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Carbon congestion effects in China's industry: Evidence from provincial and sectoral levels
Journal Pre-proof Carbon congestion effects in China's industry: Evidence from provincial and sectoral levels
Yue-Jun Zhang, Jing-Yue Liu, Bin Su PII:
S0140-9883(19)30432-3
DOI:
https://doi.org/10.1016/j.eneco.2019.104635
Reference:
ENEECO 104635
To appear in:
Energy Economics
Please cite this article as: Y.-J. Zhang, J.-Y. Liu and B. Su, Carbon congestion effects in China's industry: Evidence from provincial and sectoral levels, Energy Economics(2019), https://doi.org/10.1016/j.eneco.2019.104635
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Carbon congestion effects in China’s industry: evidence from provincial and sectoral levels Yue-Jun Zhanga, b, Jing-Yue Liu a,b, Bin Su c a b
Business School, Hunan University, Changsha 410082, China
Center for Resource and Environmental Management, Hunan University, Changsha 410082, China c
Energy Studies Institute, National University of Singapore, 119620, Singapore
Abstract
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Investigating carbon congestion effect can help identify congestion in production, which is of
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great significance for the rational use of resources and the effective promotion of carbon emissions reduction. Under this circumstance, this study uses the dual model of radial DEA to explore both
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undesirable/desirable congestion, returns to damage and damages to return during 2005-2015 from
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both provincial and sectoral levels. Combined with window analysis, the technical efficiency and
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emissions reduction potential of China’s industrial sectors are also discussed. The empirical results show that: (1) China’s industrial carbon congestion is obvious and the congestion effect witnesses a
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trend of regional agglomeration and evident regional and sectoral heterogeneity. In particular, undesirable congestion mainly occurs in the eastern region, and desirable congestion mainly occurs in
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the western region, followed by the eastern region; both undesirable and desirable congestions mainly occur in some sectors in the Manufacturing and Power-Gas-Water industries. (2) If all sectors produce on the production frontier, the average annual potential carbon emissions reduction would reach 722.82 million tons, with the higher potential in western region and Shanxi province of central region, as well as Manufacturing and six high-energy-consuming sectors. (3) To achieve the “win-win” of industrial development and carbon emissions reduction, China’s western region should focus on green technology innovation, while the eastern region and Power-Gas-Water industry should focus on both input resources optimisation and green technology innovation. Keywords: carbon congestion effect; carbon dioxide emissions; industry; radial DEA; China
Corresponding author. E-mail: [email protected] (Yue-Jun Zhang). 1
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1. Introduction Global warming caused by increased CO2 emissions has become one of the tough challenges facing the world (Montzka et al., 2011). Data from BP (2019) show that China has become the world’s largest contributor to CO2 emissions since 2006 and China’s CO2 emissions in 2018 reached 9428.7 million tons (Mt), accounting for 27.8% of the global total, which has caused China to face unprecedented challenges (Zhang et al., 2019). To prevent global warming and curb environmental pollution, China has committed to a low-carbon economy and green development (Zhang and Da, 2015). At the Paris Climate Conference in 2015, China set the target of reducing carbon intensity by
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60% to 65% by 2030 based on the 2005 level1. However, as the largest developing country, China is
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still in the stage of rapid development of industrialisation, and the conflict between industrial
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development and environmental protection remains stark. In this context, how to effectively achieve energy saving and emission reduction, together with industrial growth, has become a major concern
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for China.
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As stated in the report of the 19th National Congress of the Communist Party of China in 2017, it is necessary to develop spatial layout, industrial structure, and ways of working and living that help to
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conserve resources and protect environment2. However, one of the main reasons for China’s high CO2 emissions is the overuse of resources (Xu and Lin, 2019). When resources are overused, it will cause
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congestion; the conventional congestion only considers the phenomenon whereby when inputs increase to a certain degree under certain production conditions, the outputs (mainly desirable output) will decrease due to excessive inputs (Brockett et al., 1998; Cooper, 2001), while the undesirable output changes in this process are rarely considered. In fact, in the industrial production process, such undesirable output as CO2 emissions is an inevitable product of energy consumption; that is, when resources are overused, it will not only reduce the desirable output, but also increase CO2 emissions, resulting in undesirable carbon congestion (UC). In addition, under the impact of green technology innovation, China’s industry may also exist the possibility of desirable output growth and CO2 emissions reduction in the process of increasing resource input, resulting in desirable carbon congestion (DC). In terms of the concept, UC emphasizes that the increase of input will lead to 1 2
http://www.gov.cn/xinwen/2015-06/30/content_2887330.htm http://www.xinhuanet.com//politics/19cpcnc/2017-10/18/c_1121820882.htm 2
Journal Pre-proof the decrease of desirable output, while DC emphasizes that the increase of input will lead to the decrease of undesirable output (CO2 emissions) (Sueyoshi and Goto, 2012b; Sueyoshi and Goto, 2016; Sueyoshi and Yuan,2016). UC and DC can reflect the “win-win” possibility of industrial economic development and carbon emission reduction by improving resource management and green technology innovation, respectively. This is of great significance for China’s industry to reduce waste of resources, strengthen the guidance of scientific and technology innovation, and promote the high-quality industrial development, as well as the formation of a new model of modernisation with
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humans developing in harmony with nature. According to the data from National Bureau of Statistics of China (NBSC), from 2005 to 2016, the
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proportion of industrial energy consumption to national energy consumption increased from 64.6%
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to 68.0%, while the proportion of industrial added value to national GDP decreased from 41.6% to
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33.3%. Meanwhile, from 2005 to 2015, China’s industrial carbon emissions accounted for about 85% of the national carbon emissions (CEAD database). Then, based on the increase in the proportion of
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China’s industrial energy consumption, the decrease in the proportion of industrial added value and the high proportion of carbon emissions, we would like to explore whether there exists the UC
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caused by waste of input resources. The Guidance on Building a Market-Oriented Green technology innovation System issued by China in 2019 emphasizes that green technology innovation is the first
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driving force for green development. Then, under the influence of green technology innovation, is there the state of DC in China's industry? how about the relationship between industrial economic growth and CO2 emissions in China? what is the changing trend and distribution of the technical efficiency and carbon reduction potential in China’s industrial production process? and how to promulgate the “win-win” development strategy between industrial growth and environmental protection for different regions and different industrial sectors in China? China’s energy-intensive industries are the main sources of real economic growth as well as the main drivers of energy consumption and CO2 emissions (Wang and Wei, 2014). The analysis above can not only help Chinese government to realise resource utilisation in the industrial sector and avoid carbon emissions and potential economic decline caused by resources waste, but also promote the development of science and technology to realize the “win-win” of carbon emission reduction and industrial economic 3
Journal Pre-proof growth. In summary, based on the concept of congestion, this paper divides the carbon congestion effect which considers CO2 emissions as undesirable output, into UC and DC according to different types of decreased output, and develops a dual model of DEA to investigate the carbon congestion, returns to damage (damages to return), technical efficiency, reduction potential and strategies at both the provincial and sectoral levels. The main contribution of this study includes the following three aspects: (1) this study is based on the dual model of DEA with undesirable outputs, which can identify whether there is carbon
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congestion in the technical efficient provinces or sectors. (2) This paper investigates China’s industrial carbon congestion from both the provincial and sectoral levels, which helps us to identify the key
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regions and key sectors for targeted CO2 emissions reduction. (3) This congestion study includes both
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undesirable and desirable carbon congestions to investigate the balance between CO2 emissions
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reduction and industrial growth; and based on the results of China’s industrial carbon congestion, this paper also examines the returns to damage, damages to return, technical efficiency, and CO2
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emissions reduction potential of China’s industry by region. The rest of this paper is organised as follows: Section 2 reviews the literature, Section 3
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introduces the research methods and data descriptions, Section 4 presents the empirical results and discussion thereof, Section 5 shows the robustness test, and the conclusions and corresponding
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policy implications are drawn in Section 6.
2. Literature review
The study of congestion is mainly based on the DEA model. The DEA model is a non-parametric method that is first proposed by Charnes et al. (1978), and is widely applied to technical efficiency evaluation, including environmental efficiency evaluation. For example, Choi et al. (2012) use the DEA model to analyse the CO2 emissions efficiency and energy efficiency in China and Sueyoshi and Goto (2014b) measure the environmental efficiency of Japanese chemical and pharmaceutical firms using the DEA model. Meng et al. (2016) review the DEA studies published in 2006-2015 on measuring China’s regional energy and carbon emission efficiency using different DEA models. The DEA models for studying congestion are mainly divided into two categories: radial DEA 4
Journal Pre-proof model and non-radial DEA model. The representative method for the radial DEA model is the FGL model. Färe and Grosskopf (1983) first propose a feasible solution to judge the congestion by using the non-parametric method, and Färe, Grosskopf, and Lovell use the DEA method to develop the first measure model (Färe et al.,1985). Later scholars call this the FGL model, which is a two-stage radial DEA model and measures input congestion by comparing technical differences between input variables under conditions of both strong and weak disposability. The FGL model allows real congestion to be measured, and it has become the only way to judge input congestion (Zhou et al.,
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2017)3. Within the theoretical framework of DEA, Cooper, Thompson and Thrall propose a two-stage
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non-radial DEA model based on slack variables to measure the input congestion: this is called the CTT
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model (Cooper et al., 1996). Brockett et al. (1998) study the input congestion before and after China’s
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economic reform in 1978 based on CTT model, and extend the CTT model to their new BCSW model. Cooper et al. (2002) improve the two-stage BCSW model to a one-stage model. Radial DEA and
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non-radial DEA are the key research methods used to study the same problem from different perspectives, so there has always been a comparison and debate about them (Cooper et al., 2001;
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Cooper, 2001; Färe and Grosskopf, 2001). The former focuses on the economic analysis of technical efficiency in the sense of production frontiers, while the latter focuses on the optimisation of loss and
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redundancy of variables in efficiency calculation based on the mathematical meaning of the optimisation method.
The methods above are widely applied in measuring congestion. Most scholars study congestion from the perspective of inputs. For example, Jahanshahloo and Khodabakhshi (2004) analyse the input congestion in Chinese textile industry based on the non-radial DEA model. Simões and Marques (2011) assess the performance and input congestion of Portuguese hospitals using three different DEA methods. Wu et al. (2016) analyse the input congestion of Chinese industrial sectors using the radial DEA model and find that China’s industry does exist energy input congestion and congestion mainly occurs in provinces with higher energy intensity. In recent years, some scholars incorporate
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Input congestion refers to the conventional congestion that the decrease of input will lead to the increase of desirable output without considering the undesirable output; it identifies congestion mainly by setting input as weak disposability, so it is called input congestion. 5
Journal Pre-proof the undesirable outputs into congestion research. Based on the radial DEA model, Wu et al. (2013) and Chen et al. (2016) study the industrial congestion effects in Chinese regions in 2010 and 2012, respectively. It is worth mentioning that most studies are based on radial or non-radial DEA model, and consider that the congestion effect is a more serious technical inefficiency, and mainly occurs in technically inefficient decision-making unit (DMU) (Brockett et al., 2004). However, in recent years, some studies show that, when considering both desirable and undesirable outputs, even if a DMU is
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technically efficient (i.e. with technical efficiency of one), congestion may still occur, which can be measured using the dual model of the DEA (Fang, 2015; Sueyoshi and Goto, 2012b; Sueyoshi and
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Yuan, 2016). In addition, some scholars focus on undesirable and desirable congestions in recent
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years. For example, Sueyoshi and Yuan (2016) study the undesirable and desirable congestions of 30
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provinces in China from 2005 to 2012 using the dual model of radial DEA, with undesirable outputs of PM10, SO2 and NO2. Therefore, the method used in this paper is based on the research of Sueyoshi
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and Yuan (2016). Besides, Tone and Sahoo (2004), Sueyoshi and Goto (2014b), Sueyoshi and Yuan (2016) and other scholars explore the relationship between congestion effect and elasticity, such as
RTD and DTR in this study.
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Returns to Scale (RTS), Returns to Damage (RTD) and Damages to Return (DTR); thus, we also include
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From the research above on the congestion effect in China, they mainly study congestion from the perspective of provincial industries, and their undesirable outputs do not consider CO2 emissions, such as Wu et al. (2013) and Chen et al. (2016). In fact, China attaches great importance to CO2 emissions reduction and in December 2017, the China’s national carbon trading market for power industry was officially launched; thus, it is necessary to study the congestion specifically on CO2 emissions. As for the study of carbon congestion, Sueyoshi and Goto (2012b) measure the congestion effects of Japanese power industry and manufacturing industries from 2007 to 2009 using the dual model of radial DEA, and the undesirable output is CO2 emissions. Their results show that both power companies and manufacturing companies have undesirable and desirable congestions, and have been committed to achieving carbon reduction through technology innovation. Sueyoshi and Goto (2016) evaluate the congestion effect and technical efficiency of 68 coal-fired power plants in USA in 2010 6
Journal Pre-proof based on the dual model of radial DEA, and the undesirable outputs are SO2, CO2 and NOX; the results show that most plants belong to strong undesirable congestion while strong desirable congestion only occurs in a few power plants. There are few studies on carbon congestion in China’s industries. In summary, current related research mainly studies the conventional input congestion which only considers the desirable outputs, while little consideration is given to undesirable outputs, such as CO2 emissions. Besides, current research into congestion effects in China does not focus on carbon congestion based on the idea of energy conservation and emissions reduction, and is mainly based
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on the provincial level. Industry is the highest CO2 emitter, but existing literature often ignores the research at the industrial sector level, from which it is difficult to reveal the state of carbon
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congestion and carbon reduction potential of China’s industrial sectors, and it is not conducive to the
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targeted implementation of energy conservation and emissions reduction in China’s industry.
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Moreover, as for the research methods, most related studies adopt the DEA model and their measured congestion effect mainly occurs in technically inefficient DMUs, but ignores the congestion
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effect of technically efficient DMUs, from which it is difficult to obtain accurate measurement for congestion. Finally, it is necessary to consider both UC and DC in the analysis of carbon congestion,
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which not only reveals whether or not there is any waste of resources, but also reflects the potential to reduce CO2 emissions through green technology innovation. Therefore, this study focuses on the
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industrial carbon congestion in China, from both the levels of provincial industry and industrial sectors. Specifically, this study uses the dual model of radial DEA to discuss UC and DC, RTD and DTR, and combines window analysis to discuss the technical efficiency and carbon reduction potential. On this basis, the targeted strategies of industrial development and carbon emissions reduction are proposed.
3. Methods and data 3.1 Methods (1) Congestion effect In this study, the desirable output ( g ) refers to the industrial added value while the undesirable output ( b ) refers to CO2 emissions. Congestion means that the decrease of input will lead to the 7
Journal Pre-proof increase of output ( g or b ), as shown in Fig.1 (a). We assume that five DMUs are located on the production frontier CDEAF, and when point F moves to point A, it can reduce its input while increasing its output, which indicates congestion in point F. Congestion is different with from
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technical inefficiency (Brockett et al. 2004)4.
Weak UC (No RTD)
D
C IRTD
0
F
f0
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CRTD
A
Undesirable Output (b)
(b) Undesirable carbon congestion
Undesirable Output (b)
E
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DRTD
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Strong UC (Negative RTD)
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Desirable Output (g)
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(a) Congestion
Strong UC (Negative DTR)
DDTR
E CDTR
A
F
Weak UC (No DTR)
D
f1
C IDTR
0
Desirable Output (g) (c) Desirable carbon congestion
Fig. 1. Congestion, undesirable carbon congestion, desirable carbon congestion and five types of RTD and DTR. Note: IRTD: increasing RTD, CRTD: constant RTD, DRTD: decreasing RTD, NRTD: negative RTD, IDTR: increasing DTR, CDTR: constant DTR, DDTR: decreasing DTR, NDTR: negative DTR.
The undesirable and desirable carbon congestion effects are shown in Fig. 1(b) and Fig. 1(c), respectively. In Fig. 1, strong UC (strong DC) occurs when the desirable (undesirable) output 4
Technical inefficiency indicates that the output can be increased at a given input, or the input can be reduced at a given output (Farrell,1957). As shown in Fig.1 (a), in a given input, point M and point N can move to point A and point F, respectively, by expanding their output; this is a case of technical inefficiency. Congestion indicates that the decrease of input will lead to the increase of output, or the increase of input will inevitably lead to the decrease of output (Färe and Svensson, 1980). When point N reaches point F, it can be moved to point A by reducing input and this process is accompanied by the increase of output; this is a case of congestion. Point N represents technical inefficiency and congestion, and Point F represents technical efficiency and congestion. 8
Journal Pre-proof decreases with an increase in undesirable (desirable) output; thus, strong DC is the best state a society can expect to achieve (Sueyoshi and Yuan, 2016). The present research into UC and DC is based on natural disposability and managerial disposability, respectively (Sueyoshi and Goto, 2012b). Natural disposability reflects the idea of giving priority to economic performance. Under the paradigm of natural disposability, input and undesirable output increase or decrease in the same direction: a DMU can reduce undesirable output by reducing input, thereby increasing the desirable output as much as possible. The production possibility set of UC under natural disposability is , where X , G and B
denote input
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n n n n N PUC ( X ) G, B : G G j j , B B j j , X X j j , j 1, j 0, j 1,L , n j 1 j 1 j 1 j 1
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factor, desirable output factor, and undesirable output factor, respectively. The production possibility set of UC is similar to that of directional distance function; both of them belong to the conventional
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DEA framework and regard the undesirable output as a by-product of the desirable output, which
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confirms to real production process and reflects the production capacity constraints when considering undesirable output. Managerial disposability reflects the idea of giving priority to
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environmental performance. Under the managerial disposability paradigm, a DMU can increase
innovation.
The
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desirable output by increasing input while reducing undesirable output through green technology production
possibility
set
of
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n n n n M PDC ( X ) G, B : G G j j , B B j j , X X j j , j 1, j 0, j 1,L , n j 1 j 1 j 1 j 1
DC
under
managerial
is
. It can be seen that the most obvious
feature of the two production possibility sets is that B is weakly disposable in M PDC (X )
disposability
N PUC (X )
, while in
, G is weakly disposable. The production possibility set of DC belongs to a new framework,
which looks for the status of sustainability and reflects the possibility of green technology innovation (Sueyoshi and Goto, 2012b; Sueyoshi and Goto, 2014a). It is worth mentioning that the production frontier of DC is higher than that of UC due to green technology innovation; that is, when a technically inefficient DMU attains f0, it is possible to shift f0 to f1 after green technology innovation (Sueyoshi et al., 2013; Sueyoshi and Wang, 2014a). For more details on natural and managerial disposability, as well as the relationship between UC and DC, please see Sueyoshi and Goto (2016) and Sueyoshi and Yuan (2016). In addition, according to the state of carbon congestion, the relationship between industrial 9
Journal Pre-proof added value and CO2 emissions can also be obtained, that is, Returns to Damage (RTD) and Damages to Return (DTR)5. As can be seen from Fig. 1(b), when no UC occurs, RTD is positive, which can be subdivided into the increasing, constant, and decreasing types at points C, D and E, respectively. When strong UC occurs, RTD is negative (NRTD), such as at point F. When weak UC occurs, the state of RTD is no RTD, such as at point A. The five types of DTR are shown in Fig. 1(c), in which all terminologies are similar to that in Fig. 1(b). (2) UC effect model
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The calculation method for the industrial sector is consistent with that for the provincial industrial sector, so, here we only take provincial industry as an example to introduce the calculation
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method of carbon congestion, RTD, DTR, technical efficiency, and CO2 emissions reduction potential.
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According to the actual production process and related studies (Zhang and Hao, 2017; Zhou et al.,
xij (i 1,2,3) denote labour, capital, and energy
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2017), it is assumed that input vectors
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consumption in province j ( j 1,L ,30) , respectively, and g j and b j refer to industrial added value and CO2 emissions, respectively. xij , g j and b j are greater than 0. The data ranges of all
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inputs and outputs are determined by their bounds as shown in Eq. (1):
5 max g 5 max b
j 1,L ,30 min g j
1 j 1,L ,30
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Rix 5 max xij j 1,L ,30 min xij j 1,L ,30 Rg Rb
j
j
j 1,L ,30 min b j j 1,L ,30
1
(1)
1
According to Sueyoshi and Goto (2016), this paper uses Models (2) and (3) to measure UC under natural disposability. In Model (2), the undesirable output b is weakly disposable.
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RTD (DTR) refers to the effect of undesirable (desirable) output on the desirable (undesirable) output when the undesirable (desirable) output increases in the same proportion. The calculation process of RTD (DTR) is similar to that of Returns to scale (RTS), which reflects the idea of elasticity, but they differ in concept. RTS refers to the effect of inputs on the desirable output when all inputs increase in the same proportion, reflecting the relationship between input variables and desirable output variables (Tone and Sahoo, 2005). Please see Sueyoshi and Goto (2014a) for detailed calculation of RTS. It is noteworthy that negative RTS (the most severe form of diseconomies of scale) is also the evidence of strong UC (Sueyoshi and Goto, 2012b; Wei and Yan, 2011). 10
Journal Pre-proof 3 Max s Rix dix R g d g i 1 30
xij j dix xik
s.t.
