Decoupling and decomposition analysis of carbon emissions from economic output in Chinese Guangdong Province: A sector perspective

Abstract

South China’s Guangdong Province, the Chinese largest provincial economy and the global 14th biggest economy, has been facing a huge challenge of achieving economic growth without emission growth. Developing new strategy for making economic growth compatible carbon reduction requires better understanding of the decoupling carbon emission from economic growth. In this paper, we conduct a comprehensive decoupling and decomposition analysis of carbon emission from economic output in Guangdong Province from a sector perspective. We firstly calculate carbon emission in six sectors based on the energy consumption of each sector and carbon coefficient of 13 types of fuels during 2000–2014, and then quantify the decoupling status between CO₂ emissions and economic growth in those six sectors by using the Tapio decoupling index, finally, investigate the influencing factors of emissions by using the decomposition techniques. The modeling results show that agricultural sector has strong decoupling, industrial, transport and others sectors are weak decoupling; construction and trade sectors are expansive negative decoupling. We also find that energy intensity and economic output are the major factors influencing carbon emission, also the effects of energy structure and emission factor among six sectors are studied. Some policy recommendations finally are put forward.

Keywords

decoupling index LMDI sectors perspectives Guangdong Province

Introduction

With average annual double-digit economic growth rate over the past four decades, the GDP of South China’s Guangdong Province in 2016 reached RMB 8.0 trillion (∼US$1.2 trillion),^1,2 putting it on par with the Spanish economy (the global 14th biggest economy)^,3 and approaching Russian GDP (the global 13th biggest economy). (the global 14th biggest economy)^3,4 However, the spectacular economic achievement of Guangdong is also accompanied with huge increase in pollutant, especially carbon pollution.⁵ In this paper, we try to better understand the relationship between carbon emission and economic output in Guangdong from the perspective of sectors. Better understanding those relationships can serve to develop mitigation strategy to balance economic growth and curbing carbon emission at sector level for Guangdong, as well as for other areas similar to Guangdong in China and the emerging countries. To this end, we use the energy consumption data of each sector and carbon coefficient to calculate carbon emission of each sector in Guangdong. Next, we quantify the decoupling status between economic growth and carbon emission in each sector using the decoupling econometric technique. And then, we explore the effects of carbon emission in each sector using decomposition technique. Finally, policy recommendations are offered.

Literature review

Economic growth is accompanied by the use of energy^6,7 and the production of carbon emissions.⁸ With the increasing attention on the linkage between pollution/carbon pressure and economic growth, a growing number of scholars have studied the nexus of pollution/carbon and economic growth. OECD⁹ considered the decoupling as an indicator in 2002. Juknys¹⁰ proposed the concepts of primary decoupling, secondary decoupling and doubled decoupling. Based on the study of Vehmas et al.,¹¹ Tapio¹² proposed a framework of decoupling and divided it into eight types of decoupling status, which has been widely used in the latest studies. Freitas and Kaneko¹³ investigated the decoupling of CO₂ emissions from economic increase in Brazil for 2004–2009. Andreoni and Galmarini¹⁴ examined the decoupling status in Italy for 1998–2006, and concluded that the Italian economy performed relative decoupling from energy use and CO₂ emissions. Luo et al.¹⁵ explored the decoupling between carbon emissions and agricultural economic growth across 30 Chinese provinces during 1997–2004.

In addition, many decomposition econometric techniques have been developed and used to explore the driving forces of emission. Among those techniques, Ang^16,17 concluded that the LMDI method was the “best” decomposition technique due to its no residual. González et al.¹⁸ tracked European Union carbon emissions through LMDI, wherein energy mix appeared as the main factor in emissions reduction. Liu et al.¹⁹ decomposed the carbon emissions changes from China’s 36 industrial sectors for 1998–2005, and concluded that the industrial activity and energy intensity were the overwhelming contributors to industrial carbon emissions changes. Some papers made use of the decoupling indicator to quantify the link between economic growth and carbon emissions, and then utilized the LMDI technique to explore the effects of carbon emissions. Freitas and Kaneko¹³ examined the decoupling between economic activity and CO₂ emissions in Brazil for 2004–2009, identified the effects of emissions change based on LMDI, and proposed that it was efficient to combine the decoupling model and the decomposition technique. The previous studies took different countries as the research example to explore the decoupling status.^6,20–23

