Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China

Renewable and Sustainable Energy Reviews 54 (2016) 1563–1579

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China Qiang Wang a, Shi-dai Wu a,b,n, Yue-e Zeng a, Bo-wei Wu a a b

College of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China College of Tourism, Fujian Normal University, Fuzhou 350108, China

art ic l e i nf o

a b s t r a c t

Article history: Received 20 June 2014 Received in revised form 25 June 2015 Accepted 23 October 2015

Unlike previous studies, this paper empirically investigates the impact of urbanization on energy consumption and CO2 emissions with consideration of provincial differences. The results show the following: (1) Urbanization increases energy consumption and CO2 emissions in China, but it is not the most outstanding contributor to the increases. (2) Significant differences exist between provinces in terms of the impact of urbanization on energy consumption and CO2 emissions. The distribution of urbanization strongly and relatively strongly affects energy consumption in regions with high-urbanization areas but also those with low-urbanization areas. Meanwhile, urbanization strongly and relatively strongly affects the regional CO2 emissions in northern China, where the major coal production areas, characterized by an energy-guzzling heavy industry base, are located. (3) Some evidence supports the arguments of urban environmental transition theory. Cities at a post-industrial stage (such as Beijing and Shanghai) experience a large effect from urbanization because of higher energy consumption in private residential and public service sectors, while in western and central China, the impact of urbanization can be associated with industrial development, which is characterized by low energy efficiency, high energy consumption and high emissions. In eastern China, the coexistence of light industrial structures and rapid urbanization has led to a smaller impact from urbanization on energy consumption and CO2 emissions than in the other two regions. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Urbanization Energy consumption CO2 emissions Different provinces of China

Contents 1. 2. 3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Materials and methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Calculating CO2 emissions in China's provinces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Empirical model for investigating the impact of driving factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Methods for model parameter estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Data sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. The empirical results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Relationship between urbanization and energy consumption. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Relationship between urbanization and CO2 emissions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusions and discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Policy implications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Encouraging green building in urbanized areas and supporting the development of a low-carbon economy . . . . . . . . . . . . . . . . . . . . 6.2. Developing a highly energy efficient transport mode and promoting China's transport energy savings along with urbanization . . . . 6.3. Accelerating renewable energy development and constructing long-term and effective systems in renewable energy development . 6.4. Advancing clean energy infrastructure construction related to people's livelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

n

Corresponding author. Tel.+86 13799384791 E-mail address: [email protected] (S.-d. Wu).

http://dx.doi.org/10.1016/j.rser.2015.10.090 1364-0321/& 2015 Elsevier Ltd. All rights reserved.

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1. Introduction According to a survey released in 2010 by the International Energy Agency (IEA), urban areas are responsible for 71% of global energy-related carbon emissions, and this percentage will increase as the urbanization trend continues [1]. In China, the urban contribution to China's total energy consumption is 75.15% [2], and the CO2 emissions from urban households comprise 73% of the total [3]. Urbanization plays a leading role in energy consumption and CO2 emissions in China, and major pressures to save energy and reduce emissions in China exist in its cities [4]. With the implementation of the “Reform and Opening-up” policy in 1978, China entered a period of rapid urbanization, which occurred at an unprecedented rate [5,6]. The rate of urbanization rose from 19.39% to 53.70% between 1980 and 2013, which is close to the global average but still behind that of developed countries, where the proportion of the urban population is 70–90%. If the current trend holds, China's urban population could exceed 1 billion people in the next two decades [6]. Based on the National New-type Urbanization Plan released by the government, the proportion of the urban population in China will increase by 0.92% per year and reach 60% by 2020. China has witnessed and is still witnessing large increases in urban growth [7]. However, rapid urbanization has posed some tremendous challenges related to environmental pressures, including energy consumption and CO2 emissions [4,8–13]. According to China's census report and statistics published by the U.S. Energy Administration, energy consumption increased from 987 million tons of standard coal equivalent (mtce) in 1990 to 3750 mtce in 2013 [14], while the amount of CO2 emissions rose from 2269 million tons (mt) in 1990 to 8106 mt in 2012 [15], as shown in Figs. 1 and 2, respectively. China has already passed the United States as the largest emitter of CO2 [16]. As urbanization continues to increase rapidly, much still needs to be done to achieve energy security and environmental sustainability. Due to growing concern about urbanization's impact on the environment and on increasing energy crises, a large number of researchers have investigated the connection between urbanization, energy consumption and CO2 emissions from various perspectives. In the existing literature, the relationships between urbanization, energy consumption and CO2 emissions pose an academic puzzle as well as a practical challenge [4]. A quick review of the literature shows that there has been increasing interest in the debate about whether urbanization is positively related to energy consumption and CO2 emissions from a national perspective, without considering regional differences [2]. It is well known that China is a vast territory with apparent regional differences in resource endowments and patterns of urbanization [13] and that energy consumption and CO2 emissions should be impacted by 4,000

regional features. Consequently, the following question is critical to the research in this field: How does urbanization affect energy consumption and CO2 emissions at the national and provincial levels and is there a significant difference in terms of these effects between provinces? However, at present, a systematic analysis on this topic is lacking. Therefore, this paper investigates this issue at the provincial level, based on a dataset covering 30 provinces over the 1990–2011 period. This paper serves to provide an understanding of how the impact of urbanization can differ in terms of energy consumption and CO2 emissions across regions and highlights the establishment of a good foundation for readers who may want to make some constructive proposals and offer their thoughts on urban planning, energy consumption and CO2 emissions policy. The remainder of the paper is as follows: Section 2 briefly reviews related literature and previous studies on the influences of urbanization on energy consumption and CO2 emissions. Section 3 describes the econometric method and sample data. Section 4 presents the empirical results. The conclusions and discussions are presented in Section 5. Section 6 details a series of policy recommendations.

2. Literature review A number of studies have explored the relationship between urbanization, energy consumption, and CO2 emissions in many academic peer-reviewed journals over the past five years. These studies are listed chronologically in Table 1. Based on the literature survey, our review reaffirms an extensive concern in existing research with the relationship between urbanization, energy consumption and CO2 emissions at the national and household levels. At a national level, there is no widely accepted consensus that all scholars can follow. Most studies have found a positive link between urbanization and both energy consumption and CO2 emissions. For the case of China, Wang [5] empirically investigated the effects of China's urbanization on residential energy consumption and production energy consumption through a time series analysis. Li et al. [17] found that urbanization is one of the main factors affecting China's energy consumption. Jiang and Lin [18] claimed that China's energy demand will sustain high growth in the mid-term through the process of urbanization and industrialization. On the basis of a comparison of characteristics between the US and China, Lin and Ouyang [19] found that energy demand has rigid growth characteristics in the rapid urbanization stage. Additionally, Sun et al. [20] explored factors related to the influence of urbanization on household energy consumption and suggested that residential energy consumption in urban areas is a

Energy consumption

60

Urbanization

50

3,000 40

2,500

30

2,000 1,500

20

1,000 10

500

0

0 1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

Fig. 1. Changes in energy consumption and urbanization from 1990 to 2013.