(i 1, 2,3),
j 1 30
g j j d g gk gk , j 1 30
b j j bk bk ,
(2)
j 1 30
j 1, j 1
j 0 ( j 1,L ,30),
: free,
0 (i 1, 2,3), d 0 .
where k represents the province under evaluation,
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g
s is a very small number, which is 0.0001 in
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dix
this study, and j refers to the intensity variable. Besides, dix and d are slack variables related to
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g
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inputs and desirable output, respectively. Model (2) has the following dual formulation: 3
v x i 1
i ik
3
v x
ug j wb j 0 ( j 1,L ,30),
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s.t.
ug k wbk
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Min
i 1
i ij
ug k wbk 1,
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vi s Rix
(i 1, 2,3),
(3)
u sR , g
w : free,
: free.
where is a dual variable and derived from
30
j 1
in Model (2). The optimal solutions
j 1
vi* (i 1, 2,3) , u * , w* and * can be obtained by running Model (3), in which the state of UC can *
be judged by w
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with the assumption on a unique optimal solution (Sueyoshi and Goto, 2016)6.
It is worth noting that following Sueyoshi and Goto (2016) and Sueyoshi and Yuan (2016), this study assumes that Model
(3) produces a unique optimal solution. If province
k has multiple solutions, it is necessary to measure the upper and
lower limits of * to determine the occurrence of UC. 11
Journal Pre-proof * * Specifically, w 0 , w 0 , or w 0 indicate that province k stays in a state of strong UC,
*
no UC, or weak UC, respectively. The technical efficiency (Farrell, 1957) of province k , incorporating the UC state under natural disposability, can be obtained as follows:
3 TEN (UC ) 1 * s Rix dix * R g d g * i 1
(4)
3 1 vi* xik u* g k w*bk * i 1
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The reduction potential of CO2 emissions (CRP) can be obtained through the technical
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inefficiency and CO2 emissions (Wang and Wei, 2014), which is called efficiency-based CRP, as shown
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in Eq. (5):
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* 3 x x * CRPE bk s Ri di R g d g * i 1
(5)
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3 bk vi* xik u* g k w*bk * i 1
3
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According to the following conditions, the type of RTD of province k can be determined: (1) 3
w * 0 and * vi* xi 0 , indicating IRTD; (2) w * 0 and * vi* xi 0 , indicating i 1
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i 1
3
* * CRTD; (3) w * 0 and vi xi 0 , indicating DRTD; (4) w * < 0 , indicating NRTD; i 1
otherwise, (5) No RTD.
(3) DC effect model Here Models (6) and (7) are used to measure the state of DC under managerial disposability:
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Journal Pre-proof 3 Max s Rix dix R b d b i 1 30
s.t.
xij j dix xik
(i 1, 2,3),
j 1 30
g j j gk gk , j 1 30
b j j d b bk bk ,
(6)
j 1 30
j 1, j 1
j 0 ( j 1,L ,30),
: free,
0 (i 1, 2,3), d 0. b
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dix
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Model (6) has the following dual formulation:
i 1 3
vi xij ug j wb j 0( j 1,L ,30),
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s.t.
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3
Min vi xik ug k wbk
i 1
vi s Rix u : free,
(i 1,2,3),
(7)
: free.
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w s Rb ,
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ug k wbk 1,
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By running Model (7), the optimal solutions vi* (i 1, 2,3) , u * , w* and * can be obtained, and in province k , u* 0 , u* 0 , or u* =0 indicate that the province stays in a state of strong DC, no DC, or weak DC, respectively.
The technical efficiency of province
k , incorporating a DC state under managerial disposability,
can be measured by:
* 3 x x * TEM ( DC ) 1 s Ri di Rb d b* i 1 3 * 1 vi xik u* g k w*bk * i 1 The five types of DTR in target province 13
(8)
k can be determined: (1) u* 0 and
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3
3
i 1
i 1
* vi* xi 0 ,indicating IDTR; (2) u* 0 and * vi* xi 0 , indicating CDTR; (3) u* 0 and 3
* vi* xi 0 , indicating DDTR; (4) u* 0 , indicating NDTR; otherwise, (5) no DTR. i 1
(4) Window analysis method Since the TEN(UC) and TEM(DC) calculated by the models above are not comparable in different years, in order to achieve dynamic comparison across years, this paper introduces the window
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analysis method (Sueyoshi and Wang, 2018; Zhang and Chen, 2018). In this study, the number of DMUs in one period is 30, and the time span T 11 (from 2005 to 2015), and according to existing
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research (Halkos and Tzeremes, 2009; Wang et al., 2013), the width of the window d is generally
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set to 3. Therefore, T d 1 windows, i.e., 9 windows with 3 30 DMUs, are established.
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Specifically, the first window covers data from 2005 to 2007, and the second window covers data from 2006 to 2008, and so on, until the ninth window covers data from 2013 to 2015. Finally, the
3.2 Data description
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average efficiency of each province in the same year is taken as the final TEN(UC) or TEM(DC).
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This paper uses panel data, including industrial data from 30 provinces (excluding Tibet) and 34 industrial sectors in China from 2005 to 20157, covering China’s latest two five-year plans periods, i.e.
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11th Five-Year Plan (2005-2010) and 12th Five-Year Plan (2011-2015). Here, labour in the 30 provinces is represented by the number of employees in the entire industrial sector published in China City Statistical Yearbook, while labour in the 34 industrial sectors is represented by the average number of workers from China Industry Statistical Yearbook. Since there is no official indicator of industrial capital stock in China, capital is calculated by the perpetual inventory method (Chen, 2011; Meng et al., 2016). According to existing relevant studies (Wang and Wei, 2014; Wu et al., 2012), the
7
As a result of the Notice of the National Bureau of Statistics on the Implementation of the National Standard for the Classification of New National Economic Industries, the new Industries classification for national economic activities has changed the number of industrial sectors from 39 to 41 since 2012. In order to facilitate comparison, this study organizes 39 or 41 sectors into 34 sectors. Specifically, the sectors of “Other mining”, “Comprehensive utilization of waste resources”, “Production and supply of gas”, “Production and supply of tap water” are deleted. In the 39 sectors from 2005 to 2011, the sectors of “Rubber products” and “Plastics products” are merged into “Rubber and plastic products”. In the 41 sectors from 2012 to 2015, the sectors of “Mining auxiliary activities” and “Repair of metal products, machinery and equipment” are deleted, and the sectors of “Automobile products” and “Equipment for railway, ship, aerospace, and other transportation” are merged into “Transport equipment”. 14
Journal Pre-proof indicator of desirable output is industrial added value and this data for the 30 provinces are sourced from NBSC. Since NBSC has not released added values of the industrial sectors since 2008, the data are collected and balanced with the national data at the sectoral and regional levels by our own (Su and Ang, 2010, 2014). The capital stock and industrial added value in this paper are all at constant 2010 prices. Finally, energy consumption and CO2 emissions are sourced from China Emission Accounts and Datasets8. According to the division of NBSC, 30 provinces in China can be divided into four regions:
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northeastern, eastern, central, and western regions9. According to China’s Industries Classification for National Economic Activities, 34 industrial sectors in China can be divided into three major industries:
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Mining, Manufacturing, and Power-Gas-Water10. Descriptive statistics of various indicators for China’s
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provincial industry and industrial sector are shown in Table 1. In the four regions, the average values
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for all indicators in the eastern region are the highest while those in the western region are the lowest, and except energy consumption, the average values for all indicators in Power-gas-water are
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higher than that in the other two industries.
Table 1 Descriptive statistics of various indicators, 2005-2015. Northeast
835.27
747.61
(Billion RMB) Labor
(746.40) 295.49
(425.12) 209.05
(10,000 persons) Energy consumption
(322.16) 52.23
(Million tons, Mt) Industrial added value
China’s industrial sector
East
Central
West
Industry
Mining
Manufacturing
Power-Gas-Water
1212.11
756.80
439.81
742.73
537.11
590.73
6026.69
(992.01) 521.30
(457.61) 278.85
(346.73) 108.63
(1145.78) 305.16
(584.87) 149.19
(602.14) 276.72
(2081.63) 331.94
(104.50) 62.60
(430.37) 70.26
(123.91) 64.42
(81.41) 36.74
(338.20) 44.57
(174.18) 16.49
(193.74) 51.06
(17.43) 25.52
(38.35) 647.85
(39.40) 531.09
(58.73) 987.12
(29.96) 628.68
(21.32) 299.39
(111.63) 569.37
(16.00) 482.71
(123.63) 559.98
(3.76) 1265.60
(Billion RMB) CO2 emissions
(597.06) 227.71
(285.74) 239.13
(782.20) 284.83
(351.56) 259.57
(248.64) 155.67
(464.54) 243.42
(417.35) 34.31
(457.65) 117.52
(318.33) 3208.62
(Mt)
(163.12)
(103.84)
(219.29)
(108.48)
(101.45)
(672.68)
(35.58)
(329.98)
(679.38)
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China Capital stock
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China’s provincial industry
Note: The values in parentheses are standard deviations and the values without parentheses are the mean values.
8
http://www.ceads.net/ The northeastern region includes Liaoning, Jilin, and Heilongjiang; the eastern region includes Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; the central region includes Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan; and the western region includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. 10 The Mining industry includes the following five sectors: Coal mining and dressing, Petroleum and natural gas extraction, Ferrous metals mining and dressing, Nonferrous metals mining and dressing, and Nonmetal minerals mining and dressing. The Power-Gas-Water industry only includes the sector of Electric power, steam and hot water production and supply. The remaining 28 sectors belong to Manufacturing industry. 15 9
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4. Empirical results 4.1. Analysis of carbon congestion based on China’s provincial industry This section first discusses carbon congestion based on China’s provincial industry, and then describes the technical efficiency and CO2 emissions reduction potential of China’s provincial industries. Finally, according to the states of carbon congestion and technical efficiency of China’s provincial industry, the provincial strategies for future development and CO2 emissions reduction are
4.1.1. The state of carbon congestion The optimal solutions vi (i 1, 2,3) , u* , w* and *
can be obtained by using Models (3)
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*
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proposed.
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and (7). According to the aforementioned research methods, the state of carbon congestion, RTD,
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and DTR in China’s 30 provinces during 2005-2015 are obtained, and the results are shown in Tables A1 and A2 in the Appendix. Fig. 2 shows the results of 2005, 2010 and 2015 as representatives.
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First, it can be seen from Fig. 2 that UC and DC in China’s provincial industry is serious and obvious. The provinces with strong UC (NRTD) in China accounted for 43.33%, 40% and 46.67% of all
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provinces in 2005, 2010 and 2015, respectively, while the provinces with strong DC (NDTR) in China
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accounted for 66.67%, 60% and 46.67% of all provinces in 2005, 2010 and 2015, respectively. Combining with the concept of UC and DC, the strong UC (NRTD) states in Fig.2 indicate that there exists overflow of CO2 emissions in China’s provincial industries due to over-investment in resources. This also reflects that China has a strong resource-based CRP, that is, reducing investment resources will be beneficial to carbon emission reduction and industrial growth. The strong DC (NDTR) states indicate that there is also strong technology-based CRP in China’s provincial industries; in other words, CO2 emissions reduction can be achieved through green technology innovation while promoting industrial economic growth, which also reflects significant potential for technological innovation in China’s provincial industry.
16
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H e ilo n g jia n g
H e ilo n g jia n g
Jilin
X in jia n g
Jilin X in jia n g
G a n su
In n e rM o n g o lia
N in g xia
Q in g h a i
Be ijin g Lia o n in g G a n su
H e b e i Tia n jin
Sh a n xi Sh a n d o n g
Strong UC (NRTD) No UC (DRTD) No data
H ong Kong M a ca o H a in a n
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H e b e i Tia n jin
Tib e t
Jia n g su
H e b e i Tia n jin N in g xia Sh a n xi Sh a n d o n g
H ong Kong M a ca o H a in a n
(d)2010 DC [原创]ExcelPro的图表博客 [修改]@数据化分析
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(c)2010 UC
Strong DC (NDTR) No DC (DDTR) No data
Be ijin g
Lia o n in g
Jia n g su Sh a a n xiH e n a n Sh a n g h a i Sic h u a n H ub ei A nhui C h o n g q in g Zh e jia n g H u n a nJia n g xi G u izh o u Fu jia n Yunna n Ta iw a n G u a n g xiG u a n g d o n g
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Yunna n
H u n a nJia n g xi Fu jia n Ta iw a n G u a n g xiG u a n g d o n g H ong Kong M a ca o H a in a n
G u izh o u
re
Sh a n g h a i A nhui Sic h u a n H ub ei C h o n g q in g Zh e jia n g
Strong UC (NRTD) No UC (DRTD) No data
Jilin
In n e rM o n g o lia
Q in g h a i
Sh a n xi Sh a n d o n g
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Tib e t
G a n su
Be ijin g Lia o n in g
Sh a a n xi H e n a n
H e ilo n g jia n g
X in jia n g
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X in jia n g
Q in g h a i
Sh a n d o n g
(b)2005 DC [原创]ExcelPro的图表博客 [修改]@数据化分析
Jilin
N in g xia
H e b e i Tia n jin N in g xia Sh a n xi
Jia n g su Sh a n g h a i Sic h u a n H ub ei A nhui C h o n g q in g Zh e jia n g H u n a nJia n g xi G u izh o u Fu jia n Yunna n Ta iw a n G u a n g xiG u a n g d o n g
Strong DC (NDTR) No DC (DDTR) No data
H e ilo n g jia n g
G a n su
Lia o n in g
Sh a a n xiH e n a n
Tib e t
(a)2005 UC
In n e rM o n g o lia
In n e rM o n g o lia
Q in g h a i
Sh a a n xi H e n a n Jia n g su Sh a n g h a i A nhui Sic h u a n H ub ei C h o n g q in g Zh e jia n g H u n a nJia n g xi G u izh o u Fu jia n Yunna n Ta iw a n G u a n g xiG u a n g d o n g H ong Kong M a ca o H a in a n
Tib e t
Be ijin g
H e ilo n g jia n g
H e ilo n g jia n g
Jilin
X in jia n g
Q in g h a i Tib e t
Strong UC (NRTD) No UC (DRTD) No data
In n e rM o n g o lia
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G a n su
N in g xia
Jilin
X in jia n g
Be ijin g Lia o n in g
G a n su
H e b e i Tia n jin
Sh a n xi Sh a n d o n g
In n e rM o n g o lia N in g xia
Q in g h a i
Jia n g su Sh a a n xi H e n a n Sh a n g h a i A nhui Sic h u a n H ub ei C h o n g q in g Zh e jia n g H u n a nJia n g xi G u izh o u Fu jia n Yunna n Ta iw a n G u a n g xiG u a n g d o n g H ong Kong M a ca o H a in a n
Tib e t
Strong DC (NDTR) No DC (DDTR) No DC (IDTR) No data
Be ijin g Lia o n in g H e b e i Tia n jin
Sh a n xi Sh a n d o n g
Jia n g su Sh a a n xi H e n a n Sh a n g h a i A nhui Sic h u a n H ub ei C h o n g q in g Zh e jia n g Yunna n
H u n a nJia n g xi Fu jia n Ta iw a n G u a n g xiG u a n g d o n g H ong Kong M a ca o H a in a n
G u izh o u
(f)2015 DC
(e)2015 UC
Fig. 2. Carbon congestion for China’s provincial industry in 2005, 2010 and 2015.
Second, the carbon congestion effect in industrial production in China’s provinces shows a trend of regional agglomeration and presents obvious regional heterogeneity. From Fig. 2, we can see that from 2005 to 2015, the strong UC (NRTD) and strong DC (NDTR) agglomerated from the four regions 17
Journal Pre-proof to the eastern and western regions, respectively. In 2015, the strong UC (NRTD) is mainly concentrated in the eastern region and the eastern region accounted for 57.14% of the provinces with strong UC in 2015. It indicates that compared with other regions, the excessive investment in the eastern region is relatively serious, which not only results in unnecessary CO2 emissions, but also undermines the industrial output. Actually, the eastern region is the most economically and technologically developed region in China (Xu and Lin, 2015). Benefiting from the coastal geographic location and the policy of reform and opening up, the eastern region has developed industries and
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abundant labour force; its higher industrial outputs have continuously attracted significant outside resources and investment (Wang and He, 2017). Table 1 also shows that resource inputs in the
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eastern region are the highest among the four regions in China. Although the eastern region
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continues to attract investment, its resource carrying capacity and development capacity are limited,
re
and over-investment will lead to strong UC. Therefore, appropriate investment reduction is more beneficial to carbon emission reduction and industrial development of the eastern region. The
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disappearance of strong UC in the northeastern, central and western regions from 2005 to 2015 is mainly due to the fact that in recent years, China has placed the construction of ecological civilization
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in a prominent position (Zhang and Liu, 2019)11, committed to eliminated backward production capacity, and promoted industrial transformation and upgrading. In particular, in 2010 and 2013,
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China promulgated the Notice of the State Council on Further Strengthening the Work of Eliminating Backward Capacity and the Guiding Opinions of the State Council on Resolving Serious Overcapacity Contradictions. In fact, China’s backward industries are mainly concentrated in these regions, especially in the western region.
The strong DC (NDTR) is mainly concentrated in the western region, followed by the eastern region, and the western region accounted for 50% of the provinces with strong DC in 2015, indicating that there is significant potential for industrial growth and CO2 emissions reduction through green technology innovation. The reason is that the industrial base of China’s western region is relatively weaker and its industrialisation process is relatively slower than other regions; meanwhile, due to the
11
China's 11th Five-Year Plan in 2006 proposed the target of energy conservation and emissions reduction for the first time. Since then, China has formulated a series of policies on environmental protection and emissions reduction. 18
Journal Pre-proof long-term retention during conventional heavy industrialisation, its industrial carbon intensity is relatively higher. Therefore, the larger CO2 emissions and relatively backward technological level of the western region make it possible to realize the huge dividends of CO2 emissions reduction and industrial transformation and upgrading once it devotes itself to green technology innovation. In addition to the western region, the eastern region also has a high proportion of strong DC in 2015. This is because the eastern region has strong technology innovation capability, which even represents the highest level of scientific and technological innovation in China. The eastern region’s advantages
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in innovative infrastructure, talent and technology make it still have the potential to further reduce CO2 emissions through green technology innovation.
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However, unlike the western region, the proportion of strong DC in the eastern region
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experienced a downward trend during 2005-2015. Similarly, the disappearance of strong DC also
re
exists in the northeastern and central regions. On the one hand, this indicates that during this period, the socio-economic levels of these three regions have developed rapidly, and a significant carbon
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emission reduction has been achieved by improving green technology innovation (Jiao et al., 2018). On the other hand, this also indicates that it will become more difficult for these regions to further
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reduce CO2 emissions by improving their relatively high level of technology innovation, and they may face such problems as great difficulty in innovation, long innovation cycle and insignificant innovation
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effects. By contrast, the western region has become the region with the greatest potential for technological development and with the greatest technology-based CRP. Third, there remains significant scope for industrial development in the northeastern, central and western regions and it is unsustainable for China to develop its industrial growth through consuming fossil fuels like before. As shown in Fig. 2(e), the northeastern, central and western regions showed no UC (DRTD) in 2015. On the one hand, this indicates that consuming fossil fuels still plays a positive role in the industrial development of these regions; thus, strengthening the revitalisation of northeastern China and the development of the central and western regions remain important in promoting the development of Chinese industry (Mi et al., 2017). On the other hand, this also shows that the contribution of fossil fuel consumption to industrial development shows a diminishing marginal effect, indicating that the production mode of realising industrial development 19
Journal Pre-proof through CO2 emissions from fossil fuel consumption is unsustainable and it is thus necessary to find new modes of economic growth. 4.1.2 Technical efficiency Based on the window analysis, the technical efficiency of China’s provincial industry can be calculated according to Eqs. (4) and (8). In this study, the technical efficiency can be divided into TEN(UC) and TEM(DC), as shown in Figs. 3 and 4. As seen from Fig. 3 (a), TEN(UC) in the eastern region appears the highest, followed by that in the central and northeastern regions, and it is the
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lowest in the western region; their average TEN(UC) from 2005 to 2015 is 0.97, 0.83, 0.81, and 0.8, respectively. This indicates that the eastern region has the highest level of industrial development,
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and their CRP through upgrading its technical efficiency is relatively smaller (i.e., efficiency-based
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CRP), while the other regions have a lower level of industrial development and greater
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efficiency-based CRP, especially the western region, which is consistent with the results of many studies on China’s environmental efficiency (Wang and Wei, 2014). Besides, TEN(UC) in the
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northeastern and central regions shows a fluctuating upward trend from 2005 to 2011, but declines significantly since 2012. This transformation in northeast China is mainly caused by its economic
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structural characteristics (i.e., heavy industry-oriented). Since the implementation of the strategy of revitalizing the old industrial bases in northeast China in 2003, the northeastern region has entered a
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stage of rapid economic development (Zhang, 2008); however, when Chinese economy entered the “new normal” state, the economic growth rate in the northeastern region has dropped sharply since 2012, especially in its heavy industry12. The main reason for the decline of TEN (UC) in the central region since 2012 may be the downturn of Shanxi’s industrial economy. Shanxi is the mainly coal mine province and the continuous decline in coal prices since 2012 drags Shanxi’s industrial economy into the bottom13. The environmental protection performance in the eastern region is the best, while that in the western region is worse; as shown in Fig. 4 that the average TEM(DC) from 2005 to 2015 in the northeastern, eastern, central and western regions are 0.74, 0.93, 0.77 and 0.72, respectively.
