competitive domestic production, but in the future the
8
scale-dependent effect in the wind power market can reduce the growth figures
[8].
The main drivers in the increasing installations of photovoltaic power in
China are the reducing costs of the solar panels (< 0.6 USD/Wp), the support
by the Renewable Energy Law of China through the feed-in tariff and the Special
Renewable Energy Fund. The feed-in-tariff was 1 Yuan per kWh in 2012. In
August, 2012, National Energy Administration (NEA) published ”The Twelfth
Five-year Plan of Renewable Energy Development” with the increased target of
PV from 15 GW to 21 GW focusing on distributed PV market and in December
2012 it increased the target from 21 GW to 35 GW and published the target of
100 GW for 2020. [11]
In September 2012 the Chinese government unveiled plans to stimulate local
PV demand with a programme to promote distributed solar generation. The
measures were brought in to address overcapacity in China’s PV manufacturing
industry and trade tensions with Europe and the US. China’s National Energy
Administration (NEA) is formulating plans to set up distributed PV genera-
tion demonstration plants across the country. The plans were announced at a
meeting convened by the NEA in June 2012. The pilot projects will operate in
National Economic Development Zones and industrial parks across China with
large numbers of industrial companies, high energy usage and prices, and strong
solar resources. The NEA requires that the chosen parks should be financed by
a single development team, using a ’self-generate/consume’ model, and that the
demand of the subsidies should be no more than 0.45 CNY / kWh. The ex-
pected fast increase of the natural gas use in China in the future relies on gas
imports and the utilization of shale gas. China has largest shale gas deposits in
the world [35] but the start-up phase has been slowed down due to the under-
developed natural gas infrastructure in terms of pipelines and other facilities
(see e.g.[21]).
Carbon Capture and Storage (CCS) is seen as one possibility of reducing
CO2 emissions in future energy systems. The development of the technology
9
has been slow worldwide due to the high costs. Under the IEA1 BLUE Map
Scenario, which calls for global CO2 emissions from the energy sector to be
reduced by 50% from 2005 levels by 2050, it is anticipated that CCS could
contribute about 19% of total emissions reduction. Under this scenario, China
would need to build 10 to 12 large-scale projects by 2020 and ramp up to 600
projects by 2050 [3]. China is planning several coal and gas power plants that
can bury their carbon deep underground and is looking into offsetting the extra
cost of these power plants by using the captured carbon to recover oil from
nearby wells ([24, see e.g.]).
2.6. Energy markets
During the first decade of the 2000’s, China has turned into a net energy
importer 2. Figure 2 shows the percentage of energy use in China during the
period 1971–2011. The current net energy imports are more than 10% of the
total energy use [39]. China is the worlds largest coal producer, but in the last
years, even coal consumption has exceeded production and China has become
a net coal importer. As United States seems to become more self-sufficient and
demand in EU is not likely to grow significantly, China will probably emerge
as the most important importer of oil. China’s largest oil fields are mature and
their production has already peaked [34].
The possibility of severe problems in supplying oil and gas to China before
2015 has been explored in recent energy scenarios by Shell [28]. The share
of imported energy will increase in the future, because demand is expected to
increase at a much faster rate than domestic energy production. Thus, the global
energy market is an increasingly important driver of China’s energy future. The
rapid change of China’s position from a major energy supplier to one of the
World’s largest energy importers has brought a really big player in the global
oil, gas and coal markets, and made policymakers and businessmen in other
parts of the world anxious [25].
1International Energy Agency
10
-10
-5
0
5
10
15
Energy imports, net (% of energy use) in China 1971-2011
% o
f ene
rgy
use
Figure 2: China’s net energy imports 1971–2011 [39]
China’s increasing oil demand has been driven more by investments in heavy
industry than consumption, which has been a surprise especially in the oil mar-
ket [25]. Imports of natural gas have been at a modest level, but LNG terminals
in the coastal areas of China and a pipeline from gas-rich Siberia in Russia have
been planned. China has a significant share of the World’s coal reserves, but
it is also one of the world’s largest coal importers. Firing coal is still the most
economical way of large-scale electricity production, and increasing coal demand
is due to increasing need of electricity.
3. ChinaLINDA model
4. Model structure
ChinaLINDA is a case or application of a more general LINDA model used in
the analysis of economy from an energy and emissions perspective. The LINDA
(Long-range Integrated Development Analysis) model is based on intensity ap-
proach, building on the Extended Kaya Identity, which is used for calculation of
11
CO2 emissions. Equation 1 presents the Kaya identity used in LINDA model.
CO2 =CO2
TPES× TPES
FEC× FEC
GDP× GDP
POP× POP (1)
where
CO2 is carbon dioxide emissions from fuel combustion;
TPES is total primary energy supply (including all fuels and other forms of pri-
mary energy, before the combustion process and transfer and distribution
of electricity or heat);
FEC is final energy consumption, meaning consumption of energy carriers such
as district heat and electricity, and fuels used in residential heating and
transport;
GDP is gross domestic product in real prices; and
POP is the amount of population.
This Kaya identity forms the basic conceptual framework behind the LINDA
model and the choice of modeled factors is somewhat based on the Extended
Kaya Identity. ChinaLINDA is a so-called accounting framework-type of model,
and compared against the Extended Kaya Identity the model is much more
detailed including different fuels and electricity, electricity production as well
as different sectors of economy in the calculation procedures. In addition, the
population, accounted as households, is divided into rural and urban groups.
Figure 3 shows a simplified diagram for the calculation procedure of resi-
dential electricity demand. The change rates and other parameters the model
needs are provided by experts of relevant fields: The dashed lines in the figure
indicate the flow of expert knowledge inputs. Similar calculation procedure is
applied for the calculation of different fuels used at households.
