Provincial energy intensity in China: The role of urbanization

Energy Policy 86 (2015) 635–650

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Provincial energy intensity in China: The role of urbanization Huijie Yan The Department of Applied Foreign Languages, Faculty of Arts, Humanities, Languages, and Social Sciences, Aix-Marseille University, 29, Avenue Robert Schuman, 13621 Aix-en-Provence Cedex 1, France

H I G H L I G H T S


This paper investigates the determinants of China's energy intensity.
Urbanization is responsible for the increase in China's energy intensity.
The fluctuation in China's energy intensity is also affected by other key factors.

art ic l e i nf o

a b s t r a c t

Article history: Received 16 March 2015 Received in revised form 6 August 2015 Accepted 7 August 2015

Chinese policymakers have attached great importance to energy intensity reduction. However, the unprecedented urbanization process exercises additional pressure on the realization of energy intensity reduction targets. A better understanding of the impacts of urbanization is necessary for designing effective policies aimed at reaching the next energy intensity reduction targets. This paper empirically investigates the impacts of urbanization on China's aggregate and disaggregated energy intensities using a balanced panel dataset of 30 provinces covering the period from 2000 to 2012 and panel estimation techniques. The results show that urbanization significantly increases aggregate energy intensity, electricity intensity and coal intensity. & 2015 Elsevier Ltd. All rights reserved.

JEL Classification: Q41 Q43 Q48 Keywords: Energy intensity Urbanization China

1. Introduction As one of the largest emitters, China's involvement in global climate change mitigation has been given perennial concern by the international community. Under great pressure from international climate change negotiations, China eventually has pledged to cut its unit GDP carbon emissions by 40–45% by 2020 compared with the 2005 level. To reduce carbon emissions, Chinese policymakers have been attaching great importance to the improvement of energy intensity. The central government has set energy intensity reduction targets by 20% and 16% during the eleventh Five-Year Plan period (2006–2010) and the twelfth Five-Year Plan period (2011–2015), respectively. The fulfillment of both targets has been considered as crucial efforts of meeting the 2020 voluntary emission reduction target. However, it remains a challenge to achieve energy intensity reduction targets (Xia et al., 2014). In 2006 and 2007, energy E-mail address: [email protected] http://dx.doi.org/10.1016/j.enpol.2015.08.010 0301-4215/& 2015 Elsevier Ltd. All rights reserved.

intensity was cut by only 1.2% and 3.27%, respectively, which were below the 4% annual average cut mandated by the eleventh FiveYear Plan (Zhao and Ortolano, 2010). In the first two years (2011– 2012) of the twelfth Five-Year Plan, energy intensity was cut by only 5.54%, which was below the 6.4% reduction mandated by the twelfth Plan (Liu et al., 2014). Lagging behind schedule in reducing energy intensity seems to stem from the unprecedented rate of urbanization. Acceleration of urbanization has been a national policy priority aimed at stimulating domestic consumption-driven economic growth. In 2012, China's urbanization rate reached 52.6%, implying a total of 711.8 million urban residents (NBS, 2013). UN projection shows that China's urban population will account for 76% of the total population by 2050 (UN, 2014), suggesting that fast-paced urbanization development will continue. Therefore, motivated by the context characterized by a trend of rapid increasing urbanization along with a slight decline in energy intensity (except for a few years) in the period 2000–2012 (see Fig. 1), this paper addresses the question: Is urbanization responsible for meeting the energy intensity reduction targets? The existing literature has indicated that urbanization can

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Fig. 1. Trends of energy intensity and urbanization in China over 2000–2012. Note: Energy intensity is expressed in tons coal equivalent per RMB 10,000 GDP in 2000 constant prices. Urbanization is expressed as the percentage of urban population in the total population.

induce complicated changes in energy use through four channels. First, urbanization affects energy use through transforming production. Urbanization spurs the expansion of energy-intensive industries and promotes the realization of agricultural modernization, which further leads to an increasing energy demand (Jones, 1991; Madlener and Sunak, 2011; Sadorsky, 2013). Meanwhile, urbanization concentrates economic activities in city, which brings about economies of scale and opportunity for improving energy efficiency (e.g., preference for centralized modern energy sources) (Madlener and Sunak, 2011; Sadorsky, 2013). Second, urbanization affects energy use through changing consumer behavior. Urban dwellers with increasing income may shift their consumption patterns towards energy-intensive goods and services (Dhakal, 2009; Liu and Xie, 2013; Madlener and Sunak, 2011; Sadorsky, 2013), on the one hand, and urban dwellers with enhancing awareness of energy-saving may shift their consumption patterns towards green products (Zhang and Lin, 2012), on the other hand. Third, urbanization affects energy use through providing public infrastructures. The increased urban population not only calls for constructing and maintaining additional public infrastructures that drive up demand for energy-intensive materials (Jones, 1991; Parikh and Shukla, 1995; Poumanyvong and Kaneko, 2010), but also creates potential for the efficient use of public infrastructures that helps to reduce energy demand (Madlener and Sunak, 2011; Poumanyvong and Kaneko, 2010; Sadorsky, 2013). Finally, urbanization affects energy use through influencing transport services. The concentration of population and economic activity requires public transport services over long distances for transporting food products, raw materials and finished goods into and out of urban areas (Jones, 1991; Madlener and Sunak, 2011; Sadorsky, 2013). The efficient public transport system, in turn, reduces energy demand for individual transport (Madlener and Sunak, 2011; Sadorsky, 2013). The complex urbanization's relationship with energy use has spurred extensive research in the context of developing and developed countries (Jones, 1991; Liddle, 2004; Parikh and Shukla, 1995; Poumanyvong et al., 2012; Poumanyvong and Kaneko, 2010; Sadorsky, 2013, 2014; York, 2007). However, there seems not coming to any consensus about the conflicting results. For example, Parikh and Shukla (1995) provided an early analysis of the effect of urbanization on energy consumption by using a sample of developing countries over the period 1965–1987. Their results indicated that urbanization increases per capita energy consumption. Analyzing data from 14 countries in the European Union over the period 1960–2000, York (2007) noted that urbanization substantially contributes to an increase in energy consumption. These results are challenged by Sadorsky's (2014) and Poumanyvong and Kaneko's (2010) work. Focusing on 18 emerging countries over the years 1971–2008, Sadorsky (2014) proved that urbanization has a negative impact on energy

consumption in the long run. Using a sample of 99 countries covering the period 1975–2005, Poumanyvong and Kaneko (2010) found that the impact of urbanization on energy consumption is sensitive to panel estimation technique and further demonstrated that urbanization increases energy consumption in the middle- and high-income countries, whereas it decreases energy consumption in the low-income countries. Liddle (2004) showed that urbanization contributes a decline in transport energy consumption in OECD countries from 1961 to 2000. This finding is at odds with Poumanyvong et al.'s (2012) result of a positive relationship between urbanization and transport energy consumption in 92 countries during 1975–2005. Currently, the impact of urbanization on energy intensity is still rarely discussed in the existing literature, and therefore very little known about this impact from previous empirical research (Ma, 2015; Sadorsky, 2013). Using a data set of 59 developing countries in 1980, Jones (1991) concluded a positive impact of urbanization on energy intensity. In contrast, analyzing data from 76 developing countries over the years 1980–2010, Sadorsky (2013) argued that it is difficult to observe a strong relationship between urbanization and energy intensity. With accelerated urbanization and the increasing pressure of energy-saving, research on urbanization's relationship with energy use at the national and provincial (region) levels in China has gained wide attention in the recent literature. Table 1 lists previous empirical studies on the impact of urbanization on China's energy use. Although these studies have made significant contributions to our understandings on this impact, three challenges remain. First, the existing studies provide conflicting results, which implies that it is still difficult to pinpoint this impact. For example, applying the cointegration model, Jiang and Lin (2012) and Lin and Ouyang (2014) revealed that urbanization contributes to the increase in energy consumption. In contrary, using panel data technique, Sheng et al. (2014) reported that the coefficient of urbanization is merely significant at 10% level. In the panel data context, Lin and Ouyang (2014) and Lin and Du (2015) further argued that urbanization does not affect energy consumption. Second, considerable studies have investigated the impact of urbanization on energy use from a national perspective without consideration of the regional difference. In fact, China is vast in territory with apparent regional differences in the patterns of urbanization (Zhang and Lin, 2012), implying that the impact of urbanization among provinces is highly variable. The research without accounting for these regional differences may lead to biased estimation. Third, the studies regarding the impact of urbanization on China's energy intensity are rather limited and this impact still poses an academic puzzle. As shown in Table 1, only three studies have investigated the effect of urbanization on China's energy intensity. Applying fixed effect model, Song and Zheng (2012) showed the positive influence of urbanization upon aggregate energy intensity. In contrary to this study, Ma (2015) found that the impact of urbanization on aggregate energy intensity is not statistically significant in the fixed effect model. In addition, Liu and Xie (2013) revealed a non-linear causal relationship between urbanization and energy intensity by estimating two-regime threshold vector error correction model.1 Finally, the differentiated impacts of urbanization on disaggregated energy use remain unclear, although revealing these impacts could provide a further understanding regarding energy transition dynamics in China (Ma, 2015). To my knowledge, only one study by Ma (2015) has provided 1 The non-linear causal relationship between urbanization and energy intensity implies an asymmetric adjustment process occurs between these two variables. The existence of the asymmetric dynamic adjusting process is due to the fact that not all increases in the urbanization processes will have an equally strong impact on decrease in the energy intensity because of gradually improved energysaving technologies, whereas more efficient energy use shall not guarantee a rapid urbanization process (Liu and Xie, 2013).

