Transportation infrastructure development and China’s energy intensive industries - A road development perspective

Accepted Manuscript Transportation infrastructure development and China’s energy intensive industries - A road development perspective

Ruipeng Tan, Kui Liu, Boqiang Lin PII:

S0360-5442(18)30269-X

DOI:

10.1016/j.energy.2018.02.041

Reference:

EGY 12341

To appear in:

Energy

Received Date:

03 August 2017

Revised Date:

08 December 2017

Accepted Date:

09 February 2018

Please cite this article as: Ruipeng Tan, Kui Liu, Boqiang Lin, Transportation infrastructure development and China’s energy intensive industries - A road development perspective, Energy (2018), doi: 10.1016/j.energy.2018.02.041

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ACCEPTED MANUSCRIPT 1

Transportation infrastructure development and China’s energy intensive

2

industries - A road development perspective

3

Ruipeng Tan a Kui Liu a Boqiang Lin b *

4 5

a School

of Economics, China Center for Energy Economics Research,

Xiamen

University, Xiamen, Fujian, 361005, PR China.

6

b School

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Innovation Center for Energy Economics and Energy Policy,

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Fujian, 361005, PR China.

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*corresponding author at : School of Management, China Institute for Studies in

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Energy Policy, Collaborative Innovation Center for Energy Economics and Energy

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Policy,

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[email protected], [email protected] (B.Lin)

of Management, China Institute for Studies in Energy Policy, Collaborative Xiamen University,

Xiamen University, Fujian, 361005, PR China. E-mail address:

13 14 15 16 17 18 19 20 21 22 1

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Abstract

2

In this paper, we investigate how road infrastructure affects the energy consumption

3

and development of energy intensive industries in China. From the perspective of

4

profit function, the endogeneity problem which may be caused by reverse causality

5

can be avoided. China’s provincial data for the period 2000-2013 and seemingly

6

uncorrelated regression method are used to estimate the parameters. The results show

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that increase in road density will increase energy consumption and promote the

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development of energy intensive industries. The short term elasticity of output with

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respect to road infrastructure is smaller than the long run elasticity. However, this is

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not the case for energy consumption. Additionally, increase in road density can reduce

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energy intensity only in the long run and the decrease is largest in western China.

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Therefore, more road construction in the western regions will be more helpful for

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energy intensive industries in their bid to reduce energy intensity. Furthermore, we

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find evidence of energy price distortion in China’s energy intensive industries. Lastly,

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we calculate the changes in output and energy consumption of China’s energy

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intensive industries both in the short and long run caused by the increase in road

17

density from 2000 to 2013.

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Keywords: road infrastructure; China’s energy intensive industries; short term

19

elasticity; long term elasticity

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I. Introduction

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Over the past thirty years, China has witnessed dramatic infrastructural and economic

3

development. With the GDP surpassing that of Japan, the year 2010 marked the

4

historic milestone of China’s economic development. Since then, China has ranked

5

second globally in terms of total GDP. In this process, the role of infrastructure cannot

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be ignored since the research of Demurger [1] has proven that infrastructure can

7

stimulate the economy. Infrastructure refers to the material and engineering facilities

8

that can serve social production and residential life. Its function is to guarantee the

9

normal social and economic activities of a region or a country. Generally speaking,

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infrastructure includes transportation, power grid, post and telecommunications, water

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and electricity supply, commercial and scientific research service, landscaping,

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cultural education, public health system and some other municipal buildings. Without

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any doubt, infrastructure can impact the development of energy intensive industries,

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and the channels and extents vary across different types of infrastructure [2]. In this

15

paper we focus on transportation infrastructure.

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Also, we cannot overlook the fact that electricity, crude oil, oil products, steel and

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iron, cement, non-ferrous metals products and so on have played an indispensable role

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in this development process. These are the products of the six energy intensive

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industries listed in China Statistical Report for National Economic and Social

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Development 2010. They include (1) processing of petroleum, coking and nuclear fuel

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industry, (2) raw chemical materials and chemical products manufacturing industry,

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(3) non-metallic mineral products manufacturing industry (building materials 3

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industry), (4) smelting and pressing of ferrous metals industry (iron and steel

2

industry), (5) smelting and pressing of non-ferrous metals industry, (6) production and

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supply of electric and heat power industry. These six energy intensive industries need

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special attention since they are not only the fundamental and pillar industries for

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China’s development, but also the main energy consumers and CO2 emitters.

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Figure 1 here

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Figure 1 depicts the energy consumption and gross industrial output value of China’s

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energy intensive industries from 2000 to 2014. Both maintain an increasing trend

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during the period. However, the growth rate decreases significantly from 2013 to

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2014. The average growth rates of energy consumption and gross industrial output

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during the period 2000-2013 are 9.8% and 19.8% respectively; but decrease sharply to

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2.5% and 8.3% respectively in 2013-2014.

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Figure 2 here

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Figure 2 shows the development of transportation infrastructure in China in terms of

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the mileages of rail, waterway and road transport in the period 2000-2014. The data

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on mileages of transportation infrastructure are obtained from China Statistical

17

Yearbook. Some distinct characteristics emerge in the figure. The average mileages of

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road, waterway and rail are 3085.4, 123.49, and 82.72 thousand kilometers

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respectively. The mileages of waterway are almost constant from 2000 to 2014, since

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they are determined by resource endowment. Railways are more difficult to construct

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than road, with the mileages of the former increasing from 58.66 thousand kilometers

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in 2000 to 111.82 thousand kilometers at the end of 2014, and the mileages of the 4

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latter increasing from 1402.70 kilometers to 4463.91 kilometers at the end of 2014,

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extending many areas of China. So road transport can work in places without railway

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and waterway transport infrastructure, which is the major reason for choosing road

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transport as the research object. Year 2005 to 2006 marks the period of intensive road

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construction in China. The mileages of road at the end of 2006 are more than twice

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that of 2005. In 2006, many key road construction projects, such as Tianjin section of

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Beijing-Shanghai Highway, Jiayuguan section of Lianyungang-Huoerguosi Highway,

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the highway from Huangling to Yanan in Shaanxi province and so on were

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completed. In addition, the newly constructed roads in rural China amounted to 260

10

thousand kilometers. Road infrastructure is characterized by its flexibility. The

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density of road network is more than ten times of railway and waterway, and unlike

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the latter two, the degree of privatization of road transport is higher, making it more

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convenient, which is the second reason for choosing road transport as the research

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object.

