Download 2 - EcoMod

A Research on the treads of multi-nation and multi
-section carbon emissions and energy uses under the
drive of the progress of technology
Zheng Wang*, Gaoxiang Gu
（Institute of Policy and Management, Chinese Academy of Sciences ）
Abstract The progress of process technology which is caused by the improvement of the
productive technologies can reduce the demands of the intermediate inputs in the productive
process, and then reduce the energy demands and the carbon emissions. Thus, to improve the level
of process technologies is an important way for the decrease of global carbon emission. In this
paper, based on Jin (2012)’s model, a general equilibrium model of multi-nation and multi-section
economy was built. Coupled with the climate system of RICE model, a climate-economy
integrated assessment model been built with the interactions between the economic system and the
climate system. Based on this model, the carbon emissions and the energy demands of different
countries and sections were been studied. The simulated outcomes show that the progress of
process technology can bring on early peaks of energy demands and carbon emissions. Under the
three different scenarios, China will reach its carbon emission peak at the year of 2034, 2030, and
2022 respectively. In the more bold scenario 3, the accumulated carbon emission in China can
reduce to 93GtC, accomplishing the abatement target of 100GtC. Besides, along with the progress
of the process technologies, the developing countries like China and India have greater potential to
reduce emissions. In all walks of life, energy industry has the greatest potential, and service
industry follows.
Keywords: the progress of process technology; general equilibrium; carbon emission peak; energy
demand
1. Introduction
Global climate change and climate protection are hot issues in recent years, but also the most
challenging issues (Gardiner, Hartzell-Nichols 2012), which have been widely and intensively
discussed in the world. Recognized cause of global climate change is the heavy use of fossil fuels
and the emission of greenhouse gases like carbon dioxide, which caused by the rise of earth’s
temperature (IPCC Climate Change 2007) since industrialization. Since 1750, global
concentration of carbon dioxide, methane and nitrous oxide has significantly increased, far more
than that before industrialization. Among them, carbon dioxide is the major greenhouse gases.
Therefore, the control and reduction of global CO2 emissions play a significant role in the future
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development of human society
The progress of energy-saving technology is significant for reducing the emission of carbon
dioxide de Groot et.al, 2001). The use of energy -saving technologies can reduce energy
consumption of per unit of GDP, which is an important way to reduce the emission of industrial
greenhouse gases. Although there has always been a view that the decline in the energy intensity
caused by technological innovation in the process of production will lead to a decline in energy
prices , Consequently causing excessive energy consumption (Herring, Roy 2009), but from
macro and long-term perspective improved production process will reduce the production and
other intermediate process of energy, which plays an important role in the decline of energy use,
the decrease of CO2 emission and the protection of climate.
Narrow energy -saving technologies can reduce energy use in the process of production, and
broad energy -saving technologies should include all technologies that can reduce intermediate
inputs, namely process technology or technology, because all the production of intermediate inputs
also consumes energy. Reducing investment in intermediate products will reduce the use of energy.
This paper considers the energy -saving technology in the broad sense.
Research on the relationship between technological progress and the decline in energy
intensity is a hot issue in recent years in research field . Popp (2001) thought that technological
innovations play an important role in reducing energy consumption, especially from long-term
perspective, playing a more important role than factor substitution, especially from the perspective
of the long-term , with more significant than the factor substitution effect . Fisher-Vanden et.al
(2004) found that R & D investment , the change of industrial structure and energy prices are are
important driving factors of the decline of energy intensity in China. Gallagher et.al (2004)
considered the world's energy technology innovation is the key to maintaining economic
prosperity and ensuring that the global climate is not destroyed because of the use of fossil fuels.
the world's energy technology innovation is to maintain economic prosperity and ensure that the
global climate is not critical because of the use of fossil fuels have been destroyed . Feng et al
( 2008 ) suggests that the increase of proportion of technological progress and the tertiary industry
in GDP
can significantly reduce China 's energy intensity ,
while in the long term, the
reduction of energy intensity ultimately has to rely on technological progress. Sagar, van der
Zwaana (2006) studied the impact on energy economy of the rate of diffusion of technological
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innovation and technological innovation based on R & D and the model of learning by doing. de
Groot et.al (2001) took the Netherlands as an example to study improved choice of
environmental policy and their impact on energy intensity in different environmental policies and
sectors. Sue Wing (2008) analyzed the power source of America’s decline of energy intensity in
the past fifty years through the method of measure, the results showed that the change of industrial
structure and the improvement of energy use efficiency are major power source. he results showed
that the change of industrial structure and the improvement of energy use efficiency are major
power source, and alternative energy caused by the price has only short-term effects . The above
work mainly took statistical methods, measurement and data analysis –based method or a partial
microscopic scale study, The study was limited to a particular country , and there is no study on
the impact of the progress of energy technology on global climate change and economic
development.
This paper argues that the research on global decline in the energy intensity, national
economic development and the impact of carbon emissions research should be carried out against
the background of global climate change, to reflect the feedback effects of climate change and the
economy, to improve the accuracy and reality of the study, which formed the integrated problems
of global economic development and climate change. For this type of problem, the international
community is more popular to use integrated assessment models, especially taking the DICE
model by Nordhaus (1992) and the RICE model by Nordhaus, Yang (1996) as the representative
also including the FOUND model by a Link, Tol (2004 ), and MERGE model by Manne, Richel
(1996,1999) etc. However, the economic systems of these models have some shortcomin, the main
problem is that these models or economic system are too simple, without considering the general
equilibrium of global economy; decline in exogenous energy intensity is defined without
considering endogenous technological progress.
Among them, the economic mechanisms of RICE and DICE model is too simple, without
considering the equilibrium of the market, and its mode of technological progress does not obtain
endogenesis. Meanwhile, RICE model does not consider the economic ties between countries, but
regard their economic growth as mutually independent events. In addition, the region dividing of
RICE model is relatively simple. MERGE model also lacks economic ties between countries and
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economies are divided only to the national level. Divisions for economic development and
changes of industrial structure of various departments lack more detailed studies. Although
FOUND model divides economy into departmental level, and considers the inter-regional
economic ties, but it does not consider the feedback effects of climate change on economic
development.
Improving the work of the RICE model, Popp (2004) Improved the DICE mode by adding a
model of endogenous technological progress, but the world will be reduced to a country in
economic level, without considering the differences between countries different Therefore, he
failed to study that countries of different development levels in face of carbon emissions decline
problems would encounter different economic and industrial issues. MRICES model by Wang ,
Zhang, et al (2012) and Zhang (2012) takes into account economic ties and endogenous
technological progress mode between countries. but its economic ties based on GDP overflow
forms of expression cannot accurately describe the economic relations between countries under
global economic integration. Besides the economic system of its models only countries under
describe the impact of changes of industrial structure on energy intensity.
In response to these deficiencies in the work, based on Jin’s (2012) model, we use a general
equilibrium approach to build a general equilibrium model of biochemical technical level in many
nations and sectors, describing the interaction of economy between the nation and production
departments. On this basis, the paper will based on Jin’s (2012) model, we use a general
equilibrium approach to build a general equilibrium model of biochemical technical level in many
nations and sectors, describing the interaction of economy between the nation and production
departments making the interaction between economic models and climate models to build a
dynamic and
integrated assessment of climate change, based on economic interaction model of
general equilibrium in many nations and sectors, By changing the rate of the progress of
technological process, we will research the economic development rate, energy consumption and
changes of CO2 emissions in different countries and various sectors under different energy
intensity and rates, and compare the potential of reducing CO2 emissions between different
nations and sectors.
2 modeling and data
4
In this paper, the model includes multi-national multi-sectoral economic model based on
general equilibrium theory, a climate model and a carbon accounting module. In the model, the
world is divided into a number of countries, which are represented by j, while the production
departments are represented by i in this paper The precondition of the hypothesis is that capital
and goods can flow freely between nations; there is no trade barriers; there does not exist labor
mobility between the nations; country's internal labor can move freely.
2.1 Economic Module
In this paper, we describe production behavior of various departments in two nested layers,
namely the department's total output value is made up of added values and intermediate inputs,
and added values are composed of labor and fixed capital. Based on the constitution form of total
outputs defined by Leontief’s production function definition and constitution form of added values
defined by Cobb-Douglas’s production function definition, this paper follows Jin’s (2012)
assumption in the production process: the same department has a unique output elasticity; the
same country has a unique production technology; production technology is fully applied to labor,
to form an effective workforce.
j

