The tug of war between local government and enterprises in reducing China's carbon dioxide emissions intensity

Journal Pre-proof The tug of war between local government and enterprises in reducing China's carbon dioxide emissions intensity

Xilong Yao, Xiaoling Zhang, Zhi Guo PII:

S0048-9697(19)36136-4

DOI:

https://doi.org/10.1016/j.scitotenv.2019.136140

Reference:

STOTEN 136140

To appear in:

Science of the Total Environment

Received date:

13 September 2019

Revised date:

6 December 2019

Accepted date:

14 December 2019

Please cite this article as: X. Yao, X. Zhang and Z. Guo, The tug of war between local government and enterprises in reducing China's carbon dioxide emissions intensity, Science of the Total Environment (2019), https://doi.org/10.1016/j.scitotenv.2019.136140

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© 2019 Published by Elsevier.

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The tug of war between local government and enterprises in reducing China’s carbon dioxide emissions intensity

Xilong Yaoa, Xiaoling Zhangb*, Zhi Guoa

College of Economics and Management, Taiyuan University of Technology, Taiyuan

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a

Department of Public Policy, City University of Hong Kong, Tat Chee Avenue,

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b

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030024, China

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Kowloon, Hong Kong, China; City University of Hong Kong, Shenzhen Research

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Abstract:

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Institute, Shenzhen, PRC.

The respective interests of local government and enterprises has led to a tug of war

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that may be affecting the level of carbon dioxide emission reduction intensity in China. In order to investigate the conflicts involved, we build a model to measure the influence of local government and enterprises on actual carbon dioxide emissions reduction levels based on a two-tier stochastic frontier analysis model. This can help identify the attitudes and practical action of Chinese local governments and enterprises towards carbon dioxide emission reduction, and find out the important factors that influence their attitudes towards carbon dioxide emission reduction. The 

Corresponding author. Tel/fax: +86 3516014057(X. L. Yao), +852 34422402(X. Zhang) E-mail addresses: [email protected]; [email protected] (X. L. Yao), [email protected] (X. Zhang) 1

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results show that an overwhelming majority of local governments have a greater influence on carbon dioxide emission intensity reduction than enterprises do, with the reduction being driven by the local government, and enterprises generally facing the resulting pressure. In this context, enhancing the participation of enterprises in carbon dioxide emission intensity reduction has become the key to achieving China’s

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reduction targets.

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Keywords: carbon dioxide emissions intensity; two-tier stochastic frontier analysis

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model; government; enterprises

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1. Introduction

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Recently, increasing attention has been paid to energy saving and carbon dioxide

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emission reduction from both global north and global south countries, including China (Song et al., 2019). For instance, the Chinese government voluntarily issued a series

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of emission targets involving the reduction of carbon dioxide emission intensity by 40% to 45% by 2020, and decreasing carbon dioxide emissions per unit of gross domestic product by 60%-65% compared to 2005 (Wang et al., 2011). It is therefore vitally important that enterprises, as major emission contributors, fulfil these plans. Unfortunately, this is not always the case, as explained by "energy-efficiency gap" theory or the "energy efficiency paradox" (e.g., Ahmed and Stater, 2017). That is, improvements in energy efficiency lag far behind targets because of market information asymmetry, the short-term behavior of consumers, and the costs involved in energy saving and carbon dioxide emission reduction. 2

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Herein, a question may arise - as both local government and enterprises are able to effect the carbon dioxide emission reduction, which of them will be more influential? If local government is more influential in reducing carbon dioxide emissions, then enterprises may face more pressure, which might affect the long-term sustainable development of the economy. In contrast, a low efficiency in reduction

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may arise if enterprises are more powerful and influential (Bode, 2006). Therefore, an

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analysis of the game theory between enterprises and local government is needed for

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formulating a scientific plan in order to reduce carbon dioxide emissions.

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The extant literature mainly focuses on information asymmetry and the behaviors

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of government and enterprises in the process of energy saving and carbon dioxide emissions reduction (Blackman, 2010; Zhang et al., 2016). Moledina et al. (2003) use

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a dynamic game model to find that enterprises adopt different measures to deal with

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different government policies to minimize their costs. On the one hand, government may adopt economic measures to promote the enterprises' abatement costs involved in energy saving and carbon dioxide emissions reduction (Yu and Yao, 2012; Ding et al., 2019). Elnaboulsi et al. (2018) find that access to publicly disclosed information enables the fine-tuning of the tax rules towards specific environmental circumstances and improves the ability of the regulator to levy firm-specific environmental taxes. governments at various levels should take into account environmental information released by ENGOs and consider appropriate measures to improve local environment quality using the obtained information. Li et al. (2018) suggest that governments at 3

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various levels should take into account environmental information released by Environmental non-governmental organizations and consider appropriate measures to improve local environment quality using the obtained information. On the other hand, information asymmetry can cause enterprises to not only to falsely report data to lower their future tax commitment, but also to avoid their adoption of advanced

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technology that can reduce carbon dioxide emissions (De-Quan et al., 2016). Hence, it

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is difficult for the government to accurately formulate economic incentive policies

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relating to energy saving and carbon dioxide emissions reduction.

