Analysis on the carbon emission peaks of China's industrial, building, transport, and agricultural sectors

Journal Pre-proof Analysis on the carbon emission peaks of China's industrial, building, transport, and agricultural sectors

Xi Chen, Chenyang Shuai, Ya Wu, Yu Zhang PII:

S0048-9697(19)35763-8

DOI:

https://doi.org/10.1016/j.scitotenv.2019.135768

Reference:

STOTEN 135768

To appear in:

Science of the Total Environment

Received date:

11 September 2019

Revised date:

14 November 2019

Accepted date:

24 November 2019

Please cite this article as: X. Chen, C. Shuai, Y. Wu, et al., Analysis on the carbon emission peaks of China's industrial, building, transport, and agricultural sectors, Science of the Total Environment (2019), https://doi.org/10.1016/j.scitotenv.2019.135768

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© 2019 Published by Elsevier.

Journal Pre-proof

Analysis on the carbon emission peaks of China’s industrial, building, transport, and agricultural sectors Xi Chen a Chenyang Shuai b* Ya Wuc Yu Zhangd a College of Economics and Management, Southwest University, Chongqing, China b School for Environment and Sustainability, University of Michigan, United States c College of Resources and Environment, Southwest University, Chongqing, China

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d Department of Building and Real Estate, Hong Kong Polytechnic University, Hong Kong

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*indicates the corresponding author

Abstract

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Carbon emission peak has become a focus of political and academic concern in global

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community since the launch of Kyoto Protocol. China, as the largest carbon emitter, has committed to reaching the carbon peak by 2030 in Paris Agreement. This

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ambitious national goal requires the endeavors of individual sectors, particularly those carbon-intensive ones. Predicting the sectoral peaks under current endeavors and

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understanding driving forces for the carbon emission changes in the past years are substantial for guiding the allocation of the country’s future efforts. In the past studies

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contextualized in China, the prediction of its carbon peaks seldom appeared at the sectoral level, which is considered as a research gap. Therefore, this study predicts the peaks at four carbon pillar sectors (i.e. industrial, building, transport and agricultural sectors) and identifies the driving forces for the carbon emission changes of them. This study hypothesized Carbon Kuznets curve (CKC) as the theoretical model for predicting the peaks and used Logarithmic mean divisia index (LMDI) as the method to identify the driving forces. The results show that the carbon emission in the country will peak in 2036, six years later than the agreed year. The lateness of the national peak can be attributed to the significant lateness of three pillar sectors’ peaks, occurring in 2031 for the industrial sector, 2035 for the building sector, 2043 for the 1

Journal Pre-proof transport sector, peak for the agricultural sector occurs four years earlier in 2026 though. Furthermore, the results show that carbon emission is significantly driven by the booming economic output and inhibited by decreasing energy intensity, but the slight fluctuation of energy structure plays a minor role in the four sectors. Policy adjustments are proposed for effectively and efficiently urging the on-time occurrence of the national peak.

LMDI; China

List of Abbreviation

UNCCC

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United Nations Convention on Climate Change

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Keywords: carbon emission peak; carbon emission reduction; sectoral level; CKC;

Carbon Kuznets Curve

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International Energy Agency

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Intergovernmental Panel on Climate Change

IPCC IEA CKC LMDI

Environmental Kuznets Curve

EKC

Europe Union

GDP

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gross domestic product

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Logarithmic mean divisia index

EU

Development

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Organisation for Economic Co-operation and OECD

Statistics Database of Economic and Social SDESDC Development of China whole period

WP

Chinese Five-Year Plan

FYP

Research and Development

R&D

1. Introduction

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Journal Pre-proof Climate change caused by carbon emission has induced unprecedented threats to the survival and development of the human race, such as extreme weather, species extinction and food shortage (Dong et al., 2019; Dong et al., 2018a). Faced with this alarming fact, the United Nations Convention on Climate Change (UNCCC) was formed in 1992 to stabilize the content of greenhouse gas in the atmosphere. Afterwards, in 1997, the signing of Kyoto Protocol opened a new chapter in the pursuit of reducing carbon emission, which strictly restricted greenhouse gas

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emissions as most countries gained economic interest. The fifth report by Intergovernmental Panel on Climate Change (IPCC) underlined the global carbon emission must peak around 2020 with a temperature change less than 2°C (Pachauri

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et al., 2014). Clearly understanding the determinants and tendency of regional carbon

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emissions is important for targeting to the largest emitters with efficient strategies (Dong et al., 2019; Wu et al., 2019a; Wu et al., 2019b)

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China, as the largest emitter in the world, is burdened with tremendous carbon emission reduction pressure. In fact, China accounts for nearly 30% of global total

