March, 2013
Journal of Resources and Ecology
J. Resour. Ecol. 2013 4 (1) 056-062
DOI:10.5814/j.issn.1674-764x.2013.01.008
www.jorae.cn
Vol.4 No.1
Article
The Relationship between Patterns of Economic Development
and Increasing Carbon Emissions in Western China
JIAO Bing* and YANG Fengming
Research Center of Resources Environment and Regional Economics, Xi’an University of Finance and Economics, Xi’an 710100, China
Abstract: With the implementation of the “Development of Western China” strategy, this region has
become the fastest growing economic area in China. However, rapid economic growth has resulted in
a substantial increase in carbon emissions and affected energy reduction goals. In order to effectively
control the rapid increase in carbon emissions across western China, we need a comprehensively
analyze the main factors causing these increases. Here, we analyze the relationship between economic
development patterns and carbon emissions. The findings suggest that consumption upgrades and
industrial transformation have a positive correlation with carbon emissions in this region. We then
conducted an econometric FGLS analysis on the relationship and its transmission mechanism between
economic growth and CO2 emissions with cross-province panel data from 1991 to 2009. A positive
correlation was found, and the relationship is more significant after the implementation of the western
development strategy. The influence coefficient of change in primary, secondary and tertiary industries
is 16.4. The influence coefficient of increased share of heavy industry and extractive industry in the
secondary industry is 14.3, and the influence coefficients of per-capita living expenditure and per capita
traffic expenditure are 5.6 and 6.5. Traditional population size and income scale have a weak impact on
carbon emissions, and the influence coefficients of population size and income scale are only 0.73 and 0.86.
GDP increases have a second major impact on the carbon emissions. Energy intensity has a negative
relationship with carbon emissions and urbanization level has a positive relationship (coefficients are -8.2
and 4.65).
Key words: economic growth pattern; carbon emission; consumption upgrade; industrial transformation
1 Introduction
Reducing greenhouse gas emissions and achieving lowcarbon economic development have become important
measures to combat climate change internationally. With
rapid economic development, China is facing increasingly
grim carbon emissions. According to the IEA, China’s carbon
emissions had surpassed the USA and become the world’s
largest emitter in 2009. As a responsible and large country,
China has committed to an emissions reduction target of
40%–45% that of 2005 levels.
With implementation of the “Development of Western
China” strategy, this region in China has become the fastest
growing region in China. In order to stimulate economic
growth, the government proposed a series of policies. As
these policies are implemented, the western region faces
challenges from economic structure adjustment, industrial
transformation and consumption upgrade. These changes
will lead to increased carbon emissions, so the relationship
between patterns of economic development and carbon
emissions needs to be understood.
2 Literature review
After nearly 20 years of exploration, Yoichi Kaya, a Japanese
researcher, proposed the Kaya identity in Intergovernmental
Panel on Climate Change (IPCC) seminar in 1990. He
suggested that increases in carbon emissions depends on
four factors: population (P), per capita GDP (G), the energy
consumption per unit of GDP (IE, energy intensity) and the
energy structure (IC, carbon intensity). The Kaya identity
Received: 2012-10-17 Accepted: 2013-01-24
Foundation: Humanity and Social Science Youth foundation of Ministry of Education of China (12YJC790082); National Social Science Fund
Key Project (11AJL007).
* Corresponding author: JIAO Bing. Email: [email protected].
57
JIAO Bing, et al.: The Relationship between Patterns of Economic Development and Increasing Carbon Emissions in Western China
As a result, this partially reduced carbon emissions.
(4) The process of urbanization. Martínez-Zarzoso et
al. (2007) examined the relationship between urbanization
and carbon emissions using provincial panel data from
1975 to 1998 in 86 countries. They found that the process
of urbanization in developing countries had a significant
positive impact on carbon emissions.
Overall, the spatial dimension of research on the
relationship between economic development and carbon
emissions is basically focused on the transnational level.
Studies focused on the regional level are very few.
These researches had tended to not focus on the impact
of industrial transformation and consumption on carbon
emissions.
Fan et al. (2010) measured carbon emissions reduction
targets for each country on the basis of final consumption.
Lin and Jiang (2009) made an empirical analysis on the
relationship between carbon emissions and economic
development using the Environmental Kuznets model.