As mentioned above, the existing studies are focused on national-level²⁴ and sub-national-level, such as East China, South China^25,26 relative to China. The exiting studies relative to China’s sector are dominated by industrial sector^6,27–30 and sub-industrial sectors, such as cement industry, steel industry, electricity industry.^6,31,32 China is always paying attention to decreasing carbon emission,^6,8,33 few has analyzed the decoupling status combined with the influencing factors at provincial-level and from a perspective of different sectors taking the Guangdong Province as an example. Given this, this paper studies the decoupling status of CO₂ emissions from economic growth and the influencing factors of CO₂ emissions from six sectors in Guangdong Province during 2000–2014.

Methods and data

Methods

Calculation of CO₂ emissions

Based on the guidelines given by IPCC,³⁴ the total energy-related CO₂ emissions can be estimated as follows:

C_{i} = \sum_{j = 1}^{13} E_{ij} \times {NCV}_{ij} \times F_{ij} \times {OR}_{ij} \times (\frac{44}{12})

(1)

where j is the fossil fuel type; E is the energy use (Mt); NCV is the low calorific value (TJ/Mt); F is the carbon emission factor; OR is the carbon oxidization rate; 44/12 is the molecular weight of CO₂ to C; i = 1, 2, 3, 4, 5, 6, respectively, represents Farming, Forestry, Animal Husbandry, Fishery Water Conservancy (Agriculture); Industry; Construction; Transportation, Storage, Postal Telecommunications Services (Transport); Wholesale, Retail Trade and Catering Service (Trade); Residential Consumption and Others (Others). j = 1, 2, … , 13, represents 13 types of fossil fuels, respectively.

Decoupling model

The decoupling index of the ith industry from year 0 to year t can be measured as follows, which is proposed by Tapio¹²:

ε_{i} = \frac{Δ C_{i} / C_{i}^{0}}{Δ G_{i} / G_{i}^{0}}

(2)

where

ε_{i}

is the decoupling index of energy-related CO₂ emission from GDP of the ith industry;

Δ C_{i} / {C_{i}}^{0}

is the percent change of energy-related CO₂ emission in the ith industry;

Δ G_{i} / {G_{i}}^{0}

is the percent change of GDP in the ith industry. The decoupling status can be divided as Table 1. Negative decoupling contains three possibilities: expansive negative decoupling indicates that GDP and CO₂ emission both increase, but emission grows faster; weak negative decoupling represents GDP and CO₂ emission both decrease, and GDP declines faster; strong negative decoupling denotes that carbon emission grows in economic recession. Decoupling embodies three statuses: recessive decoupling resembles the weak negative decoupling, however, emission declines faster; weak decoupling is similar to the expansive negative decoupling, however, GDP grows faster; strong decoupling implies economy grows but emission decreases. Coupling occurs when both GDP and CO₂ emission increase or decline, and change slightly.

Table 1.

The standards of decoupling status.

Decoupling status		$ε$	$Δ C$	$Δ G$
Negative decoupling	Expansive negative decoupling	$ε > 1.2$	$> 0$	$> 0$
	Weak negative decoupling	$0 < ε <$ 0.8	$< 0$	$< 0$
	Strong negative decoupling	$ε < 0$	$> 0$	$< 0$
Decoupling	Recessive decoupling	$ε > 1.2$	$< 0$	$< 0$
	Weak decoupling	$0 < ε <$ 0.8	$> 0$	$> 0$
	Strong decoupling	$ε < 0$	$< 0$	$> 0$
Coupling	Expansive coupling	$0.8 < ε < 1.2$	$> 0$	$> 0$
Coupling	Recessive coupling	$0.8 < ε < 1.2$	$< 0$	$< 0$