12
http://www.xinhuanet.com//politics/2016lh/2016-03/11/c_1118307999.htm
13
http://www.mlr.gov.cn/xwdt/jrxw/201606/t20160622_1409427.htm 20
Journal Pre-proof Besides, from Fig. 3(b), it can be found that China's TEM(DC) shows an upward trend from 2005 to 2015, rising from 0.8 to 0.84, reflecting the growing role of energy conservation and emission reduction in China’s development. It is also found that TEM(DC) generally declines significantly in 2007, which mainly due to the high industrial growth in 2007, resulting in greater environmental protection pressure. Actually, 2007 is the last sprint year of industrial high-speed growth since the implementation of the reform and opening-up policy. According to the data from NBSC, in 2007, the industrial added value increased by 13.5% over the last year and the secondary industry boosted GDP
(a) TEN(UC)
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expansion of China’s heavy industry has come to an end.
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by 7.1%, both of which are the highest growth rates since the 21st century; so far, the capacity
(b) TEM(DC)
Efficiency
1 0.8 0.6 0.4
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Fig. 3. TEN(UC) and TEM(DC) in China and four regions from 2005 to 2015. TEN(UC)
TEM(DC)
0.2
Liaoning Jilin Heilongjiang Beijing Tianjin Hebei Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Shanxi Anhui Jiangxi Henan Hubei Hunan Inner Mongolia Guangxi Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang
0
Northeast
East
Central
West
Fig. 4. Average TEN(UC) and average TEM(DC) of 30 provinces in China. In addition, it can be seen from Fig. 4 that TEN(UC) is generally higher than TEM(DC), suggesting that compared with environmental protection, China has paid more attention to industrial 21
Journal Pre-proof development. Besides, compared with the other three regions, the eastern region has a higher TEN(UC) and TEM(DC) and a smaller difference between TEN(UC) and TEM(DC), indicating that the eastern region performs well with regard to both industrial development and environmental protection, such as Shanghai, Jiangsu, Guangdong and Hainan. However, it is worth noting that high technical efficiency does not mean that there is no congestion. Although China’s eastern region has played the leading role in both industrial development and environmental protection, it still has the potential for further improvement in light of its carbon congestion state. As for the western region, its
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technical efficiency is relatively lower, and under the current production level, there is a gap between the technical efficiency of the western region and the production frontier represented by the eastern
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region, which indicates that the western region in China has great efficiency-based CRP. Moreover,
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combined with the strong DC state, the western region also has a huge potential to further achieve
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carbon emission reduction through green technology innovation, which could expand its production frontier.
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4.1.3 Reduction potential and reduction strategy of CO2 emissions Based on Eq. (5) and the CO2 emissions of various provinces in China from 2005 to 2015, the
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average efficiency-based CRP of Chinese industry can be calculated and the results are shown in Fig. 5. It can be seen that, the average annual total CO2 emissions could be reduced by 784.27 Mt by
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improving technical efficiency, of which 122.02 Mt, 73.32 Mt, 280.17 Mt, and 308.76 Mt arise in the northeastern, eastern, central, and western regions, respectively. Therefore, the western region has the largest efficiency-based CRP and its CRP accounts for 39% of the total. From the perspective of the average CRP of all provinces in different regions, the average values of provinces in the four regions are 40.67, 7.33, 46.7 and 28.07, respectively, of which the northeastern and central regions have higher values. From the perspective of the provinces, the province with the largest CRP is Shanxi, followed by Guizhou, Liaoning and Anhui, with 151.46, 75.16, 53.81 and 50.42 Mt, respectively. Therefore, the western region, the northeastern region and Shanxi province should be the key targets for carbon emission reduction in China.
22
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Fig. 5. Efficiency-based CO2 reduction potential for China’s provincial industry14.
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As mentioned above, under the current production frontiers, the province can eliminate strong
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UC by reducing its resource investment, so as to promote the decoupling of CO2 emissions from
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China’s industrial development (Sueyoshi and Goto, 2016). If a province exhibits strong DC, this province can devote itself to green technology innovation to promote the “win-win” between
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industrial development and CO2 emissions reduction (Sueyoshi and Yuan, 2016). If a province exhibits technical inefficiencies, it indicates that there is greater CRP and industrial development if this
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province produces on the current production frontier (Du et al., 2015). And based on the results of UC, DC and TEN(UC), this paper proposes possible future development and emissions reduction
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strategies for each province (see Table 2) (Sueyoshi and Goto, 2014a; Sueyoshi and Wang, 2014b)15. Table 2 divides the reduction strategies for CO2 emissions into two types, namely: a resource-based strategy (based on strong UC) and a technology-based strategy (based on strong DC). Technology-based emissions reduction needs to expand the production frontier through technology innovation; thus, compared with the resource-based strategy, a technology-based strategy incurs high costs, and it is time-consuming, but effective and sustainable (Sueyoshi and Goto, 2012a; Sueyoshi and Yuan, 2016). It can be inferred from Table 2 that many provinces in eastern China have both
14
This result only reflects the potential of carbon emission reduction that can be achieved through the improvement of technical efficiency. 15 In order to more accurately propose strategies, we use the data of three years (i.e., 2013-2015) instead of the latest one year (i.e., 2015) to show the congestion in recent years, and the TEN(UC) in Table 2 is the mean value of TEN(UC) in 2013-2015. The state of congestion that occurs two or more times in the three years is taken as the final congestion state of a province in recent years. 23
Journal Pre-proof strong UC and DC16, such as Beijing, Hebei, Jiangsu, Shandong, Guangdong, and Hainan, so eastern China should pay more attention to the optimisation of input resources and green technology innovation in the future. For the other three regions, under the current production level, various means should be adopted to improve their technical efficiency according to the socio-economic and production consumption situation of each province (Mi et al., 2019). More importantly, the central region should pay more attention to the optimisation of input resources, and the northeastern and western regions should pay more attention to the improvement of green technology innovation to
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achieve long-term development.
Region
Province
Northeast
Central
TEN(UC)
2015
2013
2014
2015
Liaoning
No
No
No
No
No
No
0.92
Jilin Heilongjiang
No S
No No
No No
S S
No No
0.80 0.59
S
S
Tianjin Hebei
No S
No S
Shanghai
No
No
Jiangsu Zhejiang
S S
Fujian Shandong
S S
Guangdong Hainan
S S
Shanxi
S S
R
T
S
S
0.99
√
√
No S
No S
No S
0.97 0.99
√
√
No
S
S
S
0.92
S
S
E √
√ √
No S
re
Beijing
-p
2014
√ √
√
S S
S No
S No
S No
0.98 0.96
√ √
S S
S S
No S
No S
No No
1.00 0.98
√ √
√
S S
S S
S S
S S
S S
1.00 0.98
√ √
√ √
No
No
No
S
S
S
0.53
No S
S No
S No
No No
No No
No No
0.82 0.84
√
√ √ √
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Anhui Jiangxi
West
DC
2013
na
East
UC
ro
Table 2 Strategies of industrial development and carbon emission reduction in different provinces in China.
√ √
√
Henan
S
S
No
No
No
No
0.83
√
Hubei Hunan
No S
No S
No S
S No
No No
No No
0.88 1.00
√
Inner Mongolia Guangxi
S S
S S
S S
No S
No S
No S
1.00 0.99
√ √
Chongqing
No
No
No
No
S
No
0.87
Sichuan Guizhou
No S
No S
No S
S No
S No
S No
0.90 0.65
√
Yunnan Shaanxi
No S
No S
No S
S No
S No
S No
0.76 0.93
√
√
√
√ √ √
√ √
√
√ √
Gansu
No
No
No
S
S
S
0.53
√
√
Qinghai Ningxia
No No
No No
No No
S S
S S
S S
0.97 0.41
√ √
√
Xinjiang
No
No
No
S
S
S
0.61
√
√
Note: R: Resource-based strategy; T: Technology-based strategy; E: Low technical efficiency; UC: Undesirable carbon congestion; DC: Desirable carbon congestion; S: strong UC or DC; No: no UC or DC; TEN(UC): Technical efficiency under
16
UC and DC reflect conventional and sustainable ideas, respectively, which indicates that these provinces may have production capacity constraints in given production frontier, but also have potentials to expand their production frontier through green technology innovation. 24
Journal Pre-proof natural disposability.
4.2 Analysis of carbon congestion based on China’s industrial sector 4.2.1 The state of carbon congestion The state of carbon congestion in China’s 34 industrial sectors from 2005 to 2015 can be obtained by Models (3) and (7), and the results are shown in Tables A3 and A4 in the Appendix. According to China’s Industries Classification for National Economic Activities, China’s 34 industrial sectors can be divided into three major industries, i.e., Mining, Manufacturing, and Power-Gas-Water.
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Table 3 shows the results in 2005, 2010 and 2015 as representatives.
2010
UC
RTD
S
N
1
Coal mining and dressing
2
Petroleum and natural gas extraction
3
Ferrous metals mining and dressing
S
4
Nonferrous metals mining and dressing
S
5
Nonmetal minerals mining and dressing
6
Food processing
7
Food production
8
Beverage production
9
Tobacco processing
10
Textile industry
11
Garments and other fiber products
12 13 14
Furniture manufacturing
15
Papermaking and paper products
16
Printing and record medium reproduction
17 18
2015
2005
2010
2015
UC
RTD
UC
RTD
DC
DTR
DC
DTR
DC
S
N
No
D
No
I
No
I
No
DTR I
D
S
N
No
D
No
D
No
I
S
N
N
No
I
No
D
No
D
No
D
No
D
N
No
I
No
I
No
D
No
D
No
D D
re
No
N
S
N
No
I
S
N
No
D
No
S
N
S
N
S
N
No
I
No
I
No
I
No
D
S
N
S
N
No
D
No
D
No
D
No
I
No
D
S
N
No
D
No
D
No
D
S
N
S
N
S
N
No
D
No
D
No
D
No
D
S
N
S
N
S
N
No
D
No
D
No
D
S
N
No
D
S
N
S
N
S
N
Leather, furs, down and related products
No
D
No
I
No
I
S
N
S
N
S
N
Timber processing, bamboo, cane, palm & straw products
No
I
No
I
No
D
No
D
No
D
No
D
S
N
No
I
No
I
No
D
S
N
S
N
No
I
No
D
No
D
No
D
No
D
No
D
No
I
No
I
No
I
S
N
S
N
S
N
Cultural, educational and sports articles
S
N
S
N
No
I
S
N
S
N
S
N
Petroleum processing and coking
S
N
No
D
No
D
S
N
No
D
S
N
19
Raw chemical materials and chemical products
S
N
No
D
No
D
S
N
S
N
S
N
20
Medical and pharmaceutical products
No
I
No
D
S
N
No
D
No
D
No
D
21
Chemical fiber
No
I
No
I
No
I
S
N
S
N
S
N
22
Rubber and plastic products
No
D
No
D
No
D
S
N
No
D
S
N
23
Nonmetal mineral products
No
D
S
N
S
N
S
N
No
I
No
I
24
Smelting and pressing of ferrous metals
S
N
S
N
S
N
S
N
S
N
S
N
25
Smelting and pressing of nonferrous metals
No
D
No
D
No
D
No
D
No
D
No
D
26
Metal products
No
D
S
N
S
N
No
D
No
D
No
D
27
Ordinary machinery
S
N
S
N
S
N
No
I
No
I
No
I
28
Equipment for special purpose
No
D
No
D
No
D
No
D
No
D
No
D
29
Transportation equipment
No
D
No
D
No
D
No
I
No
I
S
N
30
Electric equipment and machinery
S
N
No
D
No
D
No
I
No
I
No
I
31
Electronic and telecommunications equipment
No
D
No
D
No
D
S
N
No
D
No
I
32
Instruments, meters cultural and office machinery
No
I
No
I
No
I
No
D
S
N
No
D
33
Other manufacturing industry
No
D
No
I
S
N
S
N
No
D
No
D
34
Electric power, steam and hot water production and supply
S
N
S
N
S
N
S
N
S
N
S
N
na
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S
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Mining Manufacturing PGW
Sector
-p
2005 Code
ro
Table 3 Carbon congestion for China’s industrial sectors in 2005, 2010 and 2015.
Note: UC: Undesirable carbon congestion; DC: Desirable carbon congestion; RTD: Returns to damage; DTR: Damages to return; S: Strong; W: Weak; No: No UC or DC; N: Negative; I: Increasing; D: Decreasing; Mining includes sectors with codes 1 to 5, Manufacturing includes sectors with codes 6 to 33, and Power-Gas-Water (PGW) only includes sector with code 34. 25
Journal Pre-proof First, it can be seen that UC and DC in China’s industrial sector is obvious, and both strong UC and strong DC mainly occur in Manufacturing and Power-Gas-Water industries. From 2005 to 2015, the sector number of both strong UC and strong DC fluctuates around 13, accounting for 38.24% of total sectors. In 2015, the proportion of strong UC in Mining, Manufacturing, and Power-Gas-Water is 0 (0/5), 39.29% (11/28), and 100% (1/1)17, respectively, indicating that there are varying degrees of waste of resources in the Manufacturing and Power-Gas-Water, resulting in a serious overflow of CO2 emissions. Specifically, the wastes of resources in Food processing (sector 6), Tobacco processing
of
(sector 9), Nonmetal mineral products (sector 23), Smelting and pressing of ferrous metals (sector 24), Ordinary machinery (sector 27) and Electric power, steam and hot water production and supply
ro
(sector 34) are quite serious, as these sectors are almost all in the strong UC state from 2005 to 2015.
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Besides, the Mining industry and some sectors in the Manufacturing industry have strong technology-based CRP. As shown in Table 3, in 2015, the proportions of strong DC in Mining,
re
Manufacturing, and Power-Gas-Water are 20% (1/5), 39.29% (11/28), and 100% (1/1)18, respectively.
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This indicates a great potential for green technology innovation and rich technology-based CRP in Manufacturing and Power-Gas-Water, especially in the following sectors: Garments and other fiber
na
products (sector 11), Leather, furs, down and related products (sector 12), Furniture manufacturing (sector 14), Printing and record medium reproduction (sector 16), Cultural, educational and sports
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articles (sector 17), Raw chemical materials and chemical products (sector 19), Chemical fiber (sector 21), Smelting and pressing of ferrous metals (sector 24) and Electric power, steam and hot water production and supply (sector 34). These sectors are almost all in the strong DC state from 2005 to 2015, indicating that these sectors emit huge amount of CO2 emissions and can easily achieve energy saving and emission reduction as long as they are committed to green technology innovation. Actually, these sectors also belong to the 16 categories of heavily polluting industries announced in China’s Guidelines for Environmental Information Disclosure of Listed Companies19, such as metallurgy, chemicals, building materials, paper, fermentation, textile, leather, mining. As important objects of
17
The total sector number of three major industries is 5, 28 and 1, respectively; the sector number of strong UC of three major industries is 0, 11 and 1, respectively. 18 The sector number of strong DC of three major industries is 1, 11 and 1, respectively. 19 http://www.gov.cn/gzdt/2010-09/14/content_1702292.htm 26
Journal Pre-proof carbon emissions regulation by Chinese government, these heavily polluting sectors also have the most incentive to carry out green technology innovation. Third, there is still a large space for industrial development in Mining, and the promotional effect of fossil fuel consumption on the development of Mining witnesses a marginal diminishing trend. As shown in Table 3, from 2005 to 2015, the strong UC state of the Mining shows a downward trend while its no UC state is on the rise, and by 2015 the Mining industry is dominated by a state of no UC (IRTD, DRTD). This implies that the fossil fuel consumption will still play a large part in promoting the
of
development of Mining and there will still be scope for large industrial development and strong carbon consumption tendencies in Mining, especially in the Nonferrous metals mining and dressing
ro
(sector 4) and Nonmetal minerals mining and dressing (sector 5). At the same time, it also indicates
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that the production mode of industrial development of mining through CO2 emissions from fossil fuel
re
consumption is unsustainable, especially in sectors of Coal mining and dressing (sector 1), Petroleum
4.2.2 Technical efficiency
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and natural gas extraction (sector 2), and Ferrous metals mining and dressing (sector 3).
Based on the window analysis, the technical efficiency of various industrial sectors in China can
na
be calculated according to Eqs. (4) and (8). Figs. 6 and 7 show the efficiency results for the three major industries and 34 sectors, respectively.
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It can be seen from Fig. 6 that the technical inefficiency of China’s industry is evident, especially in Mining. Specifically, from 2005 to 2015, the average TEN(UC) for Mining, Manufacturing, and Power-Gas-Water is 0.57, 0.61, and 0.93, respectively, which means that the level of industrial development of Power-Gas-Water is relatively higher while that of Mining is relatively lower. Mining is an energy-intensive industry, and the distribution of mineral resources is mainly concentrated in the economically underdeveloped western region of China. The industrial added value of Mining has increased rapidly since the 21st century; this consecutive industrial development is accompanied by surging energy consumption and CO2 emissions, and its overall production mode is relatively extensive, all of which bring the lower technical efficiency of Mining (Liu et al., 2016). Unlike Manufacturing and Power-Gas-Water, the TEN(UC) of Mining declines from 2005 to 2015. The reasons for this decline are the rising cost of the Mining industry, the slowdown in downstream 27
Journal Pre-proof demand in Mining, the oversupply of its production capacity, and the downward pressure of mineral product prices in recent years (Ma et al., 2019)20. All of this leads to a decline in the efficiency of Chinese mining industry. However, this also shows that there is some room for improving the technical efficiency in Mining. For example, reducing energy intensity and optimising structure are considered effective ways to improve the technical efficiency of Mining. The significant decline in TEN (UC) of Power-Gas-Water in 2008 is mainly due to the impact of international financial crisis, which leads to a decline in external demand and a downturn in this industry; besides, the implementation
of
of tight monetary policy and the increase of fuel costs in 2008 also make this industry difficult to
na
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re
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ro
operate.
Fig. 6. TEN(UC) and TEM(DC) of three major industries in China from 2005 to 2015.
0.9 0.8
Efficiency
TEM(DC)
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TEN(UC)
1.0
0.7
0.6 0.5 0.4 0.3
0.2 0.1 0.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Mining
Manufacturing
Power-gas-water
Sector code
Fig. 7. Average TEN(UC) and average TEM(DC) of 34 industrial sectors in China21.
20
http://money.people.com.cn/stock/n1/2015/1217/c67815-27940260.html
21
The sector codes of 34 industrial sectors are shown in Table 3. 28
Journal Pre-proof
In addition, green technology innovation is the key for China to achieve sustainable energy savings and emissions reduction in the future. From 2005 to 2015, the national TEM(DC) shows an upward trend, rising from 0.63 to 0.72, indicating that China has made efforts to achieve energy saving and emission reduction through green technology innovation since 2005. The average TEM(DC) for Mining, Manufacturing, and Power-Gas-Water is 0.55, 0.76, and 0.96, respectively, which means that Power-Gas-Water performs better in energy saving and emission reduction, and Mining and
of
Manufacturing have great potential for improvement in energy saving and emission reduction performance. This view also conforms to some existing related studies. For example, Jordaan et al.
ro
(2017) argue that energy technology innovation is increasingly considered to be the key to promoting
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global emissions reduction, which is mainly due to technological innovations that can guide the
re
transformation of energy structures and change the modes of economic growth, thus promoting carbon emissions reductions. Yii and Geetha (2017) point out that technological innovation is
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negatively correlated with CO2 emissions; thus, for the sustainable development of economy and environment, policymakers should promote green technology innovation without delay.
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Finally, as shown in Fig. 7, some sectors have a very low TEN(UC) but a very high TEM(DC), such as Timber processing, bamboo, cane, palm & straw products (Sector 13), Papermaking and paper
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products (Sector 15), and Chemical fiber (Sector 21). This indicates that these sectors currently perform well in energy saving and emission reduction, but are inefficient in industrial development. This may be due to the low profitability and rising raw material costs in Sectors 13 and 15, the “new normal” state with low-speed growth and intensified competition in Sector 21, and the increasing pressure of green environmental protection22. The TEN(UC) of some sectors appears obviously higher than their TEM(DC), such as Food processing (Sector 6), Nonmetal mineral products (Sector 23), and Ordinary machinery (Sector 27), indicating that these sectors pay more attention to industrial development than to environmental protection. Finally, the TEN(UC) and TEM(DC) of Tobacco processing (Sector 9), Smelting and pressing of ferrous metals (Sector 24), Electronic and telecommunications equipment (Sector 31), and Electric power, steam and hot water production and 22
http://www.paper.com.cn/news/daynews/2018/180906165849780261.htm 29
Journal Pre-proof supply (Sector 34), are all higher than 0.9, indicating that these four sectors have achieved better performance in both industrial development and environmental protection. 4.2.3 Reduction potential and reduction strategy of CO2 emissions According to Eq. (5) and the CO2 emissions of various industrial sectors from 2005 to 2015, the efficiency-based CRP of China’s industrial sector can be calculated and the results are listed in Table 4. It can be seen that, through the improvement of technical efficiency, 661.36 Mt of CRP can be realised. Among them, the CRP of Mining, Manufacturing, and Power-Gas-Water industries are 37.88
of
Mt, 422.22 Mt, and 201.27 Mt, respectively, accounting for 5.73%, 63.84%, and 30.43% of the total CRP, respectively. It can be inferred that, under the current production level, Manufacturing and
ro
Power-Gas-Water have rich CRP through the improvement of their technical efficiency.