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Another strand of literature studies the role of government. Lu (2011), for

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example, investigates the situation using static games of complete information and argues that the enterprises’ carbon dioxide emissions reduction levels can be improved

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significantly by more stringent government supervision. Similarly, Zhao (2016) and

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Wu et al. (2017) find government incentives to be the key to enterprises actively pursuing a low carbon development strategy. However, such policies are not always effective. Zhao et al. (2014), for example, propose that both environmental regulations and economic incentives are ineffective and inefficient in reducing enterprises' carbon dioxide emissions, claiming that China's reduction targets would be achieved if no external pressure was imposed on enterprises. Yuan et al. (2018) examine the effects of environmental regulation on industrial innovation and green development, and find that environmental regulation has inhibited patent outputs so that the “weak” version of Porter hypothesis is not underpinned. Shapiro and Walker 4

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(2018) find that the changes in environmental regulation account for most of the emissions reductions between 1990 and 2008. Wang et al. (2019) find that the impact turns to be adverse when the environmental regulation policy is stringent over a certain level, as the compliance cost effect is higher than innovation offset effect. These studies mainly focus on the influence of local government on carbon

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dioxide emissions reduction, and fail to reflect the influence of enterprises or to

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distinguish between the roles of local government and enterprises. In response, we use

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a two-tier stochastic frontier analysis model (two-tier SFA) to measure the role of

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government and enterprises in reducing carbon dioxide emissions. Two-tier SFA is

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commonly used to study the impact of conflicting parties on the bargaining process, such as the bargaining between employees and employers over wages and the

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bargaining between doctors and patients over the price of medical services

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(Kumbhakar and Parmeter, 2009; Lu et al., 2011). In this case, there is a conflict of interest between local government and enterprises over the carbon dioxide emission reductions, which conforms to the basic assumptions of the two-tier SFA model. China is one of the few developing countries since the Copenhagen conference that has explicitly put forward carbon emission reduction targets (Wang et al., 2011), and is therefore particularly suitable for such an analysis. Moreover, the mandatory reduction plan formulated by the Chinese government is bound to conflict with the economic interests of enterprises (Zhang et al., 2014). Hence, the role of the Chinese government and enterprises in carbon dioxide emissions reduction deserves further 5

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study. In addition, as with many multi-regional countries, there is some incongruity between China's central government and local governments. The issue of whether local governments would seriously adopt stringent measures to promote the reduction in enterprises’ carbon dioxide emissions is a problem faced by many countries (Sreenivasamurthy,2009). Therefore, the conclusion drawn from this paper will

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provide a reference for other developing countries.

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This paper extends prior research in three ways. First, we build a carbon dioxide

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emissions intensity reduction model based on a two-tier SFA model to reflect the

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influence of local governments and enterprises. The model is able to depict the

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benchmark level of the reduction of enterprises and express actual reduction by a regression model involving the local government's expected minimum reduction level

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and the enterprises’ expected maximum reduction level. This allows for an analysis of

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the degree of influence of local governments and enterprises on reductions, which will help judge enterprise pressure for reducing carbon dioxide emissions intensity. Second, we use the model to measure the impact of local governments and enterprises on reduction in various provinces of China. This helps us identify how much local governments have seriously implemented the country’s reduction plans. Third, we analyze the difference between the local governments and enterprises in their influence on carbon dioxide emission reduction based on such factors as industrial structure, energy structure, environmental regulation and technological progress. It is found that the influences are different in different quantiles of these four factors, 6

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which can help local governments formulate a multi-factor and multi-level policy for reducing carbon dioxide emissions.

2. Methodology 2.1 Model

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It is assumed that there are two main participants in the carbon dioxide emissions

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intensity reduction scenario in a certain region of China. One is the designer of the

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reduction scheme – the government; and the other is the executor of the reduction

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scheme – the enterprises. The local government, whose role is to care for the public

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good, anticipates that the enterprises’ carbon dioxide emissions intensity could decline

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at a faster rate. The enterprises, however, hope to slow down the rate of reduction to reduce their costs. It is assumed that the local government, in consideration of its

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economic and social goals such as tax and employment, would not establish a reduction target that is far beyond the ability of enterprises to achieve. On the other hand, it is also assumed that the government will not tolerate a zero reduction of the enterprises. Therefore, following Lu et al. (2011), the actual level of reduction (i.e., reduction in carbon dioxide emissions per unit GDP) is given by

CI  CI   (CI  CI )

(1)

where CI is carbon dioxide emissions per unit GDP; CI is the the actual level of reduction in carbon dioxide emissions per unit GDP, which equals to the difference between the value of carbon dioxide emissions per unit GDP in t  1 year and in t 7

Journal Pre-proof year. The index of CI can reflect the changing trend of carbon dioxide emission intensity, and it is aslo influenced by enterprises and local government. Therefor, this paper identifies the influence of government and enterprises on actual carbon dioxide emissions intensity reduction through the econometric analysis of this index. CI is the enterprises' minimum reduction expected by local government;

CI

CI is the

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maximum reduction that enterprises could achieve.  (0    1) is used to measure

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the amount of local government intervention. Thus,  (CI  CI ) captures the local

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government's ability to lead the reductions. The premise of this model is that the

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enterprise's emission reduction decision is self-interest, and the enterprise will arrange

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the plan for reducing carbon dioxide emissions from its own economic benefits. For one thing, enterprises do have the willingness and ability to reduce carbon dioxide

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emissions because they plan to increase profits by reducing the cost of energy

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consumption. For instance, equipment upgrading and production process optimization will reduce energy consumption of enterprises, thus reducing carbon dioxide emissions. This is a voluntary emission reduction behavior of enterprises, which is defined as enterprises' minimum reduction. For another, more reduction in carbon dioxide emissions means that enterprises need to bear more additional costs, and the highest cost that enterprises can afford is the maximum reduction that enterprises could achieve. Both the above situations can be described at the same time by this model except for the following two cases: some enterprises are likely to ignore economic benefits in order to achieve the emission reduction target set by local 8

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governments, also, some local governments may compel enterprises to stop production in order to reduce carbon dioxide emissions. Here, we further decompose Eq. (1) to capture the intervention capacity of local government and enterprises simultaneously in the model. Given both the individual basic characteristics and environmental regulations ( x ), we describe the reduction

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baseline level  ( x )  E ( x ) , where  exists in reality but we are unable to know

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it. Note that CI   ( x)  CI , thus, CI   ( x) represents the expected surplus

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in the enterprises’ reduction level. A larger value of CI   ( x) denotes a greater

 ( x)  CI

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capacity for enterprises' to reduce levels. In contrast,

 ( x)  CI denotes

a

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government’s expected surplus reduction. A larger value of

represents the

smaller capacity for the government to reduce levels (Fig.1).