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Table 1 Empirical studies on the impact of urbanization on China’s energy use. Author

Data

Method

Energy use

Determinants

Main results

Jiang and Lin (2012)

National level, 1978–2008

– Cointegration model

– Energy consumption

Urbanization increases energy consumption.

Lin and Du (2015)

Provincial level, 1997–2009

– Tobit model

– Energy consumption

– Urbanization – Other determinants – Urbanization – Other determinants – Urbanization – Other determinants – Urbanization – Other determinants

– 2SLS Lin and Ouyang National and pro(2014) vincial level, 2001– – Cointegration model 2011 Liu (2009) National level, – Autoregressive distributed 1978–2008 lag – Factor decomposition model – Two-regime threshold vecLiu and Xie National and retor error correction model (2013) gional levels, 1978–2010 Ma (2015) Provincial level, – Pooled OLS 1986–2011 – Fixed effects – Fixed effects – instrumental variable – Mean group – Common correlated effects mean group – Augmented mean group Sheng et al. Provincial level, – GMM (2014) 1978–2008 Song and Zheng (2012)

Provincial level, 1995–2009

–

Wang (2014)

National level, 1980–2011

–

Zhang and Lin (2012)

Provincial level, 1995–2010

– – – – –

– Energy consumption – Energy consumption

– Aggregate intensity

energy – Urbanization

The coefficients of urbanization in most of the models are not significant. The coefficient of urbanization in 2SLS model is not significant, while urbanization increases energy consumption in cointegration model. The existence of a unidirectional Granger causality running from urbanization to energy consumption in both long run and short run. A non-linear causal relationship between urbanization and energy intensity is observed.

– Aggregate energy – Urbanization – Other intensity determinants – Coal intensity – Electricity intensity

Heterogeneous estimation results show that the effects of urbanization on aggregate and electricity intensities are generally statistically positive, but the effect of urbanization on coal intensity remains ambiguous.

– Energy consumption

The coefficient of urbanization is significant at 10% level.

– Urbanization – Other determinants Fixed effect – Aggregate energy – Urbanization intensity – Other determinants Decomposition analysis – Energy – Urbanization consumption – Other determinants – Energy – Urbanization Fixed effects consumption – Other Two-way fixed effects determinants Feasible generalized least squares Panel-corrected standard errors Linear regression with Driscoll–Kraay standard errors

empirical evidence of urbanization's impacts on disaggregated energy intensities in China over the period 1986–2011. Conversely, China's energy issues from national and provincial (region) perspectives have long been of interest to energy researchers. Four research strands have emerged. The first strand of studies has extensively investigated the causal relationship between energy consumption and economic growth in China. 2 However, consensus either on the existence or on the direction of the causal relationship has not been produced. 3 For example, using annual time series during the

2 According to the existing literature, there are four types of hypothesis in the causality test and each of them has important policy implications (see Apergis and Payne, 2011; Ozturk, 2010). (1) “growth hypothesis”: the uni-directional causality running from energy consumption to economic growth. It implies that energy conservation policies may adversely affect economic growth. Under this hypothesis, energy is considered as an essential input in production and as a complement to capital and labor. (2) “conservation hypothesis”: the uni-directional causality running from economic growth to energy consumption. It implies that energy conservation policies have little or no adverse effect on economic growth. (3) “feedback hypothesis”: bi-directional causality between energy consumption and economic growth. It implies that energy consumption and economic growth interact each other. (4) “neutrality hypothesis”: no causality between energy consumption and economic growth. It implies that energy conservation policies have any effect on economic growth. 3 Apergis and Payne (2011), Herrerias et al. (2013b), Payne (2010) and Ozturk (2010) provided a good review of literature.

Urbanization level increases energy intensity.

Urbanization slows growth of per capita residential energy consumption. Urbanization increases energy consumption.

period 1972–2006, Y. Wang et al. (2011) performed a multivariate causality framework by incorporating capital and labor variables into the model between energy consumption and economic growth based on neo-classical aggregate production theory. The empirical result showed a unilateral Granger causality running from energy consumption to economic growth in the long run in China. Applying the error–correction model to the time series for the period 1971–2000, Shiu and Lam (2004) found a unidirectional Granger causality running from electricity consumption to economic growth in the short run in China. In contrast, using panel cointegration techniques, Herrerias et al. (2013b) revealed a long run unidirectional causal relationship running from economic growth to energy consumption for the period 1999–2009. Employing a vector autoregression procedure and a multivariate model including carbon emissions, capital and urban population, Zhang and Cheng (2009) reported a unidirectional causal relationship running from economic growth to energy consumption in China over the period 1960–2007. Furthermore, S.S. Wang et al. (2011) discovered a bidirectional causal relationship between economic growth and energy consumption by adopting panel vector error correction modeling techniques based on the panel data for 28 provinces in China over the period 1995–2007. Soytas and Sari (2006) proved no causal relationship between energy consumption and economic growth over the period

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1971–2002 by applying the Toda–Yamamoto procedure that does not require pre-testing for cointegration. From these studies, we could see that the discrepancy in results in the previous research may come from the differences in sample periods or in econometric methods, or from the omission of important variables affecting the relationship between energy consumption and economic growth (Li and Leung, 2012; Soytas and Sari, 2006). The second strand of studies has devoted to assess the energy issues and/or the energy-related greenhouse gas emissions in China. Cluster analysis and input–output model have been widely used in this strand of studies. The cluster analysis is a statistical technique that classifies observations into the same group in which they are similar to each other, whereas observations in different groups are dissimilar (Kaufman and Rousseeuw, 2005). For example, Xia et al. (2011) classified China's 38 industrial sectors for the period over 2002–2007 into five clusters and identified the main features with regard to energy security, energy efficiency and carbon emission in each cluster. Shao et al. (2014) clustered 35 industries in Tianjin for the period 1999–2010 into four classes based on the combination features of total carbon emissions and carbon emission intensities. The input–output model provides a technique to analyze the interdependence of industries in an economy (Miller and Blair, 2009). The input–output model, which captures the quantitative relationships among industrial sectors, is applied in the previous studies to trace energy use and related environmental pollution associated with interindustry activity in China (Miller and Blair, 2009; Zhang et al., in press).4 For example, numerous studies employed the input–output model to analyze direct and indirect energy consumption (Z. Li et al., 2014; Liu et al., 2012; Yuan et al., 2010), energy efficiency (Jiang et al., 2015), carbon emissions (J.S. Li et al., 2014; Xia et al., 2015) and methane emissions (Zhang et al., 2014). Taking into account the spatial linkages of industries between any two regions, multi-regional input–output model was increasingly applied for analyzing energy consumption and carbon emissions (see Cui et al., 2015; Liang et al., 2007; Zhang et al., 2013). The third strand of studies has paid attention to China's energy performance evaluation in the form of efficiency score (Wang and Wei, 2014).5 In the existing literature, various methods evaluating China's energy performance can be generally classified as parametric and non-parametric approaches (Bian et al., 2013).6 The parametric approach estimates cost or production functions based on the econometric techniques, while the non-parametric approach uses mathematical programming to analyze the deterministic cost or production frontier (Carvalho and Marques, 2014; Fried et al., 2008). Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA) are respectively the major parametric and nonparametric methods, and they are widely applied to evaluate China's energy performance (Sadjadi and Omrani, 2008).7 For instance, 4 More detailed review of the input–output model in energy and environment analysis can be found in Liu et al. (2012) and Zhang et al. (2013). 5 This strand of studies applies a total factor energy efficiency indicator to evaluate China’s energy efficiency performance (Lin and Du, 2013). The total factor energy efficiency indicator, first proposed by Hu and Wang (2006), is defined as a ratio of the optimal-to-actual energy input under a multi-factor framework. 6 Here I just provide a brief review on the methodology used for evaluating China’s energy efficiency performance. There are already several detailed reviews on this issue. See, for example, Lin and Du (2015), Zhang and Choi (2014), Zhao et al. (2014) and Zhou et al. (2008). 7 The methods of SFA and DEA share the same basic idea: an efficiency frontier should be first estimated, and then the efficiency is calculated as the relative distance between the actual output or input and the frontier (Lin and Wang, 2014). The SFA method considering stochastic noises can discern the influence of various factors on inefficiency and therefore this method has higher discriminating power for evaluating the energy efficiency performance (Lin and Du, 2013; Lin and Wang, 2014; Zhou et al., 2012). The DEA method does not need to impose a specific