Urbanization (%)

Energy consumption(mtce)

3,500

Q. Wang et al. / Renewable and Sustainable Energy Reviews 54 (2016) 1563–1579

9,000

CO2 CO2emissions emissions

1565

60

Urbanization

8,000 50 40

6,000 5,000

30 4,000 20

3,000

Urbanization (%)

Emissions (mt)

7,000

2,000 10 1,000 0

0 1990

1993

1996

1999

2002

2005

2008

2011

Fig. 2. Changes in CO2 emissions and urbanization from 1990 to 2012

crucial area of focus for energy conservation and emissions reduction. In other countries, Shahbaz and Lean [21] confirmed the existence of a long-term relationship between energy consumption and urbanization in Tunisia from 1971 to 2008, namely, that urbanization increases energy consumption in Tunisia, but there is no causality from energy consumption to urbanization. Solarin and Shahbaz [22] investigated the causal relationship between urbanization and electricity consumption in the case of Angola, and their results indicate the existence of bidirectional causality between these. Shahbaz et al. [23] found that the relationship between urbanization and CO2 emissions is positive in the United Arab Emirates and that urbanization Granger causes CO2 emissions. Shahbaz et al. [24] investigated the impact of urbanization on energy consumption in the case of Malaysia; their results validate the existence of cointegration and expose urbanization as a major contributor to energy consumption. However, opposing links between urbanization, energy consumption and CO2 emissions have also been found in recent studies. For instance, a study by Ala-Mantila et al. [25] indicated that the relationship between urbanity and direct and indirect emissions is negative in Finland and that the indirect greenhouse gas consequences of urbanization are less obvious. Ghosh and Kanjilal [26] examined a cointegrating relationship between energy consumption, urbanization and economic activity for India and found that there is no cointegration between energy consumption and urbanization. Similar results were found by O'Neill et al. [27]. Currently, there is an increasing level of interest in investigating the different mechanisms through which urbanization leads to substantial increases in energy consumption and CO2 across different levels of development or income. Madlener and Sunak [28] identified the different processes and mechanisms of urbanization that substantially affect urban energy demand and the fuel mix. They found that the relevance of these mechanisms differs considerably between developed and developing countries. Similarly, Poumanyvong and Kaneko [29] suggested that the impact of urbanization on energy use and emissions varies across stages of development. Surprisingly, urbanization decreases energy use in the low-income group, while it increases energy use in the middle- and high-income groups. The impact of urbanization on emissions is positive for all income groups, but it is more pronounced in the middle-income group than in the other income groups. Further analyses have taken different types of countries into account and investigated different mechanisms and relations. For developing countries, Martínez-Zarzoso and Maruotti [30] analyzed the impact of urbanization on CO2 emissions from 1975 to 2003, taking into account dynamics and the presence of heterogeneity in the sample of countries. The results show an inverted-U-shaped relationship between urbanization and CO2 emissions. Thus, the elasticity of emission-urbanization is positive

for low urbanization levels, which is in accordance with the higher environmental impact observed in less-developed regions. Hossain [31] empirically examined the dynamic causal relationships between CO2, energy consumption, and urbanization for a panel of newly industrialized countries using time series data for the 1971– 2007 period and found that higher urbanization in newly industrialized countries promotes faster economic growth and consequently produces greater CO2 emissions. Al-mulali et al. [32,33] found a long-term bi-directional positive relationship between urbanization, energy consumption, and CO2 emissions in Middle Eastern and North African countries, and the significance of this long-term relationship varied across the countries based on their levels of income and development. For EU member and candidate countries, the study conducted by Kasman and Duman [34] indicated a short-term unidirectional panel causality running from urbanization to carbon emissions. Of course, in some studies, causality in the opposite direction may occur, and there is little evidence of a relationship between urbanization, energy consumption and CO2 emissions. Liddle and Lung [35] found that electricity consumption unidirectionally Granger causes urbanization in the long run for 105 countries and that evidence that urbanization Granger causes electricity consumption is less supported. Similar conclusions can be found in the study by Salim and Shafiei [36], who analyzed the impact of urbanization on renewable and non-renewable energy consumption in OECD countries. Sadorsky [37] found there are two opposing effects of urbanization on CO2 emissions, an increasing effect and a decreasing effect, which tend to cancel each other out, leaving the net impact of urbanization on CO2 emissions statistically insignificant. Chikaraishi et al. [38] found similar results when investigating the effects of urbanization on carbon dioxide emissions based on an unbalanced panel dataset of 140 counties. Zhu et al. [39] did not find evidence in support of an inverted-U curve between urbanization and CO2 emissions in a sample of 20 emerging countries over the 1992–2008 period. Additionally, the impact of an urban lifestyle on energy use and CO2 emissions based on urbanization's effect on residential CO2 emissions was addressed in the following studies. Donglan et al. [40] estimated and compared the CO2 emissions from urban and rural residential energy consumption from 1991 to 2004 in China and found that increases in income and energy consumption in urban China increased CO2 emissions. Similar evidence was also observed by Feng et al. [41]. However, the findings of Chun-sheng et al. [42] did not support the above conclusions and showed that the difference in social emissions from energy consumption between urban and rural households decreases. In addition, Poumanyvong et al. [43] found that urbanization positively influences national transport and road energy use. However, the magnitude of its influence varies among the three income groups, and the

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Table 1 Summary of urbanization, energy consumption, and CO2 emissions analyses, 2010–2015. Study

Dependent variable

Poumanyvong and Kaneko [29] Donglan et al. [40]

Energy use and CO2 emissions

Feng et al. [41] Li et al. [17] Martínez-Zarzoso and Maruotti [30] Hossain [31] Madlener and Sunak [28] O'Neill et al. [27]

Chun-sheng et al. [42] Poumanyvong et al. [43] Zhang and Lin [4] Zhu et al. [39] Jiang and Lin [18] Al-mulali et al. [32] Shahbaz and Lean [21] Solarin and Shahbaz [22] Al-mulali et al. [33] Wang et al. [44] Liddle and Lung [35] Lin and Ouyang [19] Wang et al. [67] Sun et al. [20] Shahbaz et al. [23] Ren et al. [45] Ala-Mantila et al. [25] Salim and Shafiei [36] Ghosh and Kanjilal [26] Sadorsky [37] Wang [5] Chikaraishi et al. [38] Yuan et al. [46] Shahbaz et al. [24] Kasman and Duman [34]

Method

Country/ies (period)

Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model Logarithmic mean Divisia index decomposition analysis Residential CO2 emissions method Household energy consumption, resi- Gray model dential CO2 emissions Energy consumption Three-variable Energy Demand Model (urbanization, population, economy) Semi-parametric mixture effects model CO2 emissions CO2 emissions, energy consumption

Johansen Fisher panel cointegration test method

Energy demand Energy consumption

NA Integrated Population-Economy-Technology-Science (iPETS) model, computable general equilibrium (CGE) model Household energy consumption, resi- STIRPAT model dential CO2 emissions National transport and road energy STIRPAT model use STIRPAT model Energy consumption and CO2 emissions Semi-parametric panel data model with a fixed effects CO2 emissions model Energy demand Cointegration model Energy consumption and CO2 Fully modified ordinary least squares (FMOLS) model emissions Energy consumption Autoregressive Distributed Lag (ARDL) bounds testing approach Electricity consumption ARDL bounds testing approach, vector error correction model (WECM), Granger causality test Energy demand Pedroni cointegration test method, dynamic OLS models, Granger causality test CO2 emissions STIRPAT model Electricity consumption Energy demand Energy consumption and CO2 emissions Residential energy consumption CO2 emissions Energy consumption and carbon emissions Total, direct, and indirect CO2 emissions Renewable and non-renewable energy consumption Energy consumption CO2 emissions Energy consumption CO2 emissions Residential indirect CO2 emissions Energy consumption CO2 emissions, economic growth, energy consumption, and trade

99 countries (1975–2005) China (1991–2004) China (2005–2007) China (1990–2008, 2009–2020) Developing countries 10 newly industrialized countries (NIC) The world India and China (2000–2100)