The LINDA model, based on the given inputs, calculates future scenarios for
the economy and the energy use in different sectors. Figure 4 gives a simplified
12
Number of rural households
Persons per rural household
Electricity use per rural household
Annual % change (given by user)
Annual % change (given by user)
History Future
Future number of rural households
Future electricity use per rural household
Annual % change (given by user)
Future persons per rural
household
Future rural population
Future rural electricity use
Future urban electricity use
Rural
Urban
Future residential electricity use
Annual % change
Annual % change
Annual % change
Figure 3: Linda model procedure for calculation of electricity demand and population
illustration of the main calculation blocks of the model for energy demand con-
struction in the different production sectors. This calculation is carried out for
the different fuels and electricity in each modelled economic sector (agriculture,
industry, transport, commercial, construction, others).
Historical economic development - Sector 1 - Sector 2 - . . .
Historical energy use - Sector 1 - Sector 2 - . . .
Historical energy intensity - Sector 1 - Sector 2 - . . .
Future intensity changes (by user) - Sector 1 - Sector 2 - . . .
Future energy demand - Sector 1 - Sector 2 - . . .
Future economic development - Sector 1 - Sector 2 - . . .
Future economic growth rates (by user) - Sector 1 - Sector 2 - . . .
Figure 4: Calculation procedures of the LINDA model
Figure 5 indicates the linkages of different calculation modules in the LINDA
model. The historical data is given as input in the model. This includes time
13
series data of: a) the use of different fuels and electricity in different economic
sectors; b) the electricity production; c) the power plant capacity using different
energy sources; d) technical information of power plant operation (plant capacity
factor and efficiency); e) the value added in different economic sectors; and f) the
population data.
The energy demand for different fuels and electricity in different sectors of
economy is calculated based on the historical data and user given inputs about
the future changes in energy intensities and economic growth in each sector.
The future economic growth rates that are used in the model can be based on
government plans, econometric models or expert information. The change rates
in future energy intensities in different sectors can be based on information of
different technological development trends, plans for new investments in dif-
ferent sectors (e.g. investments in energy intensive metal production) or other
expert information (see e.g. discussion by Steenhof[31]).
In the electricity production sector the investment plans for different types
of power plants are given as input data for future scenarios. The fuel efficiency
and plant load factor in addition to the installed capacity determine the needed
fuel input to produce the electricity needed to cover the demand. The LINDA
model does not utilize hourly load curves to calculate the electricity demand
but uses a more straightforward calculation based on yearly averages.
The LINDA model output is future energy use by fuel in different sectors and
related CO2 emissions. The emissions are calculated using the IPCC guidelines.
[16] The model can be used to create different scenarios by varying for instance
the future economic growth figures in different sectors or the energy intensities.
The shares of different fuels can also be varied easily to illustrate e.g. changes
in government policies.
4.1. Input data
ChinaLINDA model draws input data on multiple sources. The population
data in the model is supplied by Institute of population and labor economics,
Chinese academy of Social Science [15]. The energy data is from Energy Statis-
14
Agriculture
Industry
Transport
Commercial
Construction
Others
Electricity Heat
Residential
Historical data
Energy data Economic data
Input
Scenario
CO2
Energy use
Population data
Fuels
Figure 5: Linkages of LINDA model calculation modules
tics Division, Chinese National Bureau of Statistics [9]. Economic data and data
on other issues such as transportation and construction is from the China Sta-
tistical Yearbook [22]. Some input data concerning the sub-sectoral breakdown
of the economy comes from a 2011 paper by Chen [4].
5. Scenarios
Our scenario study concentrates on the impacts of economic structure on
energy demand and related CO2 emissions. The starting point has been the
construction of a reference scenario projecting the development in China up
to year 2030. The reference scenario is based on the economic growth rates
presented in a CASS2 research paper [29]. These figures are quite close to the
growth rates of NBSC 3 and DRC 4 [7]. The purpose of the reference scenario
is to provide a robust general projection of future developments in China that
2Chinese Academy of Social Science3National Bureau of Statistics of China4Development Research Center of the State Council
15
can be used as a point of reference for the scenario comparisons.
The ChinaLinda model uses the sub-sectoral differentiation of the industrial
sector shown in table 2. A clear strength of the ChinaLINDA model in com-
parison to many other systems of providing projections for energy demand and
emissions in China is that the scenario construction is based on data and projec-
tions of the sub-sectoral developments. An obvious but not always accounted-for
fact is that all sub-sectors of the industry do not grow at the same speed. The
energy intensities of different sub-sectors of industry vary considerably (see fig-
ure 9) as well as the potential of decreasing energy intensity in these sub-sectors.
Due to these facts, the differences in the growth patterns within the industrial
sub-sectors will lead to considerable differences in energy demand and emissions
even when the total value added in the industrial sector would be equal in the
different set-ups. The same is also true for the general sectoral composition
of the economy: Different growth rates for service sector and industrial sector
will lead to very different levels of energy demand and emissions. The 12th five
year plan has a strong emphasis on developing the service sector, and the future
growth rate of the service sector is very important from this point of view.
We answer the research questions presented in section 1.2 by comparison
of three scenarios constructed with the ChinaLINDA model. The scenarios are
called a) Reference scenario, or REF for short, b) Policy scenario, or POL for
short and c) Heavy industry scenario, or IND for short.
The economic growth rate of different sectors in the Reference scenario
(REF ) is presented in table 1. The industrial growth rate is high in this scenario:
it represents a continuation of historical trends and policies emphasizing indus-
trial growth in China. The total GDP growth rate is equal to the projections
by Liu [29].
The future sectoral growth rates for the second scenario, titled ”Policy”
(POL), are also presented in table 1. The Policy scenario plays out the imple-
mentation of energy and climate policy targets in China. In this scenario, the
industrial growth is much slower than in the Reference scenario, but the growth
in commercial sector is faster. The total growth rate in these two scenarios is
16
identical.