 M 1,i ,t
X  min 
,
 a1, j ,i ,t

j
i ,t
X i*, j ,t   Ki ,jt 
i
,
M kj,i ,t
ak , j ,i ,t
,
,
M Ij,i ,t
aI , j ,i ,t


, X i*, j ,t  , k  1,


,I
 A L  Z 
j
t
j 1i
i ,t
Z
j  j ,t
t
（1）
（2）
In the formula (1), X i ,jt represents added value in t phase in i department of j country;
X i*, j ,t represents the initial increase through the combination of its labor and capital; M i ,jt,k means
the number of products that i department actually put in k department in the process of production;
ak , j ,i ,t means the middle coefficient of demands in various departments. In formula (2), Ki ,jt
represents the fixed capital in t phase in i department of j country; Lij,t means the number of labor;
At j means technological level of labors;  i means the output elasticity of departments.
Wage rate equals the marginal output of workers. Because workers in this country have full
mobility, the wage rate in various departments is the same in the country under equilibrium
conditions.
5
wij,t 
X i ,jt
Lij,t
pi ,t  (1  i )
X i ,jt
Lij,t
（3）
pi ,t
In Formula (3), pi ,t means the price of products i, Because of the absence of trade barriers
between countries, the price of one product in a phase is unified. According to Abel (2003), and
Jin (2012), the capital in this paper are accumulated and updated in Cobb-Douglas form:
Ki ,jt 1  a( Ii ,jt ) ( Ki ,jt )1
（4）
Where, Formula (4), I i ,jt indicates the number of investment obtained by i department of j
country in t phase. including domestic investment and foreign investment,
 is the output
elasticity of capital production , a is its total factor productivity. In formula (3), to calculate the
partial derivatives of I i ,jt , the marginal product can invest, which is the inverse of the price of
capital.
q  1/
j
i ,t
Ki ,jt 1
I i ,jt
j
1  I i ,t


a  Ki ,jt
1



（5）
The total GDP of the countries in each period consists of two parts, namely, he added value of
products production and capital increase in value production.
GDPt j   pi ,t X i ,jt 
i
Formula (6),
p
i ,t
1