The result showed that the theoretical inflection point of
Chinese CKC should be reached when per capita income
hits 37 170 CNY in 2020. Xu and Song (2010) studied the
existence of an Environmental Kuznets Curve for China
based on Chinese Provincial Panel Data from 1990 to 2007.
The result showed an upside-down U-shaped EKC curve
in central and eastern China. However, the curve does
not exist for western China. Zhang (2010) looked at the
impact of economic development mode changes on carbon
emission intensity from 1987 to 2007 using the input-output
structural decomposition method.
3 Preliminary statistical observation
Growth in GDP per person from 1991 to 2009 was used as
the economic growth indicator, and an average annual rate
of carbon emissions from 1991 to 2009 was used as carbon
emissions indicator (Fig. 1). Data includes 11 western
provinces and autonomous regions in China.
Carbon emissions in the wealthier areas of the western
region, such as Inner Mongolia Autonomous Region and
Shaanxi, are higher than for underdeveloped areas, such as
Yunnan, Guizhou and Ningxia Hui Autonomous Region.
28
Economic growth rate (%)
had been accepted by the IPCC and become the world’s first
system to estimate carbon emissions. Cramer and Cheney
(2000) estimated the impact of California’s population
growth on the environmental pollution using the Kaya
identity. The result showed that only CO2 emission had a
positive correlation with the size of the population, other
environmental pollutants had no significant relationship
with population size. Holdren (2000) proposed another
IPAT method to estimate carbon emissions. The core idea
of the method is that a region’s carbon emissions depend on
three factors, namely population size (P), the social impact
(A) and technology effects (T). Fischer (2001) used IPAT
to estimate carbon emission impact factors and found that
income is the most important factor, and the influence of
technical factor on carbon emissions is the most extensive
factor. IPAT has become one of the major carbon emission
estimation methods.
Since Grossman and Krueger (1995) first proposed
the carbon environmental Kuznets curve (CKC),
many empirical tests found that there was an inverted
U-shaped curve between carbon emissions and economic
development, and the CKC inflection point emerged in the
USA, Germany and other developed countries. Copeland
and Taylor (2009) used the IPAT method and introduced
a per capita income indicator to estimate the relationship
between carbon emissions and economic growth. They
found that CKC is not a U-shaped curve, but monotonically
increasing in developed countries. Auffhammer and Carson
(2009) tested CKC of China with Chinese cross-province
panel data provided by China Environmental Protection
Bureau in 1985–2004. By constructing a CGE model, they
found that China would have maintained rapid carbon
emission increases for a long time and would not soon
emerge from the CKC inflection point.
There are four main themes in this research field :
(1) Economies of scale. The scale effect has two main
parts: population size and income scale. Shi (2003) tested
the relationship between population and carbon emissions
by taking panel data of 96 countries from 1975 to 1996,
and found that the impact of population size on carbon
emissions is greater in low-income countries than highincome countries. Hamilton and Turton (2002) found that
per capita income and the size of the population are more
important for carbon emission increases in OECD countries
than other factors.
(2) Structural effects. Structural effects mainly refer to
the energy intensity structure and input-output structure.
Stern (2002) found that the change in energy intensity
structure had a significant impact on increasing carbon
emissions, whereas the change of input-output structure had
little impact.
(3) Technical effect. Bruvoll and Medin (2003) revealed
that energy intensity increases induced by economic
structural adjustment was the top cause of the rapid rise in
carbon emissions from 1913 to 1970. After 1970, technical
progress was an important reason to reduce energy intensity.
Inner Mongolia
26
24
22
Shaanxi
20
Guizhou
18
Yunnan
16
14
5
Guangxi
Xinjiang
Ningxia
Qinghai
Sichuan
10
15
20
Growth rate of CO2 emission (%)
25
30
Fig. 1 Relationship between economic growth and carbon
emissions in western China, 1991–2009.
58
Journal of Resources and Ecology Vol.4 No.1, 2013
1.5
Inner Mongolia
Sichuan
CO2 emission (100 Mt)
Carbon emissions (100 Mt)
1.5
1.0
Shaanxi
0.5
0
Ningxia
Xinjiang
Yunnan
Guangxi
Guizhou
Qinghai
30
35
40
Engel coefficient (%)
Sichuan
1.0
Yunnan
0.5
Guangxi
Guizhou
Xinjiang
Shaanxi
Ningxia
Qinghai
0
45
Inner Mongolia
35
50
40
45
50
55
60
Industry structure (%)
Fig. 2 Relationship between consumption upgrade and
carbon emission in western area, 1991–2009.