Lin and Wang (2014) and Lin and Long (2015) applied the SFA method to evaluate the energy efficiencies of China's provincial iron and steel industry for the period 2005–2011 and of China's provincial chemical industry for the same period. Hu and Wang (2006) evaluated energy efficiencies of 29 administrative regions in China for the period 1995–2002 based on the DEA method. Bian et al. (2013) and Wang et al. (2012) applied the DEA method, which took undesirable output into consideration as well, to evaluate China's regional energy efficiency performance. Recently, the hypothesis of homogeneous technology in the case of China has been criticized by some studies. In order to address this shortcoming in the existing studies, Lin and Du (2013), Wang et al. (2013) and Lin and Du (2014b) proposed a parameter metafrontier method, a metafrontier DEA method and a latent class stochastic frontier method to measure China's regional energy efficiency under heterogeneous technologies, respectively. In addition, Xia et al. (2014) argued that the full frontier estimation such as DEA is sensitive to the extremes or outliner of data. For this reason, Xia et al. (2014) performed a partial frontier analysis in order to eliminate the influence of extremes and estimate a reasonable frontier in the case of China.8 The fourth strand of studies has focused on analyzing the driving forces governing energy intensity changes in China (see Table 2). The existing literature mainly falls into two categories from methodological perspectives. The first category is decomposition analysis. Index decomposition analysis and structural decomposition analysis are the two most commonly used decomposition techniques in the previous studies (see Garbaccio et al., 1999; Lin and Du, 2014a; Song and Zheng, 2012; Wang et al., 2014; Wu, 2012; Zeng et al., 2014). For example, Lin and Du (2014a) applied the index decomposition analysis to explore the changes in China's provincial energy intensity over the period 2005–2010. Garbaccio et al. (1999) decomposed the changes in national energy intensity over the period 1987–1992 into technological change and structural change by using structure decomposition analysis. In the previous studies there seems to be general agreement about the key role played by technological progress in explaining China's declining energy intensity, while the contribution of structural change to the overall change in energy intensity remains a controversial issue (Herrerias et al., 2013a). Song and Zheng (2012) and Wu (2012) concluded that structural change plays a minor role in reducing energy intensity. In contrary to these studies, Garbaccio et al. (1999) found that structural changes within the industrial sector actually increased energy intensity over the period 1987–1992. Zeng et al. (2014) even argued that structural changes arising from the shift of Chinese economy to more energy-intensive industries explained the increase in energy intensity during 2002–2007. The decomposition analysis has been increasingly criticized for failing to investigate the extent to which other fundamental forces drive the changes in energy intensity over time (Ma et al., 2010; Metcalf, 2008; Song and Zheng, 2012). To address this criticism, the second category of literature emerges. This category applies econometric analysis to derive more key factors responsible for the changes in energy intensity. The previous studies have included a set of factors, including technological progress, industrial structure, energy price, export, Foreign Direct Investment (FDI), and enterprise ownership, in regression analysis. It has been widely accepted that technological progress is a main contributor of improvement in energy intensity (see Herrerias et al., 2013a; (footnote continued) functional form on the underlying technology and thus this method can avoid model misspecification (Bian et al., 2013; Lin and Du, 2013). 8 The partial frontier method uses part of the sample to estimate the frontier, and therefore they are less sensitive to outliers or extreme values (Carvalho and Marques, 2014).

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Table 2 Major studies of China’s energy intensity. Author

Data

Method

Determinants

Garbaccio et al. (1999)

National level, 1987–1992

– Structure decomposition analysis

Hang and Tu (2007)

National level, 1985–2004

– Seemingly unrelated regression

Herrerias et al. (2013a)

Provincial level, 1985–2008 – Panel-corrected standard errors

Herrerias et al. (2015)

Provincial level, 2006–2010 – Panel corrected standard errors

Jiang et al. (2014)

Provincial level, 2003–2011 – Spatial Durbin error model

Li et al. (2013)

Regional level, 2000–2009

– Random effect

Li and Lin (2014)

National level, 1980–2009

– Threshold cointegration model

Lin and Du (2014a)

Provincial level, 2005–2010 – Index decomposition analysis

Ma (2015)

Provincial level, 1986–2011

– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –

Song and Zheng (2012)

– – – – – – Provincial level, 1995–2009 – –

–

Wang et al. (2014)

National level, 2002–2007

Wei et al. (2009)

Provincial level, 1997–2006 – – –

Wu (2012)

Provincial level, 1997–2007 – – –

Yu (2012)

Provincial level, 1988–2007 –

Yuxiang and Chen (2010) Provincial level, 1996–2006 –

Zeng et al. (2014)

National level, 1997–2007

–

Technological change Structural change Energy price Tertiary share in GDP Investment ownership, Imports FDI Share of industry Crude oil prices Corporate ownership Share of industry Electricity price Oil price Imports Income Share of the secondary sector in GDP Share of investment to GDP Ratio of capital to labor FDI Energy reserves Energy consumption structure Economic structure Technological change Industrial structure Technology progress Energy price Energy consumption structure Economic structure Technological change Urbanization Industrial proportion Income

Pooled OLS Fixed effects Fixed effects instrumental variable Mean group Common correlated effects mean group Augmented mean group Fisher Ideal Index method – Technological change Fixed effect – Structural change – Income – Energy price – Investment – Capital–labor ratio – FDI – Urbanization – Energy resource endowment Divisia method – Structural change – Technological change – Industry share Generalized least – Service share squares – State-owned share Tobit models – Composition of energy – Technology expenditure – Government expenditure Index decomposition analysis – Technological change Fixed effect model – Structural change – Per capita gross regional product Static and dynamic GMM – Energy prices – Capital–labor ratio – Growth rate of capital stock Spatial panel data model – Per capital real GDP – Transportation infrastructure – Ratio of export to GDP – Proportion of tertiary industry in GDP – Ratio of heavy industries to total industries – Ratio of coal consumption to total energy consumption – Proportion of technology expenditures in GDP One step system GMM estimator – Government consumption expenditure for the provision of public services – Purchasing price index for raw material, fuel – Power and tertiary sector Structural decomposition analysis – Sectoral energy efficiency – Final demand composition – Production structure

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Jiang et al., 2014; Li et al., 2013). It has also been well documented that no-state ownership enterprise is a driving force of energy intensity decline (see Herrerias et al., 2013a, in press; Wei et al., 2009). Despite these common findings, the existing studies have yielded conflicting results regarding the underlying effects of industrial structure, energy price and export on the changes in energy intensity (see Hang and Tu, 2007; Herrerias et al., 2013a, in press; Jiang et al., 2014; Li et al., 2013; Song and Zheng, 2012; Wu, 2012; Yu, 2012; Yuxiang and Chen, 2010). For example, Herrerias et al. (in press) and Jiang et al. (2014) reported a positive impact of industrial structure on energy intensity, while the independent effect was observed by Herrerias et al. (2013a), Li et al. (2013) and Yuxiang and Chen (2010). A significant impact of rising energy price on the declining of energy intensity was observed by Hang and Tu (2007) and Wu (2012), but not by Yuxiang and Chen (2010). In addition, the positive impact of export on overall energy intensity suggested by Zheng et al. (2011) is challenged by the evidence provided by Yu (2012) who reported that an independent effect of export on energy intensity. From the previous literature, we see that though the topic on identifying the driving forces of changing China's energy intensity has attracted a large amount of research efforts, there are still two main shortcomings. First, the empirical literature examining the driving forces behind the changes in disaggregate energy intensity is scant (Fisher-Vanden et al., 2004; Hang and Tu, 2007; Herrerias et al., 2013a). Second, some fundamental factors which may greatly affect China's energy intensity have not been well understood, and therefore a further study on the impacts of these factors is still necessary. In this context, this paper aims to highlight the importance of urbanization in determining energy intensity and identify the key factors that drive fluctuations in energy intensity in China. These research efforts are expected to help policy makers to design effective policies directed at reaching the next energy intensity targets. The main contribution of this paper is twofold. First, this paper enriches the scant evidence by providing an empirical research regarding urbanization's impacts on disaggregated form of energy intensity from the provincial perspective. Revealing empirically the differentiated impacts of urbanization on disaggregated energy intensity could provide a further understanding regarding energy transition dynamics in China. These empirical findings are thus expected to deserve greater attention from policy makers. Second, this paper provides additional evidence by investigating how the hotly debated factors (e.g., energy price and export) affect both aggregate and disaggregated energy intensities. The use of disaggregated data could capture the extent to which different economic activities depend on various energy resources. In addition, understanding the underlying drivers of changes in disaggregated energy intensity may help policy makers to adjust energy structure that is crucial for improving the aggregate energy intensity. The paper is organized as follows. Section 2 describes empirical

model, variables and estimation methods. Section 3 discusses the estimated results. Section 4 forecasts provincial energy intensity up to 2020. Section 5 offers conclusions and policy implications.

2. Methods 2.1. Empirical model To identify the fundamental factors that drive the change in provincial energy intensity, the following empirical model is specified:

⎛K⎞ ln (E )it = β0 + β1ln(URB)it + β2 INDUit + β3ln ⎜ ⎟ ⎝ L ⎠it ⎛ PE ⎞ +β4 ln ⎜ T ⎟ + β5EXPit ⎝ P ⎠it + β6 (FDI*HC)it + β7 SEit + εit

(1)

where Eit denotes aggregate energy intensity (EI), electricity intensity (EIE) and coal intensity (CI) of province i in year t, respectively. URBit denotes urbanization level in province i in year t. INDUIit represents industry for province i in year t. K is the ratio of L

capital stock to labor.