It j  It j  X t j 
1

It j
X i ,jt means added value of products production,
i
（6）
1

I t j  I t j means capital
increase in value production.
The conditions of the clearing of product market is that production plus consumption equals
the sum of intermediate inputs and investment Assuming Yi ,gt is the global output of i department
in t phase , clearing conditions of the product i is as follows:
Yi ,gt   j Yi ,jt   j cij,t   j  i xkj,i ,t   j  i  k M kj,i ,t pit
（7）
According to Jin (2012), clearing equation of price in each production departments is
expressed as
6
pi ,t 
i
X
g
i ,t
X tg
（8）
clearing equation of every country’s labor can be expressed as:
L
i
 Ltj
j
i ,t
（9）
2.2 Capital flows Module
Employing wage and price equations in a condition of general equilibrium, you can get the
total wage of full market is:
Wt g   (1   i ) X i ,jt
j
i
X tg
 i  X tg  (1   i ) i
g
X i ,t
i
Full market total wages as fixed share of total output
 (1   )
i
（10）
i
, the ratio of the number
i
of full-market investment to the total output in each phase is:
I tg    s j  (1   i ) i X t j
j
（11）
i
In Formula (11), s j represents country j's savings rate. This article assumes that capital
flows consist of two parts, so that the pursuits of economic scale and rate of capital return are
fulfilled, the ratio of obtained investment to total investment of several countries in the various
departments herein is :
Ri ,jt   R1 (i, j, t )  (1   ) R2 (i, j, t )
In Formula (12),
the R1
（12）
and R2 represents ratio of investment when pursuing economic
scale and ratio of investment respectively;   0,1 are weighting coefficients of R1 .
Expression and Jin (2012) consistent pattern of international capital flows, the basic idea is
designed to ensure that the unit is equal to the return on investment in all sectors of the countries
obtained:
X 
(1   ) si ,t sl
 i i
R1 (i, j , t ) 
E  ig,t 1  
E  Ri ,jt 1 
sk  (1   ) s j sl  X i ,t 1  sk  (1   ) s j sl
j
j
Form from (13) point of view, is a kind of added value of the investment division of capital
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（13）
allocation model. R2 is the ration of investment in many countries and departments based on
capital attractiveness. Different from R1 , R2 , from the microscopic point of view, takes a
bottom-up perspective to reflect the profit-driven process of capital flows, namely the high rate of
return on capital flows department. Here we refer to the Caniëls (2000) and Caniëls, Verspagen’s
(2001) Knowledge spillover strength model to represent the strength of inter-regional knowledge
spillovers. Since this paper studies inter-regional capital flows, we use capital stock, total wages
and marginal product of capital intensity as factors to influence capital attraction, and we employ
inter-regional travel to replace the knowledge gap in knowledge spillovers model. Wang, Ge, et al
(2007) has been attractive regional capital intensity decay is exponential decay
TK
x, y
i, j
K w L
j
i ,t
y
t

R2 (i, j , t ) 
y
t
 x X xy,t
K xy,t


X tj
exp   ln y  1
Xt


TK xi ,, yj
x, y

i, j
TK
i, j
x, y
  y 
 1   S x ,t 


I tg
（14）
（15）
In (15), TKix, ,jy is the attractiveness of the capital intensity of sector x in the state y,
 means that the value of the ratio of the two countries to increase the attractiveness of
the impact on the capital coefficient, S xy,t represented by the state sector savings. In
Formula (21), TKix, ,jy demonstrates the attractiveness of the capital intensity of department x in
the country y for department i in country j.  means influence coefficient of the ratio of added
value in two countries on capital attractiveness. S xy,t represents the savings of department x in
country y.
As the real international capital flow exist some obstacles, and different countries employ different
policies on capital investment, international capital investment cannot flow freely. In this paper,
we introduce investment weighting
 j to correct the behavior of international capital
investment and overcome the errors caused by the simulation. The meaning of
 j is
reflected interference with international geopolitical factors on capital flows caused. Thus,
the ultimate ration of investment of different countries can be expressed as:
8
Ri*, j ,t  A j Ri ,jt
（16）
In Formula (16), A indicates normalized parameters. The significance of Formula (16)
lies in re-normalization of investment ration in different countries and departments after t
adding the correction factor  , to make sure
j
R
j
i
*
i , j ,t
1
. the actual number of
investments in various sectors States obtained as follows:
Ii ,jt  Ri*, j ,t Itg
（17）
2.3 Technology Progress Module
In this paper, the advancement of technology embodies two aspects, namely, the increase of
j
levels of process technology At , and the decrease of coefficient of intermediate demands aij,t, k .
The former reflects the improvement of productive capability, while the latter reflects the
decrease in intermediate inputs in the process of production , namely the progress of process
technology. For the production technology level At j , Based on Arrow (1962), Romer ‘s
(1986) hypothesis, we set up a biochemical approach, concerning that level of process
technology is related with capital accumulation of the department, and the following is its
expression:
At j  Bt j ( Kt j )
（18）
In the course of the progress of process technology, for coefficient of intermediate
demands , in accordance with Lorentz, Savona’s (2008) work, we take the method of
random cycle shock, randomly shocking the coefficient of intermediate demands of
different countries and departments for several times in each phase and simulating the
decrease in intermediate products of production departments.
ln(ak' , j ,i ,t )  ln(ak*, j ,i ,t ,n )   j ,k ,i ,t ,n
 j , k ,i ,t , n
（19）
N (0;  )
（20）
In Formula (19), n means that the nth iteration, ak*, j ,i ,t ,n , which means intermediate demand
coefficient at t that after the nth iteration of the national department j of products is  j ,k ,i ,t ,n
9
subject to a random number distribution. This produces a new set of intermediate demand factor
a
'
1, j ,i ,t
, aI' , j ,i ,t  . When the cost of production per unit in this new set of
, ak' , j ,i ,t ,
,
intermediate demand coefficient is less than the cost of production per unit before, this new set of
intermediate demand coefficient is accepted; otherwise maintain the original intermediate demand
factor is maintained.
a
 a