China (2009) Low, middle and high income countries (1975–2005) 30 provinces of China (1995–2010) 20 emerging countries (1992–2008) China(1978–2008, 2009–2020) East Asia and the Pacific countries (1980–2009) Tunisia (1971–2008) Angola (1971–2009)

Heterogeneous panel methods Cointegration model Regression analysis model

The Middle East and North African countries(1980–2009) Guangdong Province of China (2005– 2009) 105 countries (1971–2009) China (1980–2012, 2013–2030) 30 provinces of China (1995–2011)

Tobit and Ordinary Least Squares (OLS) models ARDL bounds testing approach Carbon emissions coefficient and sector energy consumption methods Regression analysis model

China (1990–2012) United Arab Emirates (1975–2011) Shandong Province of China (1985– 2009, 2015, 2020) Finland (since 2006)

STIRPAT model

OECD countries (1980–2011)

ARDL bounds testing approach STIRPAT model Structural Decomposition Analysis (SDA) method STIRPAT model Structural Decomposition Analysis (SDA) model

India (1971–2008) 16 emerging countries (1971–2009) China (1980–2011) 140 counties (1998–2008) Four economic regions of China (2002–2007) Malaysia (1970–2011) EU member and candidate countries over the 1992–2010 period

STIRPAT model Panel unit root tests, panel cointegration methods and panel causality tests

impact of urbanization on transport and road energy use is greater in the higher income group. In China, several studies have focused on the magnitude of the relationship between urbanization, energy consumption and CO2 emissions in certain regions of China. Zhang and Lin [4] found that the effects of urbanization on energy consumption vary across regions and decline continuously from the western region to the central and eastern regions. The impact of urbanization on CO2 emissions in the central region is greater than that in the eastern region. The study conducted by Wang et al. [44] explored the relationship between urbanization and CO2 in Guangdong and found that the urbanization level significantly affects the CO2 emissions. Ren et al. [45] designed a model to examine the impact of urbanization on the air environment in Shandong

Province and found that it has distinct positive effects on energy consumption and carbon emissions and that this influence is more obvious in the intermediate stage of urbanization than in the early stage. Yuan et al. [46] found that the expansion of urbanization plays important roles in the growth of residential indirect emissions in four economic regions of China. It can be seen from the brief review of the literature to date that few studies have examined or compared the magnitude of the relationships between urbanization, energy consumption and CO2 emissions across regions in a country and accounted for regional differences. Therefore, a further study on the impact of urbanization on energy consumption and CO2 emissions in terms of regional and provincial aspects is still necessary to establish a solid

Q. Wang et al. / Renewable and Sustainable Energy Reviews 54 (2016) 1563–1579

foundation for readers who want to reduce China's energy consumption and CO2 emissions from an urbanization perspective.

3. Materials and methodology 3.1. Calculating CO2 emissions in China's provinces Fossil fuel combustion is responsible for three-quarters of the CO2 emissions in China [47,48]. Because there are no official statistics on CO2 emissions from fossil fuel consumption, we must first quantify the amount of CO2 emissions in different provinces. The use of the carbon-emissions-coefficient method (CECM) to calculate CO2 emissions has recently gained popularity [49,45,4]. To calculate CO2 emissions for China's 30 provinces (data for Tibet and Taiwan are not available for most years) from 1990 to 2012, we use the CO2 emission coefficients of the 8 types of fuels from the Intergovernmental Panel on Climate Change [50], including coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, and natural gas; electricity was not considered. The fuel emission coefficients are often assumed to be the same across regions and over time [51]. Then, the general form of calculating CO2 emissions is given below: 8 44  ∑ EC nt  ef n  on CEt ¼ 12 n ¼ 1

ð1Þ

where CEt represents CO2 emissions from energy consumption (unit: mt) in year t; ECnt is the consumption of fuel type n (unit: mtce) in year t; efn denotes the carbon emissions coefficient of fuel type n (unit: t C/tce); and on is the rate of carbon oxidation of fuel type n (Table 2). 3.2. Empirical model for investigating the impact of driving factors In the literature, much attention has been paid to the IPAT model in the analysis of the effect of economic activity on energy consumption and the environment proposed by Ehrlich and Holdren[52]. It can be written as follows: I¼P AT

ð2Þ

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written as follows: lnI it ¼ a þ bðlnP it Þ þ cðlnAit Þ þdðlnT it Þ þeit

ð4Þ

where t denotes the year. Furthermore, many studies have shown that in addition to urbanization, quality improvements in rural life [55,56], transportation patterns [57,58], and primary energy consumption structures [59–61] are the main driving factors determining changes in energy consumption and CO2 emissions. To investigate the impact of urbanization on energy use and CO2 emissions in this paper, the level of urbanization and other variables, such as non-agricultural industrial structure, car ownership, peasant income, and primary energy consumption structure are selected as the affecting factors and combined with China's actual situation to create the following econometric model. o

o

lnEC it ¼ ao þ b ðlnP it Þ þ co ðlnAit Þ þ d ðlnT it Þ þ mo ðlnUrbit Þ þ no ðlnCarit Þ þ po ðlnIndit Þ

þr o ðlnRur it Þ þ eoit

ð5Þ

' ' lnCM it ¼ a' þ b ðlnP it Þ þ c' ðlnAit Þ þ d ðlnT it Þ þ m' ðlnUrbit Þ þ n' ðlnCar it Þ þ p' ðlnIndit Þ

þ q' ðlnRur it Þ þr ' ðlnCoait Þ þeit'

ð6Þ

The definitions and statistical descriptions of variables are shown in Table 3. 3.3. Methods for model parameter estimation The important hypothesis of linear regression is that there are not highly linear relationships among all variables. Considering the complex form of the STIRPAT model, it is necessary to determine if multi-collinearity exists before the regression. Therefore, a correlative analysis is undertaken on all of the independent variables in each of the final models for the entire nation and its provinces. The test results indicate that there is significant correlativity between any two variables. For instance, at the national level, except LnCoa, the correlation coefficient between any two variables is above 0.90, as shown in Table 4. To eliminate the effect of multi-collinearity, a ridge regression is adopted to fit the model. Ridge estimates, which can obtain acceptably biased estimates with smaller mean square errors in independent variables through bias-variance tradeoffs, were proposed by Hoerl and Kennard [62,63]. The standard model for multiple linear regressions can be written as follows:

I denotes the impact, P represents population size, A is the affluence or consumption level per capita, usually expressed in terms of GDP per capita, and T is the technological factor, such as energy efficiency, which is generally identified as the impact on environment per unit GDP. The pivotal limitation of IPAT is that it does not permit hypothesis testing because the known values of some terms determine the value of the missing term [53]. To overcome the limitations of the IPAT model, Dietz and Rosa (1997) proposed the STIRPAT model]as follows [54]:

where X is a t  n matrix of independent variables, β is a n  1 vector of unknowns, and ε is the errors such that E½ε ¼ 0 and E½εε0  ¼ δ2 I. The unbiased estimate of β is normally given by

I ¼ aP bi Aci T di ei

β^ ¼ ðX 0 XÞ  1 X 0 Y

ð3Þ

This model preserves the multiplicative framework of the IPAT model; P, A and T are the same as they are in Eq. (2). In the model, a represents the constant term, while b, c and d are the elasticities of the environmental impact of P, A, and T to be estimated, respectively, e denotes the error term, and the subscript i is the province, as it is a regional analysis. To eliminate possible heteroscedasticity, all variables take a logarithmic form. Eq. (3) can be