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Two types of variable can be used as the proxy variables for transportation

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infrastructure: one is in the form of currency such as fixed assets investments in

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transportation infrastructure construction; and the other is in physical form, such as

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the mileages of road, railway or waterway. We deem that the former is not suitable for

19

our study because of two reasons. The first is that we cannot obtain the price index of

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transportation infrastructure construction to adjust the transportation infrastructure

21

construction investment to some benchmark price. The second lies in the fact that

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higher investment in transportation infrastructure construction do not necessarily 5

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mean more advanced transportation infrastructure in a region, and the service it

2

supplies such as the effect of breaking market segmentation is not better either. For

3

example, the Qinghai-Tibet Railway requires large amounts of investment, but the

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transportation infrastructure of the provinces along the line is not developed

5

proportionally [2]. Hence, we use the physical form of road infrastructure in each

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province to measure it.

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Theoretically speaking, road infrastructure has reciprocal effects on the development

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and energy consumption of energy intensive industries. In other words, road

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infrastructure and energy intensive industries are dependent on each other, which is a

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source of endogeneity problem. Specifically, on the one hand, the construction of road

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infrastructure requires the products of energy intensive industries, such as cements

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and steels. Increased demands for these products can stimulate the developments of

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energy intensive industries and increase energy demand. On the other hand, the

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development of energy intensive industries can promote the economic development of

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a region, and consequently stimulate the development of road infrastructure.

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Considering the important roles of energy intensive industries and road infrastructure

17

in the economic development and energy consumption of a country, this paper will

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focus on how road infrastructure affects the development and energy consumption of

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China’s energy intensive industries. We calculate the elasticities of output and energy

20

consumption in China’s energy intensive industries with respect to road infrastructure.

21

Our attention is concentrated to the period of 2000-2013 in which energy intensive

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industries developed intensively. We contribute to the existing literature in the 6

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following aspects: (1) we incorporate road infrastructure in the input-output system of

2

energy intensive industries from the aspect of profit function. This method can avoid

3

the endogeneity problem which may be caused by reverse causality between energy

4

intensive industries and road infrastructure. (2) We test how transportation

5

infrastructure influences the development and energy consumption of energy

6

intensive industries in China. (3) We find another evidence of energy price distortion

7

in China. (4) We prove that road infrastructure can reduce energy intensity only in the

8

long run.

9

The rest of this paper is organized as follows: The second section is the literature

10

review. We provided a brief introduction of the methodology and data in the third

11

section. Section four is the results and discussion of the results. Lastly, we conclude

12

the paper and propose some policy suggestions.

13

14

II. Literature review

15

Paul Rosenstein-Rodan did a pioneering research on the Big Push Theory, in which he

16

proposed that infrastructure was the most important and indispensable part of a

17

country’s comprehensive development [3]. Infrastructure construction should be done

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prior to other constructions. The differences in natural conditions can be removed for

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the formation of uniform market and economic development when transportation

20

infrastructure is constructed [3]. Aschauer [4] contributed considerably to

21

infrastructure research. He is the first to include public capital as a kind of 7

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independent factor into the production function. But his work was also criticized

2

because they think he only identifies the correlation relationship but not the casual

3

relationship between infrastructure investment and output. After his research, the

4

literature on infrastructure and output from both macro and micro levels can be

5

roughly divided into four categories according to the methods or models they adopted.

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The first is applying econometric methods to time series data or panel data. For

7

example, Wang [5] adopt VAR method to investigate the cointegration and Granger

8

causal relationship between the main infrastructure index and output in China and

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concludes that there is a long term equilibrium relationship among infrastructure,

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output and economic structural changes. Liu et al. [2] use spatial panel data model

11

(SPDM) to study the relationship between China’s transportation infrastructure and

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the growth of total factor productivity (TFP). Liu and Liu [6] use the panel data of

13

medium and large manufacturing enterprises in each province of China in the period

14

2004-2008 to determine the micro mechanism of transportation infrastructure

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promoting economic growth. The second is using production function. This method

16

incorporates infrastructure into the production function as a kind of input and obtains

17

the impact of infrastructure on economic development by estimating its output

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elasticity. Bronzini and Piselli [7] use Cobb-Douglas production function to study the

19

long-run relationship between productivity and three types of capital, including

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human capital, R&D and infrastructure in different regions of Italy. Morrison and

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Schwartz [8] concluded that infrastructure investment could increase the output and

22

productivity growth of U.S. manufacturing industries. However, endogeneity problem 8

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becomes a common issue in production function approach and as a result cost

2

function and profit function methods are employed to address this problem [9]. The

3

third is using cost function. Moreno et al. [10] estimated the influences of public and

4

private capital investments on Spain manufacturing industry. Satya et al. [11] found

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evidence that public infrastructure could significantly improve the productivity of

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Canadian manufacturing industries. The profit function is the fourth method used in

7

the subject. He [12] adopted it to calculate the influence of power grid on energy

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consumption and regional output in China. She concluded that the influences of

9

power grid on energy consumption and economic development vary in different

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regions of China.

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The specific researches addressing transportation infrastructure, output, energy

12

consumption and emissions include: Farhadi [13] shows evidence that transportation

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infrastructure improve labor productivity and total factor productivity in the long run

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in 18 OECD countries. Pradhan and Bagchi [14] use India as a case study to

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investigate the linkage between transportation infrastructure and economic

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development. Agénor and Aizenman [15] conclude that the adjustment costs of

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enterprises decrease since more advanced road infrastructure can lower the costs of

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new factories construction and large machinery transportation. Achtymichuk [16]

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develops a model to assess the impacts of different transportation infrastructure

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investments on energy consumption and emissions in Canada. Burgess [17] tests

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whether developing the high-speed rail between cities in the USA can be seen as a

22

wise strategy to mitigate greenhouse gas emissions in the long term. In addition, more 9

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advanced transportation infrastructure can stimulate innovation and create a network

2

effect, bring in the benefits of scale economy and agglomeration effects, and improve

3

inventory management, which can reduce production costs and increase industrial

4

output ([18], [9], [19]).

5

In view of the large energy consumption and high output of China’s energy intensive

6

industries, it is necessary to investigate how they are influenced by road

7

infrastructure. However, this issue is not investigated in the existing literature. We use

8

the profit function model to achieve the objective of this study. There are two

9

advantages of this model. Firstly, the endogenous problem caused by reverse causality

10

between infrastructure and output can be avoided because we estimate the equation

11

set developed by the profit function. Secondly, we do not have to assume that the

12

prices of production factors are determined by marginal output in the profit function

13

model. This satisfies the fact that China’s infrastructure is invested by the government

14

so it is difficult to price the infrastructure by its marginal output [12].

15

16

III. Methodology and data

17

3.1 Methodology

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Following the research of Demetriades and Mamuneas [20], Kratena [21] and He

19

[12], the method used in this paper is described below:

20

We assume that the firm use variable input and invariable inputs simultaneously in the

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production process. Since transportation infrastructure in China is mainly supplied by 10

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the government, the firms have no power to decide how much infrastructure is used,

2

so transportation infrastructure is regarded as an invariable input in the short run.