2
Petroleum and natural gas extraction
3
Ferrous metals mining and dressing
6
Food processing
7
Food production
8
Beverage production
9
Tobacco processing
10
Textile industry
11
Garments and other fiber products
12
Power-Gas-Water
Sector
CRP
13.54
Code 4
Nonferrous metals mining and dressing
2.86
0.54
5
Nonmetal minerals mining and dressing
8.29
Total
37.88
12.65 20
Medical and pharmaceutical products
7.30
21
Chemical fiber
5.51
19.33
22
Rubber and plastic products
7.47
0.00
23
Nonmetal mineral products
139.40
15.35
24
Smelting and pressing of ferrous metals
13.23
3.89
25
Smelting and pressing of nonferrous metals
19.60
2.50
26
Metal products
7.78
9.36
27
Ordinary machinery
6.19
14
Leather, furs, down and related products Timber processing, bamboo, cane, palm & straw products Furniture manufacturing
0.49
28
Equipment for special purpose
6.61
15
Papermaking and paper products
40.63
29
Transportation equipment
2.14
16
Printing and record medium reproduction
1.00
30
0.34
17
Cultural, educational and sports articles
0.82
31
18
Petroleum processing and coking
60.05
32
19
Raw chemical materials and chemical products
30.94
Electric equipment and machinery Electronic and telecommunications equipment Instruments, meters cultural and office machinery Other manufacturing industry
34
na
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1.89
17.72
13
Manufacturing
CRP
Coal mining and dressing
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Mining
Sector
1
re
Code
-p
Table 4 Efficiency-based CO2 reduction potential in China’s industrial sector (unit: Mt-CO2)
33 Total
Electric power, steam and hot water production and supply
Total of Mining, Manufacturing, and Power-Gas-Water
0.24 1.39 1.06 422.22 201.27 661.36
Note: CRP means CO2 emissions reduction potential.
Of the 34 industrial sectors, the sector of Electric power, steam and hot water production and supply (Sector 34) has the highest CRP of 201.27Mt, followed by Nonmetal mineral products (Sector 23) with 139.4 Mt. Besides, there are three other sectors with more than 30 Mt of CRP, including Papermaking and paper products (Sector 15), Petroleum processing and coking (Sector 18), and Raw chemical materials and chemical products (Sector 19). Actually, some of the sectors above belong to 30
Journal Pre-proof six high-energy-consuming industrial sectors in China's national statistical classification (Sectors 18, 19, 23, 24, 25, 34), and the CRP of six high-energy-consuming sectors has reached 464.48 Mt, accounting for 70.23% of the total CRP; thus, we should pay more attention to these sectors in the future. To target China’s CO2 emissions reduction effort more effectively, and promote CO2 emissions decoupling, according to the congestion theory (Cooper et al., 2002; Sueyoshi and Goto, 2016) and the results of carbon congestion and technical efficiency of China’s industrial sector, some strategies
of
for future development and emissions reduction in China’s industrial sectors are proposed (see Table 5)23. It can be seen that, to achieve a “win-win” for CO2 emissions reduction and industrial growth,
ro
the Power-Gas-Water industry could adopt both resource-based and technology-based strategies. In
-p
the Mining industry, Petroleum and natural gas extraction (Sector 2) could adopt technology-based
re
emissions reduction strategy, and other four sectors need to take advantage of comprehensive means to improve their technical efficiency. The Manufacturing industry is more complicated and thus
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should be judged by different sectors. Specifically, (1) sectors such as Food processing (Sector 6) exhibit strong UC, indicating that the future development of these sectors can adopt the
na
resource-based strategy, that is, reducing the input of resources in these sectors. (2) some sectors, such as Garments and other fiber products (Sector 11), exhibit strong DC, indicating that
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strengthening green technology innovation to promote the decoupling of industrial development and CO2 emissions is crucial for achieving the aforementioned “win-win” in these sectors. Thus, these sectors can adopt the technology-based strategy, which should include attention to ecological innovation, increasing investment in science and technology, introducing clean production technology, promoting renewable energy development, and realising industrial transformation and upgrading in these sectors. (3) The TEN(UC) of some sectors is relatively lower, such as Papermaking and paper products (Sector 15). Therefore, these sectors should also focus on improving their technical efficiency by optimising their industrial structure, improving their management, rendering outdated equipment and technology obsolete, reducing and controlling emissions, and so on.
23
TEN(UC) in Table 5 is the mean value of TEN(UC) in 2013-2015. The state of congestion that occurs two or more times in three years is taken as the final congestion state of a sector in recent years. TEN(UC) below 0.95 is defined as low technical efficiency. 31
Journal Pre-proof Table 5 Strategies of industrial development and carbon emissions reduction in different sectors in China.
PGW
UC
Sector
2013
DC
2014
2015
2013
2014
2015
TEN(UC)
R
T
E
Coal mining and dressing
No
S
No
No
No
No
0.78
2
Petroleum and natural gas extraction
No
No
No
S
S
S
0.99
3
Ferrous metals mining and dressing
No
No
No
No
No
No
0.07
√
4
Nonferrous metals mining and dressing
No
No
No
No
No
No
0.22
√
5
Nonmetal minerals mining and dressing
No
No
No
No
No
No
0.09
6
Food processing
S
S
S
No
No
No
0.96
√
7
Food production
S
S
S
No
No
No
0.33
√
√
8
Beverage production
S
S
S
No
No
No
0.32
√
√
9
Tobacco processing
S
S
S
No
No
No
1.00
√
10
Textile industry
S
S
S
No
No
No
0.82
√
11
Garments and other fiber products
No
No
No
S
S
S
0.41
√
√
12
Leather, furs, down and related products
No
No
No
S
S
S
0.32
√
√
13
Timber processing, bamboo, cane, palm & straw products
No
No
No
No
No
No
0.11
14
Furniture manufacturing
No
No
No
S
S
S
0.68
15
Papermaking and paper products
No
No
No
No
No
No
0.05
16
Printing and record medium reproduction
No
No
No
S
S
S
0.47
√
√
17
Cultural, educational and sports articles
No
No
No
S
S
S
0.35
√
√
18
Petroleum processing and coking
No
No
No
No
No
S
0.65
19
Raw chemical materials and chemical products
No
No
S
S
S
0.97
20
Medical and pharmaceutical products
21
Chemical fiber
22
Rubber and plastic products
23
Nonmetal mineral products
24
Smelting and pressing of ferrous metals
25
Smelting and pressing of nonferrous metals
26
Metal products
27
Ordinary machinery
28
Equipment for special purpose
29
Transportation equipment
30 31 32
Instruments, meters cultural and office machinery
33 34
-p
ro
of
1
No
√ √
√
√
√ √
S
S
No
No
No
0.57
No
No
S
S
S
0.16
√
√
No
No
No
S
S
S
0.50
√
√
S
S
S
No
No
No
1.00
√ √
re
S
No
√
S
S
S
S
S
0.99
No
No
No
No
No
0.95
S
S
S
No
No
No
0.58
√
S
S
S
No
No
No
0.97
√
No
No
No
No
No
No
0.59
No
No
No
S
S
S
0.99
Electric equipment and machinery
No
No
No
No
No
No
0.99
Electronic and telecommunications equipment
No
No
No
No
No
No
0.97
No
No
No
S
No
No
0.96
Other manufacturing industry
S
S
S
No
No
No
0.99
√
Electric power, steam and hot water production and supply
S
S
S
S
S
S
1.00
√
lP
√ √
S
na
√
√
No
Jo ur
Manufacturing
Mining
Code
√
√ √ √ √ √
√
Note: PGW: Power-Gas-Water; T: Technology-based strategy; E: Low technical efficiency; UC: Undesirable carbon congestion; DC: Desirable carbon congestion; S: strong UC or DC; No: no UC or DC; TEN(UC): Technical efficiency under natural disposability.
5. Robustness test To verify the robustness of the results and the advantage of the method used in this paper, this section employs some other commonly used methods to recalculate the provincial UC results in 2015, including the DEA model proposed by Färe et al. (1985) and the dual model of the direction distance function proposed by Fang (2015). Among them, the DEA model proposed by Färe et al. (1985) is widely used to identify the congestion, but it only considers the desirable output and calculates input 32
Journal Pre-proof congestion rather than UC. Therefore, in order to facilitate comparison, according to the problem considered, this paper extends the DEA model proposed by Färe et al. (1985) to a model that can identify UC, as shown in Models (9) and (10) (Sueyoshi et al., 2018; Wu et al., 2016). Max
Max
30
xij j xik
30
(i 1, 2,3),
xij j xik
s.t.
j 1 30
g j j gk gk ,
g j j gk gk ,
j 1 30
j 1 30
b j j bk bk ,
b j j bk bk ,
(9)
j 1 30
j 1 30
j 1,
(10)
j 1, ,30),
j 1
: free
j 0 ( j 1,L ,30),
ro
j 1
j 0 ( j 1,
(i 1, 2,3),
j 1 30
of
s.t.
: free.
-p
Assuming that and are the optimal results of Models (9) and (10), respectively, UC can
re
be identified by and . Specifically, UC occurs only if 1 1 1 (Cooper et al., 2000;
lP
Sueyoshi, 2003).
The directional distance function and its dual model proposed by Fang (2015) are shown in
na
Models (11) and (12), respectively. As with the method used in this paper, this dual model can identify the congestion state of provinces with technical efficiency of 1. 30
s.t.
Jo ur
Max
xij j + xk xik j 1 30
g j j gk gk , j 1 30
3
Min
(i 1, 2,3),
vi xik ug k wbk i 1 3
s.t.
vi xij ug j wb j 0 ( j 1,L ,30),
i 1 3
(11)
(12)
vi xik ug k wbk 1,
b j j bk bk , j 1
i 1
j 0 ( j 1,L ,30)
vi 0
(i 1, 2,3),
u 0,
w : free.
The criteria for UC here is the same as that before, i.e., UC occurs only if w 0 . Table 6 summarizes the results from different methods. It can be seen that based on the method of Färe et al. (1985), only Zhejiang, Anhui, Guizhou, and Shaanxi provinces have strong UC; when using the method in this paper, these four provinces also display strong UC and technical inefficiency, while the remaining provinces with strong UC have the technical efficiency of 1. This phenomenon also 33
Journal Pre-proof corroborates some existing research, that is, the congestion identified by the envelopment model of DEA is mainly based on the provinces with technical inefficiency (Brockett et al., 2004; Wei and Yan, 2004). In other words, the method used in this paper, which is based on the dual model of DEA rather than the envelopment model of DEA, does not miss the congestion results identified by Färe et al. (1985)’s method and is able to better identify the congestion state of the province with technical efficiency of one. In addition, the provinces in the congestion state based on the method in this paper also experience congestion under the method of Fang (2015), indicating that the results of this paper
of
have certain robustness24. Table 6 Comparison of UC results of 30 provinces in China under different methods, 2015.
ro
Method in Fang (2015) UC 1 -0.05 0.91 S 0.07 0.89 No -0.23 0.83 S 2.11 1.00 No 0.52 1.00 No -0.04 0.94 S 0.73 0.93 No -0.03 0.85 S -0.07 0.96 S -0.10 1.00 S -0.04 0.86 S -3.88 1.00 S 0.89 0.85 No -0.08 0.76 S -0.12 0.91 S -0.26 0.89 S -0.07 0.85 S -0.10 0.88 S 0.01 1.00 No -120.48 1.00 S -0.48 1.00 S 0.08 0.88 No 0.04 0.82 No -0.34 0.91 S 0.11 0.90 No -0.07 0.94 S 0.19 0.72 No 0.49 0.91 No -144.31 1.00 S -58.28 1.00 S
re
-p
w
na
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Method in Färe et al. (1985) UC 1 1 0.95 0.95 No 0.77 0.77 No 0.55 0.55 No 1.00 1.00 No 1.00 1.00 No 1.00 1.00 No 0.93 0.93 No 1.00 1.00 No 0.98 0.99 S 1.00 1.00 No 1.00 1.00 No 1.00 1.00 No 1.00 1.00 No 0.44 0.44 No 0.80 0.81 S 0.80 0.80 No 0.81 0.81 No 0.93 0.93 No 1.00 1.00 No 1.00 1.00 No 1.00 1.00 No 0.85 0.85 No 0.88 0.88 No 0.67 0.84 S 0.81 0.81 No 0.90 0.91 S 0.46 0.46 No 0.98 0.98 No 0.43 0.43 No 0.69 0.69 No
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Province Liaoning Jilin Heilongjiang Beijing Tianjin Hebei Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Shanxi Anhui Jiangxi Henan Hubei Hunan Inner Mongolia Guangxi Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang
Method in this paper TEN(UC) UC 0.09 0.95 No 0.16 0.77 No 0.21 0.55 No -97.86 1.00 S 0.43 1.00 No -83.17 1.00 S 0.93 0.93 No -0.63 1.00 S -0.03 0.99 S -0.91 1.00 S -99.24 1.00 S -123.54 1.00 S -34.15 1.00 S 0.16 0.44 No -0.07 0.81 S 0.44 0.81 No 0.06 0.81 No 0.25 0.93 No -0.31 1.00 S -77.61 1.00 S -147.72 1.00 S 0.74 0.85 No 0.24 0.88 No -1.72 0.84 S 0.23 0.81 No -0.08 0.91 S 1.34 0.46 No 0.94 0.98 No 0.68 0.43 No 0.24 0.69 No
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Note: UC: Undesirable carbon congestion; TEN(UC): Technical efficiency under natural disposability; S: Strong UC; No: no
24
However, the number of provinces in congestion calculated by Fang (2015)’s method is more than that of this paper, which is mainly caused by the difference between the two methods. In the method of Fang (2015)’s method, the measurement of technical inefficiency by the envelopment model includes three aspects: input, desirable output and undesirable output, as shown in Eq. (11); while in our method, the measurement of technical inefficiency by the envelopment model only includes desirable output and undesirable output, as shown in Eq. (2). 34
Journal Pre-proof UC.
In summary, compared with Färe et al. (1985)’s method, the method in this paper has better capability to identify UC, which can identify the congestion state of provinces with technical efficiency of 1. Meanwhile, compared with Fang (2015)’s method, the congestion results based on the method in this paper are robust and credible.
6. Conclusions
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This paper uses the dual model of radial DEA to study the carbon congestion in China’s provincial
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industry and industrial sectors from 2005 to 2015, which can be divided to undesirable carbon congestion and desirable carbon congestion. On this basis, the states of returns to damage and
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damages to return, as well as the technical efficiency and carbon emissions reduction potential are
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also analysed. The main conclusions are as follows:
First, China’s industrial carbon congestion is obvious during the sample period. In China’s
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provincial industries, the proportion of undesirable carbon congestion fluctuates around 43.33%, and although the proportion of desirable carbon congestion decreases from 66.67% to 46.67% from 2005
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to 2015, the desirable carbon congestion is still obvious. In China’s industrial sector, from 2005 to
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2015, the proportion of undesirable carbon congestion and desirable carbon congestion fluctuate around 38.24%. Undesirable carbon congestion means that China’s waste of industrial resources is serious and this has caused overflows of CO2 emissions. Desirable carbon congestion means that China has great potential for carbon reduction and industrial growth through green technology innovation. In addition, China’s mode of production involving realisation of industrial growth through the burning of fossil fuels is unsustainable, especially in the northeastern, central and western regions and in the Mining industry. Second, the congestion effect shows a trend of regional agglomeration and obvious regional heterogeneity. From 2005 to 2015, undesirable carbon congestion and desirable carbon congestion show the trend of agglomeration from the whole country to eastern and western regions in China, respectively. In 2015, undesirable carbon congestion is mainly concentrated in the eastern region; while desirable carbon congestion mainly exists in the western region, followed by the eastern region. 35
Journal Pre-proof In addition, the congestion effect presents obvious sectoral heterogeneity. Both undesirable and desirable carbon congestions mainly occur in the Manufacturing and Power-Gas-Water industries. Therefore, under current production level, the “win-win” for CO2 emissions reduction and industrial development can be achieved in the eastern region, and some sectors in Manufacturing (such as Food processing, Food production, Beverage production, Tobacco processing, Textile industry) and Power-Gas-Water industries through eliminating undesirable carbon congestion by optimising the input of resources. The “win-win” state in the western and eastern regions, and some sectors in
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Manufacturing (such as Garments and other fiber products, Leather, furs, down and related products, Furniture manufacturing, Printing and record medium reproduction, Cultural, educational and sports
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articles, etc.) and Power-Gas-Water industries, will be reliant on improving the level of production
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and green technology innovation.
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Finally, the average annual CO2 emissions could be reduced by 722.82 Mt25, if all provinces or sectors produce on the production frontier. Regionally, China’s western region has the highest carbon
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emissions reduction potential; among the provinces, Shanxi has the greatest potential for emissions reduction. Sectorally, the greatest potential for reduction of CO2 emissions is mainly found in the
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Manufacturing and Power-Gas-Water industries. In particular, China’s six high-energy-consuming sectors (including Petroleum processing and coking, Raw chemical materials and chemical products,
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Nonmetal mineral products, Smelting and pressing of ferrous metals, Smelting and pressing of nonferrous metals, and Electric power, steam and hot water production and supply) have the greatest potential for reducing emissions, accounting for 70.23% of the total sectors’ CRP. Based on the conclusions above, this study also proposes three important policy implications: first, it is expectable that China’s industry achieves the desired “win-win” for industrial development and CO2 emissions reduction. According to the state of carbon congestion, the whole-of-industry can implement precise emissions reduction according to local conditions and sectoral conditions. Besides, green technology innovation proves the power source for China’s industry to achieve sustainable emissions reductions. Chinese government should increase its support for technology innovation in
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Here, for the sake of prudence, the potential emission reductions of China’s national industries refers to the average between emissions reduction potential of provincial industry and that of 34 industrial sectors. 36
Journal Pre-proof the fields of energy saving and emission reduction and new energy technologies, and upgrade industrial technological innovation capabilities. Second, Chinese government can scientifically guide the allocation of resources according to the state of carbon congestion, which helps to improve the current imbalance in the development of China’s regions and industrial sectors. For example, there remains significant scope for industrial development in the western region and in the Mining industry of China. Therefore, Chinese government should provide more policy support to these regions and sectors and guide more labour
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and material resources, especially technological innovation resources, to them. To improve the economic and environmental benefits of these regions and sectors, it is also necessary to enhance
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the clean, safe, low-carbon and efficient development and utilisation of coal, promote the use of
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high-quality and clean coal, promote “coal to gas”, “coal to electricity” and so on.
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Finally, when formulating the emissions reduction plan, Chinese government can take resource and efficiency-based strategies as its short-term emissions reduction plan, and adopt the
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technology-based strategy for its long-term emissions reduction plan. For example, Chinese government can implement the resource-based strategy for its short-term emissions reduction target
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as enshrined in the 13th Five-Year Plan26, supplemented by the technology-based strategy to lay a solid foundation for the long-term emissions reduction targets in the Paris Agreement, prompting
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emissions reduction tasks to be completed on time and to a high standard. In addition, the construction of China’s carbon trading market should be unswervingly promoted, and undesirable carbon congestion can thus be eliminated through the initial quota allocation to achieve carbon emissions reduction.
It should be noted that there is still much relevant work to be done in the future. For example, this study uses a dual model of DEA to assess carbon congestion, and in the future, more optimized models can be developed to calculate specific congestion values and obtain more detailed results. In addition, further work can be linked to carbon emissions targets and carbon trading constraints for each emission-intensive industry. Besides, we can also combine the provinces and industrial sectors
26
The 13th Five-Year Plan for National Economy and Social Development of the People’s Republic of China highlights that by 2020, China’s carbon intensity will fall 18% compared with the level in 2015. 37
Journal Pre-proof to investigate the carbon congestion, that is, to target the research to some industrial sectors in any provinces.
Appendix Some supplementary tables to this article can be found online.
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Acknowledgements
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We are grateful to the financial support from the National Natural Science Foundation of China (nos. 71774051), National Program for Support of Top-notch Young Professionals (no. W02070325),
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Changjiang Scholars Program of the Ministry of Education of China (no. Q2016154), Science Fund for
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Distinguished Young Scholars of Hunan province (no. 2018JJ1010), and Hunan Youth Talent Program.