Y ¼ βX þ ε

ð7Þ

ð8Þ

When a high degree of multicollinearity exists among the X variables, the X 0 X matrix is ill conditioned, i.e., the value of its determinant X 0 X  0. Attempts to calculate the ðX 0 XÞ  1 matrix are therefore highly sensitive to slight variations in the data. By controlling the inflation and general instability associated with OLS, as well as by estimating β, ridge regressions incorporate a small positive quantity k along the diagonal of the normalized independent variable

Table 2 Carbon emissions coefficients (Unit: t C/tce). Resources

Coal

Coke

Crude oil

Gasoline

Kerosene

Diesel

Fuel oil

Natural gas

Carbon emissions coefficient Rate of carbon oxidation

0.7480 0.913

0.8550 0.928

0.5850 0.979

0.5538 0.980

0.5714 0.986

0.5921 0.982

0.6185 0.985

0.4440 0.990

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matrix X 0 X: ^ βðkÞ ¼ ðX 0 X þ kIÞ  1 X 0 Y

ð9Þ

^ ^ and ridge Obviously, βðkÞ is a class of biased estimators for β, regressions can be converted back to OLS regressions as a special case by setting k¼ 0 [27]. Therefore, the above equation creates a variance in parameter estimation that is less than that estimation by OLS regression under the k Z0 condition. Therefore, by choosing an appropriate k value and accepting minimal bias, a substantially reduced variance is possible, thereby remarkably improving estimation. In addition, we provide the degree of importance of various factors by investigating the standard regression coefficients in order to quantitatively reveal the causes of energy consumption and CO2 growth in different provinces. 3.4. Data sources The data on yearly fossil fuel consumption in this paper were collected from the China Energy Statistical Yearbook for the 1990– 2012 period. These data include yearly fossil fuel consumption and social development indicators for different regions in China. The data on provincial demographics and the urban population are also collected from the China Statistical Yearbook. The data on GDP per capita were calculated at constant prices (1990 ¼ 100). In addition, the data on energy use were provided by the China Energy Statistical Yearbook.

4. The empirical results To enable a contrastive analysis, we estimate the sample divided into three regions: the eastern region (including Jiangsu, Zhejiang, Fujian, Shandong, Hebei, Guangdong, Hainan, Liaoning, Beijing, Tianjin, and Shanghai provinces), the central region (including Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan, Jilin, and Heilongjiang provinces) and Table 3 Definition of all relevant variables used in the study. Variables Definition

CMit Pit Ait Tit Urbit Carit Indit Rurit Coait

4.1. Relationship between urbanization and energy consumption Table 5 gives the estimation results of the impact of urbanization on energy consumption for the entire sample. From the model, it can be seen that except for Ln Ind, other estimated coefficients are statistically significant at the 0.05 level or lower. Among the driving factors, the elasticity of the urbanization level is 0.401, indicating that a 1% increase in the urbanization level leads to a 0.401% increase of the total energy consumption when other elements remain constant. The estimate result conforms to the research results from Zheng [64] and Zhang and Lin [4], who argued that when the urbanization level rises by 1%, energy consumption would increase 0.353% and 0.410%, respectively. However, these findings appear to be very different from those in the study by Poumanyvong and Kaneko [29], who thought that the impact of urbanization on energy use is greater in middle- and high-income countries; their elasticities of the urbanization levels are 0.507 and 0.907, respectively. This finding appears to support the argument for urban compaction in China. Factually, as many cities in China still lack basic public services, particularly in western and central China, lower elasticity is unlikely to be the result of economies of scale for public infrastructure. Rather, it could possibly be due to the insignificant improvement in average urban living quality during the rapid urbanization process. From the study conducted by Fan et al. [65], we find that the actual living levels of more than 200 million rural migrant workers included in the total urban population are very low. Their survey shows that these workers' household energy consumption levels are half of the urban per capita energy consumption level, basically approaching the rural average energy consumption standard. Table 5 Estimation results: energy consumption model for entire sample, 1990–2012.

Unit of measurement

Total primary energy consumption of region i in year t Total CO2 emissions of region i in year t Population size of region i in year t Per capita GDP of region i in year t Energy efficiency of region i in year t Urbanization level of region i in year t Car ownership of region i in year t Proportion of non-agricultural industry of region i in year t Rural per capita income of region i in year t Proportion of coal of region i in year t

ECit

the western region (including Guangxi, Sichuan including Chongqing, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, and Inner Mongolia provinces). Classification by region is common in China, taking China's economic development level combined with geographic position into account.

Indicators

Coefficients

Standard errors

Contribution rates (%)

Ln Pop Ln Ind Ln Urb Ln Car Ln A Ln T Ln Rur Constant R2 F

0.693** 0.396 0.401*** 0.179*** 0.210***  0.388*** 0.093***  5.496 0.983*** 125.754

0.261 0.350 0.073 0.018 0.015 0.082 0.026 3.286

8.5 5.7 21.4 37.6 29.9  23.0 15.5 0.0

mtce mt 104 persons RMB RMB/tce Percent 104 cars Percent RMB/person Percent

Notes: *,** and *** is significance level at 0.1, 0.05 and 0.01 respectively.

Table 4 Pearson correlations of variables. Variables

Ln P

Ln Ind

Ln Urb

Ln Car

Ln A

Ln T

Ln Rur

Ln Coal

Ln Pop Ln Ind Ln Urb Ln Car Ln A Ln T Ln Rur Ln Coa

1 0.982*** 0.983*** 0.959*** 0.983*** 0.977*** 0.980***  0.486

1 0.954*** 0.926*** 0.955*** 0.961*** 0.941***  0.503

1 0.981*** 0.990*** 0.942*** 0.965***  0.443

1 0.994*** 0.913*** 0.973***  0.316

1 0.942*** 0.985***  0.363

1 0.960***  0.603

1  0.38

1

***

denotes that the correlation is significant at the 0.01 level.

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EC1990

EC2012

1569

R 10

3,000 2,500

9 8

7.90

7

2,000

6.20

6 5

1,500 4.27 1,000 500

4 3

Growth rate (%)

Energy consumption (mtce)

9.21

2

1.54

1

0

0 Agriculture

Industry and construction

Transportation

Other trade, commerce and services

Household Consumption

Fig. 3. Comparison of energy consumption in different sectors between 1990 and 2012.

P1990

P2012

P2012-P1990

80

6

70

4.12

4 2.16

1.79

50

2 0

40 30 20

-2

Diiference (%)

Proportion (%)

60

-3.04 -4

10

-5.04 -6

0 Agriculture

Industry and construction

Transportation

Other trade, commerce and services

Household Consumption

Fig. 4. Comparison of energy consumption patterns in different sectors between 1990 and 2012.