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carbon emission in 2014 and 60% of the world’s rise in carbon emission since 2000, which is promoted by rapid fossil-fuel-dominated urbanization and industrialization

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process (World Bank, 2017). China signed the Kyoto Protocol in 1998 and had

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committed to peak carbon emission by 2030 in the 2015 Paris Agreement. Therefore, whether and how China can achieve the peaking goal on time will have strong impacts and implications on the global carbon emission reduction. Obviously, this ambitious national goal has triggered considerable pressures to reduce carbon emission on many sectors. Industrial, building, transport and agricultural sectors are regarded as the four pillars of energy consumption, accounting for approximately 88.22% of total carbon emission of China in 2017 (IEA, 2019). Hence, it is considered imperative to identify the peaks of carbon emission not only at the national level but also at the sectoral level. Furthermore, it is

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Journal Pre-proof also essential to examine the key driving forces on carbon emission in order to strategically achieve the peaking goal on time. Nevertheless, most of previous studies focused on predicting carbon emission peak at the national level (Hao et al., 2016; Jalil and Mahmud, 2009) or regional level (Shen et al., 2018a; Wei et al., 2017) in China, however, studies on carbon emission peak at the sectoral level are absent. Zhang et al. (2019) has investigated the global CKC for manufacturing and construction sectors by studying 121 countries where

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China is included though. However, all the four pillars of China, i.e. industrial, building, transport, and agriculture, are great carbon emitters, which should be paid enough attention. Therefore, this study aims at predicting the carbon emission peaks

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at the sectoral level and identifying the driving forces, which may bring a substantial

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understanding of the carbon emission status and trends of the individual sectors to governors so as to tailor the effective policies for the country to punctually achieve

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the peaking goal. The rest of the paper is structured as follows: section 2 presents the literature on studies on carbon emission reduction and Carbon Kuznets Curve (CKC),

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section 3 introduces the methods and data employed in this study, section 4 displays the results of the CKC hypothesis tests and identified driving forces using

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Logarithmic mean divisia index (LMDI) method, section 5 discussed the results and

in Section 6.

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driving forces sector by sector, and conclusion and policy implications are presented

2. Literature review Carbon emission peaks have been examined with Environmental Kuznets Curve (EKC) in most of previous studies (Dong et al., 2018b; Shen et al., 2018b), which hypothesizes an inverted U-shaped relationship between income per capita and environmental pollution, suggesting that while levels of environmental damage first increase with rising gross domestic product (GDP) per capita, then subsequently decline after reaching the peak (Grossman and Krueger, 1995).

Since

environmental dependent is carbon emission when examining the peak, EKC can be 4

Journal Pre-proof referred to as CKC (Shuai et al., 2017). In fact, there are a body of literature employed CKC to test the peak of carbon emission at the sectoral level in many countries. For example, Kharbach and Chfadi (2017) verified the applicability of CKC hypothesis in the road transport sector in Moroccan over the period 1971-2011, and concluded that carbon emission peaked in 1981. Using the panel data with multilevel mixed-effects models, Pablo-Romero and Sánchez-Braza (2017) confirmed the CKC hypothesis in the residential building sectors in Europe

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Union (EU)-28 countries and presented that Denmark, Luxembourg and Finland had reached their carbon peaks. Similarly, Pablo-Romero et al. (2017) also validated the existence of CKC in the transport sector in EU-27 countries using the

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panel data techniques, and stated that their carbon emission are yet to peak. The

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study by Fujii and Managi (2013) on the carbon emission in the industrial sectors in 23 The Organisation for Economic Co-operation and Development (OECD)

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countries supported the CKC hypothesis, and identified the carbon peak in each country. Zafeiriou and Azam (2017) in the agricultural sectors in three EU

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countries (i.e. France, Portugal and Spain) proved the CKC hypothesis and pointed out the carbon peaks were yet to come. By examining the CKC with bounds test

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approach, Dogan (2016) suggested that it is far from reaching carbon peak in

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agricultural sector in Turkey. The research by Ngarava et al. (2019) used the autoregressive distributive lag bounds-test and the error correction model to ensure the existance of CKC in agricultural sector in South Africa. On the contrary, some studies refuted to support the hypothesis and identify the carbon peaks in sectors, which includes the transport sector in Saudi Arabia (Alshehry and Belloumi, 2017) and Tunisia (Abdallah et al., 2013; Shahbaz et al., 2015), and agriculture sector in Sub-Africa (Ogundari et al., 2017). However, within the context of China, most of relevant studies focused at the regional or national level rather than sectoral level. For example, Hao et al. (2016) applied the panel data from 29 provinces between 1995 to 2011 to validate the existence of CKC