Table 1: Sectoral annual growth rates: historical rates, ”Reference” scenario rates, ”Policy”
scenario rates and ”Heavy industry” scenario growth rates.Historical Reference Policy Heavy industry
1990-95 1995-98 1998-2001 2001-06 2006-10 2011-15 2016-20 2021-30 2011-15 2016-20 2021-30 2011-15 2016-20 2021-30
Agriculture 4.2 % 4.0 % 2.7 % 4.4 % 4.4 % 4.0 % 3.5 % 2.5 % 4.0 % 4.0 % 4.0 % 4.0 % 4.0 % 4.0 %
Industry 17.7 % 10.9 % 9.0 % 11.7 % 11.4 % 10.2 % 8.1 % 6.1 % 7.1 % 4.8 % 3.9 % 7.1 % 4.8 % 3.9 %
Transportation, communication 10.9 % 9.5 % 9.8 % 11.3 % 11.3 % 9.0 % 9.0 % 6.0 % 10.0 % 9.0 % 6.0 % 10.0 % 9.0 % 6.0 %
Commercial 10.9 % 9.5 % 9.8 % 11.3 % 11.3 % 9.0 % 10.0 % 9.0 % 14.0 % 14.0 % 10.0 % 14.0 % 14.0 % 10.0 %
Construction 14.6 % 7.1 % 5.6 % 12.4 % 14.4 % 9.0 % 9.0 % 5.0 % 10.0 % 9.0 % 4.0 % 10.0 % 9.0 % 4.0 %
Others 10.9 % 5.6 % 5.8 % 11.3 % 9.0 % 6.0 % 6 % 0 % 11.0 % 10 % 5 % 11.0 % 10 % 5 %
Total 12.2 % 9.1 % 5.3 % 12.5 % 10.9 % 8.6 % 7.5 % 5.2 % 8.6 % 7.4 % 5.2 % 8.6 % 7.5 % 5.2 %
In order to compare the impacts of industrial structure on the emissions
we have derived the scenario titled ”Heavy industry” (IND) from the policy
scenario. In this scenario, the sectoral growth rates are equal to the Policy
scenario but the growth rates of industrial sub-sectors are different. The energy
intensive sub-sectors are growing faster than in the Policy scenario, but the
total industrial growth rates are equal. The growth rates of value added in
the industrial sub-sectors in the three scenarios is presented in table 2; The
differences in the value added of the industrial sub-sectors in 2030 in the three
scenarios are shown in figure 6.
The fastest growth in all industrial sub-sectors occurs in the Reference sce-
nario. The Heavy industry scenario derived from the Policy scenario differs
from Policy scenario in that the growth is higher in paper, chemical, medicines,
rubber and minerals and basic metals industries, but is correspondingly slower
in electrical and electronics industry and the metal products industry, summing
to an equal total growth for the industrial sector in both scenarios.
Figures 7 and 8 illustrate the differences in development of absolute sectoral
value added between Reference and Policy scenarios as well as the relative share
of the sectors in the economy. The Heavy industry scenario is equivalent in the
development of value added of economic sectors to the policy scenario, from
which it is derived, so it is not presented here.
In the Reference scenario, the share of commercial sector is declining while
the share of industry continues to grow fast. The service sector will not reach
the 2015 target of 47 % of GDP set in the 12th Five Year Plan as can be seen
17
Table 2: Annual industrial sub-sector growth rates: historical rates, ”Reference” scenario
rates, ”Policy” scenario rates and ”Heavy industry” scenario growth rates.Historical Reference Policy Heavy industry
1990-95 1995-98 1998-2001 2001-06 2006-08 2009-15 2016-20 2021-30 2009-15 2016-20 2021-30 2009-15 2016-20 2021-30
Food, beverages and tobacco 18.0 % 13.8 % 4.5 % 6.9 % 10.4 % 10 % 7 % 5 % 4 % 3 % 2 % 4 % 3 % 2 %
Textile, leather 12.8 % 8.7 % 7.0 % 8.3 % 10.4 % 10 % 7 % 5 % 4 % 3 % 2 % 5 % 3 % 2 %
Wood, paper and printing, education, artwork 20.5 % 13.7 % 6.7 % 8.1 % 13.1 % 8 % 6 % 4 % 5 % 4 % 3 % 10 % 8 % 5 %
Chemical 13.6 % 10.1 % 7.5 % 8.7 % 15.2 % 12 % 9 % 6 % 7 % 5 % 4 % 12 % 7 % 7 %
Coal and gas, mining and processing 7.7 % 3.8 % -2.9 % 2.3 % 1.2 % 0 % -1 % -2 % 0 % -1 % -2 % 0 % -1 % -2 %
Mining and processing ores 16.0 % 13.9 % -6.3 % 2.9 % 10.5 % 9 % 6 % 5 % 7 % 4 % 3 % 10 % 4 % 3 %
Medicines, rubber, plastics, mineral products 18.6 % 12.2 % 8.2 % 8.3 % 11.9 % 11 % 8 % 7 % 8 % 5 % 4 % 12 % 5 % 4 %
Smelting and processing metals 14.4 % 3.9 % 13.0 % 14.1 % 11.2 % 10 % 8 % 6 % 7 % 4 % 3 % 12 % 10 % 8 %
Metal products and machinery, transport equipment 21.0 % 7.7 % 8.8 % 13.0 % 19.3 % 12 % 9 % 6 % 8 % 5 % 4 % 5 % 4 % 3 %
Electrical and electronics 31.0 % 19.6 % 20.4 % 19.6 % 9.7 % 10 % 9 % 7 % 9 % 6 % 5 % 5 % 3 % 2 %
Electricity, gas and water 16.1 % 4.0 % 7.7 % 6.9 % 15.6 % 4 % 3 % 2 % 4 % 3 % 2 % 5 % 3 % 2 %
Total 17.7 % 10.9 % 9.0 % 11.7 % 8.0 % 10.2 % 8.1 % 6.1 % 7.1 % 4.8 % 3.9 % 7.1 % 4.8 % 3.9 %
0 20 000 40 000 60 000 80 000 100 000
Food, beverages and tobacco
Textile, leather
Wood, paper and printing, education,artwork
Chemical
Coal and gas, mining and processing
Mining and processing ores
Medicines, rubber, plastics, mineral products
Smelting and processing metals
Metal products and machinery, transportequipment
Electrical and electronics
Electricity, gas and water
100 mill CNY
Reference
Policy
Heavy Industry
Figure 6: The value added in industrial subsectors in 2030 in Reference, Policy and Heavy
industry scenarios
in table 3. In the Policy scenario the share of service sector grows and in this
scenario the government target is almost reached.