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(Insert Fig.1 here)

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Both local government and enterprises seek to obtain more expected surplus to ensure their own interests, the acquisition of which hinges on each participant’s current condition and each other's interventions. For instance, the enterprises’ expected surplus is influenced by their level of technological progress, energy structure, scale effect, and other factors, and the government’s expected surplus is affected by such factors as industrial structure and level of environmental regulation. Based on the expected surplus of local government and enterprises, therefore, we obtain

9

Journal Pre-proof CI   ( x)  [CI   ( x)]  [CI   ( x)]  [CI   ( x)]

(2)

  ( x)  [CI   ( x)]  (1   )[  ( x)  CI ] where  ( x ) represents the baseline reduction level; surplus

reduction

(1  )[ ( x)  CI ]

obtained

by

government

[CI   ( x)] represents the through

public

rights;

represents the surplus reduction from the enterprises' lobbying

ability. The government's ability to influence the reduction hinges on its

 and the enterprises' total expected surplus

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decision-making capability

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CI   ( x) . This means government can rely on its power to raise the reduction level,

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but the outcome would be affected by the enterprises. Likewise, the surplus that can

 ( x)  CI . That is, the enterprises can reduce

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government's total expected surplus

and

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be obtained by the enterprises depends on their lobbying ability 1  

but

the

outcome

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their carbon dioxide emissions intensity reduction level through their lobbying ability, will

be

affected

by

government.

The

net

surplus

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NS  [CI   ( x)]  (1   )(  ( x)  CI ) can describe the differences in intervention ability between government and enterprises to make the reductions.

According to

the above assumptions and inferences, governments with a green development awareness and public interest may endeavor to raise the actual carbon dioxide emissions reduction level, while enterprises with the goal of maximizing their economic benefit may strive to reduce the actual reduction level. Put differently, there is an inverse relationship in the direction of government and enterprises in the bringing about the actual reduction level, which satisfies the typical characteristics of

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the two-tier stochastic frontier analysis model (Kumbhakar & Christopher，2009). Thus, based on Eq. (2), we obtain

CI   ( x)  i where

 ( x)  xi  ,

 is parameter vector;

(3)

xi is the individual basic characteristics

and environmental regulations of the sample, such as the scientific and technological

i  i  u(i)  v(i) , i  i [CI   ( xi )]  0 , u  (1  i )[CI   ( xi )] ,

i

denotes the reduction level that the

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where v (i ) is a random error term;

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regulations.

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level of enterprises, industrial structure and the stringency of environmental

ui denotes the reduction level

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government can obtain through its public power; and

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that enterprises can decrease through their influence and lobbying ability. Here, we use maximum likelihood estimation (MLE) to estimate model (3).

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Meanwhile, we assume that both  i and ui follow an exponential distribution due

we assume assume that

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to their one-sided nature, i.e., 𝜔𝑖 ~𝑖. 𝑖. 𝑑. 𝐸𝑥𝑝(𝜎𝜔 , 𝜎𝜔2 ), 𝑢𝑖 ~𝑖. 𝑖. 𝑑. 𝐸𝑥𝑝(𝜎𝑢 , 𝜎𝑢2 ), and

vi follows a normal distribution, i.e., 𝑣𝑖 ~𝑖. 𝑖. 𝑑. 𝑁(0, 𝜎𝑣2 ). Moreover, we

i , ui

independent of f i  

, and

vi are independent of each other, and they are all

xi . Thus, the probability density function of 𝜉𝑖 is

exp  ai  exp  bi   exp  ai  exp  bi  Φ  ci     z  dz  Φ  ci     hi   u    u     hi u   u  

(4)

where  () and  · are the probability density function and cumulative distribution function respectively of the normal distribution. Other parameters are set as

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ai 

 v2 i  v2 i  b   , , i 2 u2  u 2 2  

hi 

i  v ,    ci   i        u

For samples containing n observed values, the logarithmic likelihood function can be expressed as n

n l nu      ln e ai   ci   ebi   hi  

l n L x ;  

(5)

i 1

where θ    ,  v ,  u ,    . The maximum likelihood estimation of all parameters can '

(6)

 exp  i   i /  v  ci  exp  bi  ai    hi   exp  ai  bi    ci  

(7)

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λ  1/  u  1/   . Thus the conditional expectation of ui and i are given

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where

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 exp  i    i /  v  hi    hi   exp  ai  bi    ci 

f  ui |i  

f i |i  

ui and i are given by

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The conditional distributions of

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be obtained by maximizing the logarithmic likelihood function.

by

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2    hi   exp  ai  bi  exp  v / 2   v ci   ci   v  E 1  e |i   1  1    hi   exp  ai  bi    ci   ui

2    ci   exp  bi  ai  exp  v / 2   v hi    hi   v  E 1  e |i   1  1  exp  bi  ai    hi   exp  ai  bi    ci   i

(8)

(9)

Hence, we obtain



 



i NS  E 1 ei |i  E 1eu i i |  E eu(  e ii |

Note that

u

and

Hence, the values of

  are

u

and

)

(10)

identifiable because they are in different equations.



are entirely determined by the estimation result

rather than presupposition. This is an advantage of the two-tier stochastic frontier analysis method used here. 12

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According to Eq. (1) and Eq. (2),

i

denotes the effort which comes from

government that affect the carbon emission reduction, and

ui denotes the effort

which comes from enterprises that affect the carbon emission reduction. can be calculated by Eq. (8).

i

can be

ui can be can be calculated by Eq. (9).