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Journal Pre-proof in China, and further suggested that the national carbon emission could not peak in the near future. Similarly, the study by Jalil and Mahmud (2009) on the existence of CKC with the time-series data of 1975-2005 stated that China is still far from reaching the carbon peak. Liu et al. (2019) recently validated the existence of CKC between China’s economic growth and carbon dioxide emissions. Li et al. (2019) investigated the spatial effects of economic performance on the carbon intensity of well-being through examining the CKC in China and suggested the overall planning

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of a China has significant effects on reginal environmental improvement. On the contrary, by testing the CKC with panel data at the city level, Wang and Ye (2017) found the monotonic increasing relationship between GDP per capita and carbon

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emission per capita which indicates carbon emission would not decrease

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automatically as income increases. By testing the CKC hypothesis with LMDI, Shen et al. (2018a) predicted the carbon emission in Beijing would peak in 2022. Zhang et

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al. (2017a) presented that the carbon emission in Henan province in China would peak in 2039 after validating the existence of CKC. Other studies have also tested the

2018a),

Changzhou

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CKC in other regions in China such as Qingdao city in Shandong province (Wu et al., city

in

Jiangsu

province

(Zhang

et

al.,

2017b),

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2017).

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Beijing-Tianjing-Hebei region (Wei et al., 2017) and Shanxi province (Li et al.,

The above literature review suggests that most of previous studies have examined the carbon emission peaks at the sectoral level of other countries, and at national or regional level in China with CKC. However, there is a lack of studies on examining the peaks at the sectoral level in China, which could impede the allocation of the carbon emission reduction tasks to individual sectors. To fill the research gap, this study aims to test the CKC hypothesis, predict the sectoral carbon peaks, and identify the carbon driving forces using the data over the period 1998-2015 of four sectors majorly responsible for the carbon emission in China, i.e. industrial, building, transport and agricultural sectors.

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3. Methods and data For fulfilling the research aims of the study, three methods are employed: carbon emission calculation to calculate the sectoral carbon emission amount, CKC model to test the CKC hypothesis and predict the sectoral carbon peaks, and LMDI to identify the carbon emission driving forces in each sector respectively. The details of the methods and data are presented as follows.

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3.1 The calculation method of carbon emission

This study applied the widely used formulas published in the IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2006) as follows. (1)

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44

𝐶 = ∑𝑖=1 12 × 𝐸𝑖 × 𝐿𝐶𝑉𝑖 × 𝐶𝐹𝑖

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Where C denotes the total carbon emission in year y for a particular sector, 44/12 is a

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constant meaning the molecular weight ratio of carbon dioxide to carbon, 𝐸𝑖 denotes the energy consumption of the fuel type i in year y, and 𝐿𝐶𝑉𝑖 represents the lower

3.2 CKC model

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fuel type i in year y.

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calorific value of the fuel type i, 𝐶𝐹𝑖 represents the carbon emission factors of the

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EKC hypothesis in its general format can be specified as follows (Shen et al., 2018b): 𝐿𝑛𝐶 = α + 𝛽1 𝐿𝑛𝑌 + 𝛽2 (𝐿𝑛𝑌)2 + 𝜀

(2)

Where α represents the constant variable, 𝐶 represents carbon emission, 𝑌 represents GDP per capita which is measured with constant 2010 US dollars and 𝜀 is the standard error term. 𝛽1 and 𝛽2 denote the estimated coefficients. After establishing the model of CKC hypothesis, the peak can be identified by taking the derivative of the known quadratic functions of the CKC hypothesis above, and peak of carbon emission (measured by GDP per capita) is presented as follows: 𝛽

𝛽

𝐿𝑛𝑌 = − 2𝛽1 , 𝑌 = 𝑒𝑥𝑝 (− 2𝛽1 ) 2

(3)

2

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Journal Pre-proof If Y0 and Y1 denote GDP per capita in the base year and in the carbon peaking year 𝛼 respectively and 𝜃 denotes the average annual growth rate of GDP per capita, further calculation can be conducted for estimating α using the following formula: 𝑌0 × (1 + 𝜃)𝛼 = 𝑌𝛼

(4)

3.3 LMDI method Decomposition method has been extensively used to investigate the carbon emission driving forces of its variation during a period. Although there is considerable body of

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decomposition approaches, Ang (2004) opined that LMDI method, which is based on Kaya identity, is the optimal one, due to its incomparable advantages in theoretical

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foundation, ease of use and result interpretation. LMDI is widely used in identifying the driving forces of carbon emission in recent literature (Ma and Cai, 2018; Ma et al.,

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2017; Shen et al., 2018a; Wu et al., 2018b), and therefore is applied in this study.