Fig. 3 Relationship between industrial transformation and
carbon emissions, 1991–2009.
Therefore, there is a positive correlation between economic
development and carbon emissions.
Consumption upgrade in the western region will reduce
the Engel coefficient, and the increase in the expenditure
of durable consumer goods, automobiles and housing
will inevitably have an impact on carbon emissions. The
relationship between urban residents’ Engel coefficient and
carbon emissions is shown in Fig. 2.
It can be seen from Fig. 2 that the lower the Engel
coefficient, the higher the carbon emissions. This is
consistent with empirical results (Martínez-Zarzoso et al.
2007).
With the implementation of the “Development of
Western Regions” Strategy, the proportion of secondary
industry has grown in western China. As the development
of the secondary industry requires a huge amount of energy,
it will result in increased carbon emissions in the west (Fig.
3).
Provinces with a higher proportion of secondary industry
in the western region will emit more carbon (Fig. 3), which
also is consistent with experience.
There is a positive correlation among economic growth,
consumption upgrade, industrial transformation and
increased carbon emissions in the western region of China.
The change in economic development had a significant
effect on carbon emissions.
constant vector, a1, a2, a3 and a4 represent the coefficient
vector, and ε represents random disturbance.
Because of differences in economic development levels,
population size and regional characteristics, the absolute
value index is not suitable for lateral regional comparative
assessment, so relative value indicators were chosen to
measure economic variables.
4 Model specification and variable description
∑ NX
4.1 Model specification
Based on improvements to Holdren (2000) and MartínezZarzoso et al. (2007), we built up the following panel data
regression model:
Eti=a0+a1lnGDPti+a2Cti+a3INDti+a4Zti+εti
where, Eti represents the annual growth rate of carbon
emissions, ln GDPti represents the natural logarithm of the
growth rate of per capita GDP, C t i represents the
consumption structure vector, INDti represents the vector of
the industrial structure, Zti represents the vector set to join
other control variables, i is corresponds to the cross-section
of the various provinces units, t represents the year, a0 is a
4.2 Variable description
4.2.1 Description of carbon emissions
The current method to calculate carbon emissions is IPAT,
but we argue that this approach has several drawbacks:
(1) It relies on fixed carbon emission factors of each
type of energy, but it is difficult to reflect the impact of
technological advances.
(2) In the calculation of carbon emissions, it is difficult
to determine the carbon reduction target among all regions.
Therefore, we use the national income method provided by
Fan et al. (2010) to calculate carbon emissions.
Here, an expenditure approach is used to calculate
national income in western China as follows:
Y = C + I + NX
where, Y represents western national income; C represents
total consumption; I represents total investment; NX
represents net exports. Import and export impacts will offset
each other over the long-term. Therefore, we believe that
∞
t =0
t
= 0 (t represents time).
Then, the calculation of carbon emissions will only
consider two components of consumption and investment as
follows:
E = (C + I) × T × f
E represents total carbon emissions; T represents energy
consumption per unit of GDP; f represents the carbon
emission coefficient of energy consumption per unit of
GDP.
In the process of calculating carbon emissions we used
consumer spending and investment spending data as well
as energy consumption per unit of GDP from the Statistical
Yearbook (Fig. 4). Carbon emission factors are weighted
7
6
5
4
3
2
1
0
59
4.2.3 Data sources
9
8
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Carbon emissions (100 Mt)
JIAO Bing, et al.: The Relationship between Patterns of Economic Development and Increasing Carbon Emissions in Western China
Year
Fig. 4 Carbon emissions of western China in 1991–2009.
based on carbon emission factors of three kinds of primary
energy as calculated by the IPCC.
As shown in Fig. 4, the amount of carbon emissions in
western China from 1991 to 2009 continues to increase.
Especially during 2000 to 2009, the amount of carbon
emissions has tripled. Therefore, China’s western region
is facing severe pressure to reduce carbon emissions. We
used indicators of carbon emission average growth rate per
annum (E) as a measure of carbon emissions.