2.2 Variables and data

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2.2.1 Variables

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The decline in carbon dioxide emissions intensity (i.e., reduction in carbon

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dioxide emissions per unit GDP) is selected as the dependent variable, which can be expressed as

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CI it  CIit  CIit 1

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(11)

where i denotes provinces and t denotes years.

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In order to measure the baseline reduction level  ( x ) , we employ the variables

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of individual basic characteristics (involving technological progress, energy structure, industrial structure, scale effect and carbon dioxide emissions intensity and environmental

regulations)

and

environmental

regulations

(containing

command-and-control environmental regulations and market-based environmental regulations). According to previous results (Gruber, 1996; Nemet, 2006; Yu et al., 2011)，the scale effect can be expressed by (𝑠𝑖𝑧𝑒𝑡 /𝑠𝑖𝑧𝑒𝑡−1 )𝜙 , where 𝑠𝑖𝑧𝑒𝑡 is the output scale in year 𝑡; and 𝜙 is a scaling factor，ranging from -0.07 to -0.20. Here, follow Gruber (1996) in assigning the scaling factor the value of -0.18. 13

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With regard to the environmental regulations variable, it should be noted that an accurate measure of this variable is important for evaluating the moderating effect of a given regulation. The literature contains a number of approaches. For example, pollution level, compliance cost, pollution abatement and control expenditures, and pollution abatement fees are popular proxy options (Hamamoto, 2006; Yang et al.,

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2012). Arguably, all of these are appropriate for the background in which the object is

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studied, but they are far from an ideal proxy for general use. As noted above, there are

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mainly two types of environmental regulations - command-and-control and

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market-based instruments. Here, these two types of environmental regulations are

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measured based on Xie et al.(2017) and Wang et al.(2015) in China, who use pollutant discharge fees as a proxy for market-based regulations, and environmental

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investments in new construction projects or environmental administrative punishment

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cases as a proxy for the stringency of command-and-control regulations. Specifically, we use the ratio of pollutant discharge fee to gross industrial output value as a proxy for market-based regulations, and environmental administrative punishment cases as a proxy for command-and-control regulations. This is because they are more appropriate for reflecting the stringency of environment regulations. Energy structure is measured by the proportion of coal energy in industrial primary energy consumption, because there is substantial coal consumption in China, and the pollution from coal consumption is one of the major sources of industrial air pollutant emissions (Lin and Jiang, 2009). Industrial structure is measured by the ratio of output value of the high-energy consuming industrial sector to gross industrial 14

Journal Pre-proof output value, because this can measure the change of industrial structure in different regions of China (Shao et al., 2011). 2.2.2 Data Table 2 summarizes the descriptive statistics of the variables. The China Science and Technology Statistics Yearbook (2001-2015) provided data for industrial patents

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and technology import expenditure; the China Environment Yearbook (2001-2015) for

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industrial air pollutants emission, environmental administrative punishment cases, and

(2001-2015) for industrial output value.

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pollutant discharge fees; and the China Industry Economy Statistical Yearbook

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(Insert Table 1 here)

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(Insert Table 2 here)

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3. Results and discussion 3.1Model testing

Table 3 shows the estimation results of the two-tier stochastic frontier analysis model. Models (1)-(5) are estimated through the maximum likelihood method. First, the

command-and-control

and

market-based

environmental

regulations

are

incorporated into Model (1). Then, the scale effect, energy structure, industrial structure, and technological progress are incorporated into Model (2)-(5), with the fitting results obviously improving. The results show that the scale effect, energy structure, industrial structure, and technological progress have significant positive 15

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effects. This indicates that regions with a high proportion of fossil energy, high proportion of heavy industry, and rapid progress in technology are associated with a higher degree of carbon dioxide emissions intensity reduction. Regions with a low proportion of fossil energy, low proportion of heavy industry and slow progress in technology are therefore associated with a lower degree of reduction.

ro

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(Insert Table 3 here)

intensity reduction

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3.2.1 Overall estimation results

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3.2 The influence of government and enterprises on actual carbon dioxide emissions

Table 4 presents results of the influence of local government and enterprises on

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actual carbon dioxide emissions intensity reduction.



represents the influence of

u

the

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local government on actual carbon dioxide emissions intensity reduction;

influence of enterprises on actual carbon dioxide emissions intensity reduction;

  u

indicates the local government's reduction influence is greater than the

enterprises; and

  u

indicates local government's reduction influence is smaller

than the enterprises. As shown in Table 3, the value of



and

u

are 0.1904 and

0.1457 respectively, with a difference of 0.0477. This implies that local government's influence on carbon dioxide emissions intensity reduction is greater than the enterprises, and the actual reduction level is higher than the enterprises’ baseline reduction level. This is similar to the findings of Oikonomou et al. (2009) who claim 16

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that the local government plays a leading role in energy conservation and emission reduction. This is because the environmental regulations formulated by the government play an important role in the production process and end-of-pipe treatment (Boehmer-Christiansen, 1997). As compliance parties, enterprises need support from the government, so the government is likely to have greater bargaining

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power in the process of energy saving and emission reduction (Lu et al., 2016; Song

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et al., 2019).