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According to Kaya identity, the carbon emission in each sector could be decomposed as follows (Kaya, 1989): 𝐶

𝐸

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𝐶 = 𝐸 × 𝑉𝐴 × 𝑉𝐴 = 𝐸𝑆 × 𝐸𝐼 × 𝐸𝑂

(5)

C is carbon emission in each sector, E is total energy consumption in each sector,

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and VA is the value added in each sector. In model (5), forces of carbon emission can 𝐶

𝐸

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be decomposed as energy structure (𝐸𝑆 = 𝐸 ), energy intensity (𝐸𝐼 = 𝐺𝐷𝑃 ), and economic output (𝐸𝑂 = 𝑉𝐴). According to the LMDI method, if 𝐶 0 and 𝐶 𝑡 denote the carbon emission in the base period and period T respectively, the change of carbon emission (∆𝐶) from the base period to period T can be decomposed into three types of effects, including the change of energy structure (∆𝐸𝑆), change of energy intensity (∆𝐸𝐼), and change of economic output (∆𝐸𝑂) shown as follows: ∆C = 𝐶 𝑡 − 𝐶 0 = ∆𝐸𝑆 + ∆𝐸𝐼 + ∆𝐸𝑂

(6)

The calculation for carbon emission effects are according to the following formulas: 8

Journal Pre-proof 𝐶 𝑡 −𝐶 0

𝐸𝑆 𝑇

𝐶 𝑡 −𝐶 0

𝐸𝐼 𝑇

∆𝐸𝑆 = 𝑙𝑛𝐶 𝑡 −𝑙𝑛 𝐶 0 × 𝑙𝑛 ( 𝐸𝑆0 )

(7)

∆𝐸𝐼 = 𝑙𝑛𝐶 𝑡 −𝑙𝑛 𝐶 0 × 𝑙𝑛 ( 𝐸𝐼0 ) 𝐶 𝑡 −𝐶 0

(8)

𝐸𝑂 𝑇

∆𝐸𝑂 = 𝑙𝑛𝐶 𝑡 −𝑙𝑛 𝐶 0 × 𝑙𝑛 (𝐸𝑂0 )

(9)

To express the contributions of the factors to carbon emission from the base period to period T, the contribution rate (CR) is proposed as follows: 𝐶𝑅 =

∆𝐸𝑆+∆𝐸𝐼+∆𝐸𝑂 ∆𝐶

=

∆𝐸𝑆 ∆𝐶

+

∆𝐸𝐼 ∆𝐶

+

∆𝐸𝑂 ∆𝐶

= 𝐶𝑅𝐸𝑆 + 𝐶𝑅𝐸𝐼 + 𝐶𝑅𝐸𝑂

(10)

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In model (10), the contribution rate CR of the factors to carbon emission can be decomposed into three contribution rate values of energy structure 𝐶𝑅𝐸𝑆 , energy

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intensity 𝐶𝑅𝐸𝐼 and economic output 𝐶𝑅𝐸𝑂 .

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3.4 Data

This study tests the CKC hypothesis in China at the sectoral level and examines the

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related impact factors based on the data from 1998 to 2015. The data in this study came from three different sources. The first is the International Energy Agency (IEA)

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database, which contains energy resources of all kinds in different sectors, including on oil, coal, gas, nuclear, bioenergy. Using these data with equation (1), the carbon

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emission in each sector in China can be calculated. The second is the Word Bank

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database, which provides GDP per capita for the studied countries to test the CKC hypothesis. The third is from the Statistics Database of Economic and Social Development of China (SDESDC), contributing the potential impact factors of carbon emission (e.g., value added in different sectors) to be examined. The whole period (WP) is divided sub-periods based on the Chinese Five-Year Plans (FYPs), i.e. 1998-2000 (the ninth FYP), 2001-2005 (the tenth FYP), 2006-2010 (The eleventh FYP) and 2011-2015 (The twelfth FYP).

4. Results 4.1 The results of CKC model

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Journal Pre-proof By applying the data in to the equations (1)-(2), CKC hypothesis is tested using Eviews 8.0. In addition, the national and sectoral peaks of China can be obtained with the equations (3)-(4), and results are shown in Table 1. The average GDP growth rate during the investigated period is used as the GDP growth rate in the calculations.