Logarithmic mean divisia index technique

According to the expanded kaya identity,³⁵ the CO₂ emissions from the ith industry can be described as follows:

C_{i} = \sum_{j = 1}^{13} \frac{C_{ij}}{E_{ij}} \times \frac{E_{ij}}{E_{i}} \times \frac{E_{i}}{{GDP}_{i}} \times {GDP}_{i} = \sum_{j = 1}^{13} (f_{ij} \times m_{ij} \times e_{i} \times g_{i})

(3)

where

C_{i j}

is the energy-related CO₂ emissions of the jth fuel from the ith industry (Mt);

E_{i j}

is the energy use of the jth fuel from the ith industry (Mtce);

E_{i}

is the energy use of the ith industry (Mtce);

{G D P}_{i}

is the gross domestic product of the ith industry (100 million yuan).

f_{i j} = \frac{C_{i j}}{E_{i j}}

represents the emission factor of the jth fuel from the ith industry;

m_{i j} = \frac{E_{i j}}{E_{i}}

represents the energy mix of the jth fuel from the ith industry;

e_{i} = \frac{E_{i}}{{G D P}_{i}}

represents the energy intensity of the ith industry;

g_{i} = {G D P}_{i}

represents the industry scale of the ith industry.

Making use of the LMDI technique,¹⁷ the total CO₂ emission changes from the period 0 to t can be shown in additive forms as follows:

Δ C_{tot} {=C}^{t} - C^{0} = Δ C_{f} + Δ C_{m} + Δ C_{e} + Δ C_{g}

(4)

where

Δ C_{t o t}

is the total changes in CO₂ emissions, which can be decomposed to

Δ C_{f}

(the effect of emission factor),

Δ C_{m}

(the effect of energy structure),

Δ C_{e}

(the effect of energy intensity) and

Δ C_{g}

(the effect of industry scale). The formulas of each factor in additive decomposition are as follows:

Δ C_{i, f} = \sum_{j = 1}^{13} \frac{C_{ij}^{t} - C_{ij}^{0}}{{InC}_{ij}^{t} - {InC}_{ij}^{0}} \times In (\frac{f_{ij}^{t}}{f_{ij}^{0}})

(5)

Δ C_{i, m} = \sum_{j = 1}^{13} \frac{C_{ij}^{t} - C_{ij}^{0}}{{InC}_{ij}^{t} - {InC}_{ij}^{0}} \times In (\frac{m_{ij}^{t}}{m_{ij}^{0}})

(6)

Δ C_{i . e} = \sum_{j = 1}^{13} \frac{C_{ij}^{t} - C_{ij}^{0}}{{InC}_{ij}^{t} - {InC}_{ij}^{0}} \times In (\frac{e_{i}^{t}}{e_{i}^{0}})

(7)

Δ C_{i, g} = \sum_{j = 1}^{13} \frac{C_{ij}^{t} - C_{ij}^{0}}{{InC}_{ij}^{t} - {InC}_{ij}^{0}} \times In (\frac{g_{i}^{t}}{g_{i}^{0}})

(8)

Data

The study period ranges from 2000 to 2014 in this paper. All energy consumption data came from China Energy Statistical Yearbook.^36–38 The carbon emission factor and the carbon oxidization rate of different fuel came from IPCC.³⁴ The low calorific values can be found in China Energy Statistical Yearbook.^36–38 This paper considered 13 types of fossil fuels, which were converted from physical quantity to standard quantity, according to China Energy Statistical Yearbook. The GDP data were obtained from Guangdong Statistical Yearbook.^1,39 GDP was normalized to 2000 constant price to eliminate the inflation.

Results and analysis

The changes of CO₂ emissions in Guangdong

As Figure 1 shows, industry represented a primary share of CO₂ emissions, which had been keeping about 60% of CO₂ emissions during the study years. The second contributor is transport, accounting for nearly 20% of the total CO₂ emissions. The third CO₂ emitter is others, contributing approximately 10% of CO₂ emissions. Agriculture, construction and trade accounted only small rates for CO₂ emissions and changed slightly.