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(Insert Table 4 here)

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Table 5 shows the influence of the local government and enterprises on the actual

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reduction level over the period 2002-2014 indicating that, except for 2003, 2007 and 2010, the local government's influence was greater than the enterprises’. This can be

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taken to mean that the government played a leading role in carbon dioxide emissions

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intensity reduction, while the enterprises played a more passive role in most of the years. The local government's impact on actual reduction increased by 0.213 on average during 2010-2014, while that of the enterprises decreased by 0.789 on average. This implies that, local governments paid more attention to economic development before 2010 due to GDP-based tournament promotion mechanism. Some local governments even pursue economic development at the expense of the environment. This may be the reason that the influence of local governments on carbon dioxide emission reduction is small before 2010. After 2010 (when the Chinese government promised to reduce carbon dioxide emissions intensity), the 17

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actual level of reduction attributed to the local government significantly improved, while the pressure on the enterprises' to reduce carbon dioxide emissions intensity significantly increased. (Inset Table 5 here) However, the private enterprises’ influence was greater than the local government

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in 2003, 2007 and 2010, with the capital growth rates of individual enterprises being

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16.557%, 15.924% and 17.899% respectively (China National Bureau of statistics,

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2002-2015), which were much higher than other years. This is because local

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governments successively issued economic policies to promote the development of

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individual enterprises in these years, and decided to ease restrictions on the carbon dioxide emissions from individual enterprises. For example, the capital expansion of

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individual enterprises in 2010 is influenced by the “four trillion industrial

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development policies”. This kind of policy pays more attention to the growth rate of economy than the environment. Correspondingly, private enterprises may pay more attention to economic benefits than the carbon dioxide caused by energy consumption. 3.2.2 Regional estimation results Table 6 presents the influence of the local government and enterprises on actual carbon dioxide emissions intensity reduction at the provincial level. In such provinces as Hebei, Hubei, and Sichuan, the local government's influence on actual reduction levels is less than the enterprises, indicating that the decline of industrial carbon dioxide emissions intensity in these areas failed to reach the baseline reduction level, 18

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and hence a large potential for increased reduction. This is because energy intensive industry, the pillar industry in these areas, accounts for more than 70% of industrial output in these regions (China National Bureau of statistics, 2002-2015), such as steel industry in Hebei, special equipment manufacturing industry in Hubei and general equipment manufacturing industry in Sichuan. Local governments in these areas are

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highly dependent on the taxes and employment brought by these industries, thus they

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are likely to exert a relaxed environmental policy on these energy intensive

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enterprises.

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(Insert Table 6 here)

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However, the local governments’ influence on actual reduction levels in these three provinces is larger than the enterprises in the other provinces, indicating have a

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strong intervention ability. Moreover, the local government's influence on actual

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reduction is far greater than the enterprises in Beijing, Jiangxi, Shanxi, and Ningxia indicating a greater pressure on enterprises in these areas to reduce carbon dioxide emissions intensity – while it is only slightly greater than the enterprises in Shanghai and Shaanxi, showing less pressure on enterprises in these latter areas. These results show that the unreasonable industrial structure and energy structure and the lag of technological progress of most provinces in China lead to a low level and a great potential of carbon dioxide emission reduction of industrial enterprises. However, the situation in Shanghai is quite special, with the influence of government on emission reduction being slightly higher than that of enterprises. This is because the reduction 19

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potential of carbon dioxide emissions in Shanghai is small, and the green production technology of enterprises is at a higher level, there is no need for Shanghai government to exert high-intensity influence on enterprises’ carbon dioxide emission reduction. Fig.2 shows the relationship between the mean of net influence value and the

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mean of the share of private economy from 2002 to 2014. With the increase in the

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share of private economy，the net influence value tends to decrease. That is, the local

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government's influence on carbon dioxide emission reduction will decrease with the

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increase in the share of private economy. This may be because the inconsistent carbon

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dioxide emission reduction goals between private enterprises and local government making it difficult to achieve the carbon dioxide target set by local governments.

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Therefore, how to guide private enterprises to actively reduce carbon dioxide

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emissions is an important problem that the government needs to pay attention to. (Insert Fig.2 here)

3.3 Estimation based on individual basic characteristics and environmental regulations To analyze the influence of local government and enterprises on carbon dioxide emissions intensity reduction further, the individual basic characteristics and environmental regulations are arranged according to their quartiles Q1, Q2, and Q3, in ascending order of the stringency of their market-based environmental regulations. 20

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Here, we mainly focus on market-based environmental regulations, industrial structure, energy structure, and technological progress. (Insert Table 7 here) The results based on market-based environmental regulations are shown in Table 7 . The first quartile value (Q1) of Market-based environmental regulations was

of

0.0002 ， with the second quartile value (Q2) and the third quartile value(Q3)

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being0.0004 and 0.0006, respectively. China's western provinces, such as Guizhou,

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Yunnan and Guizhou, had been in Q1 for a long time, because they attach importance

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on economic development and despise environmental protection. The central and

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western regions, such as Henan, Anhui and Hunan, were in Q2, because they are facing the dilemma of economic development and environmental protection. Most of

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the provinces in eastern region, such as Guangdong, Jiangsu and Zhejiang, were in Q3,

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and these provinces have begun to explore the path of green industrial development. In Q1, the reduction influence of the local government and enterprises is 12.55% and 11.36% respectively, the actual being 1.19% higher than the baseline level; for Q2, the equivalent figures are 16.93%, 11.27%, and 5.66% respectively; while for Q3, these are 17.53%, 13.33%, and 4.21%, respectively. The reduction influence of the government, therefore, greatly improved in Q2 and Q3, which implies that government can raise the actual reduction level through environmental regulation measures – a finding in line with Zhang et al. (2017), who propose carbon taxation and carbon trading to be the two main mechanisms required for carbon abatement. 21

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This is because regions with less stringent market-based environmental regulations pay more attention to economic development, for example, local governments in Q1 are weak in governance capacity and willingness for carbon dioxide emission reduction. Therefore, local governments in these regions have less influence on carbon dioxide emission reduction. However, local government in Q3

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emphasize the mutual development of environmental governance and economic

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development, thus have great influence on carbon dioxide emission reduction.