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Table 1 the results of fitting CKC model with the empirical data CKC model R-square Peak year 2 National LnC=-0.23**(LnY) +4.863**LnY-7.55* 0.987 2036 2 Industry LnC=-0.292**(LnY) +5.591**LnY-18.519** 0.957 2031 2 Building LnC=-0.334***(LnY) +6.915**LnY-24.179* 0.985 2035 2 Transport LnC=-0.183*(LnY) +3.893*LnY-13.348*** 0.991 2043 2 Agriculture LnC=-0.221*(LnY) +4.091**LnY-14.155* 0.919 2026 Note: ***Denotes the coefficient is significant at the 0.01 level **Denotes the coefficient is significant at the 0.05 level

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* Denotes the coefficient is significant at the 0.1 level

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As shown in Table 1, all the coefficients are significant, and the fitting equations have high R-square coefficients (>0.9), suggesting the CKC hypothesis exists both at the

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national and sectoral level in China. The national carbon emission is predicted to peak in 2036, which indicates the current efforts could hardly make the national goal

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achieved on time. In referring to the sectoral level, the earliest carbon emission peak is predicted to occur in the agricultural sector in 2026, four year earlier than the

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national goal. The second earliest is the industrial sector, whose carbon emission

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peaks in 2031, one year later than the national goal. The building section is five years later than the national goal. However, the carbon emission in the transport sector is predicted to peak in 2043, which occurs far later than the national goal.

4.2 The results of LMDI model Similarly, by using the data into equations (5)-(10), the effects and contribution rates of each impact factor on carbon emission of each sector are presented in Table 2. Table 2 The effects and contribution rates of each impact factor on the carbon emission of each sector Sector

Industry

Period

∆ES (Mt)

∆EI (Mt)

NFYP

-38.74

-237.42

288.10

-324.50

-1,988.71

2,413.21

TeFYP

-15.51

37.94

1,587.67

-0.96

2.36

98.61

10

∆EO (Mt)

𝐶𝑅𝐸𝑆 (%)

𝐶𝑅𝐸𝐼 (%)

𝐶𝑅𝐸𝑂 (%)

Journal Pre-proof 2,995.58

-8.29

-92.17

200.47

TwFYP

-208.40

-1,212.15

1,817.92

-52.45

-305.04

457.48

WP

-386.60

-2,788.96

6,689.28

-11.00

-79.37

190.38

NFYP

152.26

-143.79

142.52

100.84

-95.22

94.39

TeFYP

446.98

-601.62

666.54

87.32

-117.53

130.21

EFYP

222.09

-1,008.89

1,059.89

81.32

-369.43

388.10

TwFYP

121.61

-687.22

958.81

30.93

-174.77

243.84

WP

942.94

-2,441.51

2,827.77

70.94

-183.68

212.74

NFYP

-7.47

77.20

107.76

-4.21

43.50

60.71

TeFYP

-7.85

-50.50

337.14

-2.81

-18.11

120.93

EFYP

-25.71

-174.69

531.27

-7.77

-52.80

160.57

TwFYP

-11.21

-115.38

608.60

-2.33

-23.94

126.26

WP

-52.24

-263.36

1,584.77

-4.12

-20.75

124.87

NFYP

1.44

4.51

3.22

15.68

49.23

35.08

TeFYP

4.01

18.26

57.02

5.06

23.03

71.91

EFYP

-1.76

-110.20

116.07

-42.90

-2,686.06

2,828.96

TwFYP

-1.10

-60.79

112.52

-2.18

-120.05

222.23

2.58

-148.21

288.82

1.81

-103.51

201.70

WP

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Agriculture

-1,377.33

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Transport

-123.95

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Building

EFYP

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Shown in Table 2, in general, factor economic output plays major driving effect for the carbon emission increase, energy intensity is the major driving factor for carbon

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emission reduction, and energy structure plays negligible influence on carbon

5. Discussion

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emission change. The results are discussed in detail in the next section.

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After obtaining the results from CKC model and LMDI method, the discussion is conducted sector by sector.

5.1 Industrial sector The industrial sector is extremely energy intensive and responsible for 40% of global carbon emission (IEA, 2019). China follows the same principle, industrial sector accounts for more than 60% of final energy use, and more than 50% and 70% of final coal and electricity use over the period 1980–2005; however, they account for 36–53% of GDP (Liu et al., 2007). As shown in Table 1, the carbon emission in the industrial sector is predicted to peak in 2031, which is one year later than the national targeted peaking year. However, considering the massive carbon emission from the industrial 11

Journal Pre-proof sector, the predicted national peak occurs in 2036, which is six years later than the national targeted peaking year.