3

Since we assume the invariable output will lag one period, the profit function can be

4

written as:

5

 t  F ( Pt ,Wt , Z t 1 )

6

where,  t refers to the profit in period t; Pt refers to the price of output in period t; Wt

7

refers to the price of variable output in period t; Z t 1 refers to the price of invariable

8

output in period t  1 .

9

For simplicity, we use y to represent output and x to represent variable inputs. In the

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short run, the firms try to maximize profit by choosing how much of y to produce and

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how much of input x to include in the production process [22]. The maximization

12

problem becomes:

13 14

Max{ ( Pt , Wt , Z t 1 )}

(1)

(2)

 ( Pt ,Wt , Z t 1 )  Pt y ( Pt )  Wt x(Wt , Z t 1 )

(3)

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According to Hotelling’s Lemma, the partial derivative of profit over output price

16

equals to the demand for the output. The negative partial derivative of profit over

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input price equals to the demand for the input.

18 19

y ( Pt )   ( Pt , Wt , Z t 1 ) / Pt  x(Wt , Z t 1 )   ( Pt , Wt , Z t 1 ) / Wt

(4) (5)

20

To obtain the specific expression of output and input, we assume specific expression

21

for the profit function, including trans-log function ([23], [24]).

11

ACCEPTED MANUSCRIPT I

J

i 1

j 1

 t   0   i qi    j Z j ,t 1  1

1 I H  ih qi qh 2 i 1 h 1



I J 1 M J  jm Z i ,t 1Z m,t 1 +  ij qi Z j ,t 1    j Z j ,t  2 m 1 j 1 i 1 j 1 j

+

1  jj Z 2j ,t    ij Z i ,t 1Z j ,t    ij qi Z j ,t  2 j i j i j

(6)

2

where q  ( P, W ) is composed of the output price vector and variable inputs prices

3

vector; Z refers to the inputs vector.

4

Several assumptions needed to be made about equation (6), including symmetryand

5

homogeneity: ih   hi ,  mj   jm ,  i  1,  ih  0,   ij  0 .

I i

I

H

i 1 h 1

I

The

items

i 1

6

containing Z j ,t are the adjustment costs. In the long run, when the invariable inputs

7

reach

8

 t / Z j ,t |Z j ,t 0, Z j ,t 0  0 . Therefore,  j   ij   ij  0,  jj  0. In this paper, energy

9

(E) and labor (L) are variable inputs; capital (K) and road infrastructure (Ti) are

10

invariable inputs. In addition, we use the price of labor (Pl) to normalize the price of

11

output and other inputs, and then, py=Py/ Pl, we = We/ Pl, where Py and py refer to the

12

prices of output before and after normalization; We and we refer to the price of energy

13

input before and after normalization. Then equation (6) in this paper can be specified

14

as:

equilibrium

level,

the

adjustment

costs

become

zero,

i.e.

 t / wl   0   y p y   e we   k K t 1  T Tit 1 15

1  (  ee we2   yy p y2   kk K t21  TT Tit2   kk K t2 ) 2

16

 kT K t 1Tit 1   ye p y we   ek we K t 1   eT weTit 1   yk p y K t 1   yT p yTit 1

(7)

17

According to equation (4), (5) and (7), the supply function of total output and demand

18

functions of the variable inputs are: 12

ACCEPTED MANUSCRIPT 1

Yt   y   yy p y   ye we   yk K t 1   yT Tit 1

(8)

2

 Et   e   ee we   ye p y   ek K t 1   eT Tit 1

(9)

3

 Lt    p yY  we E   0   k K t 1  T Tit 1   ye p y we   kT K t 1Tit 1

4

1 1   kk K t2  (  ee we2   yy p y2   kk K t21  TT Tit21 ) 2 2

(10)

5

In equations (8)-(10), the coefficients  eT and  yT reflect the influences of road

6

infrastructure on energy consumption and output of China’s energy intensive

7

industries in the short run when we assume the capital input is fixed. So the

8

elasticities of energy consumption and output of China’s energy intensive industries

9

with respect to road infrastructure are:

 ET   eT

10

Ti Ti ,  YT   yT E Y

(11)

11

However, in the long run, the firms can change the capital input, so the output and

12

energy consumption in China’s energy intensive industries will be affected by this

13

change. When the long term equilibrium is achieved, the optimal capital stock K* can

14

be obtained by maximizing profit: .

.

.

max{ * ( p, w, K , K , Ti )  qk K }  py* ( p, w, K * , K , Ti )  wx* ( p, w, K * , K , Ti )  qk K *

15 16

(12)

17

Taking the first order derivative with respect to K on both sides of equation (12), we

18

get E(

19 20 21

 * ) | *  qK K K  K

(13)

By plugging equation (7) into equation (13), we get

K*  

 k   kT Ti   ek we   yk p y  qk  kk 13

(14)

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Therefore, the long run elasticities of energy consumption and output of China’s

2

energy intensive industries with respect to road infrastructure are:

 ET

3

YT  (

4

  Ti E E k * Ti (  * )  ( eT  ek kT ) Ti k Ti E  kk E

(15)

  Ti Y Y k * Ti  * )  ( yT  yk kT ) Ti k Ti Y  kk Y

(16)

5

We use the ratio of energy consumption to output of China’s energy intensive

6

industries to represent energy intensity, i.e. e  E / Y .Then, the elasticity of energy

7

intensity with respect to road infrastructure in the short run is:

 E T   eT '

8 9

Ti Ti   yT E Y

(17)

The long run elasticity is

 E T  ( eT 

10

'

  Ti  ek kT Ti )  ( yT  yk kT )  kk E  kk Y

(18)

11

3.2 Data

12

The data used in this paper include energy consumption and its price, capital stock,

13

labor and its price, output and its price in China’s energy intensive industries in 29

14

provinces (Tibet, Hainan, Taiwan, Hong Kong and Macao are not included) from

15

2000 to 2013. To measure road infrastructure, we use road density (mileages of road

16

per ten thousand square kilometers) in each province, and is called density of

17

transportation infrastructure1. We obtained the data on road mileages from CEIC

18

database. Taking year 2013 as an example, Shanghai, Shandong and Henan provinces

1

The unit of density of transportation infrastructure is kilometers/ten thousand square kilometers. 14

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ranked first, second and third in terms of density of transportation infrastructure at

2

19925.87, 15999.11 and 14959.94 kilometers/ten thousand square kilometers

3

respectively. Qinghai, Xinjiang and Inner Mongolia provinces have the lowest density

4

of transportation infrastructure at 972.50, 1025.03 and 1416.02 kilometers/ten

5

thousand square kilometers respectively. We classify China into eastern China, central

6

China and western China according to the research of [25], [26], [27] and [28], which

7

is basically consistent with the division based on economic performance2. Overall, the

8

density of road infrastructure in eastern China is larger than that of central and

9

western China.