The comparison shows that the the targets of the 12th five year plan are
actually considerably stringent. Reaching the energy intensity and CO2 inten-
sity targets is not easy even when extremely high speed of renewable energy
production capacity construction is assumed. The pressure to increase energy
production, especially electricity production, is high due to the rapidly increas-
ing industrial production as well as the lifestyle change towards more energy-
intensive, consumerist lifestyle associated with the urbanization process.
The development of the value added in industrial sub-sectors in the three
18
0
50
100
150
200
250
300
350
400
450
500
1990 1995 2000 2005 2010 2015 2020 2025 2030
100 billion CNY REF: Value added in China
0
50
100
150
200
250
300
350
400
450
500
1990 1995 2000 2005 2010 2015 2020 2025 2030
100 billion CNY POL: Value added in China
1990 1995 2000 2005 2010 2015 2020 2025 2030
Construction
Commercial
Transportation, communication
Industry
Agriculture
Figure 7: Comparison of sectoral value added in Reference and Policy scenarios
0 %
10 %
20 %
30 %
40 %
50 %
60 %
70 %
80 %
90 %
100 %
1990 1995 2000 2005 2010 2015 2020 2025 2030
REF: Economic structure in China
0 %
10 %
20 %
30 %
40 %
50 %
60 %
70 %
80 %
90 %
100 %
1990 1995 2000 2005 2010 2015 2020 2025 2030
POL: Economic structure in China
1990 1995 2000 2005 2010 2015 2020 2025 2030
Construction
Commercial
Transportation, communication
Industry
Agriculture
Figure 8: Comparison of sectoral value added shares in Reference and Policy scenarios
Table 3: 12th five-year plan targets for year 2015 and the outcome in the scenarios
Target Reference Policy Heavy Industry
Energy intensity reduction -16 % -7.8 % -14.6 % -8.2 %
CO2 emission intensity reduction -17 % -9.7 % -16.7 % -9.8 %
Non-fossil fuel share 11.4 % 11.4 % 12.4 % 11.5 %
Service sector share 47 % 36.0 % 43.4 % 43.4 %
19
scenarios is presented in table 4. The fast increase in the share of electrical and
electronics sub-sector is assumed to continue in the Policy scenario. In addition,
the share of metal products and machinery and transport equipment is assumed
to increase. This type of industrial development would lead to a sizeable reduc-
tion of the energy intensity of the industrial sector since the fastest growing sec-
tors are not very energy intensive. Compared to Policy scenario, the growth in
the Heavy industry scenario is higher in the paper, chemical, medicines, rubber
and mineral and basic metal industries, but slower in electrical and electronics
as well as metal products industries. This increases the overall energy intensity
considerably in the industrial sector, increasing the energy demand.
Table 4: Development of value added in different industrial sub-sectors in the scenarios. Values
as billion CNY in 1990 price.Reference Policy Heavy Industry
2000 2015 2020 2030 2015 2020 2030 2015 2020 2030
Food, beverages and tobacco 283 993 1393 2268 750 870 1060 750 870 1060
Textile, leather 226 868 1218 1984 656 760 927 688 798 972
Wood, paper and printing, education, artwork 195 718 961 1423 624 759 1020 787 1157 1884
Chemical 188 842 1295 2320 670 855 1266 842 1181 2323
Coal and gas, mining and processing 98 137 131 107 137 131 107 137 131 107
Mining and processing ores 66 162 217 354 148 180 242 170 207 278
Medicines, rubber, plastics, mineral products 321 1310 1925 3786 1142 1458 2158 1370 1748 2588
Smelting and processing metals 147 785 1153 2065 683 831 1117 859 1383 2850
Metal products and machinery, transport equipment 385 2461 3787 6781 2052 2619 3876 1782 2168 2914
Electrical and electronics 507 3317 5104 10039 3169 4241 6908 2629 3047 3715
Electricity, gas and water 100 304 353 430 304 353 430 319 370 451
Total 2515 11898 17535 31557 10336 13056 19111 10333 13059 19142
It is assumed in the scenario comparison that the changes in electricity in-
tensity of production in the industrial sub-sectors are similar in the different
scenarios. The development of electricity intensity of different industrial sectors
is shown in figure 9.
The electricity consumption increases fast in the Reference scenario due to
the fast growth of energy intensive industrial production. This naturally in-
creases also the fuel use and the related CO2 emissions from fuel combustion.
The electricity demand in the Policy scenario is much lower because the in-
dustrial growth is smaller than in the Reference scenario. The higher growth
of service sector in the Policy scenario does not increase electricity demand as
20
0
2
4
6
8
10
12
14
16
18
1990 1995 2000 2005 2010 2015 2020 2025 2030
ktoe/100mill CNY REF: Electricity intensity in industry Food, beverages and tobacco
Textile, leather
Wood, paper and printing,education, artwork
Chemical
Coal and gas, mining andprocessing
Mining and processing ores
Medicines, rubber, plastics,mineral products
Smelting and processing metals
Metal products and machinery,transport equipment
Electrical and electronics
Electricity, gas and water
Total
Figure 9: Electricity intensity in different industrial sectors in China and its development in
the scenarios. Identical development concerning electricity intensity is assumed in all three
scenarios.
much due to the lower electricity intensity of the service sector. In the Heavy
Industry scenario the electricity consumption increases fast even though the
total industrial value added is similar to the Policy scenario. As the energy
intensive sectors grow faster in the Heavy Industry scenario, faster energy use
growth follows. Figure 10 illustrates the differences in electricity consumption
in the different scenarios.