Figure 1.

The changes of CO₂ emissions from different industries in Guangdong.

Decoupling status

There are three types of decoupling status presented in Table 2: strong decoupling, weak decoupling and expansive negative decoupling. Agriculture had a negative growth in CO₂ emissions and a positive growth in GDP, which was in strong decoupling. Industry, transport and others were in the state of weak decoupling, whose GDP growth rates were higher than CO₂ emissions growth rates. In contrast to weak decoupling of transport and others, construction and trade were in the state of expansive negative decoupling. In this sense, CO₂ emissions from construction and trade were on the rise with the economy growth, but the CO₂ emissions grew faster. CO₂ emissions of all industries appeared increasing trends except agriculture. Trade was the fastest-growing CO₂ emitter. Construction and transport had faster growth in CO₂ emissions than industry and others. As for GDP, the economic scale of industry and others grew in a higher speed for the period 2000–2014, 4.77 times and 4.16 times, respectively, than the base year. GDP growth in agriculture was not significant compared with other industries.

Table 2.

The decoupling status of Guangdong in 2000–2014.

Industries	$Δ C / C$	$Δ G / G$	$ε$	Decoupling status
Agriculture	–0.0330	0.6692	–0.0494	Strong decoupling
Industry	1.1438	4.7678	0.2399	Weak decoupling
Construction	3.2312	1.5333	2.1073	Expansive negative decoupling
Transport	2.0736	3.2857	0.6311	Weak decoupling
Trade	4.5657	3.7125	1.2298	Expansive negative decoupling
Others	1.7348	4.1646	0.4166	Weak decoupling

Study on the relationship between CO₂ emissions and economic growth from different industries is not enough to guide us to optimize industrial structure and promote industrial low-carbon development. Hence, we further make use of LMDI to analyze factors influencing CO₂ emissions from different industries in order to provide Guangdong with more targeted recommendations.

Decomposition analysis of CO₂ emissions

On the whole, energy intensity and industry scale were the major effects influencing CO₂ emissions (Figure 2). Industry scale played a positive role, however, energy intensity played a negative role in enhancing CO₂ emissions except in 2002 and 2004. These results are consistent with the previous studies.^40,41 Due to the single energy use, the effects of emission factor and energy structure were relatively small. To analyze the importance and patterns of different factors influencing different industries explicitly, we presented the decomposition of CO₂ emissions changes by contribution rates from different industries in Figures 3 to 8.

Figure 2.

Decomposition of CO₂ emissions changes in Guangdong.

Figure 3.

Factors contributing to changing CO₂ emissions in Guangdong’s agriculture.

Figure 4.

Factors contributing to changing CO₂ emissions in Guangdong’s industry.

Figure 5.

Factors contributing to changing CO₂ emissions in Guangdong’s construction.

Figure 6.

Factors contributing to changing CO₂ emissions in Guangdong’s transport.

Figure 7.

Factors contributing to changing CO₂ emissions in Guangdong’s trade.

Figure 8.

Factors contributing to changing CO₂ emissions in Guangdong’s others sectors.

Agriculture was the only industry that appeared strong decoupling in Guangdong. Industry scale had been playing a positive role in CO₂ emissions. However, energy intensity played a constant negative role since 2005, due to the development of energy utilization efficiency. Energy structure did not show a constant influence on CO₂ emissions while it declined 3.08 Mt of CO₂ emissions on the whole. Although the emission factor effect from agriculture was weak, it contributed most to enhancing CO₂ emissions compared with other industries because of the high carbonization energy use in agriculture.

The change of CO₂ emissions from industry was 7461.78 Mt for the period 2000–2014. On the whole, industry scale was the dominant factor of industry, and played the most striking role in raising CO₂ emissions among different industries. Energy structure effect changed from negative in 2000 to positive in 2006. Energy intensity played a most significant negative effect in industry among different industries, which was associated mainly with continuous adjustment of industrial structure. The effect of carbon dioxide emission factor played minor roles in raising CO₂ emissions.