 a
*
1, j ,i ,t , n 1
'
1, j ,i ,t
,
, ak' , j ,i ,t ,
,
*
1, j ,i ,t , n
,
, aI*, j ,i ,t ,n 1  
, ak*, j ,i ,t ,n 1 ,
, ak*, j ,i ,t ,n ,
, aI' , j ,i ,t 
if  k 1 ak' , j ,i ,t pi ,t   k 1 ak*, j ,i ,t ,n pi ,t
J
, aI*, j ,i ,t ,n 
After the N circulate times,
J
（21）
Otherwise
a
*
1, j ,i ,t , N
, aI*, j ,i ,t , N  was given to the needs of the
,
middle coefficient:
a
1, j ,i ,t 1
,
, aI , j ,i ,t 1    a1,* j ,i ,t , N ,
, aI*, j ,i ,t , N 
（22）
2.4 Carbon Accounting and Climate Feedback
In this model, the energy used in the process of production of various departments is
supplied by the energy department, therefore the amount of energy can be calculated according to
intermediate demand coefficient for energy department and the total output of the various
departments.
Ei ,jt  aE , j ,i ,tYi ,jt
（23）
In Formula (23), a E , j ,i ,t indicates intermediate demand coefficient for energy department in
department i in country j in phase t, namely energy input that can meet the needs of total output
per unit of the department. Energy department provides a variety of energy sources, and different
intensity of its carbon emissions of different energy varies. When calculating the total carbon
emissions of a country, we need to consider the energy structure and its changes in the country. In
this article, the total carbon emissions can be obtained in accordance with the needs of a country's
total energy consumption and carbon emissions intensity of available fossil fuels.
Qt j   e e, j e,i ,t i Ei ,jt
（24）
In Formula (24),  e,i ,t means that the country in the first phase of fossil fuels to provide
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energy consumption accounts for more than, which means that the carbon intensity of the national
energy and carbon emissions. In this paper, fossil energy was divided into oil, coal and natural gas
three. The exogenous trends in fossil energy consumption are acquired by the EIA energy data
fitting.
Increase in global carbon emissions will lead to an increase in global carbon concentration,
and thus the intensity of atmospheric radiation, resulting in an increase of atmospheric temperature,
atmospheric temperature rise in turn will affect economic production. Climate module in this
paper is based on Nordhaus’s (1996) the RICE model and Pizer’s (1999) work, and the
temperature rise’s influence on economic production can be expressed as:
 j ,t 
1  b1, j
1   D0, j 9  Tt 2
（25）
In Formula (27), b1, j means p destruction factor in every country; D0, j is the loss of GDP
when temperature rises 3 ℃; Tt indicates temperature changes in phase t. Tt can be obtained
from climate change system , which is based on current carbon emissions, atmospheric carbon
concentration and surface temperature. The specific model refers to Nordhaus (1996), Pizer
(1999) and Wang , Li (2008), Wu and this paper will not discuss this in details.
2.5 Data Sources
In this paper, the model of economic data mainly come from the GTAP-07 databases,
including the value of the total output, added value, the number of fixed capital, the total
population, the number of intermediate inputs, energy consumption. The main parameters of the
climate module refer to Nordhaus (1996) and Pizer (1999). Population growth data comes from
the United Nations’ natural population growth rate, and demographic data originates from the
World Bank. Output elasticity of capital is calculated according to added value of GTAP-2007 and
wages of labor. Real savings rate reference countries Ma, Yi (2010).
In terms of industrial structure, this paper divided 57 departments in GTAP databases into
eight: agriculture, energy, mining and processing of metals and other mining, chemical,
manufacturing, construction, transportation and other services. The world was divided into seven
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regions: China, U.S., EU, Japan, Russia, India and other countries of the world.
3. An analysis of national economic development and industrial
structure change
On the basis of the model, the paper of the world in 2007 -2050 years of economic
development and energy consumption, carbon emissions are simulated. Because changes
in the industrial structure influence the energy needs and carbon emissions of the various
departments and countries greatly, this section will firstly analyze the 2007 -2050 economic
trends and changes in industrial structure between countries.
Graph 1 shows yearly GDP growth in every country. Simulation results show that, from
the overall point of view, the 2050 worldwide GDP reached 370.28 trillion U.S. dollars, and
average annual GDP growth rate is4.74 percent from 2007 to 2050. Among them, China's GDP
surpassed Japan in 2010, which agrees with reality. China will exceed the EU after 2037
years and surpass that of the United States around 2042. By 2050, China's GDP will
reach $ 51.74 trillion, and the average GDP growth rate is 6.48 percent from 2007 to 2050.
America's GDP reached 46.84 trillion U.S. dollars in 2050, equivalent to 90.53 percent
over the same period in China, and the average GDP growth rate is 2.99 percent. The
average growth rates in the European Union and Japan are slow, respectively, 2.56% and
2.19%. Russia's GDP growth rate is slightly higher than the average that of 3.83 %. India's
average GDP growth increases faster, reaching 8.72 %, the value of its GDP will
surpassed that of Japan in 2020 and exceed that of EU around 2048. The results show
that under the current pattern of international capital flows, China 's GDP will surpass the
United States in about 2040 . This conclusion is consistent with the predictions of
Goldman Sachs in 2004, Also close to Wilson, Purushothaman (2003), Dadush, Stancil
