Download Projection of China`s energy structure change under

Projection of China’s energy structure change under carbon emission
peak target by 2030
Zhu Yanshuo1 ,Wang Zheng12 ,Zhu Yongbin1 ,Shi Ying1
(1. Institute of Policy and Management, CAS, Beijing 100190, China; 2. Key Laboratory of Geographic Information
Science, East China Normal University, Shanghai 200062, China)
Abstract: This article use energy demand and energy supply equilibrium conditions for a ‘soft
connection’ to build hybrid energy models. Under the objective of minimizing costs, combined
with a variety of differences in energy technologies, simulate the evolution of energy structure
under the achievement of carbon emission peak in 2030. Results indicate that under the
objective of optimal cost, China can achieve emission peak in 2030, but the proportion of
non-fossil energy in primary energy still lower than 20% in 2030.
Keywords: Energy structure, Hybrid energy model, carbon emission peak, China
1 Introduction
The world’s largest economies–China, the US and the European Union have recently
announced ambitious targets for reducing emissions post-2020, especially China and the United
States announced a joint statement of their post-2020 emissions reduction targets in November
2014, in order to slow down climate change into a powerful force. The timing of the US-China
climate announcement is important because it comes well before the UN climate summit in Paris
next year and with significant time for other major polluters and economies to state their
post-2020 emissions reduction targets, showing how both developed and developing countries can
work together to tackle climate change . With the release of the US-China climate announcement,
the Global Climate Fund has now received commitments from the US, Canada, Germany, Japan,
South Korea, the UK and a number of other nations. Meanwhile, the recent US-China
announcement has put significant economic and geopolitical pressure on Australia to up its game
on climate change. Australia’s emission reduction target lags behind its allies and major trading
partners, so it exposed Australia’s increasingly isolated approach to climate change and energy
policy (Climate Council, 2014).
China intends to achieve the peaking of CO2 emissions around 2030 and to make best efforts
to peak early and intends to increase the share of non-fossil fuels in primary energy consumption
to around 20% by 2030. The two sides intend to continue strengthening their cooperation on
advanced coal technologies, nuclear energy, shale gas and renewable energy, which will help
optimize the energy mix and reduce emissions. China is steadily increasing the proportion of
renewable energy sources to reduce dependence on coal. New analysis from Deutsche Bank shows
that in October and November 2014 Chinese coal imports have fallen by around 50% compared to
previous months (Renew Economy, 2014). In 2013, for the first time China installed more
renewable energy capacity than fossil fuels (REN21, 2014).China has invested so much money on
renewable energy and it has had an impact on the global markets. For instance, by supplying the
domestic and international market, China has helped to drastically cut the global costs of
photovoltaic panels by 80% since 2008(Mathews&Tan,2014).At the same time, China by boosting
markets in water, wind and solar power is driving down costs and accelerating the uptake of
renewable energy (Mathews&Tan,2014). Obviously China has made many efforts on global
climate change to reducing emissions of carbon dioxide, but according to Citigroup analysts, this
announcement could result in a $ 4.5 trillion loss in revenue for ‘Big Oil’ and ‘Big Coal’ over the
next 15 years from the joint reduction of greenhouse gas emissions by the world’s two biggest
economies (Renew Economy 2014a). Therefore, whether China can ensure economic growth
while achieving peak emissions around 2030 and also ensure that increase the share of non-fossil
fuels in primary energy consumption to around 20% by 2030. This is a matter of concern for other
countries and climate organizations, it is also a main content that this paper will explore. We
attempts to use hybrid energy model to simulate the evolution of energy structure and changes of
carbon emission costs from 2010 to 2050 under peak emissions by 2030.
2 Model and Data
In order to connect macroeconomic and micro-energy technologies, we use energy demand
and energy supply equilibrium conditions for a ‘soft connection’ to build hybrid energy models.
Energy demand model reference a sub-sector intertemporal dynamic optimization model
established by Zhu&Wang et al. (2014). This model in ensuring the maximization of steady
economic growth and social welfare utility premise, simulated Chinese economic growth and
industrial structure optimization path to the future, then get China's energy consumption in the
future. Energy supply model is based on cost minimization goal and consider a variety of
alternative energy production technology, and the role of energy between the dynamic optimal
model, consisting of three parts: energy technology module, energy cost module and carbon
emission module.
Figure 1: Structure of model
3.1 Energy Technology Module
Energy supply model is an optimization model to minimize the total cost of the energy
system as the objective. The economic costs of different energy sources (including capital and
operating costs) were compared (after discount), the total cost of the smallest combination as the
optimal solution .The final choice is to ensure that the chosen technology portfolio of the entire
energy supply system to meet the energy needs in the context of the minimum cost, and therefore,
this article focuses on the evolution trends of different energy technologies and changes of
corresponding cost.