As for construction, energy intensity was the dominant reason for promoting CO₂ emissions during 2003–2005, however, the industry scale became the dominant factor in later years. There was a significant slowdown of energy intensity effect in 2007, indicating that policy influenced greatly. The use of natural gas appeared in 2013, accounting for only a small share of energy consumption. Hence, the energy system had not been changed structurally and the energy structure effect was still weak. The emission factor effect did not show a notable impact on CO₂ emissions.

Together, transport contributed 3719.25 Mt of CO₂ emissions for 2000–2014, second only to industry. Energy intensity effect had been mostly negative in CO₂ emissions, especially after 2005. Notably, possession of private vehicles in 2014 was 13 times more than it in 2000, and the trend will most likely remain in the future. And such a tendency would have adverse implications for decreasing CO₂ emissions. Industry scale had been playing a dominant positive role in CO₂ emissions. In addition, energy structure effect varied yearly, but it contributed to declining CO₂ emissions by 8.07 Mt totally. Guangdong started to use natural gas in transport since 2010, while there were no significant changes in energy structure. The effect of carbon dioxide emission factor was still negligible.

Concerning trade, energy intensity effect grew rapidly from 2002 and played an even bigger role than the industry scale in 2004. A sharp decrease of energy intensity was related to financial crisis from 2007 to some degree. Furthermore, industry scale effect had been positive across time, and the influences of industry scale will remain constantly due to the vigorous developments of the tertiary industry. Energy structure effect declined 76.42 Mt of CO₂ emissions in total, resulting from the raising use of natural gas.

Overall, industry scale and energy intensity both played major roles in others. Nevertheless, the industry scale made contributions to raising CO₂ emissions, and vice versa to the energy intensity effect. The positive influences of industry scale will remain constantly in parallel with the developments of national economy and income. Although energy structure showed certain effects, its impacts require further study. The carbon dioxide emission factor effect still played a very small role in CO₂ emissions due to the single energy use.

Conclusions and policy implications

Conclusions

Based on estimating CO₂ emissions from different industries in Guangdong for the period 2000–2014, we used Tapio decoupling index to analyze the decoupling status between CO₂ emissions and industrial growth from different industries. Furthermore, we used LMDI to decompose the changes of CO₂ emissions to four factors (emission factor, energy structure, energy intensity, industry scale) from six types of industries (agriculture, industry, construction, transport, trade, others). The main conclusions are as follows:

Differences in the CO₂ emissions across different industries were large and persistent. Industry still played a predominant role in CO₂ emissions, which had been accounting for just about 60% of CO₂ emissions. Transport and others contributed, respectively, about 20% and 10% of the total CO₂ emissions. The amount of CO₂ emissions from agriculture, construction and trade was small and changed slightly.

Different industries showed different behaviors in the decoupling status. Agriculture was the only industry that appeared strong decoupling. Industry, transport and others were in the state of weak decoupling. Construction and Trade were expansive negative decoupling.

There were differences in the importance and patterns of different factors influencing different industries. The emission factors were still weak due to the single energy use. On the whole, energy structure played positive roles in raising CO₂ emissions from industry, construction and others; however, it played negative roles in agriculture, transport and trade. Energy intensity contributed to cutting CO₂ emissions other than construction. The effects of industry scale were substantial in rising CO₂ emissions among different industries.

Policy implications

Based on the above conclusions, to optimize industrial structure and promote low-carbon industries, some recommendations are as follows:

Combined with the CO₂ emissions and decoupling status from different industries, industry and transport accounted for the main proportion of the CO₂ emissions and were in weak decoupling, which should be the key issue for industrial policies’ formulation and implementation in Guangdong. We should also pay attention to construction and trade, which were in the state of expansive negative decoupling while the amount of CO₂ emissions was small.