(2010) and Wang et al’s (2010) findings are relatively close.
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图 各国分年度 GDP 值变化（万亿美元）
Figure countries of the value of annual GDP change (one trillion U.S. dollars)
4. Analysis on Energy Demands and Carbon Emissions of
Every Country and Departments
Improving the skills and technology of departments in the process of production and
allowing companies to reduce the amount of intermediate inputs is main ways for
enterprises to reduce productive costs, and decrease in demand for intermediate products
is closely related to the reduction of energy demands in the process of national production.
By changing normally distributed variance under random shocking in the process
technology, we designed three different scenarios, each representing a different
intermediate demand coefficient, namely, decreased rate of technical progress, change of
energy demands under different rate of the progress of process technology and trends of
carbon emissions. Three scenarios are: Scenario 1:  = 0.00035; Scenario 2:  = 0.0004;
Scenario 3:  = 0.00045
4.1 carbon emissions trends in overall global with three scenarios
Table 1 shows average decline rate of energy intensity and the intensity of annual carbon
13
emissions of every country during the simulation in three scenarios. In three scenarios,
China's average energy decline rates were 4.81%, 5.43% and 6% respectively. Wang
(2010) advocates that China's energy intensity decline rate can be controlled within 5% in
view of trends of industrial structure change. Therefore, we believe that scenario1 and
scenario 2 conform to the reality, while scenario 3 is a more radical prediction of progress
of process technology. In scenario 1, China's energy intensity will reach world's most
advanced level in2007 (that is, EU level: 0.1498 Mtoe/BUSD) by 2034; in scenario 2,
China's level of energy intensity reached that of the EU in 2007 by2030, whereas in
scenario 3, this time can be advanced to the year 2027.
In three scenarios, Japan’s decline rate of energy intensity is minimum, followed by
the United States and the European Union, because Japan, apart from the energy
industry, intermediate demands coefficient is small, resulting in the potential to
reduce emissions is smaller than the United States and the European Union. India and
Russia's decline rate is higher than that of China, and Russia, whose initial energy
intensity is slightly higher than that of China, reaches the level of the EU in2007
earlier than China. In three scenarios, the time is: scenario1, at 2033 years, scenario 2,
at2029 years, scenario 3, at 2026 years. India's initial energy intensity is smaller than
that of China, but their level of energy intensity reach that of the EU in 2007, earlier
than China. They are as follows: scenario 1, at 2025 years, scenario 2, at 2022 years,
scenario 3, at 2020 years.
Table 1 National average energy intensity declined in table 13 (%) and carbon intensity (Mtoe/BUSD)
with three scenarios
Initial energy intensity
Scenario 1
China
USA
0.6119
0.1888
0.1622
0.1498
0.4248
0.6298
4.81%
3.42%
2.85%
3.49%
4.93%
5.16%
2020
0.2947
0.1291
0.1274
0.1035
0.1878
0.2895
2030
0.1777
0.0928
0.0953
0.0723
0.1150
0.1682
2040
0.1132
0.0627
0.0682
0.0480
0.0731
0.1018
2007
Average rate
of decline
14
Japan
EU
India
Russia
2050
Scenario 2
Scenario 3
0.0733
0.0423
0.0468
0.0325
0.0482
0.0643
5.43%
4.06%
3.53%
4.15%
5.57%
5.78%
2020
0.2647
0.1156
0.1138
0.0928
0.1679
0.2591
2030
0.1488
0.0774
0.0787
0.0601
0.0956
0.1407
2040
0.0897
0.0494
0.0528
0.0375
0.0571
0.0806
2050
0.0555
0.0317
0.0345
0.0242
0.0361
0.0487
6.00%
4.65%
4.17%
4.77%
6.16%
6.37%
2020 年
0.2383
0.1036
0.1017
0.0833
0.1504
0.2322
2030 年
0.1254
0.0649
0.0652
0.0502
0.0796
0.1181
2040 年
0.0719
0.0393
0.0413
0.0297
0.0451
0.0643
2050 年
0.0427
0.0243
0.0259
0.0183
0.0275
0.0371
Average rate
of decline
Average rate
of decline
Figure 2 shows global total carbon emissions of the year in three scenarios. In these three
scenarios, change trends of global carbon emissions appear to rise firstly and decline late, like an
inverted U. In scenario1, global carbon emissions reach the peak in 2032, with carbon peak value
11.08GtC. In 2050, global carbon emissions is 9.97GtC, 2GtC higher than that in 2007. In
scenario 2, global carbon emissions reach the peak in 2024, with carbon peak value 9.41GtC. In
2050, global carbon emissions is 7.68GtC, slightly lower than average level in 2007. In scenario 2,
global carbon emissions reach the peak in 2017, with carbon peak value 8.48GtC. In 2050, global
carbon emissions decline to 5.98GtC, 3 /4 of that in 2007.
图 2 三种情景下全球当年总碳排放量变化
Figure 2F The world's total carbon emissions in the year under changes with three
scenarios
15
4.2 Trend of carbon emissions of every country in three scenarios
Figure 3
shows change trends of carbon emissions in three scenarios. In scenario 1, change
of carbon emissions in China appears to be an inverse U, with carbon emissions peaking in
2034,and peak value is 3.27GtC. In 2050, its carbon emissions reach 2.99GtC, 1.5% higher than
that in 2007. India’s change trends of carbon emissions also appear to be an inverse U, and its
carbon emissions peak in 2043, with peak value 0.865GtC. Carbon emissions in United States, and
EU, and Japan also appear an inverse U. Among them, the United States’ carbon emissions reach
its peak in 2025, with peak value 1.73GtC. EU’s carbon emissions peak appeared in 2018, with
peak value1.17GtC, and Japan’s carbon emissions peak appeared in 2019, with its peak 0.39GtC.
India’s carbon emissions rise all the time, without any emissions peak. In 2050, its carbon
emissions is 1.45GtC, 3 times more than that in 2007. By contrast, Russia’s carbon emissions