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(Insert Table 8 here)

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The results based on industrial structure are provided in Table 8. The first

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quartile value (Q1) of industrial structure was 0.523，with the second quartile value (Q2) and the third quartile value(Q3) being 0.607 and 0.683, respectively. The

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industrial structure of China's developed regions, such as Shanghai, Guangdong and

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Jiangsu, is in Q1, because most of the heavy industries in these provinces have migrated to the central and western regions. Provinces such as Henan and Hubei are in Q2 because the proportion of heavy industry in these areas has increased. Northeast China, such as Heilongjiang, Jilin and Liaoning, is in Q3. These areas have long been a base of heavy industry of China, and their average proportion of heavy industry in industrial output value is 65%. The results based on industrial structure have a similar interpretation, with the government’s reduction influence level greatly improving in Q2 and Q3, while that of the enterprises reduced. This may be because industrial structure adjustment can lead 22

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to a reduction in carbon dioxide

emission, which is often favored by the local

government when formulating emission reduction measures (Li et al., 2017) . This is because provinces in Q1 is more reasonable in industrial structure, with a small proportion of enterprises in high energy consumption and high pollution, improving the overall reduction level of carbon dioxide emissions from industrial enterprises.

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Thereby, the local government's involvement in carbon dioxide reduction will also be

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reduced. Conversely, local governments in Q2 and Q3 have more pressure on carbon

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dioxide emission reduction, and may take more active measures to reduce carbon

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dioxide emissions, resulting in the increase of the influence of local governments in

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these regions.

(Insert Table 9 here)

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The results based on energy structure in Table 9. The first quartile value (Q1) of

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energy structure was 0.763，with the second quartile value (Q2) and the third quartile value(Q3) being 0.874 and 0.949, respectively. The industrial structure of most provinces in eastern China, such as Fujian, Jiangsu and Zhejiang, is in Q1. The industry of these provinces is in transition period, with the industrial development and coal consumption gradually being decoupled, leading to a decrease in the proportion of coal in primary energy consumption (Song et al., 2019). The industrial energy structure of Henan and Hubei provinces is in Q2, because these regions still rely on coal. The energy structure of Shanxi and Hebei is Q3. Most of the industrial enterprises in these areas use coal as raw material or fuel. For example, coal chemical 23

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industry in Shanxi and steel industry in Hubei are the main industries in coal consumption. The results based on energy structure shows that the local government and enterprises reduction influence level greatly improved in Q3, indicating that the government may pay more attention to carbon dioxide emissions intensity reduction

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when coal accounts for a high proportion of energy consumption. This is because

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energy structure is a driving factor behind carbon dioxide emissions (Jiang et al.,

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2017). Therefore, over dependence on coal are likely to result in strong carbon lock-in

re

effect. For example, local governments in Shanxi and Hebei that with energy structure

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in Q3 may adopt more stringent administrative measures, such as limiting production and shutting down polluting industrial enterprises, to promote carbon dioxide

na

emission reduction. The influence of local governments on emission reduction is

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higher in Q3 than that in Q1. For example, local governments in Shanxi and Hebei often severely shut down polluting enterprises or limit the production time of them in winter, which are relatively rare in regions in Q1. (Insert Table 10 here) The results based on technological progress in Table 10. The first quartile value (Q1) of technological progress was 1827，with the second quartile value (Q2) and the third quartile value(Q3) being 5147 and 17747, respectively. The technological progress in western China, such as Gansu, Xinjiang and Guangxi, has long been in Q1, which is due to the lack of technical investment in these areas. The technological 24

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progress in central region, such as Henan, Anhui and Hubei, is in Q2, because the technological investment in these regions is higher than that in western region. The technological progress in China's developed regions, such as Shanghai, Guangzhou and Jiangsu, are in Q3, because both R&D capital investment and R&D personnel investment in these regions are far higher than other regions, resulting in high level of

of

technological development.

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The results based on technological progress show the local government’s

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reduction influence level greatly decreased in Q2 and Q3. This may be because

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technological progress is conducive for reducing carbon dioxide emissions intensity

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(Li and Lin, 2016), and thus the government is likely to exert less influence when the level of technological progress is high. Industrial enterprises in Jiangsu, Guangdong

na

and other provinces where technological progress is in Q3 often form an internal

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driving force for carbon dioxide emission reduction. Compared to the provinces in Q1, the willingness to actively reduce carbon dioxide emissions of provinces in Q3 is higher than that of other regions. The conflict between local governments and the enterprises in the reduction target is further alleviated, and the reduction level of carbon dioxide emissions of the enterprises will gradually reach the maximum.

4. Conclusion and policy implications This paper develops a model to measure the influence of local government and enterprises on the actual level of carbon dioxide emissions intensity reduction in 25

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China based on a two-tier SFA model. This allows the determination of who is playing a leading role in actual reduction activities and the extent of the associated pressure on the enterprises. We also use the model to estimate the influence of local government and enterprises on actual carbon dioxide emissions reduction at the provincial level. The research provides some valuable results. First, the local government's

of

influence on reduction levels is greater than the enterprises. Second, the majority of

ro

China's local governments have a stronger influence on reduction levels than

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enterprises, strongly promoting the enterprises’ reduction activities. Third, stringency

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of environmental regulation, industrial structure, energy structure, and technological

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progress are main factors affecting provincial differences in the level of influence between government and enterprises on actual reduction levels.