The differences concerning the electricity production in the scenarios are
0
2000000
4000000
6000000
8000000
10000000
12000000
19
90
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
GWh REF: Electricity consumption
in China
0
2000000
4000000
6000000
8000000
10000000
12000000
19
90
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
GWh POL: Electricity consumption
in China
0
2000000
4000000
6000000
8000000
10000000
12000000
19
90
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
GWh IND: Electricity consumption
in China
0 2000000 4000000 6000000 8000000
10000000 12000000
1990
1995
2000
2005
2010
2015
2020
2025
2030
GWh IND: Electricity consumption in China
Others
Construction
Transportation
Residential(Rural)
Residential(Urban)
Commercial
Industry
Agriculture
Figure 10: Comparison of electricity consumption in the scenarios
21
0
2000000
4000000
6000000
8000000
10000000
12000000
19
90
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
GWh REF: Electricity production in
China
0
2000000
4000000
6000000
8000000
10000000
12000000
19
90
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
GWh POL: Electricity production in
China
0
2000000
4000000
6000000
8000000
10000000
12000000
19
90
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
GWh IND: Electricity production in
China
0 2000000 4000000 6000000 8000000
10000000 12000000
1990
1995
2000
2005
2010
2015
2020
2025
2030
GWh IND: Electricity production in China
Thermal
Wind
Nuclear
Hydro
Figure 11: Comparison of electricity production from different energy sources in the scenarios
0
500000
1000000
1500000
2000000
2500000
3000000
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
ktoe REF: Sectoral final energy use in China
0
500000
1000000
1500000
2000000
2500000
3000000
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
ktoe POL: Sectoral final energy use in China
0
500000
1000000
1500000
2000000
2500000
3000000
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
ktoe IND: Sectoral final energy use in China
0
500000
1000000
1500000
2000000
2500000
3000000
12222222
ktoe
Others
Construction
Residential(Rural)
Residential(Urban)
Transport
Commercial
Industry
Agriculture
0
Figure 12: Comparison of sectoral final energy use in the scenarios
mainly in the construction of thermal power plants and nuclear power. In all the
scenarios the construction of hydro, solar and wind power capacity is assumed
to continue fast and at the same speed. In the Reference scenario the higher
growth in electricity demand is mainly covered by construction of additional
thermal and nuclear power plant capacity. The electricity production from
different energy sources in the scenarios is shown in figure 11.
The sectoral final energy use in the different scenario is shown in figure 12.
The industrial energy use is dominant in the reference scenario due to the fast
economic growth in the industrial sector and the energy intensity of the sector.
In the Policy scenario the industrial energy use saturates by 2020 and the growth
comes mainly from residential sector (which includes the use of private cars).
The fuel use in the scenarios is shown in 13 In the Reference and Heavy
industry scenarios coal remains the main source of energy even though the use
of oil products in the transport sector is increasing fast. The use of natural
22
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
4000000 1
99
0
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
ktoe REF: Fuel use in China
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
4000000
19
90
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
ktoe POL: Fuel use in China
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
4000000
19
90
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
ktoe IND: Fuel use in China
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
4000000
1990
1995
2000
2005
2010
2015
2020
2025
2030
ktoe
Solid biomass
Natural gas
Oil
Coal
Figure 13: Comparison of fuel use in the scenarios
gas is also increasing fast both in industry and in electricity production where
the shift from coal to gas is remarkable. The fuel shares in the different sectors
are assumed to be identical in the different scenarios and the differences in fuel
consumption result from the different growth rates in different sectors.
The 12th Five Year Plan has set a target of non-fossil fuels to account for
11.4% of primary energy consumption. This target is reached in all the scenarios.
In the Reference scenario the share of non-fossil fuels would be 11.4%, in the
Policy scenario 12.4% and in the Heavy Industry scenario 11.4% (see table 3.
The target can be reached with speedy construction of wind and solar energy
capacity. In all the scenarios the capacity of wind power would be 414 GW,
solar PV power capacity 341 GW and hydro power capacity which would be
about 585 GW by the year 2030. In the Reference scenario the nuclear power
capacity would be about 194 GW in 2030 and in the Policy and Heavy Industry
scenarios 171 GW in 2030. In 2030 the thermal power capacity, using mainly
coal (80%) and gas as energy sources, would be in 2030 about 1 672 GW in
the Reference scenario, 1 104 GW in Policy scenario and 1 597 GW in Heavy
Industry scenario. The energy sector investments would need to be huge in all
the scenarios.
The development of the CO2 emissions in the different scenarios is shown
in figure 14. The CO2 emission level in 2030 compared across scenarios is also
presented in table 5. In the Policy scenario the emissions are 27% lower than
in the Reference scenario and the CO2 emissions from fuel combustion reach a
23
0
2000000
4000000
6000000
8000000
10000000
12000000
14000000 1
99
0
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
ktons of CO2
REF: CO2 emissions from fuels
0
2000000
4000000
6000000
8000000
10000000
12000000
14000000
19
90
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
ktons of CO2
POL: CO2 emissions from fuels
0
2000000
4000000
6000000
8000000
10000000
12000000
14000000
19
90
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
ktons of CO2
IND: CO2 emissions from fuels
0
2000000
4000000
6000000
8000000
10000000
12000000
14000000
1990 1995 2000 2005 2010 2015 2020 2025 2030
ktons of CO2
Natural gas
Oil
Coal
Figure 14: CO2 emissions from fuel combustion in the scenarios
level of about 6000 million tons of CO2 in 2020 and stabilize after that.