decline all the time. In 2050, its carbon emissions is 0.22GtC, accounted for 50 % of that in 2007.
In scenario 2, the China’s carbon emissions appeared in 2032, earlier than that in scenario 1,
and its peak value is also reduced to 2.7GtC. By 2050, its carbon emissions is 2.31GtC,
approximately 0.68GtC smaller than that in 2007. In scenario 2, the China’s carbon emissions
appeared in 2032, earlier than that in scenario 1, and its peak value is also reduced to 2.7GtC. By
2050, its carbon emissions is 2.31GtC, approximately 0.68GtC smaller than that in 2007. India's
carbon emissions peak appeared in scenario 2and its peak period is 2047, reaching a peak 1.11GtC;
Russia's carbon emissions change is consistent with scenario 1, in a stable downward trend.
In scenario 3, China's carbon emissions peak in early 2022, and its peak value
decreased to 2.34GtC. By 2050, its carbon emissions are 1.804GtC, 1.2GtC lower than
that in scenario 1, 0.5GtC.lower than that in scenario 2. Carbon emissions of developed
countries like U.S., E.U. and Japan emerge a stable 3-5 phase after experiencing economic crisis
in 2007 - 2010, during which their carbon emissions make little change. Since 2015, these
countries come to a phase in which their carbon emissions decline dramatically. By 2050,
U.S.’s carbon emissions are0.748GtC, 46% of that in 2007 , 58.96% of that in 2050 in
scenario 1; carbon emissions of European Union dropped to 0.422GtC , 36.4 % of that in
2007 , 56.92% of that in the same period in scenario 1 ; By 2050, Japan’s carbon
emissions are 0.142GtC, 41.7 % of that in 2007 , 58.03%of that in the same period in
16
scenario 1; carbon emissions in Russia has always been in decline, and its carbon
emissions in 2050 are 0.129GtC, 29.8% of that in 2007 .
(a)
(b)
17
(c)
Figure 3 States carbon emission trends with three scenarios
4.3 Changes of energy consumption in every country and department in three
scenarios
Table 2a shows the various departments countries reaching its peak and the number
of peak energy demand in scenarios1. In scenarios 1, apart from agriculture and other
mining , the rest departments will get their peak of energy demands after 2030 in China,
while energy peak of chemical industry , transportation and other services will be
postponed to later than 2040 , of which other services industry cannot achieve energy
peak until 2050. This, to some extent, is related with the rapid development of Chinese
service industry. Energy Summit in China's energy sector is in 2034, with its peak value
2786.14Mtoe. In developed countries, energy use in the U.S.’s energy industry, chemical
industry, the EU's energy industry and the chemical industry in Japan appear an inverted
U-shape, with peaks of energy demand, which generally come earlier. One peak of
America's energy industry energy demand appears in 2024, with the peak value
1923.32Mtoe. The EU and Japan respectively peak in 2020 and 2023, with peak value
1426.84Mtoe and 549.80Mtoe. In addition to agriculture and energy sectors, Russia’s 6
remaining sectors also emerge energy peaks, except for chemical industry. Other energy
sector’s peaks appear in 2030. In scenario 3, energy uses in India’s every sector are in a
constant upward trend. Five departments cannot reach its peak of energy before 2050.
Except that agricultural sector and obtain it in 2022, rest sectors have to peak after 2045.
Table 2b shows cumulative energy consumption of every country and department from 2007
to 2050 in scenarios 1. It can be seen that the energy sector is the main source of national
energy demand, accounting for more than 65% of the cumulative energy consumption of
countries, in which Japan's energy sector accounts for 75.77% of its accumulated energy
consumption. Other Mining is China's second largest cumulative energy consumption
sector accounting for 34.93% of the world’s cumulative energy consumption in the sector.
China's cumulative energy demands in manufacturing and construction industry are also
18
at a higher level. The former is nearly 2 times of the cumulative energy consumption in the
U.S.’s manufacturing sector, while the latter accounted for 55.73% of cumulative energy
consumption in world's construction industry. The cumulative energy consumption of
transportation sector in the U.S. and EU countries is significantly higher than that in
developing countries, accounting for 34.89% of worldwide cumulative energy
consumption in the sector.
Table 2a Scenario 1 countries under various departments peak energy demand and
energy consumption peak (Mtoe)
China
Agric
Energy
Mining
Chemi
Manu
Constr
Transp
OthServ
USA
2014(31.13)
Japan
EU
India
/
/
/
2034(2786.14)
2024(1923.32)
2020(1426.84)
2030(399.74)
2030(57.57)
2043(274.98)
Russia
2022(25.01)
/
2023(549.80)
2047(1359.38)
/
/
/
2050(180.29)
2021(33.1)
2026(156.39)
/
2017(64.34)
2050(251.56)
2033(105.92)
2037(168.22)
/
/
/
2050(88.89)
2029(7.51)
2040(79.33)
/
/
/
2049(3.28)
2020(2.55)
2043(240.77)
/
/
/
2050(140.41)
2016(78.57)
2050(156.53)
/
/
/
2050(75.83)
2036(14.96)
Note: The "/" indicates that the country under the scenario that is on or before 2007 to
reach a peak energy demand, peak energy demand does not exist during the simulation
Table 2b Scenario 1 countries under various departments 2007-2050 cumulative energy
consumption (Mtoe)
Agric
Energy
Mining
Chemi
Manu
Constr
Transp
OthServ
China
USA
Japan
EU
India
Russia
1099.71
109833.72
15672.53
9943.51
6192.43
2825.79
8797.93
4755.66
235.27
76643.93
2366.97
6363.03
3245.71
97.61
15322.72
2891.23
195.09
54638.65
2473.65
4931.78
1808.35
140.93
13186.53
2614.54
53.92
21357.37
823.40
2449.91
365.04
92.87
1978.92
1067.23
946.46
45354.46
5299.43
6635.41
2423.00
99.96
4241.24
2007.49
95.37
16957.60
1313.71
3976.78
301.78
97.07