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Highlight
It explores both undesirable and desirable congestion of China’s industrial sectors
It uses the dual model of radial DEA to quantify the state of carbon congestion
The congestion effect
East and central regions and PGW industry should optimize input resources
Northeast and west regions and PGW industry should innovate ecological technology
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witnesses evident regional and sectoral heterogeneity
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Yue-Jun Zhang, Jing-Yue Liu, Bin Su PII:
S0140-9883(19)30432-3
DOI:
https://doi.org/10.1016/j.eneco.2019.104635
Reference:
ENEECO 104635
To appear in:
Energy Economics
Please cite this article as: Y.-J. Zhang, J.-Y. Liu and B. Su, Carbon congestion effects in China's industry: Evidence from provincial and sectoral levels, Energy Economics(2019), https://doi.org/10.1016/j.eneco.2019.104635
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Carbon congestion effects in China’s industry: evidence from provincial and sectoral levels Yue-Jun Zhanga, b, Jing-Yue Liu a,b, Bin Su c a b
Business School, Hunan University, Changsha 410082, China
Center for Resource and Environmental Management, Hunan University, Changsha 410082, China c
Energy Studies Institute, National University of Singapore, 119620, Singapore
Abstract
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Investigating carbon congestion effect can help identify congestion in production, which is of
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great significance for the rational use of resources and the effective promotion of carbon emissions reduction. Under this circumstance, this study uses the dual model of radial DEA to explore both
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undesirable/desirable congestion, returns to damage and damages to return during 2005-2015 from
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both provincial and sectoral levels. Combined with window analysis, the technical efficiency and
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emissions reduction potential of China’s industrial sectors are also discussed. The empirical results show that: (1) China’s industrial carbon congestion is obvious and the congestion effect witnesses a
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trend of regional agglomeration and evident regional and sectoral heterogeneity. In particular, undesirable congestion mainly occurs in the eastern region, and desirable congestion mainly occurs in
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the western region, followed by the eastern region; both undesirable and desirable congestions mainly occur in some sectors in the Manufacturing and Power-Gas-Water industries. (2) If all sectors produce on the production frontier, the average annual potential carbon emissions reduction would reach 722.82 million tons, with the higher potential in western region and Shanxi province of central region, as well as Manufacturing and six high-energy-consuming sectors. (3) To achieve the “win-win” of industrial development and carbon emissions reduction, China’s western region should focus on green technology innovation, while the eastern region and Power-Gas-Water industry should focus on both input resources optimisation and green technology innovation. Keywords: carbon congestion effect; carbon dioxide emissions; industry; radial DEA; China
Corresponding author. E-mail: [email protected] (Yue-Jun Zhang). 1
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1. Introduction Global warming caused by increased CO2 emissions has become one of the tough challenges facing the world (Montzka et al., 2011). Data from BP (2019) show that China has become the world’s largest contributor to CO2 emissions since 2006 and China’s CO2 emissions in 2018 reached 9428.7 million tons (Mt), accounting for 27.8% of the global total, which has caused China to face unprecedented challenges (Zhang et al., 2019). To prevent global warming and curb environmental pollution, China has committed to a low-carbon economy and green development (Zhang and Da, 2015). At the Paris Climate Conference in 2015, China set the target of reducing carbon intensity by
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60% to 65% by 2030 based on the 2005 level1. However, as the largest developing country, China is
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still in the stage of rapid development of industrialisation, and the conflict between industrial
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development and environmental protection remains stark. In this context, how to effectively achieve energy saving and emission reduction, together with industrial growth, has become a major concern
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for China.
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As stated in the report of the 19th National Congress of the Communist Party of China in 2017, it is necessary to develop spatial layout, industrial structure, and ways of working and living that help to
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conserve resources and protect environment2. However, one of the main reasons for China’s high CO2 emissions is the overuse of resources (Xu and Lin, 2019). When resources are overused, it will cause
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congestion; the conventional congestion only considers the phenomenon whereby when inputs increase to a certain degree under certain production conditions, the outputs (mainly desirable output) will decrease due to excessive inputs (Brockett et al., 1998; Cooper, 2001), while the undesirable output changes in this process are rarely considered. In fact, in the industrial production process, such undesirable output as CO2 emissions is an inevitable product of energy consumption; that is, when resources are overused, it will not only reduce the desirable output, but also increase CO2 emissions, resulting in undesirable carbon congestion (UC). In addition, under the impact of green technology innovation, China’s industry may also exist the possibility of desirable output growth and CO2 emissions reduction in the process of increasing resource input, resulting in desirable carbon congestion (DC). In terms of the concept, UC emphasizes that the increase of input will lead to 1 2
http://www.gov.cn/xinwen/2015-06/30/content_2887330.htm http://www.xinhuanet.com//politics/19cpcnc/2017-10/18/c_1121820882.htm 2
Journal Pre-proof the decrease of desirable output, while DC emphasizes that the increase of input will lead to the decrease of undesirable output (CO2 emissions) (Sueyoshi and Goto, 2012b; Sueyoshi and Goto, 2016; Sueyoshi and Yuan,2016). UC and DC can reflect the “win-win” possibility of industrial economic development and carbon emission reduction by improving resource management and green technology innovation, respectively. This is of great significance for China’s industry to reduce waste of resources, strengthen the guidance of scientific and technology innovation, and promote the high-quality industrial development, as well as the formation of a new model of modernisation with
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humans developing in harmony with nature. According to the data from National Bureau of Statistics of China (NBSC), from 2005 to 2016, the
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proportion of industrial energy consumption to national energy consumption increased from 64.6%
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to 68.0%, while the proportion of industrial added value to national GDP decreased from 41.6% to
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33.3%. Meanwhile, from 2005 to 2015, China’s industrial carbon emissions accounted for about 85% of the national carbon emissions (CEAD database). Then, based on the increase in the proportion of
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China’s industrial energy consumption, the decrease in the proportion of industrial added value and the high proportion of carbon emissions, we would like to explore whether there exists the UC
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caused by waste of input resources. The Guidance on Building a Market-Oriented Green technology innovation System issued by China in 2019 emphasizes that green technology innovation is the first
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driving force for green development. Then, under the influence of green technology innovation, is there the state of DC in China's industry? how about the relationship between industrial economic growth and CO2 emissions in China? what is the changing trend and distribution of the technical efficiency and carbon reduction potential in China’s industrial production process? and how to promulgate the “win-win” development strategy between industrial growth and environmental protection for different regions and different industrial sectors in China? China’s energy-intensive industries are the main sources of real economic growth as well as the main drivers of energy consumption and CO2 emissions (Wang and Wei, 2014). The analysis above can not only help Chinese government to realise resource utilisation in the industrial sector and avoid carbon emissions and potential economic decline caused by resources waste, but also promote the development of science and technology to realize the “win-win” of carbon emission reduction and industrial economic 3
Journal Pre-proof growth. In summary, based on the concept of congestion, this paper divides the carbon congestion effect which considers CO2 emissions as undesirable output, into UC and DC according to different types of decreased output, and develops a dual model of DEA to investigate the carbon congestion, returns to damage (damages to return), technical efficiency, reduction potential and strategies at both the provincial and sectoral levels. The main contribution of this study includes the following three aspects: (1) this study is based on the dual model of DEA with undesirable outputs, which can identify whether there is carbon
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congestion in the technical efficient provinces or sectors. (2) This paper investigates China’s industrial carbon congestion from both the provincial and sectoral levels, which helps us to identify the key
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regions and key sectors for targeted CO2 emissions reduction. (3) This congestion study includes both
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undesirable and desirable carbon congestions to investigate the balance between CO2 emissions
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reduction and industrial growth; and based on the results of China’s industrial carbon congestion, this paper also examines the returns to damage, damages to return, technical efficiency, and CO2
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emissions reduction potential of China’s industry by region. The rest of this paper is organised as follows: Section 2 reviews the literature, Section 3
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introduces the research methods and data descriptions, Section 4 presents the empirical results and discussion thereof, Section 5 shows the robustness test, and the conclusions and corresponding
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policy implications are drawn in Section 6.
2. Literature review
The study of congestion is mainly based on the DEA model. The DEA model is a non-parametric method that is first proposed by Charnes et al. (1978), and is widely applied to technical efficiency evaluation, including environmental efficiency evaluation. For example, Choi et al. (2012) use the DEA model to analyse the CO2 emissions efficiency and energy efficiency in China and Sueyoshi and Goto (2014b) measure the environmental efficiency of Japanese chemical and pharmaceutical firms using the DEA model. Meng et al. (2016) review the DEA studies published in 2006-2015 on measuring China’s regional energy and carbon emission efficiency using different DEA models. The DEA models for studying congestion are mainly divided into two categories: radial DEA 4
Journal Pre-proof model and non-radial DEA model. The representative method for the radial DEA model is the FGL model. Färe and Grosskopf (1983) first propose a feasible solution to judge the congestion by using the non-parametric method, and Färe, Grosskopf, and Lovell use the DEA method to develop the first measure model (Färe et al.,1985). Later scholars call this the FGL model, which is a two-stage radial DEA model and measures input congestion by comparing technical differences between input variables under conditions of both strong and weak disposability. The FGL model allows real congestion to be measured, and it has become the only way to judge input congestion (Zhou et al.,
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2017)3. Within the theoretical framework of DEA, Cooper, Thompson and Thrall propose a two-stage
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non-radial DEA model based on slack variables to measure the input congestion: this is called the CTT
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model (Cooper et al., 1996). Brockett et al. (1998) study the input congestion before and after China’s
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economic reform in 1978 based on CTT model, and extend the CTT model to their new BCSW model. Cooper et al. (2002) improve the two-stage BCSW model to a one-stage model. Radial DEA and
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non-radial DEA are the key research methods used to study the same problem from different perspectives, so there has always been a comparison and debate about them (Cooper et al., 2001;
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Cooper, 2001; Färe and Grosskopf, 2001). The former focuses on the economic analysis of technical efficiency in the sense of production frontiers, while the latter focuses on the optimisation of loss and
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redundancy of variables in efficiency calculation based on the mathematical meaning of the optimisation method.
The methods above are widely applied in measuring congestion. Most scholars study congestion from the perspective of inputs. For example, Jahanshahloo and Khodabakhshi (2004) analyse the input congestion in Chinese textile industry based on the non-radial DEA model. Simões and Marques (2011) assess the performance and input congestion of Portuguese hospitals using three different DEA methods. Wu et al. (2016) analyse the input congestion of Chinese industrial sectors using the radial DEA model and find that China’s industry does exist energy input congestion and congestion mainly occurs in provinces with higher energy intensity. In recent years, some scholars incorporate
3
Input congestion refers to the conventional congestion that the decrease of input will lead to the increase of desirable output without considering the undesirable output; it identifies congestion mainly by setting input as weak disposability, so it is called input congestion. 5
Journal Pre-proof the undesirable outputs into congestion research. Based on the radial DEA model, Wu et al. (2013) and Chen et al. (2016) study the industrial congestion effects in Chinese regions in 2010 and 2012, respectively. It is worth mentioning that most studies are based on radial or non-radial DEA model, and consider that the congestion effect is a more serious technical inefficiency, and mainly occurs in technically inefficient decision-making unit (DMU) (Brockett et al., 2004). However, in recent years, some studies show that, when considering both desirable and undesirable outputs, even if a DMU is
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technically efficient (i.e. with technical efficiency of one), congestion may still occur, which can be measured using the dual model of the DEA (Fang, 2015; Sueyoshi and Goto, 2012b; Sueyoshi and
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Yuan, 2016). In addition, some scholars focus on undesirable and desirable congestions in recent
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years. For example, Sueyoshi and Yuan (2016) study the undesirable and desirable congestions of 30
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provinces in China from 2005 to 2012 using the dual model of radial DEA, with undesirable outputs of PM10, SO2 and NO2. Therefore, the method used in this paper is based on the research of Sueyoshi
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and Yuan (2016). Besides, Tone and Sahoo (2004), Sueyoshi and Goto (2014b), Sueyoshi and Yuan (2016) and other scholars explore the relationship between congestion effect and elasticity, such as
RTD and DTR in this study.
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Returns to Scale (RTS), Returns to Damage (RTD) and Damages to Return (DTR); thus, we also include
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From the research above on the congestion effect in China, they mainly study congestion from the perspective of provincial industries, and their undesirable outputs do not consider CO2 emissions, such as Wu et al. (2013) and Chen et al. (2016). In fact, China attaches great importance to CO2 emissions reduction and in December 2017, the China’s national carbon trading market for power industry was officially launched; thus, it is necessary to study the congestion specifically on CO2 emissions. As for the study of carbon congestion, Sueyoshi and Goto (2012b) measure the congestion effects of Japanese power industry and manufacturing industries from 2007 to 2009 using the dual model of radial DEA, and the undesirable output is CO2 emissions. Their results show that both power companies and manufacturing companies have undesirable and desirable congestions, and have been committed to achieving carbon reduction through technology innovation. Sueyoshi and Goto (2016) evaluate the congestion effect and technical efficiency of 68 coal-fired power plants in USA in 2010 6
Journal Pre-proof based on the dual model of radial DEA, and the undesirable outputs are SO2, CO2 and NOX; the results show that most plants belong to strong undesirable congestion while strong desirable congestion only occurs in a few power plants. There are few studies on carbon congestion in China’s industries. In summary, current related research mainly studies the conventional input congestion which only considers the desirable outputs, while little consideration is given to undesirable outputs, such as CO2 emissions. Besides, current research into congestion effects in China does not focus on carbon congestion based on the idea of energy conservation and emissions reduction, and is mainly based
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on the provincial level. Industry is the highest CO2 emitter, but existing literature often ignores the research at the industrial sector level, from which it is difficult to reveal the state of carbon
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congestion and carbon reduction potential of China’s industrial sectors, and it is not conducive to the
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targeted implementation of energy conservation and emissions reduction in China’s industry.
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Moreover, as for the research methods, most related studies adopt the DEA model and their measured congestion effect mainly occurs in technically inefficient DMUs, but ignores the congestion
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effect of technically efficient DMUs, from which it is difficult to obtain accurate measurement for congestion. Finally, it is necessary to consider both UC and DC in the analysis of carbon congestion,
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which not only reveals whether or not there is any waste of resources, but also reflects the potential to reduce CO2 emissions through green technology innovation. Therefore, this study focuses on the
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industrial carbon congestion in China, from both the levels of provincial industry and industrial sectors. Specifically, this study uses the dual model of radial DEA to discuss UC and DC, RTD and DTR, and combines window analysis to discuss the technical efficiency and carbon reduction potential. On this basis, the targeted strategies of industrial development and carbon emissions reduction are proposed.
3. Methods and data 3.1 Methods (1) Congestion effect In this study, the desirable output ( g ) refers to the industrial added value while the undesirable output ( b ) refers to CO2 emissions. Congestion means that the decrease of input will lead to the 7
Journal Pre-proof increase of output ( g or b ), as shown in Fig.1 (a). We assume that five DMUs are located on the production frontier CDEAF, and when point F moves to point A, it can reduce its input while increasing its output, which indicates congestion in point F. Congestion is different with from
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technical inefficiency (Brockett et al. 2004)4.
Weak UC (No RTD)
D
C IRTD
0
F
f0
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CRTD
A
Undesirable Output (b)
(b) Undesirable carbon congestion
Undesirable Output (b)
E
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DRTD
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Strong UC (Negative RTD)
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Desirable Output (g)
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(a) Congestion
Strong UC (Negative DTR)
DDTR
E CDTR
A
F
Weak UC (No DTR)
D
f1
C IDTR
0
Desirable Output (g) (c) Desirable carbon congestion
Fig. 1. Congestion, undesirable carbon congestion, desirable carbon congestion and five types of RTD and DTR. Note: IRTD: increasing RTD, CRTD: constant RTD, DRTD: decreasing RTD, NRTD: negative RTD, IDTR: increasing DTR, CDTR: constant DTR, DDTR: decreasing DTR, NDTR: negative DTR.
The undesirable and desirable carbon congestion effects are shown in Fig. 1(b) and Fig. 1(c), respectively. In Fig. 1, strong UC (strong DC) occurs when the desirable (undesirable) output 4
Technical inefficiency indicates that the output can be increased at a given input, or the input can be reduced at a given output (Farrell,1957). As shown in Fig.1 (a), in a given input, point M and point N can move to point A and point F, respectively, by expanding their output; this is a case of technical inefficiency. Congestion indicates that the decrease of input will lead to the increase of output, or the increase of input will inevitably lead to the decrease of output (Färe and Svensson, 1980). When point N reaches point F, it can be moved to point A by reducing input and this process is accompanied by the increase of output; this is a case of congestion. Point N represents technical inefficiency and congestion, and Point F represents technical efficiency and congestion. 8
Journal Pre-proof decreases with an increase in undesirable (desirable) output; thus, strong DC is the best state a society can expect to achieve (Sueyoshi and Yuan, 2016). The present research into UC and DC is based on natural disposability and managerial disposability, respectively (Sueyoshi and Goto, 2012b). Natural disposability reflects the idea of giving priority to economic performance. Under the paradigm of natural disposability, input and undesirable output increase or decrease in the same direction: a DMU can reduce undesirable output by reducing input, thereby increasing the desirable output as much as possible. The production possibility set of UC under natural disposability is , where X , G and B
denote input
of
n n n n N PUC ( X ) G, B : G G j j , B B j j , X X j j , j 1, j 0, j 1,L , n j 1 j 1 j 1 j 1
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factor, desirable output factor, and undesirable output factor, respectively. The production possibility set of UC is similar to that of directional distance function; both of them belong to the conventional
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DEA framework and regard the undesirable output as a by-product of the desirable output, which
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confirms to real production process and reflects the production capacity constraints when considering undesirable output. Managerial disposability reflects the idea of giving priority to
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environmental performance. Under the managerial disposability paradigm, a DMU can increase
innovation.
The
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desirable output by increasing input while reducing undesirable output through green technology production
possibility
set
of
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n n n n M PDC ( X ) G, B : G G j j , B B j j , X X j j , j 1, j 0, j 1,L , n j 1 j 1 j 1 j 1
DC
under
managerial
is
. It can be seen that the most obvious
feature of the two production possibility sets is that B is weakly disposable in M PDC (X )
disposability
N PUC (X )
, while in
, G is weakly disposable. The production possibility set of DC belongs to a new framework,
which looks for the status of sustainability and reflects the possibility of green technology innovation (Sueyoshi and Goto, 2012b; Sueyoshi and Goto, 2014a). It is worth mentioning that the production frontier of DC is higher than that of UC due to green technology innovation; that is, when a technically inefficient DMU attains f0, it is possible to shift f0 to f1 after green technology innovation (Sueyoshi et al., 2013; Sueyoshi and Wang, 2014a). For more details on natural and managerial disposability, as well as the relationship between UC and DC, please see Sueyoshi and Goto (2016) and Sueyoshi and Yuan (2016). In addition, according to the state of carbon congestion, the relationship between industrial 9
Journal Pre-proof added value and CO2 emissions can also be obtained, that is, Returns to Damage (RTD) and Damages to Return (DTR)5. As can be seen from Fig. 1(b), when no UC occurs, RTD is positive, which can be subdivided into the increasing, constant, and decreasing types at points C, D and E, respectively. When strong UC occurs, RTD is negative (NRTD), such as at point F. When weak UC occurs, the state of RTD is no RTD, such as at point A. The five types of DTR are shown in Fig. 1(c), in which all terminologies are similar to that in Fig. 1(b). (2) UC effect model
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The calculation method for the industrial sector is consistent with that for the provincial industrial sector, so, here we only take provincial industry as an example to introduce the calculation
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method of carbon congestion, RTD, DTR, technical efficiency, and CO2 emissions reduction potential.
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According to the actual production process and related studies (Zhang and Hao, 2017; Zhou et al.,
xij (i 1,2,3) denote labour, capital, and energy
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2017), it is assumed that input vectors
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consumption in province j ( j 1,L ,30) , respectively, and g j and b j refer to industrial added value and CO2 emissions, respectively. xij , g j and b j are greater than 0. The data ranges of all
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inputs and outputs are determined by their bounds as shown in Eq. (1):
5 max g 5 max b
j 1,L ,30 min g j
1 j 1,L ,30
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Rix 5 max xij j 1,L ,30 min xij j 1,L ,30 Rg Rb
j
j
j 1,L ,30 min b j j 1,L ,30
1
(1)
1
According to Sueyoshi and Goto (2016), this paper uses Models (2) and (3) to measure UC under natural disposability. In Model (2), the undesirable output b is weakly disposable.
5
RTD (DTR) refers to the effect of undesirable (desirable) output on the desirable (undesirable) output when the undesirable (desirable) output increases in the same proportion. The calculation process of RTD (DTR) is similar to that of Returns to scale (RTS), which reflects the idea of elasticity, but they differ in concept. RTS refers to the effect of inputs on the desirable output when all inputs increase in the same proportion, reflecting the relationship between input variables and desirable output variables (Tone and Sahoo, 2005). Please see Sueyoshi and Goto (2014a) for detailed calculation of RTS. It is noteworthy that negative RTS (the most severe form of diseconomies of scale) is also the evidence of strong UC (Sueyoshi and Goto, 2012b; Wei and Yan, 2011). 10
Journal Pre-proof 3 Max s Rix dix R g d g i 1 30
xij j dix xik
s.t.