Industry scale and energy intensity were the major influencing factors. The industry scale contributed to promoting CO₂ emissions, keeping reasonable economic growth and developing low-carbon economy are feasible choices. Energy intensity contributed to cutting CO₂ emissions other than construction, relevant measures on improving energy efficiency such as advancing and popularizing energy-saving technology and equipment should be encouraged. Furthermore, we can develop the use of low-carbon fuels such as oil and natural gas, and promote the use of renewable energy such as solar and hydro power, to optimize energy structure.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The current work is supported by Recruitment Talent Fund of China University of Petroleum (East China) (05Y16060020).

Ming-Ming Zhao is a postgraduate at School of Economic and Management, China University of Petroleum (East China) in China. Her research areas include energy economics and policy, environmental economics, and emission analysis.

Rongrong Li is a Researcher at School of Economic and Management, China University of Petroleum (East China) in China. Her research mainly focuses on energy and environmental economics and policy, economic valuation of environmental resources, and input–output analysis.

References

Statistics Bureau of Guangdong Province. Statistical yearbook of Guangdong 2017. Beijing: China Statistics Press, 2017 (in Chinese).

Verney S, Bosco A and Lobo MC. Southern Europe and the Financial Earthquake: Coping with the First Phase of the International Crisis. Abingdon, United Kingdom: Routledge, 2016.

World-Bank

State and trends of carbon pricing 2016. Washington D.C: World Bank Publications, 2016.

Porshakov

Ponomarenko

Sinyakov

Nowcasting and short-term forecasting of Russian GDP with a dynamic factor model. J New Econ Assoc 2016; 30: 60–76.

Meng

Zheng

et al . Demand-driven air pollutant emissions for a fast-developing region in China. Appl Energy 2017; 204: 131–142.

Wang

Chen

China's electricity market-oriented reform: From an absolute to a relative monopoly. Energy Policy 2012; 51: 143–148.

Wang

Journey to burning half of global coal: Trajectory and drivers of China’s coal use. Renew Sustain Energy Rev 2016; 58: 341–346.

Wang

Chen

Energy policies for managing China’s carbon emission. Renew Sustain Energy Rev 2015; 50: 470–479.

OECD. Sustainable development: Indicators to measure decoupling of environmental pressure from economic growth. Paris, France: OECD, 2002.

10.

Juknys

Transition period in Lithuania – Do we move to sustainability?

Energy 2003; 4: 4–9.

11.

Vehmas

, Luukkanen

and Kaivo-Oja

Material flows and economic growth. Finland: Turku School of Economics and Business Administration 2003; 2: 9– 11.

12.

Tapio

Towards a theory of decoupling: Degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001. Transport Policy 2005; 12: 137–151.

13.

Freitas

LCD

Kaneko

Decomposing the decoupling of CO₂ emissions and economic growth in Brazil. Ecol Econ 2011; 70: 1459–1469.

14.

Andreoni

Galmarini

Decoupling economic growth from carbon dioxide emissions: A decomposition analysis of Italian energy consumption. Energy 2012; 44: 682–691.

15.

Luo

Long

et al . Decoupling CO₂ emissions from economic growth in agricultural sector across 30 Chinese provinces from 1997 to 2014. J Clean Prod 2017; 159: 220–228.

16.

Ang

BW.

Decomposition analysis for policymaking in energy: Which is the preferred method?

Energy Policy 2004; 32: 1131–1139.

17.

Ang

BW.

The LMDI approach to decomposition analysis: A practical guide. Energy Policy 2005; 33: 867–871.

18.

González

Landajo

Presno

MJ.

Tracking European Union CO₂ emissions through LMDI (logarithmic-mean Divisia index) decomposition. The activity revaluation approach. Energy 2014; 73: 741–750.

19.

Liu

L-C

Fan

et al . Using LMDI method to analyze the change of China's industrial CO₂ emissions from final fuel use: An empirical analysis. Energy Policy 2007; 35: 5892–5900.

20.

Climent

Pardo

Decoupling factors on the energy–output linkage: The Spanish case. Energy Policy 2007; 35: 522–528.

21.

Diakoulaki

Mandaraka

Decomposition analysis for assessing the progress in decoupling industrial growth from CO₂ emissions in the EU manufacturing sector. Energy Econ 2007; 29: 636–664.