3116.32
596.24
Others
1636.36
128163.17
16919.86
30945.81
10320.12
1716.51
35064.53
9862.71
Table 3 shows the peak of energy demands and cumulative energy consumption of
every country and department under Scenario. In scenario 2, the peak of energy demand
of various departments of the countries comes significantly ahead of schedule comparing
to scenario 1. Peak of energy demand in China’s departments generally advance 4-5
years. Energy industry advances most, from 2034 to 2024, followed by construction
19
industry, six years ahead of schedule. In addition, the peak of energy demand in other
mining only comes one year earlier, while agriculture and manufacturing undergo two
years in advance. In terms of peak energy demands, in scenario 2, China 's the peak
energy in various departments were significantly lower than that simulated in scenario 1,
wherein the energy sector decreased by about 500Mtoe. Other services industry and
transportation industry fall significantly, reducing16.91% and 17.98% respectively. In
developed countries, peak of energy demands in the United States’ other mining occurs in
2025, while energy peak Japan's energy sector advances to 2017,and the peak number
decreased slightly. In scenario 2, other mining in Russia in 2015, appear peak of energy
demands, while some departments which have a peak of energy demands previously,
postponed its annual peak 5-6 years, decreasing a lot. In scebnario 2, Energy peak in
every department in India has advanced, compared with scenario 1 , with only four
departments in 2050 not achieving the peak. The peak years in the energy sector
remained at 2047, but dropped to its peak number from 1359.38Mtoe 1020.57Mtoe, fell by
a quarter. India's energy demands in scenario 2 have a decline, compared with the peak
in scenario 1, with the total decline being 23 %.
From a total energy consumption of various departments’ countries, the cumulative energy
consumption under Scenario 2 departments’ countries has declined. Where the greatest rate of
decline in the energy sector, India's energy sector accumulated energy consumption up to 18.03%
compared with a decline rate scenario, China, the United States, European Union, Japan, the rate
of decline is also more than 15%. Cumulative energy consumption in chemical industry decline
following energy industry. In scenario 1, in which Russia decline 15.8% and other countries are
above 11%. In transportation and other services, developed countries decline less than developing
countries. For example, China, India and Russia's decreased rate are above 11%, while the U.S.,
EU and Japan’s were 9.41%, 8.9% and 8.95%. In addition, cumulative energy consumption
accounting for total energy consumption is less in scenario 2.
Table 3a Table 3a Peak of energy demand s and peak value of every country and departments in scenario 2 (Mtoe)
(Mtoe)
China
Agric
2012(29.95)
USA
Japan
/
/
20
EU
India
/
2022(22.53)
Russia
/
Energy
Mining
Chemi
Manu
Constr
Transp
OthServ
2024(2288.14)
2029(352.16)
/
2025(52.19)
/
2017(489.07)
2047(1020.5)
/
/
/
2050(144.25)
2015(30.57)
2039(229.03)
/
/
/
2050(199.84)
2030(87.61)
2035(147.08)
/
/
/
2050(72.75)
2025(6.74)
2034(68.84)
/
/
/
2045(2.75)
2019(2.39)
2040(197.48)
/
/
/
2049(112.09)
/
2047(130.05)
/
/
/
2050(62.17)
2029(12.92)
Note: The "/" indicates that the country under the scenario that is on or before 2007 to
reach a peak energy demand, peak energy demand does not exist during the simulation
Table 3b Cumulative energy consumption of various departments from 2007-2050 in scenarios 2
(Mtoe)
Agric
Energy
Mining
Chemi
Manu
Constr
Transp
OthServ
China
USA
Japan
EU
India
Russia
990.69
92390.57
13855.96
8704.61
5541.94
2527.11
7620.90
4201.31
215.82
65015.37
2122.49
5640.57
2936.80
89.78
13474.28
2619.23
182.95
46319.91
2241.91
4410.52
1651.70
130.38
11701.13
2381.97
50.73
18005.99
741.60
2175.67
334.38
86.03
1771.32
971.76
830.84
37178.50
4562.72
5646.68
2122.66
88.75
3639.01
1758.34
87.12
14863.76
1158.96
3348.49
268.91
88.06
2723.54
532.04
Others
1472.79
109599.00
14777.50
26674.10
9130.41
1533.53
30271.41
8751.45
Table 4 shows under scenario 3 States the sector energy demand spikes and cumulative
energy consumption. Under scenario 3, the sector's peak energy demands advance further and
peak value reduces. China, apart from other services, and transportation, the rest of the sector can
attain peak energy demands by the year of 2035, and agriculture, the energy industry and other
mining energy peak by the year 2030. Among them, energy peak of every country and department
in scenario 3 bring forward from 2039 in scenario 2 to2030, experiencing larger range of ahead of
time. In scenario 3, United States, the European Union's energy demands in all industries have
reached the peak before 2007,without any peak energy demand in other departments. Japan 's
energy demands in energy industry will reach the peak in 2015, without any peak energy demand
in other departments. Russia has 4 departments that have peaks of energy demands, namely,
chemical industry, manufacturing, construction and other services, whose time of peak advance to
2025. In scenario 3, India peak energy demand of sector continues to advance: agriculture and
energy industries peak in 2019 and 2032; rest energy industries peak in 2040; the chemical
industry, manufacturing and other services are still unable to meet the energy demand until 2050.
In scenario 3, peaks in all sectors continued to decline, but the range is relatively small, compared
21
with an average decline of 21.85% per cent drop under scenario 2.
Under scenario 3, cumulative energy consumption of every country and department continues
to decline. The energy industry had the largest decline, national average rate of 15% percent lower