(i 1, 2,3),
j 1 30
g j j d g gk gk , j 1 30
b j j bk bk ,
(2)
j 1 30
j 1, j 1
j 0 ( j 1,L ,30),
: free,
0 (i 1, 2,3), d 0 .
where k represents the province under evaluation,
of
g
s is a very small number, which is 0.0001 in
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dix
this study, and j refers to the intensity variable. Besides, dix and d are slack variables related to
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g
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inputs and desirable output, respectively. Model (2) has the following dual formulation: 3
v x i 1
i ik
3
v x
ug j wb j 0 ( j 1,L ,30),
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s.t.
ug k wbk
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Min
i 1
i ij
ug k wbk 1,
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vi s Rix
(i 1, 2,3),
(3)
u sR , g
w : free,
: free.
where is a dual variable and derived from
30
j 1
in Model (2). The optimal solutions
j 1
vi* (i 1, 2,3) , u * , w* and * can be obtained by running Model (3), in which the state of UC can *
be judged by w
6
with the assumption on a unique optimal solution (Sueyoshi and Goto, 2016)6.
It is worth noting that following Sueyoshi and Goto (2016) and Sueyoshi and Yuan (2016), this study assumes that Model
(3) produces a unique optimal solution. If province
k has multiple solutions, it is necessary to measure the upper and
lower limits of * to determine the occurrence of UC. 11
Journal Pre-proof * * Specifically, w 0 , w 0 , or w 0 indicate that province k stays in a state of strong UC,
*
no UC, or weak UC, respectively. The technical efficiency (Farrell, 1957) of province k , incorporating the UC state under natural disposability, can be obtained as follows:
3 TEN (UC ) 1 * s Rix dix * R g d g * i 1
(4)
3 1 vi* xik u* g k w*bk * i 1
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The reduction potential of CO2 emissions (CRP) can be obtained through the technical
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inefficiency and CO2 emissions (Wang and Wei, 2014), which is called efficiency-based CRP, as shown
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in Eq. (5):
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* 3 x x * CRPE bk s Ri di R g d g * i 1
(5)
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3 bk vi* xik u* g k w*bk * i 1
3
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According to the following conditions, the type of RTD of province k can be determined: (1) 3
w * 0 and * vi* xi 0 , indicating IRTD; (2) w * 0 and * vi* xi 0 , indicating i 1
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i 1
3
* * CRTD; (3) w * 0 and vi xi 0 , indicating DRTD; (4) w * < 0 , indicating NRTD; i 1
otherwise, (5) No RTD.
(3) DC effect model Here Models (6) and (7) are used to measure the state of DC under managerial disposability:
12
Journal Pre-proof 3 Max s Rix dix R b d b i 1 30
s.t.
xij j dix xik
(i 1, 2,3),
j 1 30
g j j gk gk , j 1 30
b j j d b bk bk ,
(6)
j 1 30
j 1, j 1
j 0 ( j 1,L ,30),
: free,
0 (i 1, 2,3), d 0. b
of
dix
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Model (6) has the following dual formulation:
i 1 3
vi xij ug j wb j 0( j 1,L ,30),
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s.t.
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3
Min vi xik ug k wbk
i 1
vi s Rix u : free,
(i 1,2,3),
(7)
: free.
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w s Rb ,
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ug k wbk 1,
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By running Model (7), the optimal solutions vi* (i 1, 2,3) , u * , w* and * can be obtained, and in province k , u* 0 , u* 0 , or u* =0 indicate that the province stays in a state of strong DC, no DC, or weak DC, respectively.
The technical efficiency of province
k , incorporating a DC state under managerial disposability,
can be measured by:
* 3 x x * TEM ( DC ) 1 s Ri di Rb d b* i 1 3 * 1 vi xik u* g k w*bk * i 1 The five types of DTR in target province 13
(8)
k can be determined: (1) u* 0 and
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3
3
i 1
i 1
* vi* xi 0 ,indicating IDTR; (2) u* 0 and * vi* xi 0 , indicating CDTR; (3) u* 0 and 3
* vi* xi 0 , indicating DDTR; (4) u* 0 , indicating NDTR; otherwise, (5) no DTR. i 1
(4) Window analysis method Since the TEN(UC) and TEM(DC) calculated by the models above are not comparable in different years, in order to achieve dynamic comparison across years, this paper introduces the window
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analysis method (Sueyoshi and Wang, 2018; Zhang and Chen, 2018). In this study, the number of DMUs in one period is 30, and the time span T 11 (from 2005 to 2015), and according to existing
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research (Halkos and Tzeremes, 2009; Wang et al., 2013), the width of the window d is generally
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set to 3. Therefore, T d 1 windows, i.e., 9 windows with 3 30 DMUs, are established.
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Specifically, the first window covers data from 2005 to 2007, and the second window covers data from 2006 to 2008, and so on, until the ninth window covers data from 2013 to 2015. Finally, the
3.2 Data description
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average efficiency of each province in the same year is taken as the final TEN(UC) or TEM(DC).
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This paper uses panel data, including industrial data from 30 provinces (excluding Tibet) and 34 industrial sectors in China from 2005 to 20157, covering China’s latest two five-year plans periods, i.e.
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11th Five-Year Plan (2005-2010) and 12th Five-Year Plan (2011-2015). Here, labour in the 30 provinces is represented by the number of employees in the entire industrial sector published in China City Statistical Yearbook, while labour in the 34 industrial sectors is represented by the average number of workers from China Industry Statistical Yearbook. Since there is no official indicator of industrial capital stock in China, capital is calculated by the perpetual inventory method (Chen, 2011; Meng et al., 2016). According to existing relevant studies (Wang and Wei, 2014; Wu et al., 2012), the
7
As a result of the Notice of the National Bureau of Statistics on the Implementation of the National Standard for the Classification of New National Economic Industries, the new Industries classification for national economic activities has changed the number of industrial sectors from 39 to 41 since 2012. In order to facilitate comparison, this study organizes 39 or 41 sectors into 34 sectors. Specifically, the sectors of “Other mining”, “Comprehensive utilization of waste resources”, “Production and supply of gas”, “Production and supply of tap water” are deleted. In the 39 sectors from 2005 to 2011, the sectors of “Rubber products” and “Plastics products” are merged into “Rubber and plastic products”. In the 41 sectors from 2012 to 2015, the sectors of “Mining auxiliary activities” and “Repair of metal products, machinery and equipment” are deleted, and the sectors of “Automobile products” and “Equipment for railway, ship, aerospace, and other transportation” are merged into “Transport equipment”. 14
Journal Pre-proof indicator of desirable output is industrial added value and this data for the 30 provinces are sourced from NBSC. Since NBSC has not released added values of the industrial sectors since 2008, the data are collected and balanced with the national data at the sectoral and regional levels by our own (Su and Ang, 2010, 2014). The capital stock and industrial added value in this paper are all at constant 2010 prices. Finally, energy consumption and CO2 emissions are sourced from China Emission Accounts and Datasets8. According to the division of NBSC, 30 provinces in China can be divided into four regions:
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northeastern, eastern, central, and western regions9. According to China’s Industries Classification for National Economic Activities, 34 industrial sectors in China can be divided into three major industries:
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Mining, Manufacturing, and Power-Gas-Water10. Descriptive statistics of various indicators for China’s
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provincial industry and industrial sector are shown in Table 1. In the four regions, the average values
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for all indicators in the eastern region are the highest while those in the western region are the lowest, and except energy consumption, the average values for all indicators in Power-gas-water are
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higher than that in the other two industries.
Table 1 Descriptive statistics of various indicators, 2005-2015. Northeast
835.27
747.61
(Billion RMB) Labor
(746.40) 295.49
(425.12) 209.05
(10,000 persons) Energy consumption
(322.16) 52.23
(Million tons, Mt) Industrial added value
China’s industrial sector
East
Central
West
Industry
Mining
Manufacturing
Power-Gas-Water
1212.11
756.80
439.81
742.73
537.11
590.73
6026.69
(992.01) 521.30
(457.61) 278.85
(346.73) 108.63
(1145.78) 305.16
(584.87) 149.19
(602.14) 276.72
(2081.63) 331.94
(104.50) 62.60
(430.37) 70.26
(123.91) 64.42
(81.41) 36.74
(338.20) 44.57
(174.18) 16.49
(193.74) 51.06
(17.43) 25.52
(38.35) 647.85
(39.40) 531.09
(58.73) 987.12
(29.96) 628.68
(21.32) 299.39
(111.63) 569.37
(16.00) 482.71
(123.63) 559.98
(3.76) 1265.60
(Billion RMB) CO2 emissions
(597.06) 227.71
(285.74) 239.13
(782.20) 284.83
(351.56) 259.57
(248.64) 155.67
(464.54) 243.42
(417.35) 34.31
(457.65) 117.52
(318.33) 3208.62
(Mt)
(163.12)
(103.84)
(219.29)
(108.48)
(101.45)
(672.68)
(35.58)
(329.98)
(679.38)
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China Capital stock
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China’s provincial industry
Note: The values in parentheses are standard deviations and the values without parentheses are the mean values.
8
http://www.ceads.net/ The northeastern region includes Liaoning, Jilin, and Heilongjiang; the eastern region includes Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; the central region includes Shanxi, Anhui, Jiangxi, Henan, Hubei, and Hunan; and the western region includes Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. 10 The Mining industry includes the following five sectors: Coal mining and dressing, Petroleum and natural gas extraction, Ferrous metals mining and dressing, Nonferrous metals mining and dressing, and Nonmetal minerals mining and dressing. The Power-Gas-Water industry only includes the sector of Electric power, steam and hot water production and supply. The remaining 28 sectors belong to Manufacturing industry. 15 9
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4. Empirical results 4.1. Analysis of carbon congestion based on China’s provincial industry This section first discusses carbon congestion based on China’s provincial industry, and then describes the technical efficiency and CO2 emissions reduction potential of China’s provincial industries. Finally, according to the states of carbon congestion and technical efficiency of China’s provincial industry, the provincial strategies for future development and CO2 emissions reduction are
4.1.1. The state of carbon congestion The optimal solutions vi (i 1, 2,3) , u* , w* and *
can be obtained by using Models (3)
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*
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proposed.
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and (7). According to the aforementioned research methods, the state of carbon congestion, RTD,
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and DTR in China’s 30 provinces during 2005-2015 are obtained, and the results are shown in Tables A1 and A2 in the Appendix. Fig. 2 shows the results of 2005, 2010 and 2015 as representatives.
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First, it can be seen from Fig. 2 that UC and DC in China’s provincial industry is serious and obvious. The provinces with strong UC (NRTD) in China accounted for 43.33%, 40% and 46.67% of all
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provinces in 2005, 2010 and 2015, respectively, while the provinces with strong DC (NDTR) in China
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accounted for 66.67%, 60% and 46.67% of all provinces in 2005, 2010 and 2015, respectively. Combining with the concept of UC and DC, the strong UC (NRTD) states in Fig.2 indicate that there exists overflow of CO2 emissions in China’s provincial industries due to over-investment in resources. This also reflects that China has a strong resource-based CRP, that is, reducing investment resources will be beneficial to carbon emission reduction and industrial growth. The strong DC (NDTR) states indicate that there is also strong technology-based CRP in China’s provincial industries; in other words, CO2 emissions reduction can be achieved through green technology innovation while promoting industrial economic growth, which also reflects significant potential for technological innovation in China’s provincial industry.
16
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H e ilo n g jia n g
H e ilo n g jia n g
Jilin
X in jia n g
Jilin X in jia n g
G a n su
In n e rM o n g o lia
N in g xia
Q in g h a i
Be ijin g Lia o n in g G a n su
H e b e i Tia n jin
Sh a n xi Sh a n d o n g
Strong UC (NRTD) No UC (DRTD) No data
H ong Kong M a ca o H a in a n
of
H e b e i Tia n jin
Tib e t
Jia n g su
H e b e i Tia n jin N in g xia Sh a n xi Sh a n d o n g
H ong Kong M a ca o H a in a n
(d)2010 DC [原创]ExcelPro的图表博客 [修改]@数据化分析
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(c)2010 UC
Strong DC (NDTR) No DC (DDTR) No data
Be ijin g
Lia o n in g
Jia n g su Sh a a n xiH e n a n Sh a n g h a i Sic h u a n H ub ei A nhui C h o n g q in g Zh e jia n g H u n a nJia n g xi G u izh o u Fu jia n Yunna n Ta iw a n G u a n g xiG u a n g d o n g
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Yunna n
H u n a nJia n g xi Fu jia n Ta iw a n G u a n g xiG u a n g d o n g H ong Kong M a ca o H a in a n
G u izh o u
re
Sh a n g h a i A nhui Sic h u a n H ub ei C h o n g q in g Zh e jia n g
Strong UC (NRTD) No UC (DRTD) No data
Jilin
In n e rM o n g o lia
Q in g h a i
Sh a n xi Sh a n d o n g
-p
Tib e t
G a n su
Be ijin g Lia o n in g
Sh a a n xi H e n a n
H e ilo n g jia n g
X in jia n g
ro
X in jia n g
Q in g h a i
Sh a n d o n g
(b)2005 DC [原创]ExcelPro的图表博客 [修改]@数据化分析
Jilin
N in g xia
H e b e i Tia n jin N in g xia Sh a n xi
Jia n g su Sh a n g h a i Sic h u a n H ub ei A nhui C h o n g q in g Zh e jia n g H u n a nJia n g xi G u izh o u Fu jia n Yunna n Ta iw a n G u a n g xiG u a n g d o n g
Strong DC (NDTR) No DC (DDTR) No data
H e ilo n g jia n g
G a n su
Lia o n in g
Sh a a n xiH e n a n
Tib e t
(a)2005 UC
In n e rM o n g o lia
In n e rM o n g o lia
Q in g h a i
Sh a a n xi H e n a n Jia n g su Sh a n g h a i A nhui Sic h u a n H ub ei C h o n g q in g Zh e jia n g H u n a nJia n g xi G u izh o u Fu jia n Yunna n Ta iw a n G u a n g xiG u a n g d o n g H ong Kong M a ca o H a in a n
Tib e t
Be ijin g
H e ilo n g jia n g
H e ilo n g jia n g
Jilin
X in jia n g
Q in g h a i Tib e t
Strong UC (NRTD) No UC (DRTD) No data
In n e rM o n g o lia
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G a n su
N in g xia
Jilin
X in jia n g
Be ijin g Lia o n in g
G a n su
H e b e i Tia n jin
Sh a n xi Sh a n d o n g
In n e rM o n g o lia N in g xia
Q in g h a i
Jia n g su Sh a a n xi H e n a n Sh a n g h a i A nhui Sic h u a n H ub ei C h o n g q in g Zh e jia n g H u n a nJia n g xi G u izh o u Fu jia n Yunna n Ta iw a n G u a n g xiG u a n g d o n g H ong Kong M a ca o H a in a n
Tib e t
Strong DC (NDTR) No DC (DDTR) No DC (IDTR) No data
Be ijin g Lia o n in g H e b e i Tia n jin
Sh a n xi Sh a n d o n g
Jia n g su Sh a a n xi H e n a n Sh a n g h a i A nhui Sic h u a n H ub ei C h o n g q in g Zh e jia n g Yunna n
H u n a nJia n g xi Fu jia n Ta iw a n G u a n g xiG u a n g d o n g H ong Kong M a ca o H a in a n
G u izh o u
(f)2015 DC
(e)2015 UC
Fig. 2. Carbon congestion for China’s provincial industry in 2005, 2010 and 2015.
Second, the carbon congestion effect in industrial production in China’s provinces shows a trend of regional agglomeration and presents obvious regional heterogeneity. From Fig. 2, we can see that from 2005 to 2015, the strong UC (NRTD) and strong DC (NDTR) agglomerated from the four regions 17
Journal Pre-proof to the eastern and western regions, respectively. In 2015, the strong UC (NRTD) is mainly concentrated in the eastern region and the eastern region accounted for 57.14% of the provinces with strong UC in 2015. It indicates that compared with other regions, the excessive investment in the eastern region is relatively serious, which not only results in unnecessary CO2 emissions, but also undermines the industrial output. Actually, the eastern region is the most economically and technologically developed region in China (Xu and Lin, 2015). Benefiting from the coastal geographic location and the policy of reform and opening up, the eastern region has developed industries and
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abundant labour force; its higher industrial outputs have continuously attracted significant outside resources and investment (Wang and He, 2017). Table 1 also shows that resource inputs in the
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eastern region are the highest among the four regions in China. Although the eastern region
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continues to attract investment, its resource carrying capacity and development capacity are limited,
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and over-investment will lead to strong UC. Therefore, appropriate investment reduction is more beneficial to carbon emission reduction and industrial development of the eastern region. The
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disappearance of strong UC in the northeastern, central and western regions from 2005 to 2015 is mainly due to the fact that in recent years, China has placed the construction of ecological civilization
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in a prominent position (Zhang and Liu, 2019)11, committed to eliminated backward production capacity, and promoted industrial transformation and upgrading. In particular, in 2010 and 2013,
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China promulgated the Notice of the State Council on Further Strengthening the Work of Eliminating Backward Capacity and the Guiding Opinions of the State Council on Resolving Serious Overcapacity Contradictions. In fact, China’s backward industries are mainly concentrated in these regions, especially in the western region.
The strong DC (NDTR) is mainly concentrated in the western region, followed by the eastern region, and the western region accounted for 50% of the provinces with strong DC in 2015, indicating that there is significant potential for industrial growth and CO2 emissions reduction through green technology innovation. The reason is that the industrial base of China’s western region is relatively weaker and its industrialisation process is relatively slower than other regions; meanwhile, due to the
11
China's 11th Five-Year Plan in 2006 proposed the target of energy conservation and emissions reduction for the first time. Since then, China has formulated a series of policies on environmental protection and emissions reduction. 18
Journal Pre-proof long-term retention during conventional heavy industrialisation, its industrial carbon intensity is relatively higher. Therefore, the larger CO2 emissions and relatively backward technological level of the western region make it possible to realize the huge dividends of CO2 emissions reduction and industrial transformation and upgrading once it devotes itself to green technology innovation. In addition to the western region, the eastern region also has a high proportion of strong DC in 2015. This is because the eastern region has strong technology innovation capability, which even represents the highest level of scientific and technological innovation in China. The eastern region’s advantages
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in innovative infrastructure, talent and technology make it still have the potential to further reduce CO2 emissions through green technology innovation.
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However, unlike the western region, the proportion of strong DC in the eastern region
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experienced a downward trend during 2005-2015. Similarly, the disappearance of strong DC also
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exists in the northeastern and central regions. On the one hand, this indicates that during this period, the socio-economic levels of these three regions have developed rapidly, and a significant carbon
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emission reduction has been achieved by improving green technology innovation (Jiao et al., 2018). On the other hand, this also indicates that it will become more difficult for these regions to further
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reduce CO2 emissions by improving their relatively high level of technology innovation, and they may face such problems as great difficulty in innovation, long innovation cycle and insignificant innovation
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effects. By contrast, the western region has become the region with the greatest potential for technological development and with the greatest technology-based CRP. Third, there remains significant scope for industrial development in the northeastern, central and western regions and it is unsustainable for China to develop its industrial growth through consuming fossil fuels like before. As shown in Fig. 2(e), the northeastern, central and western regions showed no UC (DRTD) in 2015. On the one hand, this indicates that consuming fossil fuels still plays a positive role in the industrial development of these regions; thus, strengthening the revitalisation of northeastern China and the development of the central and western regions remain important in promoting the development of Chinese industry (Mi et al., 2017). On the other hand, this also shows that the contribution of fossil fuel consumption to industrial development shows a diminishing marginal effect, indicating that the production mode of realising industrial development 19
Journal Pre-proof through CO2 emissions from fossil fuel consumption is unsustainable and it is thus necessary to find new modes of economic growth. 4.1.2 Technical efficiency Based on the window analysis, the technical efficiency of China’s provincial industry can be calculated according to Eqs. (4) and (8). In this study, the technical efficiency can be divided into TEN(UC) and TEM(DC), as shown in Figs. 3 and 4. As seen from Fig. 3 (a), TEN(UC) in the eastern region appears the highest, followed by that in the central and northeastern regions, and it is the
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lowest in the western region; their average TEN(UC) from 2005 to 2015 is 0.97, 0.83, 0.81, and 0.8, respectively. This indicates that the eastern region has the highest level of industrial development,
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and their CRP through upgrading its technical efficiency is relatively smaller (i.e., efficiency-based
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CRP), while the other regions have a lower level of industrial development and greater
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efficiency-based CRP, especially the western region, which is consistent with the results of many studies on China’s environmental efficiency (Wang and Wei, 2014). Besides, TEN(UC) in the
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northeastern and central regions shows a fluctuating upward trend from 2005 to 2011, but declines significantly since 2012. This transformation in northeast China is mainly caused by its economic
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structural characteristics (i.e., heavy industry-oriented). Since the implementation of the strategy of revitalizing the old industrial bases in northeast China in 2003, the northeastern region has entered a
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stage of rapid economic development (Zhang, 2008); however, when Chinese economy entered the “new normal” state, the economic growth rate in the northeastern region has dropped sharply since 2012, especially in its heavy industry12. The main reason for the decline of TEN (UC) in the central region since 2012 may be the downturn of Shanxi’s industrial economy. Shanxi is the mainly coal mine province and the continuous decline in coal prices since 2012 drags Shanxi’s industrial economy into the bottom13. The environmental protection performance in the eastern region is the best, while that in the western region is worse; as shown in Fig. 4 that the average TEM(DC) from 2005 to 2015 in the northeastern, eastern, central and western regions are 0.74, 0.93, 0.77 and 0.72, respectively.