PE PT

is the ratio of purchasing price index for

fuels and power to price index for capital investment. EXP and SE represent export and state ownership enterprise, respectively. FDI*HC denotes the interaction between FDI and human capital. εit is the error term. β0, β1, β2, β3, β4, β5, β6, and β7 are coefficients to be estimated. Here, the symbol “ln” denotes the natural logarithm. K

Thus, the variables, including Eit, URBit, ( L )it and (

PE PT

)it are in the

form of natural logarithm in this model. 2.2. Data and variables This study uses a balanced panel dataset of 30 provinces covering the period from 2000 to 2012, yielding 390 observations. A detailed definition and description of all the variables used in this study are provided in Table 3. Aggregate energy intensity is measured as tons coal equivalent per RMB 10,000 GDP in 2000 constant prices. Electricity intensity is expressed in 1000 kWh per RMB 10,000 GDP in 2000 constant prices. Coal intensity is tons per RMB 10,000 GDP in 2000 constant prices. The data on the consumption of aggregate energy, electricity and coal come from China Energy Statistical Yearbook. The data on provincial GDP come from China Statistical Yearbook. Before performing an in-depth empirical analysis, it is necessary to provide an overview of provincial energy intensity trends. The evolution of aggregate, electricity and coal intensities across

Table 3 Definition and description of the variables used in the study over the period 2000–2012. Variable Definition

Mean

ln(EI) ln(EIE) ln(CI) ln(URB) INDU

0.432 0.432 0.338 3.773 39.65 10.98

0.464 0.46 0.703 0.322 8.124 0.742

 0.443  0.315  1.594 2.873 13.37 9.339

1.885 1.968 2.205 4.492 54.83 12.81

0.451

0.345

 0.041

1.414

K L

ln ( ) ln (

PE ) PT

EXP FDI*HC SE

Ln form of aggregate energy consumption divided by GDP Ln form of electricity consumption divided by GDP Ln form of coal consumption divided by GDP Ln form of proportion of urban population in the total population Proportion of industrial output in total GDP Ln form of capital stock to labor ratio Ratio of purchasing price index for fuels and power to price index for capital investment Ratio of total exports to GDP Interaction between ratio of FDI to GDP and percentage of the population aged 6 and above with at least a college degree Percentage of output represented by state-owned enterprise in GDP

Std. dev. Min

Max

16.85 25.24

19.87 33.25

1.484 92.02 0.347 169.6

46.53

17.88

5.177

98.08

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Fig. 2. Provincial aggregate energy intensity over 2000–2012. Note: Energy intensity is expressed in tons coal equivalent per RMB 10,000 GDP in 2000 constant prices.

regions that covers different provinces is plotted in Figs. 2–4. Fig. 2 illustrates that aggregate energy intensity in coastal, central and western regions basically followed a declining trend although this trend was temporarily reversed in some years, such as in 2004 and 2005.9 From this figure, a considerable disparity of aggregate energy intensity across regions over time is observed. The aggregate energy intensity in coastal region is remarkably lower than that in central and western areas. This observation is consistent with the results reported by Li et al. (2013). The regional disparity of aggregate energy intensity between coastal and central regions tends to gradually narrow. For example, the gap of aggregate energy intensity between central and coastal regions was 0.62 t coal equivalent per 10,000 yuan in 2003. The gap between central and coastal dropped to 0.35 t coal equivalent per 10,000 yuan in 2011. Figs. 3 and 4 show that regional disparity in disaggregate energy intensity is significantly different from that in aggregate energy intensity. For example, in western region the evolution of electricity intensity differed significantly from that of aggregate energy intensity. In this region, electricity intensity had an upward trend over the period 2008–2011, while aggregate energy intensity followed a downward trend in the same period. Fig. 4 also allows us to observe that coal intensity in central region was nearly equal to that in western region during 2000–2002. These observations provide a support for necessity of examining determinants of disaggregate energy intensities. Following the conventional approach adopted in previous studies, urbanization level is defined as the proportion of urban population in the total population (see Poumanyvong and Kaneko, 2010; Sadorsky, 2013; Zhang and Lin, 2012).10 The measure of urban population includes not only registered household population but also migrating population (Du et al., 2012). This measure is preferred to the measure of non-agriculture population that includes only the registered household population, since the share of migrant to total population is considerably larger in coastal provinces.11 In this study, I use the proportion of non-agriculture population in total population, which is also widely used in previous research, as an alternative measure of urbanization level to test the robustness of the findings.12 The data on provincial total 9 The 30 provinces are regrouped into three regions. Coastal region includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Fujian, Zhejiang, Shandong, Guangdong and Hainan. Central region includes Shanxi, Henan, Jilin, Heilongjiang, Anhui, Jiangxi, Hubei and Hunan. Western region includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang. 10 The total population is measured as the residential total population. 11 Unfortunately, the data on urban population is not available for several provinces. In this case, we have to use the data on non-agriculture population. 12 The two measures (share of urban population or share of non-agriculture population) of urbanization level used in this study are positively and highly correlated with a correlation coefficient of 0.901. The mean of the share of non-agriculture population for 2000–2012 is 35.4%, which is considerably lower than the

641

Fig. 3. Provincial electricity intensity over 2000–2012. Note: Electricity intensity is expressed in 1000 kWh per RMB 10,000 GDP in 2000 constant prices.

Fig. 4. Provincial coal intensity over 2000–2012. Note: Coal intensity is expressed in tons per RMB 10,000 GDP in 2000 constant prices.

population and urban population are collected from China Statistical Yearbook and provincial statistical yearbooks. The data on provincial non-agriculture population are taken from China Population & Employment Statistics Yearbook. Urbanization level for three regions is illustrated in Fig. 5. As shown, it has varied across regions over time. The coastal region experienced the greatest increase in its urbanization level, rising from 48.6% in 2000 to 66.1% in 2012. The urbanization level in central and western regions gradually increased but at a lower level. In 2012, the degree of urbanization was 49.8% in central region and 46.2% in western region. These observations show that regional urbanization process is at the acceleration stage.13 This implies that the urbanization process will continue until the maturing of the industrialization stage (Jiang and Lin, 2012). A set of controlled variables are also considered as follows. To describe the changing economic structure, I construct a variable to express the proportion of industrial output in total GDP by referring to the studies of Hang and Tu (2007), Poumanyvong and Kaneko (2010), Wei et al. (2009), Yu (2012) and Zhang and Lin (2012). The industrial sector has been the largest energy-consuming sector in China. It accounted for approximately 70% of energy consumption in 2005 (Liao et al., 2007), over 70% of energy consumption in 2009 (Xia et al., 2011) and 69% of energy consumption in 2012 (CESY, 2013). In addition, the industrial structure has rapidly shifted towards more energy-intensive industries especially during the period 2002–2007 (Zeng et al., 2014). For these reasons, this variable is expected to have a positive effect on (footnote continued) mean of the share of urban population of 45.9% for the same period. 13 Northam (1979) described the urbanization process as “S curve”, which includes three stages: initial, acceleration and terminal. The initial stage in which urban proportion is between 10% and 30%, the acceleration stage in which urban proportion is between 30% and 70%, and the terminal stage in which urban proportion is above 70% (Jiang and Lin, 2012).

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H. Yan / Energy Policy 86 (2015) 635–650

Fig. 5. Provincial urbanization level over 2000–2012. Note: Urbanization level is expressed as the proportion of urban population in the total population.

energy intensity. The data on the output value of industry are taken from the provincial statistical yearbooks. In line with the study of Wu (2012), I employ the ratio of capital stock to labor as a proxy of technical progress, which is expected to have a negative effect on energy intensity. Following Song and Zheng (2012), I adopt the capital stock data estimated by Zhang et al. (2004).14 I use Zhang et al.'s (2004) estimated capital stock in 2000 and further extend the data for the period of 2001–2012 by following the same perpetual inventory method.15 The labor here is measured by the annual number of employed population. The data on capital investment in the current year and labor come from provincial statistical yearbooks. To evaluate the impact of energy price on energy intensity, I introduce an energy price index in relative form into the estimated models by referring to the studies of Fisher-Vanden et al. (2004) and Hang and Tu (2007). I use an aggregate energy price index as a proxy of energy price due to the missing data of energy price indices for different sources of energy in some provinces.16 In order to capture the behavior of enterprises in face of the changing energy price, I define a variable (

PE PT

) as the ratio of purchasing price

index for fuels and power to price index for capital investment. An increase in energy price relative to the price of capital may motivate producers to substitute capital for energy. In other words, the producers may adjust their input structure by equipping energy saving technology. Thus the sign of the coefficient for this variable is expected to be negative. Two price indices are expressed in terms of 2000 constant prices. These indices are taken from the provincial statistical yearbook. I also consider the impact of export on regional energy intensity, since the empirical evidence on this impact remains inconsistent. Following Yu (2012), export is measured as the ratio of total exports to GDP. Kahrl and Roland-Holst (2008) argued that export has been an important contributor to the rise in China's energy intensity since 2002. Liu et al. (2010) considered that the energy embodied in exports of China tends to increase over time. The coefficient of this variable is thus expected to be positive. The data on export measured in US$ come from the provincial statistical yearbooks and they are converted into RMB by using the yearly exchange rates taken from the China Statistical Yearbook. The impact of FDI is also taken into account. FDI has been 14