10

Processing of petroleum, coking and nuclear fuel industry and production and supply

11

of electric and heat power industry are excluded in the study because they use energy

12

as kinds of raw material to produce another kind of energy. For example, the former

13

uses crude oil to produce oil products, such as gasoline, diesel and so on, while the

14

latter uses coal to produce electricity and heat. That is, energy is a kind of input as

15

well as a kind of output for the two industries. This process makes the data of final

16

energy consumption at the provincial level difficult to come by (although the data of

17

final energy consumption of national level can be obtained) [29].

18

For energy consumption, following Lin and Tan [29], we obtain the data of each kind

19

of energy, convert them into standard coal equivalent and take the summation as the

20

energy input. We use the weighted average of purchase price index of raw materials,

2

.

Eastern China includes Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong. The central regions include Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei and Hunan. Western China includes Sichuan, Chongqing, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Xinjiang, Guangxi, Ningxia and Inner Mongolia. 15

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fuel and power of energy intensive subsectors as the proxy variable of energy price,

2

with the weights being the level of consumption of each sub-industry. Since data on

3

energy price of each industry for each province is unavailable, purchase price index of

4

raw materials, fuels and power is taken as the proxy [30]. The purchase price index of

5

each energy intensive sub-industry of each province is obtained from the Statistical

6

Yearbook of each province and China Energy Statistical Yearbook.

7

As for capital stock, there is no direct available data, so we estimate it by using

8

perpetual inventory method. The basic formula of the method is

9

K t  K t 1 (1   t )  I t

(19)

10

Where, K t and K t 1 represent capital stock in the energy intensive industries in year t

11

and year t-1, respectively;  t represents the depreciation rate in year t; and I t

12

represents the investment in year t. Following Goldsmith [31], we calculate the capital

13

stock of energy intensive industries in each year with Formula (19) and depreciate it

14

to the base price level of year 2000. The specific steps are as follows: The first step is

15

to estimate the depreciation rate. The depreciation rate of year t equals to depreciation

16

of year t divided by original value of fixed assets of year t-1. The second step is to

17

estimate the new investment of each year. New investment of year t equals to original

18

value of fixed assets of year t minus that of year t-1. The third step is to determine the

19

capital stock of the base period. The start year of this research is 2000, so we take the

20

net value of fixed assets of 2000 as the base year capital stock following Chen [32].

21

The fourth step is to estimate capital stock of each year following Formula (19). All

22

variables with time value (original value and net value of fixed asset) are converted to 16

ACCEPTED MANUSCRIPT 1

base price level of year 2000 using price index of fixed assets investment. The data on

2

the estimation of capital stock is obtained from the Statistical Yearbook of each

3

province in China. With regard to the measurement of output, we use the gross

4

industrial output value of energy intensive industries. The producer price index (PPI)

5

of the energy intensive subsectors is used as the price of output. Since we regard the

6

summation of four energy intensive subsectors as the research object, we use the

7

weighted average of four kinds of PPI as its price. The weight is the ratio of output of

8

each subsector to the total output. The data are obtained from China Statistical

9

Yearbook and statistical yearbook of each province.

10

Finally, we use the number of employees to measure labor input, and weighted

11

average of real wages index of staff in each subsector to measure the price of labor.

12

The weights are the labor inputs. The data source is China Labor Statistical Yearbook.

13

The descriptive statistics are shown in Table 1.

14

Table 1 here

15

16

IV. Results and discussion

17

Adding the error terms to equations (8)-(10), an internal connection is deemed to exist

18

among the three equations since they are the output and inputs of the same firm, so

19

the disturbance terms are correlated with each other. Seemingly uncorrelated

20

regression (SUR) method is suitable for estimating the parameters of such a

21

multifunction system [33]. Since regional discrepancy exists, we also add dummy 17

ACCEPTED MANUSCRIPT 1

variables for regions in the regression. In order to check whether our results are

2

robust, we substitute the mileages of grade road per ten thousand square kilometers in

3

each province for that of road, and then re-estimate the equations (8)-(10). All the

4

results are shown in Table 2. Columns (1)-(3) are the estimation results of using road

5

density and columns(4)-(6) are the re-estimation results of using grade road density.

6

Comparing the two results, the coefficients and their significance levels are almost the

7

same, proving that our estimation results are robust. The following discussion is based

8

on the results in columns (1) to (3).

9

Table 2 here

10

4.1 The analysis of the elasticities of road infrastructure to output and energy

11

consumption

12

Table 3 here

13

From Table 3, the short term average elasticities of energy consumption and output

14

with respect to road infrastructure in China are positive at 0.669 and 0.303

15

respectively. This means that when the density of road infrastructure increases by one

16

percent, the output and energy consumption of China’s energy intensive industries

17

will increase by 0.669% and 0.303% respectively. The reasons are obvious. First,

18

increased road construction increases the needs for cement, steel and so on, and these

19

materials are the products of energy intensive industries. To meet the increased

20

demand for the products of energy intensive industries, they will expand their

21

production and consume more energy. Second, higher road density is beneficial to 18

ACCEPTED MANUSCRIPT 1

reduce transportation costs. Easier access to sales and raw materials markets will

2

induce more production of energy intensive products. Third, improved road

3

infrastructure may enhance faster transmission of technology breakthroughs, which is

4

also conductive for the development of energy intensive industries.

5

There is a regional difference in the short term elasticity of output and energy

6

consumption of China’s energy intensive industries with respect to road

7

infrastructure. No region ranks first in terms of the elasticities of both output and

8

energy consumption. Specifically, the short term elasticities of output are 0.267, 0.293

9

and 0.343 for eastern, central and western China respectively. In the long term, when

10

the density of road infrastructure increase by one percent, the output of energy

11

intensive industries will increase by1.530%, 1.680% and 1.966% in East, Central and

12

West China respectively. Energy consumption will increase 1.091%, 0.472% and

13

0.428% in eastern, central and western regions of China respectively when the density

14

of road infrastructure increases by one percent in the short term. In the long term, the

15

elasticities are 0.399, 0.173 and 0.157 respectively in the three regions. Eastern China

16

is the most developed region and the road density is also the highest. So when road

17

density increases in the same proportion, the benefits such as breaking market

18

segmentation and reducing transportation fees are quite limited. Thus, if gross

19

industrial output value is used to measure the output of energy intensive industries, its

20

increase will be lower in eastern region than the other two regions, but this is contrary

21

to the case of energy consumption.