4.2.2 Other variables
With a stimulus-fuelled boom in domestic demand in
western China, we chose the per capita durable consumer
goods growth rate (RC), the car growth per capita (MC),
and the per capita housing growth (HC) as measures of
consumption structure indicators.
In the process of industrial transformation in western
China, the major subject is adjustment of thrice industrial
structure and acceptance transferred industry of eastern
region. We used the proportion of secondary industry
growth rate (SIND) and the manufacturing sector growth
rate (MIND) as measures of industry structure.
According to existing literature, the model control
variables Z include population size, income scale, energy
intensity and urbanization degree. Here we selected the
annual population growth rate as a measure of population
size, average annual growth rate of per capita income (INC)
as a measure of income scale, added value of the primary
energy usage of GDP (RI) as a measure of energy intensity
indicators, and proportion change rate of urban population
(URB) in total population as a measure of urbanization.
Considering the actual situation as well as the availability
of data in China, the starting point for our research was
1991. The latest statistical data allows us to extend the
study period to 2009. Due to a lack of separate statistics
in Chongqing before 1997, we incorporated Chongqing
into Sichuan. The sample rage of the research includes
190 observation samples during 1991–2009, and the crosssectional unit contains Inner Mongolia, Guangxi, Sichuan,
Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and
Xinjiang. Data was from the China Statistical Yearbook,
China Industrial Economy Statistical Yearbook, China
Statistical Yearbook and Provincial Statistical Yearbook, the
Provinces Statistical Bulletin and INFOBANK Databases
(www.bjinfobank.com).
5 The relationship between western economic
development and carbon emissions
Panel data estimation methods included polymerization
least squares regression (Pool OLS), fixed effects (fixed
effect) model, random affect (random effect) models. From
the data structure, there are more obvious heteroscedasticity
and sequence autocorrelation in the error term of the
regression equation. So generalized least squares (FGLS)
estimation will be the most feasible estimation method. The
results are shown in Table 1.
Carbon emissions in western China rose by 230% from
1991 to 2009. Before the development of this region,
the speed of rise in carbon emissions was 10.83% in
1991–1999, an average annual growth rate of 1.2%. After
implementation of the western development strategy, the
rise in carbon emissions was 198% from 2000–2009 and the
average annual increase rate was 21.96%.
5.1 Economic growth
In 1991–2009, the impact of economic growth in
western China on carbon emissions is significant, and the
contribution rate reached 14.61%. Raupach (2007) found
that in the initial stage of economic development, economic
take-off will bring a rapid increase in carbon emissions.
Before the “Development of West Regions” campaign in
1999, the western regional average annual GDP growth rate
was only 7.3% and its impact on carbon emission growth
was 9.78%. Following implementation of the strategy the
economic growth rate soared to 11.9% and its effect on
Table1 Impact of economic development on carbon emission in western China in 1991–2009.
Consumption upgrade
Per capita
(%)
GDP growth
Phase
Per capita durable
Traffic
Per capita
rate
consumer goods expenditure
housing
(%)
spending
per capita
expenditure
1991–2009
14.61
1.14
6.5
5.6
1991–2000
9.78
3.15
4.67
3.23
2000–2009
16.37
–2.7
8.63
7.85
Industrial
Other variables
transformation (%)
(%)
Interior
InterPopulation Income Energy Urbanization
secondary
industry
scale
scale intensity
rate
industry
16.4
14.3
0.73
0.86
–8.2
4.65
9.7
9.5
0.69
0.67
1.63
2.56
18.6
16.7
0.82
1.08
–9.73
8.46
60
Journal of Resources and Ecology Vol.4 No.1, 2013
Table 2 Change in consumer structure and industry structure in western China in 1991–2009.
Year
1991
1995
1999
2002
2005
2009
Consumption upgrade (%)
Industrial transformation (%)
Growth rate of
Growth
Growth
Industry structure
The second industry growth rate
per capita durable
rate of per
rate of per
Industry
Architecture
consumer goods capita traffic capita living Agriculture Manufacture Service Mining and Heavy
Light
industry
quarry
industry
expenditure
expenditure
expenditure
industry industry
3.7
4.4
4.9
5.6
3.8
2.9
5.2
6.4
7.2
10.9
11.3
12.4
7.3
8.4
10.2
11.1
12.3
13.7
31
30
26
16
14
12
carbon emissions rose to 16.37%.