The data of capital stock is not available in statistical yearbook. In the perpetual inventory method, the capital stock is estimated by using the equation Kt ¼ Kt  1(1  δ) þIt, where Kt is the capital stock in current year, Kt  1 is the capital stock in previous year, δ is the capital depreciation rate and is equal to 9.6%, It is the capital investment in the current year. 16 The energy price index cannot measure the difference of energy prices among regions and the energy price index only reflects the energy price change in each region. However, I have to follow the previous studies by using the energy price index as the proxy of energy price, since the data on China’s regional energy price are unavailable and it may be hard to find a better proxy (Lin and Du, 2015). 15

regarded as a key channel through which advanced technology is transferred from developed to developing countries (Elliott et al., 2013; Herrerias et al., 2013a; Jiang et al., 2014). A FDI-induced technique effect should contribute to the advancement in energy efficiency through technological spillovers from foreign to domestic firms (Elliott et al., 2013; Herrerias et al., in press). However, significant technology spillovers coming from FDI are influenced by human capital level, which represents the absorptive capability in the host country (Fu et al., 2011; Fu and Gong, 2011). Therefore, a variable-an interaction term between FDI (share of total GDP represented by FDI) and human capital (percentage of the population aged 6 and above with at least a college degree) is introduced to capture regional disparity in FDI's energy effect which depends on levels of human capital. The coefficient of this interaction is expected to be negative. The data on FDI measured in US$ come from the provincial statistical yearbooks, which are also converted into RMB. The data on human capital are taken from China Population & Employment Statistics Yearbook. Finally, I introduce a variable (SE/GDP) as the percentage of output represented by state-owned enterprise in GDP to illustrate the change in ownership as suggested by Hang and Tu (2007), Herrerias et al. (2013a) and Sinton and Fridley (2000). On the one hand, the energy input in state-owned enterprise may be more poorly managed than that in non-state enterprise (Sinton and Fridley, 2000). The state-owned enterprises are also mainly specialized in energy-intensive industries (Herrerias et al., 2013a). On the other hand, the state-owned enterprises may rapidly integrate new technology into the production process (Fisher-Vanden, 2003). Thus, the net effect of this variable on energy intensity depends on the relative strength of each opposing force. The data come from the provincial statistical yearbooks. To detect the potential collinearity among the independent variables, the Pearson correlation is used in this study. The results (see Table A1 in Appendix A) show that most correlation coefficients in absolute value are less than 0.5. Thus, collinearity should not present a major problem.17 2.3. Estimation methods I estimate Eq. (1) using five different estimation methods: Pooled Ordinary Least Squares (POLS), Fixed Effects (FE), Feasible Generalized Least Squares (FGLS), Panel-Corrected Standard Errors (PCSE) and Driscoll–Kraay (DK). Considering the possibility of heterogeneity bias in the POLS estimates, the FE estimation is applied. However, the presence of heteroskedasticity is detected in all the FE models by using the modified Wald test for groupwise heteroskedasticity developed by Greene (2000).18 Moreover, serial correlation in all the FE models is also found by applying the Wooldridge test for autocorrelation in panel data (Wooldridge, 2002).19 Hereby, the results of the FE estimates could also be biased. To tackle the problems of heteroskedasticity and autocorrelation, the FGLS and PCSE estimators are employed. These two estimators can produce heteroskedasticity- and autocorrelationconsistent standard errors (Hoechle, 2007). Nonetheless, the Parks–Kmenta FGLS method is criticized by Beck and Katz (1995) for two reasons: the FGLS method is infeasible when the time dimension is smaller than the cross-sectional dimension; standard error of this method underestimates the true parameter variability. Moreover, the properties of Beck and Katz’s (1995) PCSE in finite sample are rather poor when the cross-sectional dimension 17 As a further check on collinearity, the Variance Inflation Factor (VIF) tests are carried out. The mean VIF of 2.9 suggests that collinearity is not a concern. 18 This test is implemented via the STATA command xttest3. 19 This test is implemented in STATA using command xtserial.

H. Yan / Energy Policy 86 (2015) 635–650

is larger than the time dimension (Hoechle, 2007). Therefore, referring to Zhang and Lin’s (2012) study, I further apply DK estimator.20 The DK method can produce heteroskedasticity- and autocorrelation-consistent standard errors, which are also well calibrated when cross-sectional dependence is present (Hoechle, 2007). I apply the CD test to check for whether the residuals are cross-sectionally dependent. The output of the test indicates that the residuals of DK regression models exhibit cross-sectional dependence. It further confirms that it is reasonable to choose the DK estimator. Another reason of choosing this method is that it remains appropriate even if the cross-sectional dimension is larger than the time dimension.

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Table 4 Determinants of aggregate and disaggregated energy intensities in Driscoll–Kraay regression models. Independent variables

Aggregate energy intensity (ln(EI))

Electricity intensity (ln(EIE))

Coal intensity (ln(CI))

Ln(URB) INDU

0.111**(0.051) 0.011***(0.003)  0.176***(0.027)

0.287***(0.056) 0.009***(0.002)  0.133***(0.022)

0.194**(0.079) 0.011***(0.003)  0.106***(0.027)

 0.098***(0.02)

 0.067*(0.036)

 0.254***(0.032)

EXP FDI*HC SE Constant

0.002***(0.0004)  0.001***(0.0005)  0.001(0.002) 1.62***(0.258)

0.002***(0.0006)  0.001***(0.0005) 0.0001(0.001) 0.465***(0.156)

0.002***(0.001)  0.003***(0.0005) 0.0001(0.002) 0.494(0.35)

R2 CD test Observations

0.477 66.95*** 390

0.229 32.97*** 390

0.36 58.11*** 390

K L

ln ( ) ln (

PE ) PT

3. Results and discussion 3.1. Empirical results In this section only the estimated results of the DK models are presented (see Table 4). The coefficients of the DK models are preferred, as the DK estimator addresses important econometric concerns including autocorrelation, heteroskedasticity and crosssectional dependence. The estimated results show the positive and highly significant impacts of urbanization on both aggregate and disaggregate energy intensities. These results suggest that the impact of urbanization on energy intensity differs across the composition of energy resources, with the greatest impact on electricity intensity, followed by coal intensity and aggregate energy intensity. These tend to illustrate that urbanization exerts an additional pressure on the realization of energy intensity reduction targets. The observed positive relationship between urbanization and energy intensity is consistent with the results of Song and Zheng (2012) and Ma (2015). The former study found the positive influence of urbanization upon China's aggregate energy intensity by applying the fixed effect model and using a provincial-level panel dataset for the period 1995–2009. The latter study showed the positive impacts of urbanization on coal intensity and electricity intensity in provinces of China by using static models and panel datasets for the period 1986–2011. Regarding the other drivers, INDU also has a positive and highly significant coefficient, indicating that the expansion of industrial sector drives up both aggregate and disaggregate energy intensities, as expected. These findings are consistent with Herrerias et al.'s (in press) and Jiang et al.'s (2014) results derived from provincial panel datasets of China. The estimated coefficients for K ln ( L ) suggest that a rise in the capital–labor ratio significantly decreases aggregate energy intensity, electricity intensity and coal intensity. These results confirm the common consensus in previous studies that technological progress is a key contributor to the decline in energy intensity (see Fisher-Vanden et al., 2004; Jiang et al., 2014; Ma and Stern, 2008). These findings also support the studies of Fan et al. (2007), Ma et al. (2008), Smyth et al. (2011), Su et al. (2012) and Zha et al. (2012) which reported the substitutable relationship between capital and energy. The estimated coefficients for ln (

PE PT

) indicate that a faster rise in

energy price relative to capital price involves the reduction of energy intensity regardless of the source of energy. These results are consistent with the findings of Fisher-Vanden et al. (2004), Hang and Tu (2007) and Wu (2012), showing that the rising energy price is an important driver of China's declining energy intensity. In contrast, my results are inconsistent with the findings of Song and Zheng (2012) and Yuxiang

Note: Estimation is from a balanced panel of 30 provinces covering the period 2000–2012. The CD test checks cross-sectional dependence of residuals. In CD test, the null hypothesis is cross-section independence. Standard errors are shown in parentheses. * **

Table 5 Average annual growth rate of variables for period 2000–2012 in each province (%). Province

URB

INDU

K L

PE PT

EXP

FDI

HC

SE

Coastal region Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan

0.4 1.2 3.2 1.8 0.7 3.3 1.9 2.8 7.1 1.1 1.9

 3.6  1.7  0.2 2  3.5  2.5  1.5 0.2  1.9 1  4.1

7.3 14.3 13.5 15.3 5.5 14.6 14.1 11.8 14.1 12.1 12.3

10.7 8.9 8.2 5.2 9.6 6.5 5.4 6.4 6.4 5.2 4.5

 9.6  14.7  3.8  8.1  4.6  6.9  3.7  5.3  4.3 6  7.7

 7.8  0.4  0.2 4.7 2  6.9  10.1  10.4  13.1  8.4  7.5

4.1 7 6.7 11.6 0.9 10.9 10.1 5 9.3 9.4 11.2

1.1  3.3 1.2  4.9  1.9 2 1  2.4  1.9  0.9 8.1

Central region Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan

3 0.2 1 3.8 3.5 4.6 3.4 3.2

 0.9 3.2  4.2 4.3 1.7 0.7 2.2 2.3

16.5 22.3 18.1 13.8 14.5 19.6 17.2 17.9

5.8 5.6 7.7 6 4.4 6.5 6.1 4.8

 10.3  9.1  7.8 1.7 11.4 6.7  2.9  6.5

8.5  7.8  4.5 9.8  5.9 13.8  7.6  4.5

6.2 4.2 8.8 13.8 9.8 8.3 8 6.4

1.9  1.6  6.1 1.3  4.6  0.6  0.4  1.5

Western region Inner Mongolia Guangxi Chongqing Guizhou Yunnan Sichuan Shaanxi Gansu Qinghai Ningxia Xinjiang

2.9 4.8 3.4 4.8 4.3 4.1 4.2 3.7 3.2 2.8 2.5

2.7 3.7 1.6  2.4 1 3.3 0.3  0.5 2.1 1  1.2

19.5 25.6 14.2 17 15.8 15.9 19.4 14.5 16.6 16.7 9.5

5.5 6.2 6.4 9.9 6.5 5.1 7.1 4.5 2.8 9.8 11.6

 12.5 3.9 20.9 3.8  1.6 9  8.4  4.6  15.5  13.5  2.3

 8.7  11.2  6.9 5.4 14.3  15.3  3.9 7  23.5 6 3.9

10.8 6 14.2 15.8 13.9 14 6.2 18 8.3 3.8 7.5

0.7 3.5  3.5 4  2.2  1.4  0.4 0.05  5.7 1.3  0.8

Note: URB is the urbanization level; INDU is the industry; PE

20

The DK estimation is implemented in STATA using command xtscc with fixed effects option. This option is used to control for unobserved heterogeneity across provinces.