19

ACCEPTED MANUSCRIPT 1

For whole China, the short-term elasticities of energy and output with respect to road

2

density are 0.669 and 0.303, while those of long-term are 0.245 and 1.737. Output

3

increase effect is lower than energy increase effect in the short run, but the case is on

4

the contrary in the long run, which means that the energy intensity will decrease in the

5

long run for energy intensive industries if holding other conditions remain unchanged.

6

In the long term, enterprises can adjust the input of capital in production. For

7

example, more energy intensive industries may be established when the traffic

8

conditions are improved. In addition, when the construction of roads is completed in

9

the long run, it is more convenient for the transportation of the products of energy

10

intensive industries. These two changes make the products of energy intensive

11

industries further increase in the long run. But this is different for energy

12

consumption. The construction of roads will increase the demand for energy in the

13

short run. In the long run, more convenient transportation conditions make the

14

transmission of knowledge, technology and technical staffs among the different

15

regions to be enhanced. As a result, some backward production technologies will be

16

eliminated, which is beneficial for the reduction in energy intensity and realization of

17

energy conservation. Furthermore, the newly-built factories must be equipped with

18

the most advanced machines under the management of the governments. Thus, the

19

lower energy intensity enables the long-term elasticity of energy consumption in

20

energy intensive industries lower.

21

20

ACCEPTED MANUSCRIPT 1

4.2 The analysis of the elasticity of energy intensity and price elasticity of energy

2

with respect to road infrastructure

3

Table 4 here

4

Table 4 presents the elasticities of energy intensity in China’s energy intensive

5

industries with respect to road infrastructure. It reveals that in the short run, the

6

influences on energy intensity are almost positive in all the provinces. But in the long

7

run, the increase in the density of road infrastructure can make energy intensity of

8

energy intensive industries in all the provinces to decrease. In the short term,

9

increased road mileages will provide more convenient condition for the transportation

10

of products. So in order to earn more profits, more energy intensive enterprises will

11

expand their productions, ignoring the improvement of energy efficiency. However,

12

the benefit of denser road infrastructure for energy intensity decrease can only be

13

reflected in the long term, since the diffusion of knowledge and technology takes

14

time. If the density of road infrastructure increases by 1% in the long run, the energy

15

intensity of energy intensive industries will decrease by 1.49% on average. The

16

degree of decrease is the biggest in the western region, which is followed by the

17

central and eastern regions, so more roads construction in western China is helpful for

18

the decrease of energy intensity in whole energy intensive industries.

19

From column (2) of the energy equation in Table 2, the impact of energy price on

20

energy consumption in China’s energy intensive industries is not significant even at

21

10% confidence level, meaning that we fail to reject the null hypothesis that the

22

influence of energy price on energy consumption in China’s energy intensive 21

ACCEPTED MANUSCRIPT 1

industries can be ignored. Theoretically, the demand for a good is impacted by its own

2

price. However, this is not the case for energy in this research. This phenomenon is an

3

indication of “price distortion”. Tao et al. [34] conclude that the own price elasticity

4

of energy is positive, indicating that when energy price increases, the demand for

5

energy increase as well, in contrast to the law of demand. Energy price in China is

6

controlled by the government to maintain at a relatively low level, making it hard to

7

reflect energy scarcity and the environmental externalities. In addition, the demands

8

for the products of China’s energy intensive industries are rigid and huge in the

9

process of urbanization and industrialization, so the increase in energy price cannot

10

reduce the demand.

11

4.3 The analysis of the changes in energy consumption and output of China’s

12

energy intensive industries

13

Table 5 here

14

In this part, we analyze the short and long run change in energy consumption and

15

output of China’s energy intensive industries, as a result of change in road density.

16

The figures in Table 5 are average for the period 2000-2013.

17

From 2000 to 2013, road density in eastern region increases by nearly 9%, making the

18

energy consumption and output of the region to increase by about 6.8% and 1.8%

19

respectively in the short run. The increases for road density and energy consumption

20

in central and western China are 7.1% and 5.1%, 6.9% and 5.4%. In the long term, the

21

increases in energy consumption in the three regions are 2.5%, 2.6% and 2.5% while

22

the increases in output are 10.6%, 29.1% and 31.1%. The increase in road density 22

ACCEPTED MANUSCRIPT 1

leads to about 4.9%, 2.0% and 1.5% increase in energy intensity in the eastern, central

2

and western regions respectively. However, in the long run, the increase in road

3

density results in a decrease of 8.1%, 26.5% and 21.0% in the energy intensity of

4

energy intensive industries in the eastern, western and central regions respectively.

5

This means that promoting the construction of road infrastructure in central and

6

western regions may be beneficial for both the development of energy intensive

7

industries and decrease in the energy intensity of the industries.

8

V. Robustness check

9

In order to check whether our result is robust, apart from using grade road density to

10

substitute road density in the regression, we conduct another two kinds of robustness

11

checks in this section. The results are shown in Table 6.

12

Table 6 here

13

The first to third columns are the results when we change the depreciation rate in the

14

estimation of capital stock. As we have stated in Section 3.2, the depreciation rate of

15

year t equals to the depreciation of year t divided by the original value of fixed assets

16

of year t-1, so the depreciation rate of each year is different. In the robustness check,

17

we use the average value of the original depreciation rate to re-calculate the capital

18

stock and repeat the regression again. The coefficient and significance levels of the

19

variables in the first three columns are similar to those in Table 2.The fourth to sixth

20

columns of Table 6 are the results when we use the sub-period 2005 to 2013. No

21

substantial change can be detected, proving the robustness of our results in the 23

ACCEPTED MANUSCRIPT 1

preceding part of the text.

2

VI. Conclusions and policy recommendations

3

In this paper, we use the profit function and seemingly unrelated regression (SUR)

4

method and Chinese provincial data to investigate how road infrastructure influences

5

the output and energy consumption of China’s energy intensive industries. The main

6

findings are as follows:

7

First, increase in road density can increase the energy consumption and output of

8

China’s energy intensive industries. Second, the long term elasticities of output in

9

energy intensive industries with respect to road infrastructure are greater than in the

10

short run, but the opposite is the case for energy consumption. Third, there is a

11

regional difference in the elasticity of output and energy consumption in China’s

12

energy intensive industries with respect to road infrastructure. The elasticities of road

13

intensity with respect to energy in both short and long run are the largest in eastern

14

region, which is followed by central and western regions. But the ranking of output

15

elasticities is the opposite, with western China having the largest elasticity followed

16

by central and eastern China. Fourth, the effect of road density on the decrease in the

17

energy intensity of energy intensive industries maintain over a long period of time.