Figure 1 Factor-led carbon emission changes in the industrial sector

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It can be seen from Figure 1 that the economic output plays the key role in promoting in the carbon emission in all the FYPs and the whole study period. The economic output contributes to the 288 Mt, 1,588 Mt, 2,996 Mt, 1,818 Mt and 6,689 Mt of

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carbon emission increment in the ninth, tenth, eleventh and twelfth FYPs and the

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whole study period respectively. The finding echoes with other studies (Ouyang and Lin, 2015; Xu et al., 2016). For example, the research by Ouyang and Lin (2015)

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concluded that the rapid economic growth was the major force for the energy demand

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and carbon emission increment in industrial sector. Statistics can also support the finding: during last two decades (shown in Figure 1), coupled with rapid

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industrialization, the industrial value output is rapidly increase from 3,413.5 billion RMBs in 1998 to 23,518.4 billion RMB in 2015, calculated with a growth of 589%

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(China Statistical Yearbook Database, 2016). Literature also supports that energy consumption is the major engine for the economic growth and the dramatic economic growth massive energy consumption and carbon emission. (Shuai et al., 2019). EI is the major inhibiting factor of carbon emission in most of the time period. Energy intensity reduces the 237 Mt, 1377 Mt, 1212 Mt and 2,789 Mt carbon emission in the ninth, eleventh, twelfth FYPs and the whole study period respectively. Energy intensity indicates the energy consumption per unit of GDP, i.e. the lower energy intensity means the higher energy efficiency and lower carbon emission. The result is clear to support this: the energy intensity decreased 50% in 2015 compared to 1998 during the study period and reduced 2,789 Mt carbon emission (shown in Figure 1). 12

Journal Pre-proof This is benefit for the industrial energy conservation policies during the FYPs. Chinese government also proposes energy intensity targets in the energy-intensive industrial sub-sectors like iron and steel, chemical, petrol processing and coking sectors (Ouyang and Lin, 2015). Besides, the Chinese government and companies allocating large amount of funding for industrial technology investment seeking for a solution. For instance, there are 342 thousand Research and Development (R&D) projects in Chinese industrial sector with total research grant of 202 billion RMB

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(China Statistical Yearbook Database, 2015). Energy structure contributes minor influence of carbon emission reduction in referring to all the FYPs and whole study period. The energy structure reduces the 39

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Mt, 16 Mt, 124 Mt, 208 Mt and 387 Mt in the ninth, tenth, eleventh and twelfth FYPs

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and whole study period respectively. Energy structure means the carbon emission per unit of energy consumption (i.e. carbon emission factor), i.e. lower energy structure

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means lower carbon emission per unit of energy consumption. Different energy sources have different carbon emission factors. For example, a unit of coal contributes

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more carbon emission than others, which however has been the dominant source in the energy consumption structure in China’s industrial sector for a long time (Qiao et

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al., 2019). Encouragingly, the ratio of coal in the energy structure has declined from

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65% in 1998 to 55% in 2015, which reduces the carbon emission (IEA, 2019). Nevertheless, adjusting the energy structure by reversing the coal-dependence should be the major task for the Chinese government.

5.2 Building sector There has been a growing appeal globally for reducing building energy consumption in pursuing sustainable development especially for the climate change. The assessment report by the International Panel on Climate Change reveals that building sector consumes over 40% of the world's total primary energy resources and is responsible for 24% of world’s CO2 emissions (Shen et al., 2016). Due to the unprecedented urbanization process in China, building energy consumption presents a 13

Journal Pre-proof large quantity and a rapid growth trend (Liang et al., 2019; Ma et al., 2019a; Ma et al., 2019b). Table 1 presents that carbon emission in the building sector will peak in 2035 which is five years later than the national targeted year. Figure 2 illustrates the LMDI

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results and factor trends in the building sector.

Figure 2 Factor-led carbon emission changes in the building sector It can be seen from Figure 2 that the economic output also plays the key role in

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promoting the carbon emission in considering all the FYPs and whole study period.

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The economic output contributes to the 143 Mt, 667 Mt, 1,060 Mt, 959 Mt and 2828 Mt of carbon emission increment in the ninth, tenth, eleventh and twelfth FYPs and

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whole study period respectively. The significant effect of economic output on China’s

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carbon emission can be explained as follows. China’s significant population growth and unpreceded urbanization process have brought about the rapid growth of the total

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volume of buildings, which leads to the massive energy consumption and carbon emission in the building sector. According to the data from World Bank, the urban

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population 420 million (with the ratio of 45.8%) in 1998 to 762 million in 2015 (World Bank, 2017). The existing building area in China was estimated to be 52.2 billion m2 in 2014, and officials have predicted that the area in 2030 may exceed 70 billion m2 (Ma et al., 2017). Furthermore, the rising income of the citizens allows them to improve quality of life, for example, buying household appliances such as the air conditioners, refrigerators, and microwave ovens. The data from China Statistical Yearbook Database (2015) showed there were 343 million air-conditions, 390 refrigerators, 164 million microwave ovens, 309 million water heaters, and 382 million washing machines in China for the household use.