Significance at 10% level. Significance at 5% level. Significance at 1% level.

***

PT

K L

is capital–labor ratio;

is the energy price in relative form; EXP is the export; FDI is the Foreign Direct

Investment; HC is the human capital; SE is the state-owned enterprise.

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H. Yan / Energy Policy 86 (2015) 635–650

capability may be one explanation for the insignificant effect of FDI on energy intensity reported by Song and Zheng (2012) and Jiang et al. (2014). The estimated coefficients for SE are statistically insignificant. The effects of the expansion of state-owned enterprise on energy intensity are thus not obvious in the study period. 3.2. Robustness checks

Fig. 6. Forecast results on aggregate energy intensity. Note: Aggregate energy intensity is expressed in tons coal equivalent per RMB 10,000 GDP in 2000 constant prices.

This section presents some robustness checks. These robustness tests are designed to check if the results discussed before, especially for the variable of primary interest, are altered by using an alternative measure of urbanization level. As noted in Section 2.2, there is an alternative conventional measure of urbanization level (namely, the proportion of non-agriculture population in total population), which I use as robustness checks. The results from the robustness checks provided in Table A2 are similar to those reported in Table 4. As shown in Table A2, the coefficients of the proportion of nonagriculture population in total population (ln(non_agri_pop)) confirm the positive impacts of urbanization level on both aggregate and disaggregate energy intensities. These results suggest that the empirical evidence on these impacts is robust to different measures of urbanization level. Table A2 also illustrates that the estimated coefK ficients for the variables INDU, ln ( L ) and FDI*HC continue to have their expected signs and are highly significant. In addition, most estimated coefficients for ln (

PE PT

) and EXP have identical signs and

similar levels of statistical significance to those reported in Table 4. Thus, the results of the robustness checks provide additional evidence to support the findings discussed in Section 3.1. Fig. 7. Forecast results on electricity intensity. Note: Electricity intensity is expressed in 1000 kWh per RMB 10,000 GDP in 2000 constant prices.

4. Forecasting provincial energy intensity In this section, I forecast aggregate energy intensity, electricity intensity and coal intensity at provincial level based on the estimated DK models. In this study, I limit the forecasting horizon to 2020.21 4.1. Scenario description In this section three scenarios are set up: Business As Usual (BAU), medium and high. These scenarios are governed by the following variables: urbanization level (URB), industry (INDU), K

capital–labor ratio ( L ), energy price in relative form (

Fig. 8. Forecast results on electricity intensity. Note: Electricity intensity is expressed in 1000 kWh per RMB 10,000 GDP in 2000 constant prices.

and Chen (2010), showing that China's energy intensity change is not responsive to the price change. The estimated coefficients for EXP are consistently positive and significant. These results suggest that export expansion could worsen China's energy intensity and the energy embodied in exports should receive special attentions in energy policies design. These results confirm the findings previously obtained by Zheng et al. (2011) for a dataset of China's industrial sub-sectors over 1999–2007. The estimated coefficients for FDI*HC are negative and significant at the 1% level regardless of the source of energy. These results support the argument that significant technology spillovers coming from FDI rely on region's human capital level as a measure of region's absorptive capability. These findings are consistent with Elliott et al.'s (2013) results. Elliott et al. (2013) used a prefecture-level dataset of China for the period 2005–2008 and found that the negative effect of FDI on energy intensity depends on the region's capacity to absorb technology spillovers. Thus, without considering region's absorptive

PE PT

), export

(EXP), FDI, human capital (HC) and state-owned enterprise (SE). Based on the average annual growth rate of these variables over the period 2006–2012 in each province, the three scenarios are constructed.22 Table 5 lists the average annual growth rate of all the independent variables over the period 2006–2012 for each 21

The year of 2020 corresponds to China’s 13th Five-Year Plan. The uniform setting of provincial scenarios (i.e., the independent variables of all the provinces are assumed to vary at the same rate as the national variables) may cause forecasting bias (Du et al., 2012). To solve this problem, it is necessary to set the scenarios for all the independent variables for each province over the period 2013–2020. However, the relevant information on the projection of these variables at province level is still lacking. Therefore, the average annual growth rate of the variables over the period 2006–2012 for each province is used for setting the provincial scenarios. Since 2006, achieving the energy intensity reduction targets has been a high priority in both central and local governments’ policy agenda. In this context, a set of policy measures have been issued for upgrading industry structures, adjusting exports and accelerating the deregulation of energy prices. Under the influence of these policies, the trends of the independent variables for the period 2006–2012 may be very different from those before the year of 2006. The use of the average annual growth rate over the period 2006–2012 is thus considered to be more appropriate for setting the future growth rates of the corresponding variables, given that the improvement in energy intensity remains the high priority in Chinese governments’ policy agenda for the coming years. 22

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Table 6 Forecast results on aggregate energy intensity in each province. Province

2016

2020

BAU

Medium

High

BAU

Medium

TCE

TCE

%

TCE

Coastal region Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Coastal mean

0.742 0.781 1.229 0.989 0.932 1.142 1.282 1.268 1.226 1.41 0.969 1.088

0.743 0.783 1.238 0.992 0.934 1.15 1.287 1.276 1.245 1.414 0.973 1.094

0.14 0.26 0.73 0.3 0.22 0.7 0.39 0.63 1.55 0.28 0.41 0.51

Central region Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Central mean

1.181 1.1608 1.004 1.435 1.452 1.3 1.313 1.3 1.268

1.188 1.1613 1.006 1.447 1.464 1.313 1.323 1.309 1.276

Western region Inner Mongolia Guangxi Chongqing Guizhou Yunnan Sichuan Shaanxi Gansu Qinghai Ningxia Xinjiang Western mean

1.187 1.241 1.438 1.096 1.141 1.486 1.189 1.205 1.334 0.981 1.026 1.211

1.195 1.254 1.449 1.108 1.152 1.499 1.2 1.214 1.343 0.987 1.031 1.221

High

%

TCE

TCE

%

TCE

%

0.744 0.788 1.255 1 0.937 1.166 1.298 1.292 1.281 1.421 0.981 1.106

0.27 0.9 2.12 1.11 0.54 2.1 1.25 1.89 4.49 0.78 1.24 1.52

0.666 0.614 1.084 0.714 0.822 0.954 1.11 1.153 1.073 1.227 0.844 0.933

0.668 0.617 1.099 0.72 0.825 0.968 1.12 1.167 1.105 1.232 0.85 0.943

0.3 0.49 1.38 0.84 0.37 1.47 0.9 1.21 2.98 0.41 0.71 1.01

0.669 0.624 1.13 0.731 0.83 0.995 1.138 1.196 1.171 1.245 0.864 0.963

0.45 1.63 4.24 2.38 0.97 4.3 2.52 3.73 9.13 1.47 2.37 3.02

0.59 0.04 0.2 0.84 0.83 1 0.76 0.69 0.62

1.203 1.163 1.011 1.471 1.486 1.337 1.342 1.327 1.293

1.86 0.19 0.7 2.51 2.34 2.85 2.21 2.08 1.84

0.993 1.07 0.826 1.244 1.389 1.081 1.212 1.198 1.127

1.006 1.071 0.83 1.264 1.411 1.102 1.23 1.215 1.141

1.31 0.09 0.48 1.61 1.58 1.94 1.49 1.42 1.24

1.031 1.074 0.838 1.306 1.453 1.144 1.266 1.248 1.17

3.83 0.37 1.45 4.98 4.61 5.83 4.46 4.17 3.71

0.67 1.05 0.77 1.1 0.96 0.88 0.93 0.75 0.68 0.61 0.49 0.81

1.209 1.279 1.469 1.13 1.172 1.525 1.221 1.234 1.361 0.999 1.042 1.24

1.85 3.06 2.16 3.1 2.72 2.62 2.69 2.41 2.02 1.84 1.56 2.37

1.099 1.125 1.499 0.928 0.943 1.452 1.036 1.075 1.268 0.83 0.905 1.105

1.113 1.148 1.521 0.948 0.961 1.478 1.055 1.092 1.286 0.84 0.915 1.123

1.27 2.04 1.47 2.16 1.91 1.79 1.83 1.58 1.42 1.21 1.11 1.62

1.14 1.194 1.564 0.986 0.996 1.529 1.092 1.128 1.321 0.861 0.935 1.159

3.73 6.13 4.34 6.25 5.62 5.3 5.41 4.93 4.18 3.74 3.32 4.81

Note: TCE denotes tons coal equivalent per RMB 10,000 GDP in 2000 constant prices. % is the change between medium and BAU scenarios or the change between high and BAU scenarios in that year.