We also demonstrate the contribution of major factors leading to growth in energy consumption. Urbanization is not the most outstanding contributor; its contribution rate is 21.4%, far less than increases in the rates of car ownership (37.6%) and economic growth (29.9%). There is evidence to support our finding when we observe the change in industrial energy consumption structure. The observation shows that (as shown in Fig. 3) the energy consumption(EC) in the transportation sector and the other trade, commerce and services sector increased at higher growth rates (R) of 9.21% and 7.90%, respectively, during 1990–2012, and their share of energy consumption (P) grew by more than 4.12 and 2.16 percentage points, as shown in Fig. 4. The above analysis indicates that in the past two decades, although less than 16% of the total energy consumption in China was accounted for by the tertiary sector, it has played an important role in the growth of energy consumption. At provincial level, the coefficients of urbanization are statistically significantly and positively correlated with energy consumption at the 0.05 level or lower. According to the elasticities of urbanization (Ela_urb) on energy consumption, 30 provinces can be divided into five grades: weakly affected, relatively weakly affected, generally affected, relatively strongly affected and strongly affected (as shown in Table 6). The natural breakpoint grading method in the ArcGIS technology platform is used for the type classification of 30 provinces and cities in mainland China. In addition, in order to quantitatively reveal the causes of growth in energy consumption in different regions, the degree of importance of domain factors is provided. Here, we first arrange all factors according to their contribution rates from large to small and then choose key ones for which the sum of their contribution rates is not less than 80%. It can be seen from Fig. 5 that the impact of urbanization on energy consumption differs across provinces. On average, the western

region has the greatest impact on energy consumption, where the average elasticity of urbanization on energy consumption is 0.526, followed by the central (0.411) and eastern (0.375) regions (as shown in Fig. 6). This finding is also supported by Zhang and Lin [4]. Specifically, we find that the strongly affected areas include Xinjiang Uygur A.R, Qinghai, Beijing and Shanghai, which have the highest elasticities of urbanization to energy consumption, as shown in Table 6. According to the urbanization levels, these provinces can be divided into two categories. One category includes Beijing and Shanghai, with high urbanization levels resulting in the high growth of energy consumption. Our investigation indicates that as migration and urbanization increase, the newly increased urban population stimulates the rapid growth of energy consumption in residential and non-agricultural sectors. Another category comprises Xinjiang Uygur A.R and Qinghai. We find that the urbanization of these areas depends particularly on resource exploitation, infrastructure construction and policy promoting, which may lead to a lack of sustainable development ability, weak market awareness and a lag in production technology. Thus, it has frequently been asserted that an energy-efficiency gap exists between these areas and their counterparts. In 2012, although the indicators of Xinjiang Uygur A.R and Qinghai increased to 1.89 and 1.69 thousand RMB/tce, respectively, these regions still rank at the bottom in the country for energy efficiency, only slightly ahead of Ningxia (1.27 thousand RMB/tce). In addition, economic growth in both Xinjiang Uygur A.R and Qinghai is the main cause of the increase in energy consumption. Consequently, low energy efficiency results in high levels of energy consumption along with urbanization. The relatively strongly affected areas, including Gansu, Shanxi, Inner Mongolia A.R and Jilin, are concentrated in northern China, which relies on carbon-intensive industries for economic growth due to its abundance of coal, crude oil and other mineral resources. As

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Table 6 Grading criteria and contribution rates of domain factors affecting regional energy consumption. Grades

Criteria

Provinces

Strongly affected area

Ela_urb Z 1

I: Beijing and Shanghai

Relatively strongly affected area Generally affected area

Relatively weakly affected area

Dominant factors

Urbanization, population growth, economic growth, growth of car ownership II: Xinjiang Uygur A.R and Qinghai Economic growth, urbanization, growth of car ownership, growth of rural per capita income 0.6 r Ela_urb o1 Gansu, Shanxi, Inner Mongolia A.R, and Jilin Economic growth, growth of car ownership, urbanization, rural development 0.3 r Ela_urb o0.6 I: Heilongjiang and Liaoning Growth of car ownership, economic growth II: Hebei, Henan, Jiangxi, Hunan, Guangxi Zhuang Growth of car ownership, urbanization, economic growth A.R, and Ningxia III: Shandong Growth of car ownership, population growth, economic growth 0.15r Ela_urb o 0.3 I: Tianjin and Jiangsu Population growth, economic growth II: Hubei, Shaanxi, Sichuan, and Yunnan III: Hainan and Chongqing

Weakly affected area

Ela_urb o 0.15

I: Zhejiang, Fujian, and Guangdong II: Guizhou and Anhui

Growth of car ownership, economic growth, and rural development Growth of car ownership, economic growth, growth of urbanization and rural development Growth of car ownership, economic growth, population growth, rural development Rural development, growth of car ownership, economic growth

Notes: In this table, the domain factors have been arranged according to their contribution rates from large to small, and the sum of their contribution rates is not less than 80%.

Fig. 5. Distribution pattern of elasticities of urbanization on energy consumption in 30 provinces.

discussed above, low energy efficiency always results in high levels of energy consumption along with urbanization in resource-based and less-developed areas. From 1990 to 2012, except for Jilin, the energy efficiency of these provinces was lower than the national average level (as shown in Fig. 7).

The generally affected areas are distributed throughout the old industrial base in the northeast (Heilongjiang and Liaoning), the rapidly industrializing eastern regions (Hebei and Shandong), the important grain production base in the central region (Henan, Hunan and Jiangxi), and the western transition zones (Guangxi

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Fig. 6. Elasticities of urbanization on energy consumption in 30 provinces.

Energy efficiency (10 4 RMB/tce

0.60 0.50

China

Shanxi

Jilin

Gansu

Inner Mogolia

0.40 0.30 0.20 0.10 0.00 1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

Fig. 7. Changes in energy efficiency in China and relatively strongly affected areas.

and Ningxia). Heilongjiang and Liaoning were the locations of the strongest industrial bases before 1990. To date in these provinces, the growth of car ownership and economic development has been the main drivers behind increased energy consumption due to their locations and economic foundations. Hebei, Henan, Jiangxi, Hunan and Guangxi also had the highest urbanization growth rates in eastern, central and western China during the past 22 years, growing 1.5–2.3% points annually. Shandong is an eastern province with rapid industrialization and urbanization that has benefitted from the Reform and Opening Up Policies. In addition, its population has increased quickly in response to the rapid industrialization and urbanization, and it ranks as the second most populous province, with 96.85 million persons in 2012. Relatively weakly affected areas are mainly distributed throughout eastern and western China, an area that covers two Chinese regional policies, i.e., the east taking the lead in development and western development. In addition, the relatively weakly affected areas also include Hubei. Also among these areas, Tianjin and Jiangsu are located in the eastern coastal area of China, are subject to revitalization policies, and exhibit growth in energy consumption mainly driven by transportation development, economic growth and population increases due to large economies and higher levels of social development. The growth in energy consumption in Hubei, Shaanxi, Sichuan and Yunnan is mainly caused by transportation development, economic growth,

and rural development due to the economic importance of agriculture. It can be seen that the rural economy plays an important role in promoting energy consumption. Hainan and Chongqing are unique in the main driving factors behind the growth of energy consumption: based on observation, we find that both urbanization and rural development have increased energy consumption – except for transportation and economic growth – in these two regions. Weakly affected areas are also concentrated in eastern and western China. Over the past 22 years, the regional economy and level of urbanization in Zhejiang, Fujian and Guangdong all maintained high growth rates of car ownership, population, economy, urbanization and rural development, and these factors have played an important role in increasing energy consumption. However, in Guizhou and Anhui, the above factors experienced sluggish growth during the period. For both of these provinces, rural development is the most important factor affecting growth in energy consumption, followed by economic growth, transportation and nonagricultural industry development. 4.2. Relationship between urbanization and CO2 emissions Table 7 illustrates the estimated results of the impact of urbanization on CO2 emissions for the entire sample. It can be seen that apart from the coefficients of the share of the non-agriculture sectors