18

The elasticity is negative in three regions, and the absolute value is biggest in the

19

western region. Therefore, more roads should be constructed especially in western

20

China. This will be helpful for decreasing the energy intensity of whole energy

24

ACCEPTED MANUSCRIPT 1

intensive industries. Fifth, energy price distortion still exists in China, and as a result

2

the scarcity and externality of energy consumption is not reflected in its price.

3

In the future, policy makers should give priority to the construction of road in Central

4

and Western China, because

5

greater in central and Western China than in Eastern China. In addition, the effect of

6

road infrastructure on the decrease in energy intensity of energy intensive industries is

7

largest in the western region in the long run. This phenomenon of energy price control

8

by the government is reflected in our findings. In the 13th Five-Year Plan period,

9

more emphasis is given to balanced development between the economy and the

10

environment. Marketization of energy prices should be regarded as a priority for the

11

governments. As such, control on energy price should be relaxed to make it reflect the

12

scarcity and externality of energy use.

13

Acknowledgement

14

The paper is supported by the Grant for Collaborative Innovation Center for Energy

15

Economics and Energy Policy (No: 1260-Z0210011), Xiamen University Flourish

16

Plan Special Funding (No:1260-Y07200), and Newcastle University Joint Strategic

17

Partnership Fund.

18

Reference

19

[1] Demurger, S. Infrastructure development and economic growth: an explanation for

20

regional disparities in China?. Journal of Comparative Economics 2001; 29(1):

the elasticities of road infrastructure to output are

25

ACCEPTED MANUSCRIPT 1

95-117.

2

[2] Liu, B.L., Wu, P., Liu, Y. H. Transportation infrastructure and the increase in TFP

3

in China—Spatial econometric analysis on provincial panel data. China Industrial

4

Economics 2010; (3): 54-64. [In Chinese]

5 6 7 8 9 10

[3] Rosenstein-Rodan, P. N. Problems of industrialization of eastern and south-eastern Europe. The Economic Journal 1943; 53(210/211): 202-211. [4] Aschauer, D. A. Is public expenditure productive?. Journal of Monetary Economics 1989; 23(2): 177-200. [5] Wang, R.F., Wang, J.J. Infrastructure and economic growth in China: based on VAR method. World Economy 2007;(3):13-21. [In Chinese]

11

[6] Liu, B.L., Liu, Y.H. Transport infrastructure and inventory costs reduction of

12

Chinese manufacturing enterprises. China Industrial Economics 2011; (5): 69-79.

13

[In Chinese]

14

[7] Bronzini R, Piselli P. Determinants of long-run regional productivity with

15

geographical spillovers: the role of R&D, human capital and public

16

infrastructure. Regional Science and Urban Economics 2009; 39(2): 187-199.

17

[8] Morrison, C. J., Schwartz, A. E. State Infrastructure and Productive Performance.

18 19 20 21 22

American Economic Review 1992; 86(5):1095-1111. [9] Straub S. Infrastructure and development: A critical appraisal of the macro-level literature. The Journal of Development Studies 2011; 47(5): 683-708. [10] Moreno, R., Lopez-Bazo, E., & ArtÍs, M. On the effectiveness of private and public capital. Applied Economics 2003; 35(6): 727-740. 26

ACCEPTED MANUSCRIPT 1

[11] Satya, P., Balbir, S., Bagala, P. B. Public Infrastructure and the Productive

2

Performance of Canadian Manufacturing Industries. Southern Economic Journal

3

2004; 70(4):998-1011.

4

[12] He, X.P. Impact of infrastructure on regional economic growth and the energy

5

consumption: an empirical analysis regarding power grid. China Economic

6

Quarterly 2014; (13): 1513-1532. [In Chinese]

7

[13] Farhadi, Minoo. Transport infrastructure and long-run economic growth in

8

OECD countries. Transportation Research Part A: Policy and Practice 2015; 74:

9

73-90.

10

[14] Pradhan, Rudra P., and Tapan P. Bagchi. Effect of transportation infrastructure

11

on economic growth in India: the VECM approach. Research in Transportation

12

Economics 2013; 38.1: 139-148.

13 14

[15] Agénor, P. R, Aizenman, J. Public Capital and the Big Push. Work in progress, University of Manchester (October 2006), 2005.

15

[16] Achtymichuk, Darren. Investigating the effects of transportation infrastructure

16

development on energy consumption and emissions. 51st Annual Transportation

17

Research Forum, Arlington, Virginia, March 11-13, 2010 Transportation

18

Research Forum 2010:173-179.

19

[17] Burgess E. Sustainability of Intercity Transportation Infrastructure: Assessing the

20

Energy Consumption and Greenhouse Gas Emissions of High-Speed Rail in the

21

US. Arizona State University, 2011.

22

[18] Baldwin, R., Forslid, R., Martin, P. Economic geography and public policy. 27

ACCEPTED MANUSCRIPT 1

Princeton University Press, U.S., 2005.

2

[19] Hulten, C. R., Bennathan, E., Srinivasan, S. Infrastructure, externalities, and

3

economic development: a study of the Indian manufacturing industry. The World

4

Bank Economic Review 2006; 20(2): 291-308.

5

[20] Demetriades, P. O., Mamuneas, T. P. Intertemporal output and employment

6

effects of public infrastructure capital: evidence from 12 OECD economies. The

7

Economic Journal 2000; 110(465): 687-712.

8 9

[21] Kratena K. Technical change, investment and energy intensity. Economic Systems Research 2007; 19(3): 295-314.

10

[22] Berndt E R, Fuss M A. Economic capacity utilization and productivity

11

measurement for multi-product firms with multiple quasi-fixed inputs. NBER

12

working paper 1989.

13

[23] Bergman, M. A. The restricted profit function and the application of the

14

generalized Leontief and the translog functional forms. International Journal of

15

Production Economics1997; 49(3), 249-254.

16

[24] Nadiri, M. I., Mamuneas, T. P. The effects of public infrastructure and R&D

17

capital on the cost structure and performance of US manufacturing industries.

18

National Bureau of Economic Research,1991.[25] Yao X, Zhou H, Zhang A, et

19

al. Regional energy efficiency, carbon emission performance and technology

20

gaps in China: a meta-frontier non-radial directional distance function analysis.

21

Energy Policy 2015; 84: 142-154.

22

[26] Li K, Lin B. The improvement gap in energy intensity: Analysis of China's thirty 28

ACCEPTED MANUSCRIPT 1

provincial regions using the improved DEA (data envelopment analysis) model.

2

Energy, 2015, 84: 589-599.

3

[27] Ding Y, Li F. Examining the effects of urbanization and industrialization on

4

carbon dioxide emission: Evidence from China's provincial regions. Energy,

5

2017, 125: 533-542.