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The policy implications are clear and straightforward. First, local governments

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need to focus on establishing an appropriate regional emission reduction target through administrative measures. For instance, provincial governments in Hebei, Sichuan, and Hubei need to improve the stringency of their environmental regulations initially, because of their considerable potential to make further reductions; the governments in Shanghai, Liaoning, Shandong, Jiangxi, and Shaanxi can just maintain a balance on both sides; while their counterpart in other provinces need to increase assistance from enterprises, such as by promoting the technology diffusion of carbon dioxide emission reduction and impelling enterprises to adjust their energy structure. Secondly, enterprises should shoulder all their responsibilities for meeting 26

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the emission target and avoid simply aiming for short-term economic gains. In particular, reporting false data or conducting other illegal activities (even though caused by information asymmetry) should be resolutely discouraged. Conversely, enterprises that adopt advanced green technologies or exceed reduction targets should be rewarded by their local governments, such as by reducing taxes and increasing

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subsidies. Thus, a fair and reasonable competitive environment for enterprises’

ro

emission reductions could be built. Moreover, new rules towards a low carbon society

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needs to be established or enhanced through the market to control the expansion of

re

production capacity. In short, what is needed is a “win-win” low carbon society built

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by both governments and enterprises. Experiences also show that not only the total but also the capacity of carbon dioxide emission reduction of one region need to be

na

considered when the central government formulating distribution scheme. The

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reduction capacity of carbon dioxide emissions is affected by the level of technological progress, industrial structure, energy structure and other factors. Thus, a comprehensive index of carbon dioxide emission reduction capacity based on these factors can be built as an important indicator for local governments' responsibilities in reducing carbon dioxide emissions. Although the approach in this paper serves as a suitable reference point for developing countries, there are some limitations. For instance, the study was limited by the unavailability of data at the micro level, which future work may be able to overcome. Future research could also examine a wide range of further issues, such as 27

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enriching the data set with a more comprehensive categorization of individual enterprises instead of just industrial enterprises, involve a more extensive time period, and introduce more variables that distinguish between manufacturers and other types of enterprises. In addition, the influence of central government, local government and enterprises on carbon dioxide emissions reduction is involved in this paper due to data

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unavailability.

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Acknowledgments

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This study is funded by the National Natural Science Foundation of China (Project No.

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41401655, 71834005, 71673232); the Qualified Personnel Foundation of Taiyuan

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University of Technology; the Top Young Academic Leaders of Higher Learning

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Institutions of Shanxi; the Philosophy and Social Sciences Research of Higher Learning Institutions of Shanxi; the Research Grant Council of Hong Kong, China

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[grant numbers CityU 11271716, CityU 21209715] and the CityU Internal Funds [grant numbers 9680195, 9610386].

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Journal Pre-proof Author statement Xilong Yao: Conceptualization, Methodology, Software, Writing- Reviewing and Editing. Xiaoling Zhang: Conceptualization, Data curation, Writing- Original draft preparation and Supervision.

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na

lP

re

-p

ro

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Zhi Guo: Visualization and Investigation.

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The tug of war between local government and enterprises in reducing China’s carbon dioxide emissions intensity

Xilong Yao, Xiaoling Zhang*, Zhi Guo

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* Corresponding Author

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Acknowledgments

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This study is funded by the National Natural Science Foundation of China (Project No.

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41401655, 71834005, 71673232); the Qualified Personnel Foundation of Taiyuan University of Technology; the Top Young Academic Leaders of Higher Learning

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Institutions of Shanxi; the Philosophy and Social Sciences Research of Higher

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Learning Institutions of Shanxi; the Research Grant Council of Hong Kong, China [grant numbers CityU 11271716, CityU 21209715] and the CityU Internal Funds [grant numbers 9680195, 9610386].

Declarations of interest: None



Corresponding author. Tel/fax: +86 3516014057(X. L. Yao), +852 34422402(X. Zhang)

E-mail addresses: [email protected]; [email protected] (X. L. Yao), [email protected] (X. Zhang) 36

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Fig.1. Carbon dioxide emissions intensity reduction of local government and enterprises

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na

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ro

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Fig.2. Scatter diagram of net influence value and share of private economy

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Journal Pre-proof Table List Table 1. Explanation of variable Variable Market-based regulation(MER)

Explanation The ratio of pollutant discharge fee to gross industrial output value

Command-and-control Environmental administrative punishment cases

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regulations (CCR) Calculated as yearly development of output (Nemet,2006), Scale effect (SE)

ro

(outputt/outputt-1)-0.18

-p

Heavy industry production share in gross industry Industrial structure(IS)

re

production, indicating industry structure

The decline in carbon

consumption structure

Number of technical patents

na

Technical progress(TP)

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Industry coal use per primary energy use, indicating Energy Energy structure(ES)

The decline in carbon dioxide emissions intensity (i.e.,

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dioxide emissions

reduction in carbon dioxide emissions per unit GDP)

intensity(VCI)

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Mean

Sd

Min

Max

0.0003

0.0005

0.0001

0.003

5446

5085

181.1

48000

Scale effect (SE)

0.971

0.0410

0.716

1.236

Industrial structure(IS)

0.600

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Table 2. Descriptive statistics of the variables Variable

0.286

0.859

Energy structure(ES)

0.831

0.157

0.205

1

Technical progress(TP)

18000

36000

70

270000

0.400

-2.616

3.330

Market-based regulation(MER) Command-and-control

re

-p

0.118

lP

The decline in carbon dioxide

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regulations (CCR)

-0.0660

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na

emissions intensity(VCI)

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Table 3. Estimation results Model (1)

Model (2)

Model (3)

Model (4)

Model (5)

MER

-0.077***

-0.059***

-0.067***

-0.077***

-0.037***

(-5.493)

(-4.076)

(-4.440)

(-4.564)

(-184.329)

0.003

0.005

0.005

0.006

-0.013***

(0.297)

(0.505)

(0.521)

(0.633)

(-96.504)

0.033***

0.034***

SE

0.030***

(3.633)

(362.700)

0.002**

0.002**

0.002***

(2.232)

(156.581)

0.001

0.001***

(1.385)

(113.941)

(3.715)

-p

(3.648)