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Journal Pre-proof Energy intensity is the major inhibiting factor of carbon emission in most of the time period. Energy intensity reduces the 144 Mt, 602 Mt, 1009 Mt, 687 Mt and 2442 Mt carbon emission in the ninth, tenth, eleventh, twelfth FYPs and whole study period respectively. Energy intensity has decreased nearly 50% from 1998 to 2015 (shown in Figure 2), which echoes previous studies (Chen et al., 2017; Shi et al., 2017). The decrease of energy intensity may due to the booming of the green buildings and green retrofit for existing building, which is an effective way for the building energy saving

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(Chen et al., 2019). Starting from 2008, the total area of green building has increased to 800 million m2 in 2016, and this value is expected to be 1500 million m2 in 2020. Besides, over 60% urban residential have finished the green retrofit projects

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(Qianzhan website, 2017).

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Energy structure contributes negligible influence of carbon emission increase in referring to all the FYPs and whole study period. The energy structure increases 152

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Mt, 447 Mt, 222 Mt, 122 Mt and 943 Mt in the ninth, tenth, eleventh and twelfth FYPs and whole study period respectively. Higher energy structure means the more

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carbon emission per unit, shown in Figure 2, energy structure has slightly increased during the study period. The proportion of coal has increased by approximately twice

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during the study period (IEA, 2019).

5.3 Transport sector

Transport sector is responsible for a large proportion of carbon emission. The carbon emission from transport sector increased rapidly from 306 Mt in 1998 to 1,575 Mt in 2015 with the increment of 415%. This is due to the rapid economic and population growth, industrialization, urbanization and agricultural development in the period, which boost the freight, everyday transport, and leisure-related travel. Shown in Table 1, carbon emission in transport sector is predicted to peak in 2043, occurring 13 years later than the national goal. Figure 3 presents the LMDI results and factor trends.

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Figure 3 Factor led carbon emission changes in transport sector It can be seen from Figure 3 that the economic output also plays the key role in promoting in the carbon emission during each of the FYPs and whole study period.

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The economic output contributes to 108 Mt, 337 Mt, 531 Mt, 609 Mt and 1,585 Mt of carbon emission increase in the ninth, tenth, eleventh and twelfth FYPs and whole study period respectively. This finding is reasonable and echoes with other studies

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(Wang et al., 2011; Xu et al., 2016). China is still at an early stage of motorization

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and urbanization. The improvement of economic level allows people to pursue high quality of life highly reliant on the convenient transport, and the modern logistic

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system featuring significantly in the growing economic system requires efficient

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transport system, which increase the demand of the passenger and freight transport. According to the data from China Statistical Yearbook Database (2015), there are 88

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and 198 million cars and motors for household use, and the total amount of China’s cargo by plane in 2014 is 5.95 million tons. Another crucial factor leading to the rapid

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growth of transport is the development of tourism. According to the available data from 2017, highways, railways and roads during the Spring Festival witnessed 2.98 billion person-time passengers, and this number reached more than three billion in 2018 with an average 700 kilometers per passenger (Sohu website, 2018). Energy intensity is the major inhibiting factor of carbon emission in most of the time period. Energy intensity reduced 50 Mt, 175 Mt, 115 Mt and 263 Mt carbon emission in the tenth, eleventh, twelfth FYPs and whole study period respectively. As shown in Figure 3, the energy intensity decreased nearly 30% from the tenth to twelfth FYP. This may be attribute to China’s effective measures and policies on promoting new technology, improving traffic equipment, and phasing-out energy-intensive cars and 16

Journal Pre-proof promoting alternative fuels. For example, the Chinese government invests 450 billion RMB on new-energy vehicles technologies, and there are more than 1.6 million new-energy vehicles in 2017 (Sohu website, 2018). The research by Wang et al. (2011) reported the reduction of carbon emission from railways is mainly attributed to the phase-out of the steam locomotives. Energy structure contributes minor influence of carbon emission reduction in referring to all the FYPs and whole study period. The energy structure reduces 7 Mt, 8

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Mt, 26 Mt, 11 Mt and 52 Mt in the ninth, tenth, eleventh and twelfth FYPs and whole study period respectively. The minor influence may be attributed to fuel substitution mostly occurred between diesel and gasoline, who have similar carbon emission

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coefficients. Besides, data in Figure 3 can also support this point of view: energy

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structure only declined 6% during the study period. Oil, characterized with high carbon emission coefficients, dominates (88%) the energy demand in transport sector

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in 2015 (IEA, 2019). Although the emission reduction is slight, restructuring the

5.4 Agricultural sector

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energy mix plays a significant role in the overall reduction of carbon emission. Agricultural sector is closely related to carbon emission as it is the most vulnerable

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from climate change (Xu and Lin, 2017). As a large agricultural country, China’s

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carbon emission from the agricultural sector keeps on growing rapidly from 99 Mt in 1998 to 242 Mt in 2015, which is estimated with an increase of 142%. Encouragingly, the carbon emission will peak in 2026, which occurs four years in advance of the targeted peaking year, which benefits from a series of effective measures taken by government for promoting ecological agriculture.