6 7 8 9 10 11 12 13 14 15

[28] Lin B, Tan R. China's CO2 emissions of a critical sector: Evidence from energy intensive industries. Journal of Cleaner Production 2017; 142:4270-4281. [29] Lin B, Tan R. Ecological total-factor energy efficiency of China’s energy intensive industries. Ecological Indicators 2016; 70: 480-497. [30] Wu, Y. Energy intensity and its determinants in China's regional economies. Energy Policy 2012; 41: 703–711. [31] Goldsmith, R. W. A perpetual inventory of national wealth. Studies in Income and Wealth, Volume 14. NBER, 1951: 5-73. [32] Chen, S.Y. Reconstruction of sub-industrial statistical data in China (1980-2008). China Economic Quarterly 2011; (4): 735-776. [In Chinese]

16

[33] Zellner A. An efficient method of estimating seemingly unrelated regressions and

17

tests for aggregation bias. Journal of the American statistical Association 1962;

18

57(298): 348-368.

19

[34] Tao, X.M., Xin, J.W., Huang, X., Zhou, W. The measurement of energy price

20

distortion and factor substitution in Chinese industry.

21

Quantitative & Technical Economics 2009; (11): 3-16. [In Chinese]

22

29

The Journal of

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Figures

3 4

Figure 1 The energy consumption and gross industrial output value of China's energy intensive industries

The energy consumption and gross industrial output value of China's energy intensive industries 300000 250000

200000

200000

150000

150000

100000

100000 50000

0

5

Gross industrial outpput value (100 million yuan)

Energy consumption (10 thousand SCE)

250000

50000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Gross industrial output value

6

30

Energy consumption

0

ACCEPTED MANUSCRIPT 1 2

Figure 2

3

The mileages of rail, waterway and road in China from 2000 to 2014

The mileages

The mileages of rail, waterway and road in China (ten thousand kilometers) 500 450 400

rail waterway road

350 300 250 200 150 100 50 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

year

4 5

31

ACCEPTED MANUSCRIPT 1 2 3

Tables

4

Descriptive statistics of the variables

Table 1

variable Y K L

mean 2443 1040 45.07

sd 3251 1077 35.21

min 83.19 65.99 4.8

mean 2443 1040 45.07

max 22000 6762 174.8

N 406 406 406

E

4166

3962

370.5

4166

28000

406

py

0.67

0.183

0.282

0.67

1

406

pl

2.166

0.929

1

2.166

4.993

406

we

0.692

0.174

0.29

0.692

1.024

406

road density

6298

4388

208.3

6298

20000

406

5381

4163

194.5

5381

20000

406

grade road density 5 6

32

Units 100 million yuan 100 million yuan 10 thousand persons 10 thousand tons of standard coal kilometer/10 thousand square kilometers kilometer/10 thousand square kilometers

ACCEPTED MANUSCRIPT 1 2

Table 2

3

The estimation results of equations (8)-(10) using SUR (1)

　

(2)

(3)

using mileages of road per square

(4)

(6)

using mileages of grade

kilometers

road per

square kilometers

output

energy

labor

output

energy

labor

variables

equation

equation

equation

equation

equation

equation

Kt-1

3.244***

-0.194***

-0.254***

3.261***

-0.219***

-0.249***

(0.0000)

(0.0000)

(0.0000)

(0.0000)

(0.0000)

(0.0000)

0.00601**

0.00268***

-0.000809*

0.00456

0.00410***

-0.00102**

(-0.0135

(-0.0011)

(-0.0921)

(-0.106)

(-1.28E-05)

(-0.034)

Tit-1 P yP e

Kt-1Tit-1

△Kt2

PePe P yP y

Kt-1 Kt-1

Tit-1Tit-1

Py Pe Constant Time fixed effect Province fixed effect Observations R-squared

4 5

(5)

101.1

　

110.5

(-0.479)

　

(-0.783)

-2.25E-06

　

-3.39e-06**

(-0.117)

　

(-0.0488)

-0.000114**

　

-0.000111**

(-0.0119)

　

(-0.0138)

-65.21

　

-96.96

(-0.35)

　

(-0.626)

-50.93

　

-22.5

(-0.485)

　

(-0.912)

0.000256***

　

0.000263***

(0.000)

　

(0.0000)

3.42E-08

　

7.07e-08***

(-0.153)

　

(-0.00354)

-22.98

-46.61

-274.6*

-97.43*

(-0.805)

(-0.153)

(-0.0686)

(-0.0734)

143.1

10.3

281.4*

102.5*

(-0.1)

(-0.736)

(-0.0609)

(-0.0584)

-204.4***

-46.07***

-5.281

-101.9*

-26.63

-10.37**

(-0.000638)

(-0.000617)

(-0.256)

(-0.0812)

(-0.201)

(-0.0293)

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

377

377

377

377

377

377

0.969

0.759

0.972

0.969

0.759

0.972

Notes: (a) p-values in parentheses (b)*** p<0.01, ** p<0.05, * p<0.1 33

ACCEPTED MANUSCRIPT 1

Table 3

2

The elasticity of output and energy consumption in China’s energy intensive industries with

3

respect to road infrastructure

Province Region Beijing Fujian Guangdong Hebei Jiangsu Liaoning Shandong Shanghai Tianjin Zhejiang Anhui Henan Heilongjiang Hubei Hunan Jilin Jiangxi Shanxi Gansu Guangxi Guizhou Inner Mongolia Ningxia Qinghai Shaanxi Sichuan Xinjiang Yunnan Chongqing East China Central China West China Whole China

E E E E E E E E E E C C C C C C C C W W W W W W W W W W W

Short-term elasticity road to road to energy output

Long-term elasticity road to road to energy output

3.713 0.837 0.523 0.144 0.528 0.182 0.297 2.124 1.942 0.619 0.610 0.461 0.641 0.657 0.514 0.325 0.314 0.256 0.182 0.424 0.504

0.936 0.300 0.122 0.094 0.077 0.105 0.096 0.360 0.429 0.150 0.384 0.175 0.395 0.258 0.268 0.225 0.344 0.297 0.141 0.283 0.582

1.357 0.306 0.191 0.053 0.193 0.067 0.109 0.776 0.710 0.226 0.223 0.169 0.234 0.240 0.188 0.119 0.115 0.094 0.067 0.155 0.184

5.363 1.722 0.701 0.540 0.442 0.601 0.551 2.062 2.462 0.860 2.200 1.002 2.262 1.479 1.536 1.292 1.972 1.701 0.810 1.620 3.334