than that in scenario 2, average rates lower than 1 27.6% in scenario 1. The next one is
transportation services industry, cumulative energy consumption 12.1% lower than that in scenario
2, and 23.4% lower than that in scenario 1. Under scenario 3, chemical industry and the
transportation industry of every country fell to 12.6% and 12.1% respectively in comparison
Table 4a countries in various departments under Scenario 3 peak energy demand and energy consumption peak
(Mtoe)
China
Agric
Energy
Mining
Chemi
Manu
Constr
Transp
OthServ
USA
Japan
2011(29.18)
/
/
2022(1996.06)
/
/
2025(315.91)
/
/
2030(197.14)
/
2032(129.19)
EU
India
/
Russia
2019(20.71)
/
2032(780.42)
/
/
2045(116.93)
/
/
/
2050(159.03)
2025(74.09)
/
/
/
2050(59.55)
2023(6.10)
2034(60.75)
/
/
/
2040(2.33)
2014(2.28)
2035(166.11)
/
/
/
2049(89.39)
2046(108.69)
/
/
/
2050(50.90)
2015(446.41)
/
2025(11.62)
Note: the "/" in the scenarios in the country reached a peak energy demand in 2007 or earlier, peak
energy demand does not exist during simulation
Table 4b Scenario 3 under the accumulated energy consumption in various departments
countries 2007-2050 (Mtoe)
Agric
Energy
Mining
Chemi
Manu
Constr
Transp
OthServ
China
USA
Japan
EU
India
Russia
897.48
78622.29
12317.81
7662.14
4968.05
2267.77
6649.39
3719.18
198.86
55906.52
1909.03
5033.08
2669.93
82.75
11936.05
2380.78
172.03
39771.53
2034.72
3963.1
1513.4
120.93
10458.7
2175.64
Others
47.95
15355.62
671.29
1949.3
307.4
79.88
1600.11
887.98
734.83
30888.35
3956.95
4827.16
1865.74
78.82
3134.8
1541.37
79.97
13136.39
1029.17
2854.19
240.85
80.16
2408.48
475.85
1331.2
94624.91
12997.32
23179.85
8102.38
1372.74
26400.07
7782.64
5 Summaries
Based on Jin (2012), we developed a model of multi-nation and multi-sector general
equilibrium model, adding climate module in it, and study ways of logarithmic shocking to
22
reduce the intermediate demand coefficient in various departments, to achieve the
progress of process technology. Through numerical simulation, we study the economic
development of various departments countries in 2007 -2050,and set up three different
scenarios by adjusting the speed of the progress of process technology, in order to study
the changes of energy consumption and trends of carbon emissions at different rates of
progress of process technology in various departments States. Through simulation, we
come to the following conclusions.
Energy industry is the most important economic sectors of energy consumption,
which accounting for more than 60% of the entire economy; while the chemical industry,
and transportation sector also occupy an important position in energy consumption, which
are potential sectors to reduce emissions.
Simulation results show that China's GDP will exceed the EU after 2035, and surpass
the United States in 2040. The service sector will be the main driving force for China's
economic development; changes of industrial structure in the United States is small, and it
is likely for European Union, Japan's industrial structure became close to the U.S.; India
will have a further process of industrialization in 2030,with a little rise in the proportion of
its industry.
Improvement of the progress speed of process technology can effectively advance
the peaks of nations’ carbon emissions, and significantly reduce carbon emissions in its
peak and cumulative carbon emissions. In three scenarios, China respectively in 2034,
2030 and 2022 reach a peak of carbon, while in the more radical scenario 3, China’s
cumulative carbon emissions in 2007 -2050 can be reduced to 93GtC, meeting the goals
of 100GtC emissions. Thus, it is significant for China to accelerate the progress speed of
process technology, reduce energy intensity and improve energy efficiency for CO2
emissions.
Improve the process speed of technological progress can effectively advance the
nations carbon emissions peak, while significantly reducing carbon emissions peak and
cumulative carbon States carbon emissions. The following three scenarios, China
respectively in 2034, 2030 and 2022 to reach a peak of carbon, while in the more radical
scenario 3, China 2007 -2050 year cumulative carbon emissions can be reduced to 93GtC,
23
meet 100GtC emissions goals. Thus, for China to accelerate the process of improving the
rate of technological progress, reduce energy intensity and improve energy efficiency for
CO2 emissions has important practical significance.
With the improvement of, progress speed of process technology, the peaks of energy
demand in various departments and countries com ahead of time, and its peak value also
declines. Among them, China and India’s peak come the earliest and peak value and the
cumulative energy decline in energy consumption than any other country, especially in the
energy sector between the two countries. The service sector (transportation and other
services) for developing countries such as China, India also has a large reduction
potential, which is due to industrial restructuring in the future, the services will be China,
India and such important to support economic growth in developing countries. From
Scenario 1 to Scenario 3, China and India account for the cumulative decline in the
world's total energy consumption energy consumption decreased 34.23%, while the
United States, Europe, Japan accounted for only 13.28%. For developed countries, due to
the high technical level of its own process, in different scenarios so it reduces energy use
is significantly less than the magnitude of the developing countries. In Scenario 2 and
Scenario 3, the majority of sectors in developed countries during the simulation is always
in decline, there is no peak energy use.
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