Q. Wang et al. / Renewable and Sustainable Energy Reviews 54 (2016) 1563–1579

in GDP and energy efficiency, those of other variables are statistically significant at the 0.05 level or lower. The elasticities of population, urbanization, car ownership, GDP per capita, rural per capita income, proportion of coal in energy consumption on CO2 emissions are 0.936, 0.507, 0.174, 0.166, 0.044 and 2.272, respectively. These results mean that if other elements remain unchanged, CO2 emissions in China will increase 0.507% if the urbanization level increases by 1%. The estimated result is supported by Poumanyvong and Kaneko [29], whose research reveals that the elasticity of urbanization with respect to CO2 emissions is 0.512 in middle-income countries, which is greater than the corresponding rate in Table 7. From the viewpoint of the major factors leading to growth in CO2 emissions, transportation development is the most outstanding contributor; its contribution rate of 34.4% is greater than the contribution

Table 7 Estimation results: CO2 emissions model for the entire sample, 1990–2012. Indicators

Coefficients

Standard errors

Contribution rates (%)

Ln Pop Ln Ind Ln Urb Ln Car Ln A Ln T Ln Rur Ln Coa Constant R2 F

0.936*** 0.330 0.507*** 0.174*** 0.166***  0.112 0.044** 2.272***  19.942 0.994 152.092

0.212 0.304 0.058 0.017 0.009 0.055 0.019 0.362 3.452

11.5 4.8 27.1 34.4 23.6  6.6 7.4 18.0 0.0

Notes: *,** and *** is significance level at 0.1, 0.05 and 0.01 respectively.

C_Emi1990

rates of both urbanization (27.1%) and economic growth (23.6%). There is evidence to support our finding from observing the change in the sectorial structure of total energy consumption. As can be observed in Fig. 8, the CO2 emissions (C_Emi) in the transportation sector increased at the highest growth rate (R) of 8.87% during 1990– 2012, and its proportion of CO2 emissions (P) grew by more 2.51% points, as shown in Fig. 9. The findings indicate that transportation development has played an important role in the growth of CO2 emissions. In addition, we also find that there is a positive relationship between the proportion of coal consumption in energy use and CO2 emissions. Additionally, the contribution rate of the energy consumption structure to CO2 emissions is 18.0%, which indicates that reducing China's dependency on coal would be conducive to decreasing CO2 emissions. At provincial level, the coefficients of urbanization are statistically significantly and positively correlated with CO2 emissions under the 0.10 level or lower. According to the elasticities of urbanization (Ela_urb) on CO2 emissions, 30 provinces can be divided into five grades: weakly affected, relatively weakly affected, generally affected, relatively strongly affected and strongly affected. As mentioned in the previous section, the grading method and selection criteria for domain factors are used to further analyze the differences among Chinese provinces in terms of urbanization's influence on CO2 emissions, as shown in Table 8. It can be seen from Figs. 10 and 11 that the impact of urbanization on CO2 emissions differs across regions, with the greatest impact being on the western region, where the average elasticity of urbanization on energy consumption is 0.634, followed by the central (0.354) and eastern (0.264) regions. This finding differs from the conclusions of Zhang and Lin [4] due to the data sources C_Emi2012

R

8,000 8.87

CO2 emissions (mt)

7,000 6,000 6.60 5,000 4,000

4.32

3,000 2,000 1,000

0.57 -0.09

0 Agriculture

Industry and construction

Transportation

Other trade, commerce and services

10 9 8 7 6 5 4 3 2 1 0 -1

Growth rate (%)

1572

Household Consumption

Fig.8. Comparison of CO2 emissions in different sectors between 1990 and 2012.

P1990

P2012

P2012-P1990 10

100 8.48

90

8

Proportion (%)

4

70 2.51

60 50 40

2 0

-0.91

-1.81

-2 -4

30

-6

20 10

-8.28

-8 -10

0 Agriculture

Industry and construction

Transportation

Other trade, commerce and services

Household Consumption

Fig. 9. Change in shares of sectorial CO2 emissions between 1990 and 2012.

Diiference (%)

6

80

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Table 8 Grading criteria and contribution rates of domain factors affecting regional CO2 emissions. Grades

Criteria

Provinces

Dominant factors

Strongly affected area

Ela_urb Z 1

Relatively strongly affected area

0.6 r Ela_urb o 1

I: Inner Mongolia A.R II: Xinjiang Uygur A.R and Qinghai I: Beijing and Shanghai

Economic growth, growth of car ownership, urbanization Urbanization, economic growth, growth of population Economic growth, urbanization, growth of car ownership

II: Gansu, Shanxi, and Jilin

Generally affected area

Relatively weakly affected area

Weakly affected area

Urbanization, economic growth, rural development and coal consumption 0.3 r Ela_urb o 0.6 I: Shaanxi, Henan, Anhui, Jiangxi, Hunan, Hubei, Economic growth, growth of car ownership, and urbanization and Guangxi Zhuang A.R II: Liaoning and Heilongjiang Coal consumption, urbanization, and growth of car ownership 0.15 r Ela_urb o 0.3 I: Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Growth of car ownership, population growth, economic growth Guangdong,and Hainan II: Ningxia Hui A.R, Chongqing, Guizhou Growth of car ownership, economic growth, rural development, and non-agricultural industry development Ela_urb o 0.15 I: Tianjin Coal consumption, growth of car ownership, urbanization II: Sichuan and Yunnan Growth of car ownership, economic growth

Notes: In this table, the domain factors have been arranged according to their contribution rates from large to small, and the sum of their contribution rates is not less than 80%.

Fig. 10. Distribution pattern of elasticities of urbanization on CO2 emissions in 30 provinces.

and analysis methods used. In their study, with an increase of 1% of the urbanization in eastern and central China, CO2 emissions will increase by 0.140% and 0.148%, respectively, while the increase of western China is insignificant. Specifically, we find that the strongly affected areas include Xinjiang Uygur A.R, Qinghai, and Inner Mongolia, which have the highest elasticities of urbanization to CO2 emissions. According to the domain factors affecting CO2 emissions, the three provinces

can be divided into two categories. From 1990 to 2012, the economy of Inner Mongolia A.R developed rapidly, exhibiting the highest growth rate, 9.0% among all provinces. Therefore, rapid economic growth results in the high growth of CO2 emissions. In addition to economic growth, the growth of car ownership and urbanization also affected CO2 emissions. For both Xinjiang Uygur A.R and Qinghai, as mentioned above, urbanization depends particularly on resource exploitation, infrastructure construction and

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Fig. 11. Elasticities of urbanization on CO2 emissions in 30 provinces.

policy promotion, which may lead to an increase in CO2 emissions as the most important driving factor. Relatively strongly affected areas are distributed in Beijing, Shanghai, Gansu, Shanxi, and Jilin. As the biggest cities in China, Beijing and Shanghai have attracted an enormous floating population since 1990, with their advantages of good social and economic foundations, and their population scales tripled in the past 22 years. This fast-growing population became the most important factor resulting in CO2 emissions, followed by urbanization and the increase in car ownership. In addition, Jilin is an important base for old industry, with equipment manufacturing, and Gansu and Shanxi are the main coal production base and important heavy chemical industry bases; here, urbanization, economic growth, rural development and coal consumption are the main factors driving the increase in CO2 emissions. The generally affected areas are distributed mainly in central and western China, where urbanization depends particularly on resource exploitation, opening borders to the neighborhood, and the favorable governmental policies. CO2 emissions in Shaanxi, Henan, Anhui, Jiangxi, Hunan, Hubei, and Guangxi Zhuang A.R rely on economic growth, transportation development, and urbanization due to their location and industrial bases. In addition, Liaoning and Heilongjiang are the important bases of old industry, with equipment manufacturing, and both of them maintain a higher share of coal in energy consumption. In these two provinces, CO2 emissions are promoted by coal consumption, urbanization, and the growth of car ownership. The relatively weakly affected areas are distributed throughout eastern and western China. Eastern China, including Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, and Hainan, benefited from the Reform and Opening Up Policies and achieved rapid growth from 1990 to 2012. Due to their large economies, the growth in car ownership, and the population scale, these provinces’ economies maintained high growth rates over the past 22 years, and these factors became the main drivers behind increases in CO2 emissions. In contrast, the growth of CO2 emissions in Ningxia Hui A.R, Chongqing, and Guizhou was primarily caused by transportation development, economic growth, and rural development. It can be seen that the rural economy plays an important role in promoting growth in CO2 emissions. The weakly affected areas are concentrated in Tianjin, Sichuan and Yunnan. Tianjin is located in the northeast section of North China. It is the largest open coastal city in North China and the