Various energy technologies mainly complex by the form of multiple levels nest, energy is
decomposed as shown in in the figure below. Due to the elasticity of substitution parameters can
better reflect relationship of mutual substitution between energy technologies, so this paper using
constant elasticity of substitution function for each energy technology complex, for portraying the
role of mutual substitution between energy technologies. According GTAP-E, BMR and WITCH
model, we divide energy into electric energy and non-electric energy (Nordhaus, 1999; Popp, 2004;
Bosetti et al, 2007).
Figure 2: Energy nest
Legenda: EL=Electric Energy; PC=Traditional Pulverized Coal Power Generation; USC=Ultra-Supercritical
Power Generation Technology; NEL=Non-electric Energy; ELFF=Fossil Fuel Electricity; IGCC=Integrated
Gasification Combined Cycle
Energy is a combination of electric and non-electric energy:
1
EEN  AEN  (EL EEL
In the above formula,
energy technologies.
 EN
 NEL ENEL
A j and  j
 EN
)
 EN
,
EL  NEL  1
(1)
represent the scale factor and parameters of each share of
 j is elasticity of substitution parameters (substitution elasticity is
1 ).Each factor is further decomposed into several sub-components. Factors are aggregated
1  j
using CES linear and Leontief production function. Each electric power technology produced via
capital, operation and maintenance and resource use through a zero-elasticity Leontief
aggregate:
EEL j t   min{ j K j (t ); j O & M j (t ); j X j (t )}
(2)
Capital for electricity production technology accumulates as follows:
KD j (t  1)  KD j (t )(1   j ) 
I j (t )
SC j (t )
(3)
where, for selected technologies j, the new capital investment cost SC, decreases with the
cumulated installed capacity by mean of Learning-by-Doing:
SC j (t )  B j KD j (t )
b j
(4)
2.2 Energy Cost module
For energy costs, we focus the evolution of fossil energy technologies. Nordhaus (1993)
considered fossil fuels as expendable fossil energy resources, the total amount is certain, and as
the yearly consumption of fossil fuels, the cost will continue to rise. Therefore, the price of fossil
fuels are calculated endogenously using a reduced-form cost function that allows for non-linearity
in both the depletion effect and in the rate of extraction:
f
Pf (t )   f   f [Qf (t 1) / Q f (t )]
(5)
 f is the current cost of energy extraction and transportation costs, distribution costs. The
second term is a function of increasing costs,
Q f and Q f represent the accumulation and
exploitation of the remaining reserves.
So total energy cost as follows:
Ctot   f  Pf ,t X f ,t    j OM j ,t  I j ,t 
(6)
2.3 Carbon emission module
We can calculate and obtain the total carbon emissions for each period according to the
carbon emission factor for each fossil. As shown in equation (7) below:
Em(t )   f  f X f  t 
(7)
Note: Emissions of carbon calculation in this model , only including human demand for
carbon emissions, excluding natural emissions.
2.4 Constraint and objective
The amount of energy supply and energy demand always maintain equilibrium based on the
assumption, so:
EEN (t )  E(t )
(8)
In the formula, EEN (t ) is the amount of energy supplied to each period, E (t ) is that each
period of the energy demand.
Set the minimum total energy costs as a target, plusing the discounted energy system costs,
then we can get minimum energy cost from t1 ~ t2 ：
t2
min Cost   Ctot (t )  (1  r )(1t )
(9)
t1
Wherein, r is the discount rate, which substantially equal to the long-term real interest rates,
excluding inflation or other opportunity costs.
2.5 Data
Zhu and Wang(2014) simulated China's future energy demand under different consumer
preferences patterns, it showed that carbon emissions trajectory are similar with four consumption
preference scenarios (Figure 3). Carbon emissions will reach a peak in 2030 under China
consumption preference scenario, while under the US, EU and Japanese consumption preference
scenarios China's carbon emissions peak will reach in 2032.Corresponding to China, the EU,
Japan and the United States consumer preferences scene, carbon emission peak values respectively
are 3909, 3297, 3895 and 3735Mtc. Obviously, compared with China, the EU and Japan scenario,
carbon emissions peak value is small under American scenario. The rate of decline in carbon
emissions under China scenario is significantly lower than EU and Japan scenarios. Although the
energy consumption and carbon emissions under the US scenario is significantly lower than
other three scenarios, but taking into account the objective of China's carbon emissions peak
around 2030, the actual energy consumption data from 2009 to 2013 and China's share of
manufacturing in the economy, consumption preference of the Chinese mode has been more in
line with demand for energy purpose of this study, therefore this paper use the energy demand data
from 2010 to 2050 simulated by Chinese consumption preference mode.