5.2 Consumption upgrade
From 1991 to 2009, China’s consumption structure change
was significant (as shown in Table 2). Especially after the
outbreak of the global financial crisis in 2009, China’s
economic growth pattern is gradually moving away from
export-led growth and towards domestic consumption.
The government has issued a series of positive policies to
stimulate the need of domestic consumption.
5.2.1 Per capita durable consumer goods expenditure
The growth rate of per capita durable consumer goods
expenditure in western China peaked in 2002, more than
5%. Since then, the situation has become worse and worse.
The growth rate began to decline in 2009, which was only
2.9%. Because the regeneration period of home appliances
is about ten years, the growth rate of durable consumer
goods, for the next few years at least, is comparatively little.
At the same time, with the development of environmental
protection, the impact of durable consumer goods on carbon
emission has been minimal.
5.2.2 Per capita transportation spending
The growth rate of per capita expenditure on transport in
western China continued to increase from 1991–2009.
Before 2000, per capita expenditure on transport grew only
7.2%. Since 2000, the growth rate has exploded, reaching
12.4% in 2009. Since the low-carbon development pace of
the automobile industry is relatively slow, the rise of cars
results in increased carbon emissions, especially after 2000;
the influence coefficient was 8.63.
5.2.3 Per capita living expenditure
The growth rate of per capita living expenditure in
western China increased from 1991 to 2009. In 2009 the
growth rate reached 13.7%, which shows that since 2000
and the expansion of gross floor area in western China,
carbon emissions increase. The impact of per capita living
expenditure on carbon emissions in western China is 5.6.
35
38
41
47
48
52
33
32
32
37
38
36
24
25
33
34
43
45
19
27
33
35
42
45
8
11
19
21
20
21
11
16
17
21
23
28
5.3 Industrial transformation
5.3.1 Industrial structural change
Structural changes of three industries in western China
result in increased carbon emissions from 1991 to
2009. This effect is mainly due to the sharp rise of nonagricultural sectors and a declining agricultural sector (as
shown in Table 2). The share of total output arising from
the manufacturing industry rose to 52% in 2009, up from
35% in 1991. Service industry output rose to 36% of output
in 2009, but the growth rate was relatively slow. The share
of agricultural output shrunk to 12% in 2009 from 31% in
1991. This change reflects industrial transformation across
western China. Industrial structural change contributed
16.4% of total carbon emissions in 2009.
Western China’s agricultural output fell by 14 % from
2000 to 2009, and the proportion of total industrial output
rose 11% at the same time. These changes led to the impact
of industrial structural adjustments on carbon emissions
hitting 18.6 since 2000.
5.3.2 Changes in internal structure
Changes in industry internal structure caused carbon
emissions to grow by 14.3% from 1991 to 2009. Further
analysis shows that changes in industry internal structure
are mainly from extractive industries and heavy industry
(Table 2). From 1991 to 2009, the growth rate of the
extractive industry increased from 24% to 45%. Especially
after 2005, extractive industries exhibited explosive growth
trends. The growth rate of heavy industry reached 45% in
2009. The extractive industry and heavy industry are high
energy-consumers and inevitably result in increased carbon
emissions.
5.4 Other factors
5.4.1 Population size
Since large-scale development began in western China,
the population size has been relatively stable (Fig. 5).
Population size is an important factor in other research, but
its impact on the carbon emissions of western China is only
0.73.
61
0.68
0.67
0.66
0.65
0.673
0.637
0.63
0.62
0.61
0.60
0.59
0.48
0.46
0.659
0.65
0.647
0.64
0.668
Engel coefficient
Population growth rate (%)
JIAO Bing, et al.: The Relationship between Patterns of Economic Development and Increasing Carbon Emissions in Western China
0.629
0.62
0.44
0.42
0.40
0.38
0.36
0.34
2002
2003
2004
2005 2006
Year
2007
2008
2009
Fig. 5 Population growth rate in western China from 2002–
2009.
5.4.2 Income scale
Household annual income increased on average 10%,
making western China one of the nation’s fastest growing
regions. Increasing household income leads to a reduction
in the urban resident Engel coefficient (Fig. 6).
As can be seen in Fig. 6, the urban residents Engel
coefficient declined from 47.5% in 1995 to 36% in 2009.