The 12th five-year plan has also set a target for energy intensity reduction
and CO2 intensity reduction for 2020. The target for energy intensity reduction
is 16%, but in the Reference scenario a reduction of only 8% is reached. Policy
scenario reaches 14.6% reduction while the Heavy industry scenario also ends
up to about 8% reduction (see table 3). The target for CO2 intensity reduction
is 17%. The Reference and Heavy industry scenarios reach only 10% reduction
while the Policy scenario comes very close to reaching the target with 16.7%
reduction (see table 3). The policy scenario demonstrates that the targets of
the 12th five-year plan are actually quite ambitious and require a fast shift to
more efficient production systems and non-fossil fuel use in energy production.
ChinaLINDA model can also be used for projecting the change in the labour
force demand and employment by estimating the changes in labour intensity in
the different sectors. The employment in the Reference scenario is decreasing
after 2020. This is due to the lower economic growth after 2020 and the decreas-
ing labour intensity in all the sectors. In the policy scenario the labour demand
also decreases after 2020, but the general employment rate is higher than in
the Reference scenario. This is due to the higher labour intensity in the service
sector and the faster growth in non-industrial sectors. In the Policy scenario
the demand for workforce is about 8 million persons higher in 2030 than in
the Reference scenario due to Policy scenario’s more labour-intensive economic
structure. Figure 15 illustrates the development of employment in different sec-
24
Table 5: Summary of the scenario results. The second column for scenarios ”Policy” (POL)
and ”Heavy industry” (IND) indicates the percentual difference of value in comparison to the
”Reference” (REF) scenario.
Year 2030 REF POL IND
GDP (100 billion CNY) 494 491 −1% 491 −1%
Agriculture 21 24 +19% 24 +19%
Industry 316 191 −39% 191 −39%
Commercial 110 228 +108% 228 +108%
Transport and communication 19 20 +5% 20 +5%
Construction 29 28 −5% 28 −5%
Electricity consumption (Mtoe) 885 683 −23% 853 −4%
Final Energy Consumption (Mtoe) 2 774 2 276 −18% 2 754 −1%
Fuel consumption (Mtoe) 3 584 2 655 −26% 3 464 −3%
CO2 emissions in 2030 (Mtons) 12 446 9 054 −27% 11 973 −4%
Employment (millions) 586 667 +14% 667 +14%
tors in China in the scenarios. The employment in Heavy industry scenario is
similar to the Policy scenario because no data on industrial sub-sector labour
force was available and changes in these could not be included in the model.
6. Comparison to previous scenario studies
As in most other scenario studies on Chinesev economy and emissions, all
scenarios presented feature a sharp increase in electricity use and fuel use. This
is due to high estimated economic growth, comparatively slow decrease in elec-
tricity intensity in economic production sectors and urbanization process, which
greatly increases household electricity consumption. The electricity production
figures in the presented scenarios for 2030 do not differ greatly from the sce-
narios by the International Energy Agency. The reference scenario figures are
only a little higher than IEA Current Policy scenarios, while the policy scenario
figures are little bit lower that the IEA New Policies scenario.
Comparison of the REF, POL and IND scenarios to several scenario studies
25
0
10000
20000
30000
40000
50000
60000
70000
19
90
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
10 000 persons
REF: Employment in China
0
10000
20000
30000
40000
50000
60000
70000
19
90
19
95
20
00
20
05
20
10
20
15
20
20
20
25
20
30
10 000 persons
POL: Employment in China
0
10000
20000
30000
40000
50000
60000
70000
80000
1990 1995 2000 2005 2010 2015 2020 2025 2030
10 000 persons
Other
Transport
Construction
Commercial
Industry
Agricullture
Figure 15: Employment in different economic sectors in the Reference and Policy scenarios
on Chinese economy are presented in figure 16.
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
Mton CO2
CO2 emissions in China 2030 in different scenarios
0
1000
2000
3000
4000
5000
6000
7000
Mtoe
Energy use in China in 2030 in different scenarios
Figure 16: Comparison of energy production and emissions between REF, POL and IND
scenarios and other scenario studies
The compared scenarios are constructed by International energy agency (IEA
”New Policies” scenario, ”450” Scenario, ”Current policies” scenario) [2], United
States Energy Information Administration (Scenarios IEO ”Ref”, ”High” and
”Low”) [1], Chinese Energy Research Institute (Scenarios ERI ”Baseline”, ”Low
carbon”, ”Accelerated low carbon”) [40], Lawrence Berkley National Laboratory
(Scenarios LBNL ”CIS”, ”CIS with CCS”) [40], Dai et al. (Scenario Dai et al.)
[5],
IEA New Policies IEA 450 IEA Current Policies LBNL CIS LBNL CIS with
CCS ERI Baseline ERI Low carbon ERI Accel low carbon Jiang Baseline Jiang
26
Policy Jiang Low Carbon Jiang ELC Dai et al. IEO Ref IEO High IEO Low
7. Conclusions and discussion
A comparison of the most relevant outcomes of the three scenarios is pre-
sented in table 5. The ”Policy” scenario represents a rather optimistic future
where the current policies, some actually very ambitious, are succcessfully im-
plemented: from that scenario, we can learn how great an impact could those
policies have on emissions and energy use. The ”Heavy industry” scenario,
derived from the ”Policy” scenario, then tells us how the abatement of CO2
reached in the ”Policy” scenario can largely be annulled if the industrial profile
leans towards energy-intensive activities.
The scenario analysis illustrates the stringency of the longer-term CO2 in-
tensity and energy intensity targets. Even with the optimistically fast expansion
of renewable energy capacity simulated in the policy scenario reaching these tar-
gets is difficult. On the other hand, the goals set for renewable energy capacity
[32] seem unambitious in contrast to the capacity growth levels required to reach
the CO2 intensity and energy intensity targets. Many targets previously set for
2020 have already been reached.