economic center of the Bohai coastal region. Since 1978, Tianjin's economy has grown substantially, based on investment and exports. Driven mainly by heavy industry, the surge in energy consumption forced Tianjin to increase its reliance on coal to almost 70% of its energy needs, thus resulting in high CO2 emissions. In addition, transportation development also drove the increase in CO2 emission. Unlike Tianjin, Sichuan and Yunnan are located in southwest China and are the most inaccessible provinces in the nation. Because of natural, historical and other reasons, economic and social development in these regions has lagged behind that of the central and eastern regions. However, in 2000, the Chinese government began to implement the “Western Development” strategy. Thanks to rapid development over the past 22 years, a relatively solid foundation in terms of material wealth and infrastructure has been laid in western China. From this viewpoint, the growth of CO2 emissions is driven by transportation development and economic growth.

5. Conclusions and discussions Unlike previous studies, this paper empirically investigates the impact of urbanization on energy consumption and CO2 emissions, considering provincial differences, and we find several interesting phenomena from our empirical results. The first finding is that urbanization increases energy consumption and CO2 emissions in China, as documented in publications by Zheng [64], Zhang and Lin [4]. However, several new findings have been observed in this study. Urbanization is not the most outstanding contributor to the increase in energy consumption and CO2 emissions; its contribution rate is far less than the contribution rates of the increase in car ownership and economic growth. This indicates that compared with the transfer of the population from rural areas to urban areas, the development of transportation and the economy play more important roles in driving increases in energy consumption and CO2 emissions. The second finding is that the impact of urbanization on energy consumption declines continuously from the western region to the central and eastern regions. This finding is in accordance with the findings of Zhang and Lin [4]. However, we further find that significant differences exist among provinces in terms of the impact of urbanization on energy consumption, which is worth noting by

Q. Wang et al. / Renewable and Sustainable Energy Reviews 54 (2016) 1563–1579

policymakers. Surprisingly, strongly affected areas and relatively strongly affected areas are distributed not only in highly urbanized areas that have entered the post-industrialization stage (such as Beijing and Shanghai) but also in low-urbanization areas because of the high dependency of urbanization on resource exploitation and low energy efficiency (such as Xinjiang Uygur A.R and Qinghai). Weakly affected and relatively weakly affected areas are concentrated in both eastern developed and western underdeveloped regions. In the developed regions, the tendency to switch the industrial structure toward low-carbon-intensive industries (e.g., transportation sector, trade and services sector) along with an increase in effective energy efficiency significantly curbed the increase in energy consumption during the late-middle period of industrialization. However, the increase in energy consumption in undeveloped regions is driven by development of the rural economy, especially the rise of tourism and its related industries. The third finding is that the impact of urbanization on CO2 declines continuously from the western region to the central and eastern regions. We also find that there are significant differences among provinces in terms of the impact of urbanization on CO2 emissions. Strongly affected areas and relative strongly affected areas are distributed in northern China, which is the major coal production region and is characterized by an energy-guzzling heavy industry base. Along with urbanization, from 1990 to 2012, the share of secondary industries increased by more than 10% points. Therefore, the impact of urbanization on CO2 emissions is still more significant in northern China than it is in the other regions. Weakly affected and relatively weakly affected areas are concentrated in eastern developed

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and western underdeveloped regions. Compared with the spatial structure of the elasticities of urbanization on energy consumption, that of the elasticities of urbanization on CO2 demonstrates more distinct spatial features. This can possibly be attributed to structural change in regional industrial structures and energy structures. We find that except for Beijing, Shanghai, Heilongjiang and Tianjin, the other provincial shares of secondary industry output in GDP increased by varying degrees over the 1990–2012 period (see Fig. 12). Due to the high dependency of industrial energy consumption on coal, the increase in the share of secondary industry indicates the growth of CO2 emissions. The fourth finding is that the impact of urbanization on energy consumption and CO2 emissions seems to support the argument for urban compaction in eastern China, where the urbanization level is higher than that in the central and western regions, as a complete sample. Surprisingly, when we analyze this subject at the provincial level, we find that Beijing and Shanghai have high elasticities of urbanization, with high per capita energy consumption and per capita CO2 emissions [66], which indicates that further urbanization in these two megalopolises would increase rather than decrease energy consumption and CO2 emissions. This supports the argument of the urban environmental transition theory that consumption-related issues are most pronounced in high-income countries [29]. These two megalopolises use much private and public infrastructure to support their urban populations and urban economies. Constructing, maintaining and operating this infrastructure involve significant amounts of energy resources. For instance, the per capita energy end-use in the

Fig. 12. Changes in the provincial share of secondary industry output in GDP, 1990–2012.

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Fig. 13. Per capita energy end-use in residential and service sectors in different provinces in 2012.

residential and service sectors in Beijing and Shanghai were 2218 kgce and 2053 kgce in 2012, respectively, far exceeding the national average value (708 kgce) and even the corresponding values in most central and western provinces, as shown in Fig. 13. In addition, we find that eastern China also consumes more energy in terms of the average per capita energy end-use in the residential and service sectors; the average value is 1134 kgce, far higher than the corresponding values for western and central China, which are 850 and 716 kgce, respectively. According our observation, gaps in economic development result mostly in millions of people flowing from the central-western region to the eastern region and large cities becoming the primary destinations in southeastern coastal areas. However, rapid urban population growth and a significant increase in the floating population have brought a tremendous burden on the relatively lagging living facilities and infrastructure. Consequently, material production is still the biggest energy consumer, while the effective energy supply is insufficient. In fact, the Chinese experience of urbanization has often been perceived as a “unique case” because of its peculiar pattern of rapid industrialization without a parallel growth of the urban population [67], especially in western and central China. Namely, western and central urbanization is largely driven by industrialization, which leads to the rapid growth of energy consumption and CO2 emissions, but not in the residential and service sectors. From this point of view, the larger impact of urbanization in western and central China is abnormal because of these regions’ economic growth patterns. It is well known that the Chinese government began implementing The West-East Power Transmission Project in the beginning of the 21st century. In 2012, there were 14 large-sized coal industry bases and 16 coal-fired power production bases that accounted for 91% of the national coal output in western and central China, including Shanxi, Inner Mongolia A.R, Shaanxi, Gansu, Ningxia Hui A.R, Xinjiang Uygur A.R, and Guizhou. With this background, the enormous urban population urgently needs the engagement of the mining, power production, and other energy industries. Unfortunately, the current growth pattern is characterized by low energy efficiency, high energy consumption and high emission maintenance. As a result, urbanization has resulted in more energy consumption and CO2 emissions in western and central China than the corresponding levels in eastern China. In fact, the urban living levels of western and central China are very low. Our survey also shows that these regions’ household energy consumption levels are three-quarters those in eastern China.