0.038***

ro

CCR

of

Variable

re

ES

lP

(2.427)

na

IS

Adj-R2

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TP

0.029*** (293.126)

0.033

0.234

0.547

0.632

0.715

Log likelihood

20.145

28.104

31.091

32.088

35.267

P-value

0.000

0.000

0.000

0.000

0.000

LR(χ2)

717.64

733.565

739.540

741.533

390.000

390.000

390.000

390.000

747.892

7 N

40

390.000

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Table 4. Influence of local government and enterprises on actual carbon dioxide emissions intensity reduction levels Variable meaning

Symbol

Coefficient

The influence of enterprises on 𝜎𝑢

actual carbon dioxide emissions

of

intensity reduction

-p

𝜎𝜔

0.190

re

intensity reduction

ro

The influence of government on actual carbon dioxide emissions

0.146

lP

The proportion of enterprises'

𝜎𝑢2 /(𝜎𝑢2 + 𝜎𝜔2 )

36.95%

𝜎𝜔2 /(𝜎𝑢2 + 𝜎𝜔2 )

63.05%

na

influence on actual carbon dioxide emissions intensity reduction

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The proportion of government

influence on actual carbon dioxide emissions intensity reduction

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Table 5. Influence of local government and enterprises on actual carbon dioxide emissions intensity reduction level over 2002-2014 The influence of

The influence of

Year

Net influence local government

enterprises

14.32

12.59

1.73

2003

13.92

15.52

-1.60

2004

17.84

12.71

2005

18.56

2006

15.62

2007

11.94

2008

18.92

2009

17.03

ro

of

2002

-p

16.72

5.14 1.84 3.66

13.74

-1.80

12.09

6.84

9.39

7.63

14.96

-0.76

12.96

12.60

0.36

17.68

11.26

6.43

2013

21.11

11.22

9.89

2014

15.21

11.47

3.74

mean

16.10

12.79

3.31

2011 2012

lP

na 14.20

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2010

re

11.97

42

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Table 6. Influence of local government and enterprises on actual intensity reduction at the provincial level The influence of

The influence of

Region

Net influence local government

enterprises

15.37

7.66

7.71

Tianjin

12.97

11.75

1.22

Hebei

14.29

17.23

Shanxi

22.07

12.87

Liaoning

13.57

Jilin

18.26

Heilongjiang

13.40

ro

-p

25.87

16.12

re

Inner Mongolia

of

Beijing

-2.94 9.20 9.75 0.29

15.21

3.05

10.37

3.03

11.09

0.18

11.46

10.56

0.90

10.86

8.71

2.15

Anhui

14.66

9.76

4.90

Fujian

12.41

10.49

1.91

Jiangxi

15.32

8.17

7.15

Shandong

15.09

14.73

0.37

Henan

18.52

17.01

1.51

Hubei

14.11

15.37

-1.26

Jiangsu Zhejiang

na 11.28

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Shanghai

lP

13.28

43

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16.86

10.80

6.06

Guangdong

11.11

8.89

2.22

Guangxi

15.67

12.76

2.91

Hainan

18.60

11.45

7.15

Chongqing

18.81

14.23

4.57

Sichuan

14.06

16.24

-2.18

Guizhou

19.08

14.45

Yunnan

17.41

13.36

Shaanxi

14.91

Gansu

14.78

Qinghai

19.70

Ningxia

27.92

ro

-p

na

lP

re

13.92

14.63

Jo ur

Xinjiang

of

Hunan

44

4.63 4.05 0.99

11.71

3.07

15.84

3.87

16.70

11.22

12.88

1.75

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Table 7. Influence of local government and enterprises on carbon dioxide emissions intensity reduction based on market-based environmental regulations Q1 (%)

Q2 (%)

Q3 (%)

12.55

16.93

17.53

11.36

11.27

13.33

1.190

ro

The influence of

4.210

local government

of

The influence of enterprises

5.660

-p

Net influence

re

Note：Q1, Q2, and Q3, denotes the stringency of market-based environmental regulations from small to

Jo ur

na

lP

large.

45

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Table 8. Influence of local government and enterprises on carbon dioxide emissions

Q1 (%)

Q2 (%)

Q3 (%)

14.44

16.82

16.54

13.30

of

intensity reduction based on industrial structure

12.08

12.98

4.740

3.550

The influence of local government The influence of

1.140

-p

Net influence

ro

enterprises

Jo ur

na

lP

re

Note：Q1, Q2, and Q3 are divided by the proportion of heavy industry from small to large.

46

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Table 9. Influence of local government and enterprises on carbon dioxide emissions intensity reduction based on energy structure Q1 (%)

Q2 (%)

Q3 (%)

14.72

13.68

18.30

10.14

14.65

13.07

The influence of local government

of

The influence of

4.580

-0.970

-p

Net influence

ro

enterprises

5.220

Jo ur

na

lP

re

Note：Q1, Q2, and Q3 are divided by the proportion of coal in energy consumption from small to large.

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Table 10. Influence of local government and enterprises on carbon dioxide emissions

Q1 (%)

Q2 (%)

Q3 (%)

19.63

15.76

15.41

14.23

of

intensity reduction based on technological progress

13.70

12.50

2.060

2.910

The influence of local government The influence of

5.400

-p

Net influence

ro

enterprises

Jo ur

na

lP

re

Note：Q1, Q2, and Q3 are divided by the level of technological progress from small to large.

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Journal Pre-proof Graphical abstract

Highlights 1. Build a carbon dioxide intensity reduction model to reflect the influence of local governments and enterprises. 2. Measure the impact of local governments and enterprises on carbon dioxide intensity

of

reduction.

ro

3. The Government can raise the actual reduction level through environmental regulation.

-p

4. The government played a leading role in carbon reduction, while the enterprises played a

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na

lP

re

more passive role.

49

Figure 1

Figure 2