0.062

0.062

0.023

0.355

0.427 0.165 0.648 0.229 0.151 0.451 1.469

0.632 0.142 0.541 0.116 0.130 0.366 0.779

0.156 0.060 0.237 0.084 0.055 0.165 0.537

3.620 0.812 3.103 0.666 0.745 2.098 4.466

1.091 0.472 0.428 0.669

0.267 0.293 0.343 0.303

0.399 0.173 0.157 0.245

1.530 1.680 1.966 1.737

4 5 34

ACCEPTED MANUSCRIPT 1

Table 4

2

The elasticity of energy intensity with respect to road infrastructure

Province

Region

Beijing Fujian Guangdong Hebei Jiangsu Liaoning Shandong Shanghai Tianjin Zhejiang Anhui Henan Heilongjiang Hubei Hunan Jilin Jiangxi Shanxi Gansu Guangxi Guizhou Inner Mongolia Ningxia Qinghai Shaanxi Sichuan Xinjiang Yunnan Chongqing

E E E E E E E E E E C C C C C C C C W W W W W W W W W W W

road to energy intensity short term long term

East China Central China West China Whole China 3 4

35

2.778 0.536 0.400 0.050 0.451 0.077 0.201 1.764 1.513 0.469 0.226 0.287 0.246 0.399 0.246 0.100 -0.030 -0.041 0.041 0.141 -0.077 0.000 -0.204 0.024 0.106 0.113 0.021 0.085 0.690

-4.006 -1.417 -0.510 -0.487 -0.249 -0.535 -0.442 -1.286 -1.752 -0.634 -1.977 -0.833 -2.028 -1.239 -1.348 -1.173 -1.857 -1.607 -0.744 -1.465 -3.149 -0.332 -3.464 -0.751 -2.866 -0.583 -0.690 -1.933 -3.929

0.824 0.179 0.085 0.366

-1.132 -1.508 -1.810 -1.493

ACCEPTED MANUSCRIPT 1 2

Table 5

3

The changes in energy consumption, output and energy intensity of China’s energy intensive

4

industries caused by the increase in road intensity

Province Beijing Fujian Guangdong Hebei Jiangsu Liaoning Shandong Shanghai Tianjin Zhejiang Anhui Henan Heilongjiang Hubei Hunan Jilin Jiangxi Shanxi Gansu Guangxi Guizhou Inner Mongolia Ningxia Qinghai Shaanxi Sichuan Xinjiang Yunnan Chongqing East China Central China West China Whole China

change in energy consumption

change in output

change in energy intensity

short term

long term

short term

long term

short term

long term

E E E E E E E E E E C C C C C C C C W W W

0.117 0.051 0.035 0.016 0.100 0.019 0.061 0.124 0.082 0.071 0.103 0.100 0.094 0.102 0.076 0.032 0.039 0.022 0.028 0.027 0.091

0.043 0.019 0.013 0.006 0.037 0.007 0.022 0.045 0.030 0.026 0.037 0.036 0.034 0.037 0.028 0.012 0.014 0.008 0.010 0.010 0.033

0.036 0.017 0.008 0.010 0.018 0.011 0.016 0.035 0.017 0.017 0.074 0.046 0.066 0.051 0.044 0.026 0.074 0.025 0.023 0.019 0.121

0.205 0.097 0.043 0.060 0.106 0.064 0.089 0.199 0.099 0.096 0.426 0.266 0.380 0.291 0.252 0.148 0.426 0.143 0.134 0.107 0.695

0.081 0.034 0.028 0.006 0.082 0.008 0.045 0.089 0.065 0.054 0.028 0.054 0.028 0.051 0.032 0.006 -0.035 -0.003 0.004 0.008 -0.031

-0.162 -0.079 -0.030 -0.054 -0.069 -0.057 -0.067 -0.153 -0.069 -0.070 -0.389 -0.229 -0.346 -0.254 -0.224 -0.136 -0.412 -0.135 -0.124 -0.097 -0.662

W W W W W W W W

0.005 0.039 0.021 0.097 0.024 0.041 0.050 0.336

0.002 0.014 0.008 0.036 0.009 0.015 0.018 0.123

0.005 0.056 0.019 0.081 0.012 0.036 0.038 0.187

0.027 0.323 0.109 0.467 0.069 0.207 0.219 1.070

0.000 -0.018 0.002 0.016 0.012 0.005 0.012 0.149

-0.025 -0.309 -0.102 -0.431 -0.060 -0.192 -0.200 -0.947

0.068 0.071 0.069 0.069

0.025 0.026 0.025 0.025

0.018 0.051 0.054 0.041

0.106 0.291 0.311 0.235

0.049 0.020 0.015 0.028

-0.081 -0.265 -0.286 -0.210

Region

5 36

ACCEPTED MANUSCRIPT 1 2

Table 6 Robustness check

change depreciation rate

　

subperiod (2005-2013)

variables

output equation

energy equation

labor equation

output equation

energy equation

labor equation

Kt-1

2.957***

-0.173***

-0.240***

2.982***

-0.148***

-0.241***

(0.0000)

(0.0000)

(0.0000)

(0.0000)

(-3.64E-05

(0.0000)

0.00903***

0.00244***

-0.00111**

0.00828**

0.00330**

-0.000794

(-0.000377

(-0.00273

(-0.0213)

(-0.0321)

(-0.014)

Tit-1 P yP e Kt-1Tit-1 2

△Kt PePe P yP y

Kt-1 Kt-1 Tit-1Tit-1 Py Pe Constant Time fixed effect Province fixed effect Observations R-squared

(-0.266)

120.9

　

605.5***

(-0.394)

　

(-0.000964)

-9.24E-07

　

-2.93e-06*

(-0.485)

　

(-0.0548)

-1.45E-05

　

-0.000154***

(-0.637)

　

(-0.00013)

-75.14

　

-312.7***

(-0.278)

　

(-0.000562)

-60.65

　

-292.7***

(-0.403)

　

(-0.00151)

0.000197***

　

0.000277***

(0.0000)

　

(0.0000)

3.06E-08

　

3.16E-08

(-0.195)

　

(-0.366)

-36.06

-45.53

-45.82

23.18

(0.712)

(-0.165)

(-0.761)

(-0.666)

147.4

9.595

121.5

22.53

(-0.107)

(-0.754)

(-0.384)

(-0.652)

-204.5***

8.299

-3.3

-135.9**

-89.02***

0

(-0.00116)

(-0.693)

(-0.404)

(-0.0209)

(-1.77E-05)

-

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

377

377

377

232

232

232

0.965

0.757

0.97

0.983

0.852

0.989

3

37

ACCEPTED MANUSCRIPT Highlights 1. The energy consumption elasticity with respect to road infrastructure is estimated. 2. How road infrastructure affects the development of energy intensive industries is tested. 3. The endogeneity problem which may be caused by reverse causality is avoided.