This indicates that living standards continuously improved.
However, the impact of income scale on carbon emissions
was only 0.86, a lesser value than that reported by other
studies.
5.4.3 Energy intensity
As the low-carbon development pattern was implemented,
energy intensity in western China declined from 1991–2009,
and the contribution of this factor on carbon emissions was
–8.2%. Industrial energy intensity declined from 1991–
1999, while energy intensity in the agricultural herd fishery,
service and construction industries increased. This led to a
slight rise in the production department’s energy intensity
and the contribution of energy intensity on carbon emissions
moved from positive to negative. Overall, from 1991 to
2009 the average contribution of production department
energy intensity was –8.2.
5.4.4 Urbanization
In 1991, western China’s urbanization rate was below
19.3%. However, with continuous development, the
urbanization rate rose to 36.2% in 2009. With an increasing
urban population, the impact on carbon emissions is greater
and greater. According to empirical test results, the impact
of western China’s urbanization rate on carbon emissions
was 4.65 in 1991-1999, and rose to 8.45 in 2009.
6 Conclusions and policy implications
(1) Empirical observation shows that carbon emissions
increased in step with economic development, but an
inflection point did not appear during 1991–2009. The lower
Engel coefficient in western China, the higher the amount
of carbon emissions. The larger the proportion of secondary
industry, the higher the carbon emissions.
0.32
1995
1997
1999
2001
Year
2003
2005
2007
2009
Fig. 6 Engel coefficient of urban residents in western China
in 1995–2009.
(2) Results of panel-data analysis show that there is a
positive correlation among economic growth, consumption
upgrade, industrial transformation and carbon emissions.
Increasing carbon emissions was mainly due to the rise
in per capita GDP, consumption upgrading and industrial
transformation. The impact of population size and income
scale on carbon emissions is less than for other areas. In
1991–1999, the impact of economic growth on carbon
emission was minimal, however with the implementation
of the West Development Strategy, the influence of
consumption upgrade and industrial transformation on
carbon emissions has increased.
(3) Because consumer upgrading has had a major impact
on western China’s carbon emissions, emphasis should be
placed on both the consumption process and production
process when the government is developing policy about
energy savings and emission reductions. For example, when
introducing appliances and automobiles to households,
environmentally friendly products should be subsidized.
In the meantime, when constructing traffic facilities and
houses, large buildings and fuel-hungry vehicles should be
strictly controlled.
(4) When developing western China, too much attention
was paid on extractive industries and heavy industries. This
situation has led to the adverse situation of carbon emissions
growth. In the future, the government will not only see
economic efficiency, but should pay more attention on lowcarbon and high-tech industries. During the progress of
Great Western Development, the government should adjust
and optimize industrial structures to combine geographic
advantages and natural resource endowment.
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西部地区的经济发展模式与碳排放量增长关系的实证研究
焦 兵， 杨凤明
西安财经学院 资源环境与区域经济研究中心，西安 710100
摘 要：随着“西部大开发”战略的深入实施，西部地区已经成为我国经济发展最快的区域，然而西部经济的快速增长已
经造成了碳排放量的大幅增加，严重影响了我国节能减排目标的实现。为了有效地控制西部地区碳排放量的急速增加，我们必
须全面分析引致西部碳排放量增加的主要因素。本文在已有研究的基础上，从西部地区产业转型和消费升级的视角出发，利用
1991—2009年的省际面板数据对西部地区的经济发展模式与碳排放量增长之间的相关关系及其传导机制进行了实证检验。检验
结果表明:自进入上世纪90年代以来，西部地区的经济发展与碳排放量增长之间存在显著的正相关关系，而且在西部大开发战
略实施以后，这种关系更加显著。同时，检验还发现西部地区的消费升级和产业转型对碳排放量增加产生重要影响，其中三次
产业间结构变动的影响系数达到16.4，二次产业内部采掘业和重工业比重上升的影响系数达到14.3，人均居住支出和人均交通
支出的影响系数也分别达到5.6和6.5，而传统的人口规模、收入规模则对西部地区碳排放量的影响微弱，影响系数仅为0.73和
0.86。因此在制定西部地区“十二五”节能减排战略时，需要更多的从消费升级和产业转型的视角出发。
关键词：经济发展模式；碳排放量；消费升级；产业转型