Figure 3: Projection of China’s carbon emission trends under four consumption preference scenarios,
2020-2050
In the energy supply model, non-electric energy consumption data from ‘China Energy
Statistical Yearbook’, fossil energy prices come from GTAP (Global Trade Analysis Project)
database and the US Department of Energy's Energy Information Administration (EIA),technical
data of various power electricity from annual statistical report jointly issued by the International
Energy Agency (IEA) and the Economic Cooperation Organization under the Nuclear Energy
Agency (NEA) , carbon emission data from the EIA. For renewable energy, considering the
carrying capacity of resources, set capacity limit. According to ‘renewable energy and long-term
development plan’ issued by the National Development and Reform Commission, the national
water resources economically exploitable installed capacity of up to 401.8 million kwh, the largest
potential of biomass resources turn into coal energy is 10 million tons. Wind energy resources
including the national land available and offshore areas available are totally 10 million kilowatts.
3. Simulation Results
3.1 Carbon emissions trajectory under 2030 target
Carbon emission trends from 2010 to 2050 as shown in Figure 4. It can be seen from the
figure that carbon emission peak occurs in 2030 under the optimal control of cost, then the
emission gradually decreased after 2030.Affected by energy data and oil prices, carbon emission
trends exhibited a clear ups and downs before 2030, in 2015 carbon emissions have dropped
significantly. In the energy supply model constructed in this paper, the oil price is exogenously
given, with reference to the international crude oil prices, due to a sharp decline of international
crude oil prices from the second half of 2014, leading to lower expected oil prices in 2015, While
the carbon emission factor of oil is less than coal, the decline in oil prices are favorable alternative
to coal, it will inevitably bring about a decline in carbon emissions. But for energy demand is still
in growth, carbon emissions in 2015 has decreased but not great.
Figure 4: Projection of carbon emission trend and energy consumption change, 2020-2050
3.2 Energy structure and evolution cost
According to the results of the energy dynamic optimization module obtained in the GAMS
platform, energy structure from 2010 to 2050 under optimal cost objective shown in Figure 6. It
can be found that the main source of carbon dioxide emission from coal energy. The proportion of
coal in the various types of energy has been in a dominant position, its share declined from 87
percent in 2010 to 61 percent in 2050, the proportion of 2030 has been maintained at over 80% of
all the energy supply, which is mainly due to the price of coal is much lower than oil, natural gas
and other non-fossil energy sources, as well as China's petroleum and natural gas are relative
scarcity; the proportion of coal energy is gradually decreased after 2030, mainly due to the
substitution of oil nuclear energy in electric power and biomass in non-electric energy . In the
future oil, nuclear and biomass replace coal as a major energy, their consumption increasing year
by year. Different with the expanding proportion of oil and bio-energy that although the
consumption of nuclear energy in 2044 has been growing, its’ proportion of the total energy has a
slight decrease. Changes from nuclear power and biomass energy consumption shows that in the
future as energy technology costs becoming lower, clean energy and renewable energy will be
fully utilized. Simulation results indicate that water and electricity in the beginning will has a
rapid pace, while the amount of water available is restricted so the amount of hydroelectric power
peak (1.0317 trillion kwh) will reach in 2017, then it has remained at this level until 2050; no
significant change in natural gas consumption during simulation period, the proportion has
remained at around 6%.
Figure 5: Evolution of energy consumption structure, 2010-2050
Overall, the proportion of fossil fuels in primary energy consumption was 87% in 2030,
accounting for 80% in 2040.According to China-US joint statement on climate change declaration,
in addition to reach carbon emissions peak in 2030 China also pledged the proportion of non-fossil
energy in primary energy will reach 20% in 2030. This will require China to deploy an additional
800-1,000 gigawatts of non-fossil fuel emission generation capacity by 2030-more than all the
coal-fired power plants that exist in China today and close to total current electricity generation
capacity in the US (The White House 2014). Obviously simulation results indicate that the time of
substitution by non-fossil fuels later than the time promised in joint statement, for which we need
to further analyze reasons for the delay, that is to simulate structure and cost changes of electrical
energy and non-electric energy changes from 2010 to 2050.