12
http://www.xinhuanet.com//politics/2016lh/2016-03/11/c_1118307999.htm
13
http://www.mlr.gov.cn/xwdt/jrxw/201606/t20160622_1409427.htm 20
Journal Pre-proof Besides, from Fig. 3(b), it can be found that China's TEM(DC) shows an upward trend from 2005 to 2015, rising from 0.8 to 0.84, reflecting the growing role of energy conservation and emission reduction in China’s development. It is also found that TEM(DC) generally declines significantly in 2007, which mainly due to the high industrial growth in 2007, resulting in greater environmental protection pressure. Actually, 2007 is the last sprint year of industrial high-speed growth since the implementation of the reform and opening-up policy. According to the data from NBSC, in 2007, the industrial added value increased by 13.5% over the last year and the secondary industry boosted GDP
(a) TEN(UC)
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expansion of China’s heavy industry has come to an end.
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by 7.1%, both of which are the highest growth rates since the 21st century; so far, the capacity
(b) TEM(DC)
Efficiency
1 0.8 0.6 0.4
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Fig. 3. TEN(UC) and TEM(DC) in China and four regions from 2005 to 2015. TEN(UC)
TEM(DC)
0.2
Liaoning Jilin Heilongjiang Beijing Tianjin Hebei Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Shanxi Anhui Jiangxi Henan Hubei Hunan Inner Mongolia Guangxi Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang
0
Northeast
East
Central
West
Fig. 4. Average TEN(UC) and average TEM(DC) of 30 provinces in China. In addition, it can be seen from Fig. 4 that TEN(UC) is generally higher than TEM(DC), suggesting that compared with environmental protection, China has paid more attention to industrial 21
Journal Pre-proof development. Besides, compared with the other three regions, the eastern region has a higher TEN(UC) and TEM(DC) and a smaller difference between TEN(UC) and TEM(DC), indicating that the eastern region performs well with regard to both industrial development and environmental protection, such as Shanghai, Jiangsu, Guangdong and Hainan. However, it is worth noting that high technical efficiency does not mean that there is no congestion. Although China’s eastern region has played the leading role in both industrial development and environmental protection, it still has the potential for further improvement in light of its carbon congestion state. As for the western region, its
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technical efficiency is relatively lower, and under the current production level, there is a gap between the technical efficiency of the western region and the production frontier represented by the eastern
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region, which indicates that the western region in China has great efficiency-based CRP. Moreover,
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combined with the strong DC state, the western region also has a huge potential to further achieve
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carbon emission reduction through green technology innovation, which could expand its production frontier.
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4.1.3 Reduction potential and reduction strategy of CO2 emissions Based on Eq. (5) and the CO2 emissions of various provinces in China from 2005 to 2015, the
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average efficiency-based CRP of Chinese industry can be calculated and the results are shown in Fig. 5. It can be seen that, the average annual total CO2 emissions could be reduced by 784.27 Mt by
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improving technical efficiency, of which 122.02 Mt, 73.32 Mt, 280.17 Mt, and 308.76 Mt arise in the northeastern, eastern, central, and western regions, respectively. Therefore, the western region has the largest efficiency-based CRP and its CRP accounts for 39% of the total. From the perspective of the average CRP of all provinces in different regions, the average values of provinces in the four regions are 40.67, 7.33, 46.7 and 28.07, respectively, of which the northeastern and central regions have higher values. From the perspective of the provinces, the province with the largest CRP is Shanxi, followed by Guizhou, Liaoning and Anhui, with 151.46, 75.16, 53.81 and 50.42 Mt, respectively. Therefore, the western region, the northeastern region and Shanxi province should be the key targets for carbon emission reduction in China.
22
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Fig. 5. Efficiency-based CO2 reduction potential for China’s provincial industry14.
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As mentioned above, under the current production frontiers, the province can eliminate strong
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UC by reducing its resource investment, so as to promote the decoupling of CO2 emissions from
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China’s industrial development (Sueyoshi and Goto, 2016). If a province exhibits strong DC, this province can devote itself to green technology innovation to promote the “win-win” between
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industrial development and CO2 emissions reduction (Sueyoshi and Yuan, 2016). If a province exhibits technical inefficiencies, it indicates that there is greater CRP and industrial development if this
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province produces on the current production frontier (Du et al., 2015). And based on the results of UC, DC and TEN(UC), this paper proposes possible future development and emissions reduction
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strategies for each province (see Table 2) (Sueyoshi and Goto, 2014a; Sueyoshi and Wang, 2014b)15. Table 2 divides the reduction strategies for CO2 emissions into two types, namely: a resource-based strategy (based on strong UC) and a technology-based strategy (based on strong DC). Technology-based emissions reduction needs to expand the production frontier through technology innovation; thus, compared with the resource-based strategy, a technology-based strategy incurs high costs, and it is time-consuming, but effective and sustainable (Sueyoshi and Goto, 2012a; Sueyoshi and Yuan, 2016). It can be inferred from Table 2 that many provinces in eastern China have both
14
This result only reflects the potential of carbon emission reduction that can be achieved through the improvement of technical efficiency. 15 In order to more accurately propose strategies, we use the data of three years (i.e., 2013-2015) instead of the latest one year (i.e., 2015) to show the congestion in recent years, and the TEN(UC) in Table 2 is the mean value of TEN(UC) in 2013-2015. The state of congestion that occurs two or more times in the three years is taken as the final congestion state of a province in recent years. 23
Journal Pre-proof strong UC and DC16, such as Beijing, Hebei, Jiangsu, Shandong, Guangdong, and Hainan, so eastern China should pay more attention to the optimisation of input resources and green technology innovation in the future. For the other three regions, under the current production level, various means should be adopted to improve their technical efficiency according to the socio-economic and production consumption situation of each province (Mi et al., 2019). More importantly, the central region should pay more attention to the optimisation of input resources, and the northeastern and western regions should pay more attention to the improvement of green technology innovation to
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achieve long-term development.
Region
Province
Northeast
Central
TEN(UC)
2015
2013
2014
2015
Liaoning
No
No
No
No
No
No
0.92
Jilin Heilongjiang
No S
No No
No No
S S
No No
0.80 0.59
S
S
Tianjin Hebei
No S
No S
Shanghai
No
No
Jiangsu Zhejiang
S S
Fujian Shandong
S S
Guangdong Hainan
S S
Shanxi
S S
R
T
S
S
0.99
√
√
No S
No S
No S
0.97 0.99
√
√
No
S
S
S
0.92
S
S
E √
√ √
No S
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Beijing
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2014
√ √
√
S S
S No
S No
S No
0.98 0.96
√ √
S S
S S
No S
No S
No No
1.00 0.98
√ √
√
S S
S S
S S
S S
S S
1.00 0.98
√ √
√ √
No
No
No
S
S
S
0.53
No S
S No
S No
No No
No No
No No
0.82 0.84
√
√ √ √
lP S S
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Anhui Jiangxi
West
DC
2013
na
East
UC
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Table 2 Strategies of industrial development and carbon emission reduction in different provinces in China.
√ √
√
Henan
S
S
No
No
No
No
0.83
√
Hubei Hunan
No S
No S
No S
S No
No No
No No
0.88 1.00
√
Inner Mongolia Guangxi
S S
S S
S S
No S
No S
No S
1.00 0.99
√ √
Chongqing
No
No
No
No
S
No
0.87
Sichuan Guizhou
No S
No S
No S
S No
S No
S No
0.90 0.65
√
Yunnan Shaanxi
No S
No S
No S
S No
S No
S No
0.76 0.93
√
√
√
√ √ √
√ √
√
√ √
Gansu
No
No
No
S
S
S
0.53
√
√
Qinghai Ningxia
No No
No No
No No
S S
S S
S S
0.97 0.41
√ √
√
Xinjiang
No
No
No
S
S
S
0.61
√
√
Note: R: Resource-based strategy; T: Technology-based strategy; E: Low technical efficiency; UC: Undesirable carbon congestion; DC: Desirable carbon congestion; S: strong UC or DC; No: no UC or DC; TEN(UC): Technical efficiency under
16
UC and DC reflect conventional and sustainable ideas, respectively, which indicates that these provinces may have production capacity constraints in given production frontier, but also have potentials to expand their production frontier through green technology innovation. 24
Journal Pre-proof natural disposability.
4.2 Analysis of carbon congestion based on China’s industrial sector 4.2.1 The state of carbon congestion The state of carbon congestion in China’s 34 industrial sectors from 2005 to 2015 can be obtained by Models (3) and (7), and the results are shown in Tables A3 and A4 in the Appendix. According to China’s Industries Classification for National Economic Activities, China’s 34 industrial sectors can be divided into three major industries, i.e., Mining, Manufacturing, and Power-Gas-Water.
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Table 3 shows the results in 2005, 2010 and 2015 as representatives.
2010
UC
RTD
S
N
1
Coal mining and dressing
2
Petroleum and natural gas extraction
3
Ferrous metals mining and dressing
S
4
Nonferrous metals mining and dressing
S
5
Nonmetal minerals mining and dressing
6
Food processing
7
Food production
8
Beverage production
9
Tobacco processing
10
Textile industry
11
Garments and other fiber products
12 13 14
Furniture manufacturing
15
Papermaking and paper products
16
Printing and record medium reproduction
17 18
2015
2005
2010
2015
UC
RTD
UC
RTD
DC
DTR
DC
DTR
DC
S
N
No
D
No
I
No
I
No
DTR I
D
S
N
No
D
No
D
No
I
S
N
N
No
I
No
D
No
D
No
D
No
D
N
No
I
No
I
No
D
No
D
No
D D
re
No
N
S
N
No
I
S
N
No
D
No
S
N
S
N
S
N
No
I
No
I
No
I
No
D
S
N
S
N
No
D
No
D
No
D
No
I
No
D
S
N
No
D
No
D
No
D
S
N
S
N
S
N
No
D
No
D
No
D
No
D
S
N
S
N
S
N
No
D
No
D
No
D
S
N
No
D
S
N
S
N
S
N
Leather, furs, down and related products
No
D
No
I
No
I
S
N
S
N
S
N
Timber processing, bamboo, cane, palm & straw products
No
I
No
I
No
D
No
D
No
D
No
D
S
N
No
I
No
I
No
D
S
N
S
N
No
I
No
D
No
D
No
D
No
D
No
D
No
I
No
I
No
I
S
N
S
N
S
N
Cultural, educational and sports articles
S
N
S
N
No
I
S
N
S
N
S
N
Petroleum processing and coking
S
N
No
D
No
D
S
N
No
D
S
N
19
Raw chemical materials and chemical products
S
N
No
D
No
D
S
N
S
N
S
N
20
Medical and pharmaceutical products
No
I
No
D
S
N
No
D
No
D
No
D
21
Chemical fiber
No
I
No
I
No
I
S
N
S
N
S
N
22
Rubber and plastic products
No
D
No
D
No
D
S
N
No
D
S
N
23
Nonmetal mineral products
No
D
S
N
S
N
S
N
No
I
No
I
24
Smelting and pressing of ferrous metals
S
N
S
N
S
N
S
N
S
N
S
N
25
Smelting and pressing of nonferrous metals
No
D
No
D
No
D
No
D
No
D
No
D
26
Metal products
No
D
S
N
S
N
No
D
No
D
No
D
27
Ordinary machinery
S
N
S
N
S
N
No
I
No
I
No
I
28
Equipment for special purpose
No
D
No
D
No
D
No
D
No
D
No
D
29
Transportation equipment
No
D
No
D
No
D
No
I
No
I
S
N
30
Electric equipment and machinery
S
N
No
D
No
D
No
I
No
I
No
I
31
Electronic and telecommunications equipment
No
D
No
D
No
D
S
N
No
D
No
I
32
Instruments, meters cultural and office machinery
No
I
No
I
No
I
No
D
S
N
No
D
33
Other manufacturing industry
No
D
No
I
S
N
S
N
No
D
No
D
34
Electric power, steam and hot water production and supply
S
N
S
N
S
N
S
N
S
N
S
N
na
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S
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Mining Manufacturing PGW
Sector
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2005 Code
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Table 3 Carbon congestion for China’s industrial sectors in 2005, 2010 and 2015.
Note: UC: Undesirable carbon congestion; DC: Desirable carbon congestion; RTD: Returns to damage; DTR: Damages to return; S: Strong; W: Weak; No: No UC or DC; N: Negative; I: Increasing; D: Decreasing; Mining includes sectors with codes 1 to 5, Manufacturing includes sectors with codes 6 to 33, and Power-Gas-Water (PGW) only includes sector with code 34. 25
Journal Pre-proof First, it can be seen that UC and DC in China’s industrial sector is obvious, and both strong UC and strong DC mainly occur in Manufacturing and Power-Gas-Water industries. From 2005 to 2015, the sector number of both strong UC and strong DC fluctuates around 13, accounting for 38.24% of total sectors. In 2015, the proportion of strong UC in Mining, Manufacturing, and Power-Gas-Water is 0 (0/5), 39.29% (11/28), and 100% (1/1)17, respectively, indicating that there are varying degrees of waste of resources in the Manufacturing and Power-Gas-Water, resulting in a serious overflow of CO2 emissions. Specifically, the wastes of resources in Food processing (sector 6), Tobacco processing
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(sector 9), Nonmetal mineral products (sector 23), Smelting and pressing of ferrous metals (sector 24), Ordinary machinery (sector 27) and Electric power, steam and hot water production and supply
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(sector 34) are quite serious, as these sectors are almost all in the strong UC state from 2005 to 2015.
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Besides, the Mining industry and some sectors in the Manufacturing industry have strong technology-based CRP. As shown in Table 3, in 2015, the proportions of strong DC in Mining,
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Manufacturing, and Power-Gas-Water are 20% (1/5), 39.29% (11/28), and 100% (1/1)18, respectively.
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This indicates a great potential for green technology innovation and rich technology-based CRP in Manufacturing and Power-Gas-Water, especially in the following sectors: Garments and other fiber
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products (sector 11), Leather, furs, down and related products (sector 12), Furniture manufacturing (sector 14), Printing and record medium reproduction (sector 16), Cultural, educational and sports
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articles (sector 17), Raw chemical materials and chemical products (sector 19), Chemical fiber (sector 21), Smelting and pressing of ferrous metals (sector 24) and Electric power, steam and hot water production and supply (sector 34). These sectors are almost all in the strong DC state from 2005 to 2015, indicating that these sectors emit huge amount of CO2 emissions and can easily achieve energy saving and emission reduction as long as they are committed to green technology innovation. Actually, these sectors also belong to the 16 categories of heavily polluting industries announced in China’s Guidelines for Environmental Information Disclosure of Listed Companies19, such as metallurgy, chemicals, building materials, paper, fermentation, textile, leather, mining. As important objects of
17
The total sector number of three major industries is 5, 28 and 1, respectively; the sector number of strong UC of three major industries is 0, 11 and 1, respectively. 18 The sector number of strong DC of three major industries is 1, 11 and 1, respectively. 19 http://www.gov.cn/gzdt/2010-09/14/content_1702292.htm 26
Journal Pre-proof carbon emissions regulation by Chinese government, these heavily polluting sectors also have the most incentive to carry out green technology innovation. Third, there is still a large space for industrial development in Mining, and the promotional effect of fossil fuel consumption on the development of Mining witnesses a marginal diminishing trend. As shown in Table 3, from 2005 to 2015, the strong UC state of the Mining shows a downward trend while its no UC state is on the rise, and by 2015 the Mining industry is dominated by a state of no UC (IRTD, DRTD). This implies that the fossil fuel consumption will still play a large part in promoting the
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development of Mining and there will still be scope for large industrial development and strong carbon consumption tendencies in Mining, especially in the Nonferrous metals mining and dressing
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(sector 4) and Nonmetal minerals mining and dressing (sector 5). At the same time, it also indicates
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that the production mode of industrial development of mining through CO2 emissions from fossil fuel
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consumption is unsustainable, especially in sectors of Coal mining and dressing (sector 1), Petroleum
4.2.2 Technical efficiency
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and natural gas extraction (sector 2), and Ferrous metals mining and dressing (sector 3).
Based on the window analysis, the technical efficiency of various industrial sectors in China can
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be calculated according to Eqs. (4) and (8). Figs. 6 and 7 show the efficiency results for the three major industries and 34 sectors, respectively.
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It can be seen from Fig. 6 that the technical inefficiency of China’s industry is evident, especially in Mining. Specifically, from 2005 to 2015, the average TEN(UC) for Mining, Manufacturing, and Power-Gas-Water is 0.57, 0.61, and 0.93, respectively, which means that the level of industrial development of Power-Gas-Water is relatively higher while that of Mining is relatively lower. Mining is an energy-intensive industry, and the distribution of mineral resources is mainly concentrated in the economically underdeveloped western region of China. The industrial added value of Mining has increased rapidly since the 21st century; this consecutive industrial development is accompanied by surging energy consumption and CO2 emissions, and its overall production mode is relatively extensive, all of which bring the lower technical efficiency of Mining (Liu et al., 2016). Unlike Manufacturing and Power-Gas-Water, the TEN(UC) of Mining declines from 2005 to 2015. The reasons for this decline are the rising cost of the Mining industry, the slowdown in downstream 27
Journal Pre-proof demand in Mining, the oversupply of its production capacity, and the downward pressure of mineral product prices in recent years (Ma et al., 2019)20. All of this leads to a decline in the efficiency of Chinese mining industry. However, this also shows that there is some room for improving the technical efficiency in Mining. For example, reducing energy intensity and optimising structure are considered effective ways to improve the technical efficiency of Mining. The significant decline in TEN (UC) of Power-Gas-Water in 2008 is mainly due to the impact of international financial crisis, which leads to a decline in external demand and a downturn in this industry; besides, the implementation
of
of tight monetary policy and the increase of fuel costs in 2008 also make this industry difficult to
na
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re
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ro
operate.
Fig. 6. TEN(UC) and TEM(DC) of three major industries in China from 2005 to 2015.
0.9 0.8
Efficiency
TEM(DC)
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TEN(UC)
1.0
0.7
0.6 0.5 0.4 0.3
0.2 0.1 0.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Mining
Manufacturing
Power-gas-water
Sector code
Fig. 7. Average TEN(UC) and average TEM(DC) of 34 industrial sectors in China21.
20
http://money.people.com.cn/stock/n1/2015/1217/c67815-27940260.html
21
The sector codes of 34 industrial sectors are shown in Table 3. 28
Journal Pre-proof
In addition, green technology innovation is the key for China to achieve sustainable energy savings and emissions reduction in the future. From 2005 to 2015, the national TEM(DC) shows an upward trend, rising from 0.63 to 0.72, indicating that China has made efforts to achieve energy saving and emission reduction through green technology innovation since 2005. The average TEM(DC) for Mining, Manufacturing, and Power-Gas-Water is 0.55, 0.76, and 0.96, respectively, which means that Power-Gas-Water performs better in energy saving and emission reduction, and Mining and
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Manufacturing have great potential for improvement in energy saving and emission reduction performance. This view also conforms to some existing related studies. For example, Jordaan et al.
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(2017) argue that energy technology innovation is increasingly considered to be the key to promoting
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global emissions reduction, which is mainly due to technological innovations that can guide the
re
transformation of energy structures and change the modes of economic growth, thus promoting carbon emissions reductions. Yii and Geetha (2017) point out that technological innovation is
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negatively correlated with CO2 emissions; thus, for the sustainable development of economy and environment, policymakers should promote green technology innovation without delay.
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Finally, as shown in Fig. 7, some sectors have a very low TEN(UC) but a very high TEM(DC), such as Timber processing, bamboo, cane, palm & straw products (Sector 13), Papermaking and paper
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products (Sector 15), and Chemical fiber (Sector 21). This indicates that these sectors currently perform well in energy saving and emission reduction, but are inefficient in industrial development. This may be due to the low profitability and rising raw material costs in Sectors 13 and 15, the “new normal” state with low-speed growth and intensified competition in Sector 21, and the increasing pressure of green environmental protection22. The TEN(UC) of some sectors appears obviously higher than their TEM(DC), such as Food processing (Sector 6), Nonmetal mineral products (Sector 23), and Ordinary machinery (Sector 27), indicating that these sectors pay more attention to industrial development than to environmental protection. Finally, the TEN(UC) and TEM(DC) of Tobacco processing (Sector 9), Smelting and pressing of ferrous metals (Sector 24), Electronic and telecommunications equipment (Sector 31), and Electric power, steam and hot water production and 22
http://www.paper.com.cn/news/daynews/2018/180906165849780261.htm 29
Journal Pre-proof supply (Sector 34), are all higher than 0.9, indicating that these four sectors have achieved better performance in both industrial development and environmental protection. 4.2.3 Reduction potential and reduction strategy of CO2 emissions According to Eq. (5) and the CO2 emissions of various industrial sectors from 2005 to 2015, the efficiency-based CRP of China’s industrial sector can be calculated and the results are listed in Table 4. It can be seen that, through the improvement of technical efficiency, 661.36 Mt of CRP can be realised. Among them, the CRP of Mining, Manufacturing, and Power-Gas-Water industries are 37.88
of
Mt, 422.22 Mt, and 201.27 Mt, respectively, accounting for 5.73%, 63.84%, and 30.43% of the total CRP, respectively. It can be inferred that, under the current production level, Manufacturing and
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Power-Gas-Water have rich CRP through the improvement of their technical efficiency.