22.

Enevoldsen

Ryelund

Andersen

MS.

Decoupling of industrial energy consumption and CO₂-emissions in energy-intensive industries in Scandinavia. Energy Econ 2007; 29: 665–692.

23.

Sjöström

Östblom

Decoupling waste generation from economic growth—A CGE analysis of the Swedish case. Ecol Econ 2010; 69: 1545–1552.

24.

Guan

, Peters

, Weber

, et al. Journey to world top emitter: An analysis of the driving forces of China. Geophys Res Lett 2009; 36: doi:10.1029/2008GL036540.

25.

Zhang

Wang

Decouple indicators on the CO₂ emission-economic growth linkage: The Jiangsu Province case. Ecol Indic 2013; 32: 239–244.

26.

Auffhammer

Carson

RT.

Forecasting the path of China's CO₂ emissions using province-level information. J Environ Econ Manage 2008; 55: 229–247.

27.

Zhou

, Zhang

, Zhou

, et al. A comparative study on decoupling relationship and influence factors between China. J Clean Prod 2017; 142: 783– 800.

28.

Garbaccio

Jorgenson

DW.

Controlling carbon emissions in China. Environ Dev Econ 1999; 4: 493–518.

29.

Garbaccio

, Ho

and Jorgenson

DW.

Why has the energy–output ratio fallen in China?

Energy J 1999; 20: 63–91.

30.

Sinton

Levine

MD.

Changing energy intensity in Chinese industry: The relatively importance of structural shift and intensity change. Energy Policy 1994; 22: 239–255.

31.

Lin

Moubarak

Decomposition analysis: Change of carbon dioxide emissions in the Chinese textile industry. Renew Sustain Energy Rev 2013; 26: 389–396.

32.

Tian

Zhu

Geng

An analysis of energy-related greenhouse gas emissions in the Chinese iron and steel industry. Energy Policy 2013; 56: 352–361.

33.

Wang

China should aim for a total cap on emissions: A focus on carbon intensity alone will allow emissions to grow with the economy, argues Qiang Wang. Nature 2014; 512: 115–116.

34.

Lubetsky

Steiner

Lanza

2006 IPCC guidelines for national greenhouse gas inventories, 2006.

35.

Kaya

Impact of carbon dioxide emission control on GNP growth: Interpretation of proposed scenarios IPCC energy and industry subgroup, response strategies working group, 1990.

36.

National Bureau of Statistics of China. China energy statistical yearbook. Beijing: China Statistical Press, 2005.

37.

National Bureau of Statistics of China. China energy statistical yearbook. Beijing: China Statistical Press, 2010.

38.

National Bureau of Statistics of China. China energy statistical yearbook. Beijing: China Statistical Press, 2015.

39.

Statistics Bureau of Guangdong Province . Statistical yearbook of Guangdong . Beijing: China Statistics Press, 2017 (in Chinese).

40.

Dhakal

Urban energy use and carbon emissions from cities in China and policy implications. Energy Policy 2009; 37: 4208–4219.

41.

Wang

Qiang

, Jiang

Xue-ting

, Li

Rongrong

, Comparative decoupling analysis of energy-related carbon emission from electric output of electricity sector in Shandong Province, China, Energy, 2017, 127, 78, 88, 10.1016/j.energy.2017.03.111

Decoupling and decomposition analysis of carbon emissions from economic output in Chinese Guangdong Province: A sector perspective

Abstract

Keywords

Introduction

Literature review

Methods and data

Methods

Calculation of CO2 emissions

Decoupling model

Logarithmic mean divisia index technique

Data

Results and analysis

The changes of CO2 emissions in Guangdong

Decoupling status

Decomposition analysis of CO2 emissions

Conclusions and policy implications

Conclusions

Policy implications

Footnotes

Declaration of conflicting interests

Funding

References

Calculation of CO₂ emissions

The changes of CO₂ emissions in Guangdong

Decomposition analysis of CO₂ emissions