The CO2 emissions could be as much as 27% lower assuming a strong devel-
opment of the service sector and a turn away from the energy-intensive industries
and the carbon-intensive pattern of growth, but this will also require huge in-
vestments in renewable energy capacity. The Policy scenario assumes 156 GW
of new hydropower capacity, 124 GW of new photovoltaic capacity and 140 GW
of new wind power capacity (totalling 478 GW) to be constructed during the
period 2014–2020 and another 150 GW of hydropower, 200 GW of photovoltaic
and 200 GW of wind power capacity (totalling 550 GW) to be constructed in
the later period 2021–2030. Even with the heavy investing in renewables, coal
use would need to be roughly on the same level as in 2013 for the entire period
2014–2030 to supply the needed electricity. The cumulative CO2 emissions over
the period 2011–2030 would be 202 GT in the Reference scenario. In the policy
27
scenario, the cumulative emissions would add up to 167 GT, 35 GT less. The
Heavy industry scenario would result in 195 GT of cumulative CO2 emissions
for the period.
References
[1] Administration, U. S. E. I., 2013. International energy outlook 2013. Online
database.
URL http://www.eia.gov/oiaf/aeo/tablebrowser/
[2] Agency, I. E., for Economic Co-operation, O., Development, 2012. World
Energy Outlook 2012. WORLD ENERGY OUTLOOK. OECD/IEA.
URL http://www.iea.org/publications/freepublications/
publication/WEO2012_free.pdf
[3] Best, D., Levina, E., 2012. Facing China’s Coal Future: Prospects and
Challenges for Carbon Capture and Storage. Tech. rep., OECD Publishing.
[4] Chen, S., 2011. China’s industrial sub-sectors statistics estimate 1980-2008.
China Economic Quarterly 10 (3).
[5] Dai, H., Masui, T., Matsuoka, Y., Fujimori, S., 2012. The impacts of
chinas household consumption expenditure patterns on energy demand
and carbon emissions towards 2050. Energy Policy 50, 736 – 750, special
Section: Past and Prospective Energy Transitions - Insights from History.
URL http://www.sciencedirect.com/science/article/pii/
S0301421512007057
[6] Davidson, M., 9 2013. Transforming China’s grid: Sustaining the renewable
energy push. Online article, accessed 17.1.2014.
URL http://theenergycollective.com/michael-davidson/279091/
transforming-china-s-grid-sustaining-renewable-energy-push
[7] Development Research Center of the State Council, World Bank, 2013.
China 2030. The World Bank.
28
URL http://elibrary.worldbank.org/doi/abs/10.1596/
978-0-8213-9545-5
[8] Duan, H.-B., Zhu, L., Fan, Y., 2013. A cross-country study on the
relationship between diffusion of wind and photovoltaic solar technology.
Technological Forecasting and Social ChangeIn press 18.11.2013.
URL http://www.sciencedirect.com/science/article/pii/
S0040162513001571
[9] Energy statistics division, National bureau of statistics, 2014. China
energy statistical yearbook. Online.
URL http://tongji.cnki.net/kns55/Navi/HomePage.aspx?id=
N2014030143&name=YCXME&floor=1
[10] Fan, Y., Liang, Q.-M., Wei, Y.-M., Okada, N., 2007. A model for China’s
energy requirements and CO2 emissions analysis. Environmental Modelling
& Software 22 (3), 378–393, special section: Advanced Technology for
Environmental Modelling.
URL http://www.sciencedirect.com/science/article/pii/
S1364815206000041
[11] Fang, L., Honghua, X., Sicheng, W., 2013. National survey report of pv
power applications in China 2012. Tech. rep., Institute of Electrical Engi-
neering, Chinese Academy of Sciences and Chinese PV Society, available
online.
URL http://www.iea-pvps.org/index.php?id=93&eID=dam_frontend_
push&docID=1592
[12] Guan, D., Hubacek, K., Weber, C. L., Peters, G. P., Reiner, D. M.,
2008. The drivers of chinese CO2 emissions from 1980 to 2030. Global
Environmental Change 18 (4), 626–634, local evidence on vulnerabilities
and adaptations to global environmental change.
URL http://www.sciencedirect.com/science/article/pii/
S095937800800068X
29
[13] Hesketh, T., Lu, L., Xing, Z. W., 2005. The effect of China’s one-child
family policy after 25 years. New England Journal of Medicine 353 (11),
1171–1176, pMID: 16162890.
URL http://www.nejm.org/doi/full/10.1056/NEJMhpr051833
[14] Information Office of the State Council of the People’s Republic of China,
2011. China’s policies and actions for addressing climate change. Read on
29.10.2013.
URL http://www.china.org.cn/government/whitepaper/node_
7142680.htm
[15] Institute of population and labor economics, Chinese Academy of Social
Science, 2014. Almanac of china’s population. Online.
URL http://tongji.cnki.net/kns55/Navi/HomePage.aspx?id=
N2013030124&name=YZGRK&floor=1
[16] Intergovernmental Panel on Climate Change, 2006. Guidelines for national
greenhouse gas inventories, volume 2: Energy. Online, accessed 20.9.2013.
URL http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol2.html
[17] Kaivo-oja, J., Luukkanen, J., Panula-Ontto, J., Vehmas, J., Chen, Y.,
Mikkonen, S., Auffermann, B., 2014. Are structural change and moderni-
sation leading to convergence in the {CO2} economy? decomposition
analysis of china, {EU} and {USA}. Energy 72 (0), 115–125.
URL http://www.sciencedirect.com/science/article/pii/
S0360544214005647
[18] KPMG China, 2011. China’s 12th five-year plan: Overview. Tech. rep.,
KPMG Advisory (China) Limited, accessed 29.10.2013.
URL http://www.kpmg.com/CN/en/IssuesAndInsights/
ArticlesPublications/Publicationseries/5-years-plan/
Documents/China-12th-Five-Year-Plan-Overview-201104.pdf
[19] McKinsey Global Institute, 2009. Preparing for China’s urban billion.