2
Petroleum and natural gas extraction
3
Ferrous metals mining and dressing
6
Food processing
7
Food production
8
Beverage production
9
Tobacco processing
10
Textile industry
11
Garments and other fiber products
12
Power-Gas-Water
Sector
CRP
13.54
Code 4
Nonferrous metals mining and dressing
2.86
0.54
5
Nonmetal minerals mining and dressing
8.29
Total
37.88
12.65 20
Medical and pharmaceutical products
7.30
21
Chemical fiber
5.51
19.33
22
Rubber and plastic products
7.47
0.00
23
Nonmetal mineral products
139.40
15.35
24
Smelting and pressing of ferrous metals
13.23
3.89
25
Smelting and pressing of nonferrous metals
19.60
2.50
26
Metal products
7.78
9.36
27
Ordinary machinery
6.19
14
Leather, furs, down and related products Timber processing, bamboo, cane, palm & straw products Furniture manufacturing
0.49
28
Equipment for special purpose
6.61
15
Papermaking and paper products
40.63
29
Transportation equipment
2.14
16
Printing and record medium reproduction
1.00
30
0.34
17
Cultural, educational and sports articles
0.82
31
18
Petroleum processing and coking
60.05
32
19
Raw chemical materials and chemical products
30.94
Electric equipment and machinery Electronic and telecommunications equipment Instruments, meters cultural and office machinery Other manufacturing industry
34
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1.89
17.72
13
Manufacturing
CRP
Coal mining and dressing
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Mining
Sector
1
re
Code
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Table 4 Efficiency-based CO2 reduction potential in China’s industrial sector (unit: Mt-CO2)
33 Total
Electric power, steam and hot water production and supply
Total of Mining, Manufacturing, and Power-Gas-Water
0.24 1.39 1.06 422.22 201.27 661.36
Note: CRP means CO2 emissions reduction potential.
Of the 34 industrial sectors, the sector of Electric power, steam and hot water production and supply (Sector 34) has the highest CRP of 201.27Mt, followed by Nonmetal mineral products (Sector 23) with 139.4 Mt. Besides, there are three other sectors with more than 30 Mt of CRP, including Papermaking and paper products (Sector 15), Petroleum processing and coking (Sector 18), and Raw chemical materials and chemical products (Sector 19). Actually, some of the sectors above belong to 30
Journal Pre-proof six high-energy-consuming industrial sectors in China's national statistical classification (Sectors 18, 19, 23, 24, 25, 34), and the CRP of six high-energy-consuming sectors has reached 464.48 Mt, accounting for 70.23% of the total CRP; thus, we should pay more attention to these sectors in the future. To target China’s CO2 emissions reduction effort more effectively, and promote CO2 emissions decoupling, according to the congestion theory (Cooper et al., 2002; Sueyoshi and Goto, 2016) and the results of carbon congestion and technical efficiency of China’s industrial sector, some strategies
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for future development and emissions reduction in China’s industrial sectors are proposed (see Table 5)23. It can be seen that, to achieve a “win-win” for CO2 emissions reduction and industrial growth,
ro
the Power-Gas-Water industry could adopt both resource-based and technology-based strategies. In
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the Mining industry, Petroleum and natural gas extraction (Sector 2) could adopt technology-based
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emissions reduction strategy, and other four sectors need to take advantage of comprehensive means to improve their technical efficiency. The Manufacturing industry is more complicated and thus
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should be judged by different sectors. Specifically, (1) sectors such as Food processing (Sector 6) exhibit strong UC, indicating that the future development of these sectors can adopt the
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resource-based strategy, that is, reducing the input of resources in these sectors. (2) some sectors, such as Garments and other fiber products (Sector 11), exhibit strong DC, indicating that
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strengthening green technology innovation to promote the decoupling of industrial development and CO2 emissions is crucial for achieving the aforementioned “win-win” in these sectors. Thus, these sectors can adopt the technology-based strategy, which should include attention to ecological innovation, increasing investment in science and technology, introducing clean production technology, promoting renewable energy development, and realising industrial transformation and upgrading in these sectors. (3) The TEN(UC) of some sectors is relatively lower, such as Papermaking and paper products (Sector 15). Therefore, these sectors should also focus on improving their technical efficiency by optimising their industrial structure, improving their management, rendering outdated equipment and technology obsolete, reducing and controlling emissions, and so on.
23
TEN(UC) in Table 5 is the mean value of TEN(UC) in 2013-2015. The state of congestion that occurs two or more times in three years is taken as the final congestion state of a sector in recent years. TEN(UC) below 0.95 is defined as low technical efficiency. 31
Journal Pre-proof Table 5 Strategies of industrial development and carbon emissions reduction in different sectors in China.
PGW
UC
Sector
2013
DC
2014
2015
2013
2014
2015
TEN(UC)
R
T
E
Coal mining and dressing
No
S
No
No
No
No
0.78
2
Petroleum and natural gas extraction
No
No
No
S
S
S
0.99
3
Ferrous metals mining and dressing
No
No
No
No
No
No
0.07
√
4
Nonferrous metals mining and dressing
No
No
No
No
No
No
0.22
√
5
Nonmetal minerals mining and dressing
No
No
No
No
No
No
0.09
6
Food processing
S
S
S
No
No
No
0.96
√
7
Food production
S
S
S
No
No
No
0.33
√
√
8
Beverage production
S
S
S
No
No
No
0.32
√
√
9
Tobacco processing
S
S
S
No
No
No
1.00
√
10
Textile industry
S
S
S
No
No
No
0.82
√
11
Garments and other fiber products
No
No
No
S
S
S
0.41
√
√
12
Leather, furs, down and related products
No
No
No
S
S
S
0.32
√
√
13
Timber processing, bamboo, cane, palm & straw products
No
No
No
No
No
No
0.11
14
Furniture manufacturing
No
No
No
S
S
S
0.68
15
Papermaking and paper products
No
No
No
No
No
No
0.05
16
Printing and record medium reproduction
No
No
No
S
S
S
0.47
√
√
17
Cultural, educational and sports articles
No
No
No
S
S
S
0.35
√
√
18
Petroleum processing and coking
No
No
No
No
No
S
0.65
19
Raw chemical materials and chemical products
No
No
S
S
S
0.97
20
Medical and pharmaceutical products
21
Chemical fiber
22
Rubber and plastic products
23
Nonmetal mineral products
24
Smelting and pressing of ferrous metals
25
Smelting and pressing of nonferrous metals
26
Metal products
27
Ordinary machinery
28
Equipment for special purpose
29
Transportation equipment
30 31 32
Instruments, meters cultural and office machinery
33 34
-p
ro
of
1
No
√ √
√
√
√ √
S
S
No
No
No
0.57
No
No
S
S
S
0.16
√
√
No
No
No
S
S
S
0.50
√
√
S
S
S
No
No
No
1.00
√ √
re
S
No
√
S
S
S
S
S
0.99
No
No
No
No
No
0.95
S
S
S
No
No
No
0.58
√
S
S
S
No
No
No
0.97
√
No
No
No
No
No
No
0.59
No
No
No
S
S
S
0.99
Electric equipment and machinery
No
No
No
No
No
No
0.99
Electronic and telecommunications equipment
No
No
No
No
No
No
0.97
No
No
No
S
No
No
0.96
Other manufacturing industry
S
S
S
No
No
No
0.99
√
Electric power, steam and hot water production and supply
S
S
S
S
S
S
1.00
√
lP
√ √
S
na
√
√
No
Jo ur
Manufacturing
Mining
Code
√
√ √ √ √ √
√
Note: PGW: Power-Gas-Water; T: Technology-based strategy; E: Low technical efficiency; UC: Undesirable carbon congestion; DC: Desirable carbon congestion; S: strong UC or DC; No: no UC or DC; TEN(UC): Technical efficiency under natural disposability.
5. Robustness test To verify the robustness of the results and the advantage of the method used in this paper, this section employs some other commonly used methods to recalculate the provincial UC results in 2015, including the DEA model proposed by Färe et al. (1985) and the dual model of the direction distance function proposed by Fang (2015). Among them, the DEA model proposed by Färe et al. (1985) is widely used to identify the congestion, but it only considers the desirable output and calculates input 32
Journal Pre-proof congestion rather than UC. Therefore, in order to facilitate comparison, according to the problem considered, this paper extends the DEA model proposed by Färe et al. (1985) to a model that can identify UC, as shown in Models (9) and (10) (Sueyoshi et al., 2018; Wu et al., 2016). Max
Max
30
xij j xik
30
(i 1, 2,3),
xij j xik
s.t.
j 1 30
g j j gk gk ,
g j j gk gk ,
j 1 30
j 1 30
b j j bk bk ,
b j j bk bk ,
(9)
j 1 30
j 1 30
j 1,
(10)
j 1, ,30),
j 1
: free
j 0 ( j 1,L ,30),
ro
j 1
j 0 ( j 1,
(i 1, 2,3),
j 1 30
of
s.t.
: free.
-p
Assuming that and are the optimal results of Models (9) and (10), respectively, UC can
re
be identified by and . Specifically, UC occurs only if 1 1 1 (Cooper et al., 2000;
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Sueyoshi, 2003).
The directional distance function and its dual model proposed by Fang (2015) are shown in
na
Models (11) and (12), respectively. As with the method used in this paper, this dual model can identify the congestion state of provinces with technical efficiency of 1. 30
s.t.
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Max
xij j + xk xik j 1 30
g j j gk gk , j 1 30
3
Min
(i 1, 2,3),
vi xik ug k wbk i 1 3
s.t.
vi xij ug j wb j 0 ( j 1,L ,30),
i 1 3
(11)
(12)
vi xik ug k wbk 1,
b j j bk bk , j 1
i 1
j 0 ( j 1,L ,30)
vi 0
(i 1, 2,3),
u 0,
w : free.
The criteria for UC here is the same as that before, i.e., UC occurs only if w 0 . Table 6 summarizes the results from different methods. It can be seen that based on the method of Färe et al. (1985), only Zhejiang, Anhui, Guizhou, and Shaanxi provinces have strong UC; when using the method in this paper, these four provinces also display strong UC and technical inefficiency, while the remaining provinces with strong UC have the technical efficiency of 1. This phenomenon also 33
Journal Pre-proof corroborates some existing research, that is, the congestion identified by the envelopment model of DEA is mainly based on the provinces with technical inefficiency (Brockett et al., 2004; Wei and Yan, 2004). In other words, the method used in this paper, which is based on the dual model of DEA rather than the envelopment model of DEA, does not miss the congestion results identified by Färe et al. (1985)’s method and is able to better identify the congestion state of the province with technical efficiency of one. In addition, the provinces in the congestion state based on the method in this paper also experience congestion under the method of Fang (2015), indicating that the results of this paper
of
have certain robustness24. Table 6 Comparison of UC results of 30 provinces in China under different methods, 2015.
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Method in Fang (2015) UC 1 -0.05 0.91 S 0.07 0.89 No -0.23 0.83 S 2.11 1.00 No 0.52 1.00 No -0.04 0.94 S 0.73 0.93 No -0.03 0.85 S -0.07 0.96 S -0.10 1.00 S -0.04 0.86 S -3.88 1.00 S 0.89 0.85 No -0.08 0.76 S -0.12 0.91 S -0.26 0.89 S -0.07 0.85 S -0.10 0.88 S 0.01 1.00 No -120.48 1.00 S -0.48 1.00 S 0.08 0.88 No 0.04 0.82 No -0.34 0.91 S 0.11 0.90 No -0.07 0.94 S 0.19 0.72 No 0.49 0.91 No -144.31 1.00 S -58.28 1.00 S
re
-p
w
na
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Method in Färe et al. (1985) UC 1 1 0.95 0.95 No 0.77 0.77 No 0.55 0.55 No 1.00 1.00 No 1.00 1.00 No 1.00 1.00 No 0.93 0.93 No 1.00 1.00 No 0.98 0.99 S 1.00 1.00 No 1.00 1.00 No 1.00 1.00 No 1.00 1.00 No 0.44 0.44 No 0.80 0.81 S 0.80 0.80 No 0.81 0.81 No 0.93 0.93 No 1.00 1.00 No 1.00 1.00 No 1.00 1.00 No 0.85 0.85 No 0.88 0.88 No 0.67 0.84 S 0.81 0.81 No 0.90 0.91 S 0.46 0.46 No 0.98 0.98 No 0.43 0.43 No 0.69 0.69 No
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Province Liaoning Jilin Heilongjiang Beijing Tianjin Hebei Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Shanxi Anhui Jiangxi Henan Hubei Hunan Inner Mongolia Guangxi Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang
Method in this paper TEN(UC) UC 0.09 0.95 No 0.16 0.77 No 0.21 0.55 No -97.86 1.00 S 0.43 1.00 No -83.17 1.00 S 0.93 0.93 No -0.63 1.00 S -0.03 0.99 S -0.91 1.00 S -99.24 1.00 S -123.54 1.00 S -34.15 1.00 S 0.16 0.44 No -0.07 0.81 S 0.44 0.81 No 0.06 0.81 No 0.25 0.93 No -0.31 1.00 S -77.61 1.00 S -147.72 1.00 S 0.74 0.85 No 0.24 0.88 No -1.72 0.84 S 0.23 0.81 No -0.08 0.91 S 1.34 0.46 No 0.94 0.98 No 0.68 0.43 No 0.24 0.69 No
k
Note: UC: Undesirable carbon congestion; TEN(UC): Technical efficiency under natural disposability; S: Strong UC; No: no
24
However, the number of provinces in congestion calculated by Fang (2015)’s method is more than that of this paper, which is mainly caused by the difference between the two methods. In the method of Fang (2015)’s method, the measurement of technical inefficiency by the envelopment model includes three aspects: input, desirable output and undesirable output, as shown in Eq. (11); while in our method, the measurement of technical inefficiency by the envelopment model only includes desirable output and undesirable output, as shown in Eq. (2). 34
Journal Pre-proof UC.
In summary, compared with Färe et al. (1985)’s method, the method in this paper has better capability to identify UC, which can identify the congestion state of provinces with technical efficiency of 1. Meanwhile, compared with Fang (2015)’s method, the congestion results based on the method in this paper are robust and credible.
6. Conclusions
of
This paper uses the dual model of radial DEA to study the carbon congestion in China’s provincial
ro
industry and industrial sectors from 2005 to 2015, which can be divided to undesirable carbon congestion and desirable carbon congestion. On this basis, the states of returns to damage and
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damages to return, as well as the technical efficiency and carbon emissions reduction potential are
re
also analysed. The main conclusions are as follows:
First, China’s industrial carbon congestion is obvious during the sample period. In China’s
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provincial industries, the proportion of undesirable carbon congestion fluctuates around 43.33%, and although the proportion of desirable carbon congestion decreases from 66.67% to 46.67% from 2005
na
to 2015, the desirable carbon congestion is still obvious. In China’s industrial sector, from 2005 to
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2015, the proportion of undesirable carbon congestion and desirable carbon congestion fluctuate around 38.24%. Undesirable carbon congestion means that China’s waste of industrial resources is serious and this has caused overflows of CO2 emissions. Desirable carbon congestion means that China has great potential for carbon reduction and industrial growth through green technology innovation. In addition, China’s mode of production involving realisation of industrial growth through the burning of fossil fuels is unsustainable, especially in the northeastern, central and western regions and in the Mining industry. Second, the congestion effect shows a trend of regional agglomeration and obvious regional heterogeneity. From 2005 to 2015, undesirable carbon congestion and desirable carbon congestion show the trend of agglomeration from the whole country to eastern and western regions in China, respectively. In 2015, undesirable carbon congestion is mainly concentrated in the eastern region; while desirable carbon congestion mainly exists in the western region, followed by the eastern region. 35
Journal Pre-proof In addition, the congestion effect presents obvious sectoral heterogeneity. Both undesirable and desirable carbon congestions mainly occur in the Manufacturing and Power-Gas-Water industries. Therefore, under current production level, the “win-win” for CO2 emissions reduction and industrial development can be achieved in the eastern region, and some sectors in Manufacturing (such as Food processing, Food production, Beverage production, Tobacco processing, Textile industry) and Power-Gas-Water industries through eliminating undesirable carbon congestion by optimising the input of resources. The “win-win” state in the western and eastern regions, and some sectors in
of
Manufacturing (such as Garments and other fiber products, Leather, furs, down and related products, Furniture manufacturing, Printing and record medium reproduction, Cultural, educational and sports
ro
articles, etc.) and Power-Gas-Water industries, will be reliant on improving the level of production
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and green technology innovation.
re
Finally, the average annual CO2 emissions could be reduced by 722.82 Mt25, if all provinces or sectors produce on the production frontier. Regionally, China’s western region has the highest carbon
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emissions reduction potential; among the provinces, Shanxi has the greatest potential for emissions reduction. Sectorally, the greatest potential for reduction of CO2 emissions is mainly found in the
na
Manufacturing and Power-Gas-Water industries. In particular, China’s six high-energy-consuming sectors (including Petroleum processing and coking, Raw chemical materials and chemical products,
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Nonmetal mineral products, Smelting and pressing of ferrous metals, Smelting and pressing of nonferrous metals, and Electric power, steam and hot water production and supply) have the greatest potential for reducing emissions, accounting for 70.23% of the total sectors’ CRP. Based on the conclusions above, this study also proposes three important policy implications: first, it is expectable that China’s industry achieves the desired “win-win” for industrial development and CO2 emissions reduction. According to the state of carbon congestion, the whole-of-industry can implement precise emissions reduction according to local conditions and sectoral conditions. Besides, green technology innovation proves the power source for China’s industry to achieve sustainable emissions reductions. Chinese government should increase its support for technology innovation in
25
Here, for the sake of prudence, the potential emission reductions of China’s national industries refers to the average between emissions reduction potential of provincial industry and that of 34 industrial sectors. 36
Journal Pre-proof the fields of energy saving and emission reduction and new energy technologies, and upgrade industrial technological innovation capabilities. Second, Chinese government can scientifically guide the allocation of resources according to the state of carbon congestion, which helps to improve the current imbalance in the development of China’s regions and industrial sectors. For example, there remains significant scope for industrial development in the western region and in the Mining industry of China. Therefore, Chinese government should provide more policy support to these regions and sectors and guide more labour
of
and material resources, especially technological innovation resources, to them. To improve the economic and environmental benefits of these regions and sectors, it is also necessary to enhance
ro
the clean, safe, low-carbon and efficient development and utilisation of coal, promote the use of
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high-quality and clean coal, promote “coal to gas”, “coal to electricity” and so on.
re
Finally, when formulating the emissions reduction plan, Chinese government can take resource and efficiency-based strategies as its short-term emissions reduction plan, and adopt the
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technology-based strategy for its long-term emissions reduction plan. For example, Chinese government can implement the resource-based strategy for its short-term emissions reduction target
na
as enshrined in the 13th Five-Year Plan26, supplemented by the technology-based strategy to lay a solid foundation for the long-term emissions reduction targets in the Paris Agreement, prompting
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emissions reduction tasks to be completed on time and to a high standard. In addition, the construction of China’s carbon trading market should be unswervingly promoted, and undesirable carbon congestion can thus be eliminated through the initial quota allocation to achieve carbon emissions reduction.
It should be noted that there is still much relevant work to be done in the future. For example, this study uses a dual model of DEA to assess carbon congestion, and in the future, more optimized models can be developed to calculate specific congestion values and obtain more detailed results. In addition, further work can be linked to carbon emissions targets and carbon trading constraints for each emission-intensive industry. Besides, we can also combine the provinces and industrial sectors
26
The 13th Five-Year Plan for National Economy and Social Development of the People’s Republic of China highlights that by 2020, China’s carbon intensity will fall 18% compared with the level in 2015. 37
Journal Pre-proof to investigate the carbon congestion, that is, to target the research to some industrial sectors in any provinces.
Appendix Some supplementary tables to this article can be found online.
of
Acknowledgements
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We are grateful to the financial support from the National Natural Science Foundation of China (nos. 71774051), National Program for Support of Top-notch Young Professionals (no. W02070325),
-p
Changjiang Scholars Program of the Ministry of Education of China (no. Q2016154), Science Fund for
lP
re
Distinguished Young Scholars of Hunan province (no. 2018JJ1010), and Hunan Youth Talent Program.
References
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Journal Pre-proof
Highlight
It explores both undesirable and desirable congestion of China’s industrial sectors
It uses the dual model of radial DEA to quantify the state of carbon congestion
The congestion effect
East and central regions and PGW industry should optimize input resources
Northeast and west regions and PGW industry should innovate ecological technology
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witnesses evident regional and sectoral heterogeneity
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