6. Policy implications Through the empirical results of this paper, we obtained evidence confirming the positive relationship between urbanization and energy consumption and CO2 emissions, as well as the lack of uniformity in the regional significances of these relationships. There is a possibility that the government will be able to reduce energy consumption and CO2 emissions along with urbanization. 6.1. Encouraging green building in urbanized areas and supporting the development of a low-carbon economy China is still at the medium-term stage of urbanization. The 18th national congress of the communist party of China looked on urbanization as having the greatest potential to expand domestic demand and create consumer demand. However, urbanization is the third most important factor in the rise in energy consumption and the second most important factor in the increase in CO2 emissions. It can be predicated that China will face increasing international pressure for carbon reduction and the threat of domestic energy shortages accompanying the continual rapid development of urbanization. To reduce the impact of urbanization on the growth of energy consumption and CO2 emissions, the Chinese government should adjust its fiscal policies to support green construction, encourage real estate developers to engage in green construction and expand the scale of green buildings. In addition, along with urbanization, industry is a major source of energy consumption and carbon emissions in China. There should be an emphasis on prioritizing the development of light industry with low energy intensity and of industries with highvalue-added products that are internationally competitive, accelerating the elimination of outdated production technology and equipment, and encouraging the technological innovation of industry. Furthermore, it should be noted that improving the quality of urbanization is urgent to accelerating the transformation of urbanization to be people-oriented, with city clusters as a major form of urbanization; these clusters will be supported by a comprehensive accommodation capacity and safeguarded by institutional innovation. 6.2. Developing a highly energy efficient transport mode and promoting China's transport energy savings along with urbanization Transportation should be economized by emphasizing the urban transportation network. As the most important factor in the increase in CO2 emissions and energy consumption, China's transport sectors

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are still in a period of large-scale construction and development. The trend involves increasing energy consumption [68], especially oil. According to the Asia-Pacific Economic Cooperation (APEC), China's transport petroleum consumption reached 98 mtoe (million tons of oil equivalent) in 2005, which is 21 times that in 1980 [69]. From the viewpoint of transport sectors, it has been reported that from 1980 to 2006, the proportion of energy consumption by highway transport increased from 36.4% to 61.5%, the share of railway transport energy consumption decreased from 27.3% to 2.8%, the share of civil aviation energy consumption increased from 1.3% to 7.3%, and the share of waterway transport decreased from 32.1% to 25.6% [70]. Thus, it can be seen that road transport occupies an increasingly important position in energy consumption by the transport sectors. To alleviate the pressure from the growing demand for fossil fuel energy by China's transport sectors and to eliminate pressure on congested road transport systems, the Chinese government should take a variety of measures, including developing a highly energy efficient transport mode. In general, railway and waterway transport systems have larger transport capacity and lower energy consumption and should be the main transport modes in an efficient transport system. Therefore, in the future, the government should put further effort into adjusting and optimizing road structures to connect the road transport system with other transport systems [71,72]. In addition, because rail transit is the ideal form of transport for urban groups and large cities in eastern China [72], the government should develop railway transport, especially high-speed railway transport. Furthermore, the Chinese government needs to increase the market share of low-power small cars. Meanwhile, the market share of diesel passenger cars should be appropriately increased. 6.3. Accelerating renewable energy development and constructing long-term and effective systems in renewable energy development The energy supply structure should be optimized by increasing the share of new energy and renewable energy according to local advantages and resource characteristics. As the urban economy advances and human society requires more energy, the lack of fossil energy and the pollution of the environment have given rise to the serious contradiction between energy provision, environmental protection and economic development [73]. Renewable energy, because it is renewable and non-polluting, will grow to be an effective and practical choice to guarantee future urbanization [74]. Thanks to over three decades of development, China's renewable energy policy has matured and played an important guiding and pushing the development of renewable energy. However, to date, it has not been widely used in urban industries and daily life. With regard to the main problems in China's renewable energy development, we suggest that the government pay attention to the following aspects of China's renewable energy development policy along with urbanization. First, the development of renewable energy must be based on the regional situation, and these issues should be studied in accordance with local conditions [75]. Therefore, the government should enhance policy innovation in policy regions. In concrete terms, this means that the state's mid- and long-term programming for renewable energy should be conducted to form a reasonable regional framework for industrial development and urbanization. In addition, the government should encourage regionalindustry-related policies to produce innovation and support a sustainable guided transition in energy use. Second, China's renewable energy is appropriate to serving as a Clean Development Mechanism (CDM) project [76]. Consequently, the Chinese government should actively echo the CDM, take advantage of the CDM, construct the regional echoing mechanisms for the CDM, and strive for more capital and techniques to accelerate the development of renewable energy. Third, the government should provide sustainable financial subsidies

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to technical research and development projects stage by stage to reduce the investment risk of individual enterprises and to enhance their participation in technical research and development. Fourth, the state at various levels of government should establish renewable energy development offices, which would be in charge of policy implementation, industrial dynamics management, and the provision of reports and feedback on information by subordinates to their supervisors. In addition, the government should encourage investment by various sources and diversified ownership in renewable energy fields and eventually formulate a long-term and effective development mechanism for renewable energy. In this system, the state should offer guidance with a limited amount of capital, while bank loans and self-raised capital should play a more important role for enterprises.

6.4. Advancing clean energy infrastructure construction related to people's livelihood China has entered into the accelerated development phase of urbanization; thus, the large increase in the urban population will pose a more practical demand in terms of energy consumption, especially a demand for clean and efficient energy resources. Therefore, ensuring reliable energy services and mitigating climate change crucially entail a clean energy infrastructure component [77,78]. China has been the world's fastest-growing market for renewable energy in recent years, whose installed renewable energy capacity was 191 GW in 2013. [79]. This capacity accounted for 60% of the world's total. Despite the fact that installed capacities of renewable energy technologies have increased dramatically, due to lack of coordination and consistency in regional clean energy development planning, the government has made only the supply scale of clean energy a high priority and has ignored the local consumption capacity that consequently leads to excess supply. In addition, secondary industry is the biggest energy consumer in China, consuming 70% of total energy, while per household energy consumption is less than that of several OECD and economically developed countries [80]. For instance, the total household energy consumption in China in 2012 was approximately 44% that in the United States in 2009 and 38 percent of that in the EU-27 in 2008. Especially in central and western China, most provinces lag far behind other eastern countries in energy infrastructure construction. Along with urbanization, the energy demand of the residential sector is anticipated to increase continuously with the large growth potential. One way to address the two above issues is to advance clean energy infrastructure construction related to people's livelihood, boost clean energy consumption and expand domestic demand. Therefore, the government should strengthen the clean energy infrastructure in different regions. For instance, the government should accelerate the power grid and natural gas network construction and expand its coverage in the southwestern region with abundant hydropower and natural gas resources; further, it should support the construction of solar water heat collectors and windsolar energy to mutually complement power stations in remote agricultural and pastoral areas. In addition, the country should adjust its current price mechanism to encourage the use of clean energy such as wind, photovoltaic, and solar thermal energy. Meanwhile, experience from several areas of high clean energy penetration suggests that markets with well-functioning dayahead and hour-ahead markets provide an effective means to address clean energy variability [81]. Therefore, the continuing deregulation of the electricity market is needed in the future.

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Acknowledgments We are grateful to the anonymous reviewers for their useful comments and suggestions that have improved the manuscript. We also would like to acknowledge the funding of National Natural Science Foundation of China (Grant nos: 41201171; 41271146; 41171147), Soft Science Research Project of Fujian Province of China (Grant no: 2014R0036).

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