Tech. rep., McKinsey & Company, accessed 13.6.2013.
30
URL http://www.mckinsey.com/~/media/McKinsey/dotcom/Insights%
20and%20pubs/MGI/Research/Urbanization/Preparing%20for%
20Chinas%20urban%20billion/MGI_Preparing_for_Chinas_Urban_
Billion_full_report.ashx
[20] Minx, J. C., Baiocchi, G., Peters, G. P., Weber, C. L., Guan, D., Hubacek,
K., 2011. A carbonizing dragon: Chinas fast growing co2 emissions revis-
ited. Environmental Science & Technology 45 (21), 9144–9153.
URL http://pubs.acs.org/doi/abs/10.1021/es201497m
[21] Montlake, S., 10 2013. Shale gas revolution not coming to China anytime
soon. Online article, accessed 18.11.2013.
URL http://www.forbes.com/sites/simonmontlake/2013/10/30/
shale-gas-revolution-not-coming-to-china-anytime-soon/
[22] National bureau of statistics, 2014. China statistical yearbook. Online.
URL http://tongji.cnki.net/kns55/Navi/HomePage.aspx?id=
N2013110049&name=YINFN&floor=1
[23] Peng, X., 2011. Chinas demographic history and future challenges. Science
333 (6042), 581–587.
URL http://www.sciencemag.org/content/333/6042/581.abstract
[24] Plumer, B., 10 2013. Is China the last hope for carbon capture technology?
Article in Washington Post, accessed 18.11.2013.
URL http://www.washingtonpost.com/blogs/wonkblog/wp/2013/10/
22/is-china-the-last-hope-for-carbon-capture-technology/
[25] Rosen, D. H., Houser, T., 2008. China Energy - A Guide for the Perplexed.
Oil, Gas & Energy Law (OGEL) 6 (1).
URL http://www.ogel.org/article.asp?key=2695
[26] Scissors, D., 2009. Liberalization in reverse. Tech. rep., The Heritage
Foundation, The Heritage Foundation, 214 Massachusetts Ave NE,
Washington DC 20002-4999, accessed 28.10.2013.
31
URL http://www.heritage.org/research/commentary/2009/05/
liberalization-in-reverse
[27] Scissors, D., 2010. 10 China myths for the new decade. Tech. rep., The
Heritage Foundation, The Heritage Foundation, 214 Massachusetts Ave
NE, Washington DC 20002-4999, accessed 25.10.2013.
URL http://www.heritage.org/research/reports/2010/01/
10-china-myths-for-the-new-decade
[28] Shell International BV, 2008. Shell energy scenarios to 2050. Tech. rep.,
Shell International BV.
URL http://s08.static-shell.com/content/dam/shell/
static/future-energy/downloads/shell-scenarios/
shell-energy-scenarios2050.pdf
[29] Shenglong, L., 2013. A study on the relationship between the electric de-
velopment and economic growth, working paper of the Institute of Quan-
titative & Technical Economics, Chinese Academy of Social Sciences.
[30] Steckel, J. C., Jakob, M., Marschinski, R., Luderer, G., 2011. From
carbonization to decarbonization? Past trends and future scenarios for
China’s CO2 emissions. Energy Policy 39 (6), 3443–3455.
URL http://www.sciencedirect.com/science/article/pii/
S0301421511002229
[31] Steenhof, P. A., Fulton, W., 2007. Scenario development in China’s
electricity sector. Technological Forecasting and Social Change 74 (6),
779–797.
URL http://www.sciencedirect.com/science/article/pii/
S0040162506001818
[32] The China Sustainable Energy Program, 03 12. Fact sheet: China emerging
as new leader in clean energy policies. Online, accessed 5.11.2013.
URL http://www.efchina.org/csepupfiles/news/200831315240123.
32
06345444173405.pdf/China%20Clean%20Energy%20Fact%20Sheet%
20080312.pdf
[33] United Nations, Department of Economic and Social Affairs, Population
Division, 2012. World population prospects: The 2012 revision. Online
database, accessed 29.10.2013.
URL http://data.un.org/Explorer.aspx?d=PopDiv
[34] United States Energy Information Agency, 2012. Countries: China. Online
database, accessed 9.12.2013.
URL http://www.eia.gov/countries/country-data.cfm?fips=CH
[35] U.S. Energy Information Administration, 2013. Technically Recoverable
Shale Oil and Shale Gas Resources: An Assessment of 137 Shale Forma-
tions in 41 Countries Outside the United States. Tech. rep., U.S. Energy
Information Administration, available online.
URL http://www.eia.gov/analysis/studies/worldshalegas/pdf/
overview.pdf?zscb=79906188
[36] van Vuuren, D., Fengqi, Z., de Vries, B., Kejun, J., Graveland, C., Yun,
L., 2003. Energy and emission scenarios for China in the 21st centuryex-
ploration of baseline development and mitigation options. Energy Policy
31 (4), 369–387.
URL http://www.sciencedirect.com/science/article/pii/
S0301421502000708
[37] Wang, F., 2005. Can China afford to continue its one-child policy? Tech.
rep., Honolulu: East-West Center.
URL http://www.eastwestcenter.org/sites/default/files/
private/api077.pdf
[38] Wang, T., Watson, J., 2010. Scenario analysis of Chinas emissions path-
ways in the 21st century for low carbon transition. Energy Policy 38 (7),
3537–3546.
33
URL http://www.sciencedirect.com/science/article/pii/
S0301421510001126
[39] World Bank, 2012. World development indicators & global development
finance database. Online database, accessed 29.10.2013.
URL http://data.worldbank.org/data-catalog/
world-development-indicators
[40] Zhou, N., Fridley, D., McNeil, M. A., Zheng, N., Ke, J., Levine, M. D., apr
2011. China’s energy and carbon emissions outlook to 2050, 83.
34