province. I assume that the three scenarios share the same assumptions for industry, capital–labor ratio, energy price in relative form, export, FDI, human capital and state-owned enterprise, so that I can focus on analyzing the differences among the three scenarios, which is due to urbanization level. Thus, the three different scenarios demonstrate the sensitivity of the forecasts to changes in the assumptions regarding the average annual growth rate of urbanization level. Concretely, the assumptions of the three scenarios are considered as follows:


Business As Usual scenario: this is a low growth case, which as-




sumes that urbanization level grows at a low speed. In this scenario, urbanization level is assumed to grow at half the average annual rate in past period (2006–2012) for each province for period 2013–2020. Industry, capital–labor ratio, energy price in relative form, export, FDI, human capital and state-owned enterprise are assumed to grow at the average annual rate in past period (2006–2012) for each province for period 2013–2020. Medium scenario: this is a medium growth case, which assumes that urbanization level grows at a medium speed. In this scenario, urbanization level is assumed to grow at the average annual rate in past period (2006–2012) for each province for period 2013–2020. All the other variables considered in the BAU scenario are assumed to grow at the average annual rate in past

period (2006–2012) for each province for period 2013–2020.


High scenario: this is a high growth case, which assumes that urbanization level grows at a high speed. In this scenario, urbanization level is assumed to grow at double the average annual rate in past period (2006–2012) for each province for period 2013–2020.23 All the other variables considered in the BAU scenario are assumed to grow at the average annual rate in past period (2006–2012) for each province for period 2013–2020. 4.2. Forecasting results Based on the hypothesis above, the forecasting results of both aggregate and disaggregate energy intensities are presented in this section. Figs. 6–8 illustrate the changing trends of aggregate energy intensity, electricity intensity and coal intensity under the three different scenarios for the period 2013–2020 respectively.24 From 23 For example, the average annual growth rate of urbanization for period 2006–2012 in Liaoning is 1.8% (see Table 5). Thus, the urbanization level in Liaoning is supposed to grow at the rate of 0.9%, 1.8% and 3.6% for period 2013-2020 under BAU, medium and high scenarios. 24 I also compute 95% confidence intervals by calculating standard error. The forecast results along with the 95% confidence intervals for both aggregate and disaggregate energy intensities under the three different scenarios are plotted in

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Table 7 Forecast results on electricity intensity in each province. Province

2016

2020

BAU

Medium

High

BAU

Medium

KWH

KWH

%

KWH

Coastal region Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Coastal mean

0.983 0.988 1.351 1.11 1.21 1.298 1.436 1.41 1.386 1.583 1.058 1.256

0.986 0.995 1.375 1.122 1.216 1.323 1.452 1.432 1.44 1.592 1.069 1.273

0.31 0.71 1.78 1.08 0.5 1.93 1.11 1.56 3.9 0.57 1.04 1.32

Central region Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Central mean

1.379 1.344 1.164 1.505 1.502 1.366 1.457 1.378 1.387

1.402 1.345 1.17 1.537 1.533 1.401 1.485 1.403 1.41

Western region Inner Mongolia Guangxi Chongqing Guizhou Yunnan Sichuan Shaanxi Gansu Qinghai Ningxia Xinjiang Western mean

1.367 1.356 1.587 1.161 1.207 1.533 1.372 1.334 1.444 1.18 1.192 1.339

1.389 1.393 1.617 1.193 1.235 1.569 1.405 1.361 1.47 1.199 1.208 1.367

High

%

KWH

KWH

%

KWH

%

0.989 1.009 1.424 1.144 1.225 1.371 1.483 1.479 1.552 1.613 1.092 1.307

0.61 2.13 5.4 3.06 1.24 5.62 3.27 4.89 11.98 1.9 3.21 3.94

0.915 0.795 1.248 0.796 1.095 1.135 1.293 1.328 1.284 1.422 0.965 1.116

0.919 0.806 1.293 0.813 1.105 1.179 1.322 1.37 1.386 1.438 0.984 1.147

0.44 1.38 3.61 2.14 0.91 3.88 2.24 3.16 7.94 1.13 1.97 2.62

0.925 0.83 1.387 0.846 1.122 1.267 1.379 1.46 1.609 1.478 1.027 1.212

1.09 4.4 11.14 6.28 2.47 11.63 6.65 9.94 25.31 3.94 6.43 8.12

1.67 0.07 0.52 2.13 2.06 2.56 1.92 1.81 1.59

1.448 1.35 1.185 1.603 1.592 1.47 1.541 1.451 1.455

5 0.45 1.8 6.51 5.99 7.61 5.77 5.3 4.8

1.223 1.272 0.994 1.324 1.476 1.187 1.398 1.317 1.274

1.265 1.275 1.006 1.382 1.537 1.249 1.452 1.365 1.316

3.43 0.24 1.21 4.38 4.13 5.22 3.86 3.65 3.27

1.348 1.284 1.031 1.504 1.658 1.376 1.564 1.461 1.403

10.22 0.94 3.72 13.6 12.33 15.92 11.87 10.93 9.94

1.61 2.73 1.89 2.76 2.32 2.35 2.41 2.02 1.80 1.61 1.34 2.08

1.434 1.465 1.677 1.255 1.294 1.639 1.47 1.418 1.523 1.237 1.242 1.423

4.9 8.04 5.67 8.1 7.21 6.92 7.14 6.3 5.47 4.83 4.2 6.25

1.317 1.307 1.699 1.034 1.037 1.546 1.258 1.243 1.402 1.056 1.092 1.272

1.359 1.378 1.764 1.09 1.087 1.62 1.318 1.294 1.453 1.09 1.122 1.325

3.19 5.43 3.83 5.42 4.82 4.79 4.77 4.1 3.64 3.22 2.75 4.18

1.449 1.524 1.896 1.208 1.192 1.767 1.443 1.405 1.559 1.159 1.186 1.435

10.02 16.6 11.6 16.83 14.95 14.29 14.71 13.03 11.2 9.75 8.61 12.87

Note: KWH denotes 1000 kWh per RMB 10,000 GDP in 2000 constant prices. % is the change between medium and BAU scenarios or the change between high and BAU scenarios in that year.

the figures, I find that both aggregate and disaggregate energy intensities will decrease continuously within the forecasting horizon. However, when I compare amongst the three scenarios, an obvious trend emerges, namely that a scenario with more rapid urbanization process has higher energy intensity regardless of the source of energy. For example, as shown in Fig. 6, the aggregate energy intensity will reach 1.048, 1.062 and 1.09 t coal equivalent per RMB 10,000 in 2020 under the BAU, medium and high scenarios respectively. These results imply that medium scenario realizes an increase of 1.34% compared with the BAU scenario, and the high scenario realizes a greater increase of 4.01%. Fig. 7 illustrates that the electricity intensity will be reduced by 13.6% in 2020 compared with 2013 level under the BAU scenario. However, the electricity intensity will be reduced by 11% and 5.55% compared with 2013 level under the medium and high scenarios respectively. Similarly, as shown in Fig. 8, compared with the BAU scenario, the coal intensity in 2020 will be higher by 2.35% and 7.16% under the medium and high scenarios respectively. The forecasting results of aggregate and disaggregate energy intensities in each province for the years 2016 and 2020 under the three scenarios are illustrated in Tables 6–8. The forecasted results (footnote continued) Figs. A.1–A.9 in Appendix A.

show the changes in aggregate and disaggregate energy intensities under medium and high scenarios relative to the BAU scenario considerably vary by province. As seen in Table 6, compared with the BAU scenario, the largest increase in aggregate energy intensity in 2016 under the high scenario will occur in Shandong (4.49%) located in coastal region, followed by Guangxi (3.06%) in western region and Henan (2.85%) in central region. However, the western region will be generally recorded with the largest average change in energy intensity in 2016 and 2020 under the medium and high scenarios relative to the BAU scenario, followed in turn by the central and coastal regions, as shown in Tables 6–8. These observations may suggest that the provinces in western and central regions tend to become the major contributors to the increase in China's energy intensity in the speeding-up urbanization process.