As it can be seen from Figure 6, the substitution of nuclear for coal in electric energy is
earlier than biomass in non-electric energy. Nuclear power as a major alternative energy in electric
energy its power generation capacity exhibited a rapid growth trend before 2044, from 18.5Mtoe
in 2010 to 266.3Mtoe in 2044 , nearly 14.4 times increase, after 2044 nuclear power generation
capacity had a gradually decline. In contrast, the proportion of solar, biomass in electric energy
consumption is very small, during the simulation period remained at a lower level. Oil power
generation performed the EKC curve from 2015, its peak occurred in 2031, the peak is 13.75
billion kwh. Proportion of Oil and natural gas in the sector remained basically unchanged.
Substitution of biomass in non - electric sector is very obviously, especially after 2030, its growth
from 9Mtoe to 407.8Mtoe in simulation period, about 45 times increase. Although biomass in non
- electric sector showed a significant substitution effect, however, due to the limitation of total
amount of biomass resource, which results in the consumption of fossil energy in non-electric
sector has always been a dominant status, the proportion of fossil energy consumption up to 98%
in 2030, in 2050 the proportion has decreased but still over 80%.
(a) Energy consumption structure in electricity sector
(b) Energy consumption structure in non-electric sector
Figure 6: Energy consumption structure in electricity and non-electric sector
As can be seen from Figure 7, China’s coal price remained low, which makes the energy
structure is still based on coal supply. Due to the extensive use of coal, the price of coal continued
to rise and reached 203.22USD / toe in 2050, about 4.4 times in 2010. The price of nuclear energy
has experienced a steady growth from 2010 to 2043, after 2044 price had a decline slightly, the
rate of nuclear price increase is less than the speed of coal increase, so the substitution role of
nuclear energy for coal energy gradually appear, but high install cost makes it unable to replace a
large number of coal energy. Although nuclear energy price rises before 2043, however,
accompanied by a rapid decline in the cost of nuclear power technology, the increase in price did
not prevent further use of nuclear power. So for nuclear power, the technology cost is a major
factor affecting use of nuclear energy.
Figure 6: Projection of fossil energy price change, 2010-2050
Learning-by-doing is the major engine of endogenous technical change in the energy sector.
Over time, in addition to PC technology, other energy technologies depict downward path of
install costs. By the year 2050, cost of USC and IGCC as clean coal technology will drop to
543USD / kW and 950USD / kW, with reduction of about 16% and 17% respectively compared
with 2010. Installed cost of nuclear power decreased rapidly, by 2050 it’s reduce to 1703 USD /
kW, dropped by nearly 28%. In contrast, due to resource restriction and limited decline scope,
hydropower costs only decreased about 1.6% from 2010 to 2050.Renewable energy wind power
experienced a rapid decline before 2038, then the process is slowing down; solar energy and
biomass power have decreasing costs over the simulation period, but the reduction is not obvious.
In summary, with the sharp decline in the cost of new energy technologies, these technologies,
especially nuclear power will be actively developed and applied.
Figure 8: Projection of electrical energy technology cost trends, 2010-2050
4 Conclusion and Discussion
This article use energy demand and energy supply equilibrium conditions for a ‘soft
connection’ to build hybrid energy models. Ensure dynamic mechanism between economic growth
and energy inputs, fully considering the impact of technological progress and the substitution of
energy technologies on the evolution of the energy structure. Under the objective of minimizing
costs, combined with a variety of differences in energy technologies, simulate the evolution of
energy structure under the achievement of carbon emission peak in 2030.
The simulation results show that under the objective of optimal cost, China can achieve a
emission peak in 2030, earlier than the emergence of energy demand peak. For the energy
structure, since only considering the cost, while the price of coal and cost of coal technology are
low compared with other energy sources, therefore coal in fossil energy has been in a dominant
manner. In other words, due to the coal energy consumption in the non-power sector has always
been large. China wants to achieve non-fossil energy sources accounting for 20% in the
non-electric sector around 2030, having to adjust energy consumption structure by reducing the
use of coal. The creation of the U.S.-China Clean Energy Research Center facilitates collaborative
work in carbon capture and storage technologies, energy efficiency in buildings, and clean
vehicles. Although the power sector in 2030 can achieve non-fossil energy accounting for about
20%, but it still need to optimize the energy structure, development of advanced coal technologies,
nuclear energy, shale gas and renewable energy, reduce emissions including from coal. By the
impact of the decline of nuclear power technology costs and power generation efficiency
improvement brought by the U.S.-China Clean Energy Research Center, the feasibility of nuclear
energy replace fossil energy will enhance, that is to say nuclear power will be actively developed
in future.
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