5. Conclusions and policy implications Facing the challenge of meeting energy intensity reduction targets, this paper aims to highlight the importance of urbanization in determining energy intensity and identify the key factors that drive fluctuations in China's energy intensity. Using panel datasets at the provincial level over 2000–2012 and DK regression models, this paper first investigates the impacts of urbanization on

H. Yan / Energy Policy 86 (2015) 635–650

647

Table 8 Forecast results on coal intensity in each province. Province

2016

2020

BAU

Medium

High

BAU

Medium

TON

TON

%

TON

Coastal region Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Coastal mean

0.592 0.535 1.093 0.684 0.749 0.989 1.159 1.092 1.123 1.309 0.875 0.927

0.593 0.537 1.106 0.688 0.751 1.002 1.168 1.104 1.152 1.314 0.881 0.936

0.17 0.37 1.19 0.59 0.27 1.31 0.78 1.1 2.58 0.38 0.69 0.86

Central region Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Central mean

1.147 1.217 0.91 1.152 1.293 1.105 1.206 1.147 1.147

1.16 1.218 0.914 1.169 1.311 1.124 1.222 1.161 1.16

Western region Inner Mongolia Guangxi Chongqing Guizhou Yunnan Sichuan Shaanxi Gansu Qinghai Ningxia Xinjiang Western mean

1.216 1.21 1.337 0.931 1.013 1.362 1.172 1.204 1.406 0.931 0.883 1.151

1.229 1.232 1.354 0.947 1.029 1.384 1.19 1.22 1.423 0.941 0.891 1.167

High

%

TON

TON

%

TON

%

0.595 0.543 1.132 0.698 0.755 1.027 1.184 1.128 1.212 1.326 0.893 0.954

0.51 1.5 3.57 2.05 0.8 3.84 2.16 3.3 7.93 1.3 2.06 2.64

0.524 0.366 0.953 0.344 0.635 0.815 1.011 0.996 0.993 1.138 0.769 0.777

0.525 0.37 0.977 0.348 0.639 0.836 1.027 1.017 1.045 1.146 0.78 0.792

0.19 1.09 2.52 1.16 0.63 2.58 1.58 2.11 5.24 0.7 1.43 1.75

0.528 0.377 1.024 0.358 0.646 0.878 1.057 1.062 1.156 1.167 0.802 0.823

0.76 3.01 7.45 4.07 1.73 7.73 4.55 6.63 16.41 2.55 4.29 5.38

1.13 0.08 0.44 1.48 1.39 1.72 1.33 1.22 1.1

1.186 1.221 0.921 1.203 1.345 1.162 1.253 1.188 1.185

3.4 0.33 1.21 4.43 4.02 5.16 3.9 3.58 3.25

0.957 1.142 0.738 0.822 1.241 0.852 1.126 1.075 0.994

0.979 1.144 0.744 0.845 1.275 0.882 1.155 1.101 1.016

2.3 0.18 0.81 2.8 2.74 3.52 2.58 2.42 2.17

1.022 1.149 0.757 0.895 1.342 0.942 1.215 1.153 1.059

6.79 0.61 2.58 8.88 8.14 10.56 7.9 7.26 6.59

1.07 1.82 1.27 1.72 1.58 1.62 1.54 1.33 1.21 1.07 0.91 1.38

1.256 1.275 1.387 0.981 1.062 1.425 1.228 1.255 1.457 0.961 0.908 1.2

3.29 5.37 3.74 5.37 4.84 4.63 4.78 4.24 3.63 3.22 2.83 4.18

1.148 1.145 1.394 0.765 0.771 1.351 1.03 1.092 1.362 0.779 0.742 1.053

1.173 1.187 1.43 0.793 0.795 1.394 1.064 1.122 1.396 0.796 0.756 1.082

2.18 3.67 2.58 3.66 3.11 3.18 3.3 2.75 2.5 2.18 1.89 2.82

1.224 1.271 1.501 0.85 0.847 1.478 1.131 1.186 1.463 0.83 0.785 1.142

6.62 11 7.68 11.11 9.86 9.4 9.81 8.61 7.42 6.55 5.8 8.53

Note: TON: tons per RMB 10,000 GDP in 2000 constant prices. % is the change between medium and BAU scenarios or the change between high and BAU scenarios in that year.

aggregate, electricity and coal intensities by adding a set of control variables. Then, the robustness of the empirical results is assessed by using an alternative measure of urbanization level. Finally, the trends of the three energy intensities at provincial level up to 2020 are forecasted based on the estimated DK models. Regression results reveal that the positive impact of urbanization on energy intensity differs across the composition of energy resources, which is robust to the change in urbanization measurement. The empirical results show that a faster rise in energy price relative to capital price, the technological progress and technology spillovers from FDI serve as important contributors to the decline in China's energy intensities in the study period. The results also confirm that industry and export are the drivers behind China's provincial energy intensity increase during 2000–2012. The forecast results further demonstrate an obvious trend that a scenario with more rapid urbanization process has higher energy intensity regardless of the source of energy, when the three scenarios are compared. Some policy suggestions for meeting the next energy intensity targets could be drawn from the above-mentioned results. First, local governments should formulate an integrated development framework in line with local economic development level for addressing urban development and energy savings simultaneously. Concretely, the local governments in less-developed areas should attach much importance to make the public infrastructures at each

phase of design, construction and operation more energy efficient.25 Moreover, these local governments should pay more attention to improve the public awareness of energy savings in order to change the residents' lifestyle and consumption mode.26 In contrast, the local governments in well-developed areas should involve themselves in promoting the deployment of renewable energy sources and the adoption of energy-saving products, such as new energy vehicle.27 Second, Chinese government should further advance the market-based energy pricing mechanism, so that the energy price could serve as a significant signal for motivating the enterprises to adopt the energy-saving equipment. Third, Chinese government should adjust the industrial structure and export structure in order to promote the entire industry to shift towards green production 25 Because the rapid urbanization process in the less-developed areas may call for additional large-scale public infrastructures. This may further drive up the demand of energy-intensive materials. 26 Because in these areas the dominance of more energy-consumption industries together with the relative lack of energy-saving awareness may further drive the urban dwellers’ consumption patterns to shift towards more energy-intensive goods in these regions. 27 Because the acceleration of urbanization in well-developed areas may create more favorable conditions for accumulating human capital, enhancing the awareness of energy-saving and spreading the energy-saving technologies. This may result in a shift of household behavior and consumption patterns towards green products.

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activities and reduce exports of energy intensive products. Finally, the local governments should create favorable conditions for enhancing the absorptive capacity of local host enterprises so as to benefit from the FDI-induced technique effect. However, the present paper provides only preliminary empirical evidence. Future research should consider the multi-dimensional characteristics of urban (e.g., urban form) and the threshold effects of urbanization, and apply sophisticated econometric models (e.g., spatial panel model) to advance the knowledge relating to impacts of urbanization on energy intensity in the case of China. Fig. A4. Forecast and the 95% confidence interval for electricity intensity under BAU scenario.

Acknowledgments The author would like to thank Professor Jean-Pascal BASSINO, Professor Pascale COMBES MOTEL and Professor Jie He for their comments and suggestions. An earlier version of this study was presented in 9th International Conference on the Chinese Economy organized by CERDI-IDREC, University of Auvergne, France. Comments from participants are appreciated. Useful comments on econometric methods from Charles Lai Tong are much appreciated. My special thanks to two anonymous reviewers for their insightful comments in guiding the substantial improvements on the previous version of the paper. The handling work of the editors is highly appreciated.

Fig. A5. Forecast and the 95% confidence interval for electricity intensity under medium scenario.

Appendix A See Figs. A1–A9 and Tables A1 and A2.

Fig. A6. Forecast and the 95% confidence interval for electricity intensity under high scenario.

Fig. A1. Forecast and the 95% confidence interval for aggregate energy intensity under BAU scenario.

Fig. A2. Forecast and the 95% confidence interval for aggregate energy intensity under medium scenario.

Fig. A3. Forecast and the 95% confidence interval for aggregate energy intensity under high scenario.

Fig. A7. Forecast and the 95% confidence interval for coal intensity under BAU scenario.

Fig. A8. Forecast and the 95% confidence interval for coal intensity under medium scenario.

H. Yan / Energy Policy 86 (2015) 635–650

Fig. A9. Forecast and the 95% confidence interval for coal intensity under high scenario.

Table A1 Pearson correlation coefficients.

ln(URB) INDU K L

ln ( ) ln (

PE PT

)

EXP FDI*HC SE a

ln(URB)

INDU

ln ( )

1 0.273a 0.828a

1 0.326a

1

0.494a

0.3a

0.721a

1

0.601a 0.74a 0.142a

0.248a 0.049 0.193a

0.453a 0.629a 0.186a

0.202a 0.286a 0.122b

K L

ln (

PE ) PT

EXP

FDI*HC

SE

1 0.613a  0.146a

1 0.09c

1

Significance at the 1% level. Significance at the 5% level. Significance at the 10% level.

b c

Table A2 Determinants of aggregate and disaggregated energy intensities in Driscoll–Kraay regression models—robustness checks. Independent variables

Aggregate energy intensity (ln(EI))

Electricity intensity (ln(EI))

Coal intensity (ln(CI))

ln(non_agri_pop)

0.326*** (0.056) 0.011*** (0.003)  0.189*** (0.022)  0.121*** (0.019) 0.001** (0.001)  0.001*** (0.0004)  0.001 (0.002) 1.046*** (0.307)

0.269*** (0.08) 0.011*** (0.002)  0.115*** (0.013)  0.062 (0.046) 0.001*** (0.0004)  0.001** (0.001) 2.17e  06 (0.001) 0.371 (0.317)

0.35*** (0.085) 0.011*** (0.003)  0.108*** (0.025)  0.27*** (0.022) 0.001 (0.001)  0.003*** (0.0005) 0.0002 (0.002) 0.023 (0.317)

0.5 63.29*** 390

0.214 34.75*** 390

0.37 58.31*** 390

INDU K L

ln ( ) ln (

PE ) PT

EXP FDI*HC SE Constant

R2 CD test Observations

Note: Estimation is from a balanced panel of 30 provinces covering the period 2000–2012. The CD test checks cross-sectional dependence of residuals. In CD test, the null hypothesis is cross-section independence. Standard errors are shown in parentheses. ** ***

Significance at 5% level. Significance at 1% level.

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