Figure 4 Factor led carbon emission changes in the agricultural sector 17

Journal Pre-proof It can be seen from Figure 4 that the economic output plays the key role in promoting in the carbon emission in considering all the FYPs and whole study period. The economic output contributes to 3 Mt, 57 Mt, 116 Mt, 113 Mt and 289 Mt of carbon emission increase in the ninth, tenth, eleventh and twelfth FYPs and whole study period respectively. This finding is reasonable and echoes with other studies (Xu and Lin, 2017; Xu et al., 2016). For a long time, investment and exports have been the two major drivers of economic growth in China’s agricultural sector. Throughout the

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economic growth period, agriculture, forestry and water conservation infrastructure were seriously inadequate. In order to get rid of the constraints of agricultural production due to inadequate agricultural infrastructure, the Chinese governments at

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all levels have increased agricultural investment in fixed assets. The massive

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fixed-asset investment consumes large amounts of cement, iron and steel products, and building bricks, which lead to extra carbon emission (Xu and Lin, 2015a).

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Besides, primary agricultural products play an important role in China’s exports trade. The development of agricultural production and related agricultural machinery has

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increased carbon emission in the agricultural sector (Xu and Lin, 2015a). Shown in

study period.

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Figure 4, the economic output in agricultural sector increased 371% during the whole

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EI is the major factor inhibiting carbon emission. EI reduces 110 Mt, 61 Mt and 148 Mt carbon emission in the eleventh, twelfth FYPs and whole study period. The Chinese government has achieved satisfactory results in reducing energy intensity by formulating relevant laws and regulations, promoting low-emission agricultural technologies, enhancing water use and fertilization management for agriculture, upgrading farming machinery, reinforcing intensive agricultural production, and developing biogas digesters (National Coordination Committee on Climate Change, 2012). The negligible change of energy structure leads to very slight carbon emission change in the FYPs and whole study period. The energy structure increases 1 Mt, 4 Mt and 3

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Journal Pre-proof Mt carbon emission in the ninth and tenth FYPs and whole study period respectively. On the contrary, energy structure reduces 2 Mt and 1 Mt carbon emission in the eleventh and twelfth FYPs. Figure 4 shows energy structure trend fluctuates but stabilizes just above 3.5, indicating the negligible change of energy structure in the period.

6. Conclusion and policy implications This is the first study to not only test the CKC hypothesis but also predict the

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theoretical peaks in the context of China at both national and sectoral levels. The results show that carbon emission in China will peak in 2036, indicating the national goal (i.e. peak in 2030) can hardly be achieved with the current efforts. At the sectoral

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level, the carbon emission from the industrial, building, transport, and agricultural

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sectors will respectively peak in 2031 (one year later than the targeted year of the national goal), 2035 (five years later), 2043 (13 years later), and 2026 (four years

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earlier). As to the impact factors, the economic output is found to be the major factor

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driving carbon emission increment in the four sectors, energy intensity, however, is the major factor driving carbon emission reduction, and energy structure barely plays

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influence on either increment or decrement of carbon emission in the four sectors. The predicted lateness in the industrial, building, and transport sectors call for the

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adjustment of policies to guarantee China’s on-time peaking. Policies for carbon emission reduction are suggested based on the findings on driving forces for carbon emission changes. Firstly, to effectively depress the carbon emission led by economic output, government should strictly control the total expansion of the industries committing extensive energy consumption and carbon emission. Secondly, phasing out the backward production capacities is suggested to reduce the impact of economic output on the carbon emission increment, such that the peak of the industrial sector may occur earlier than the predicted. Thirdly, government should implement the carbon tax policy, establish and complete the carbon emission trading systems particularly for 19

Journal Pre-proof the high-carbon-emission sectors (e.g. building, manufacturing, and transport). Lastly, more effectively implement economic incentives are needed, such as providing the easier loans and tax exemption for low-carbon companies, and subsidies for public transport and purchasing cleaning vehicles. In order to enhancing the effect of energy intensity on the carbon emission reduction, government should issue total energy consumption control standards (e.g. building emission standards, vehicle efficiency standards, and vehicle occupancy standards),

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which is the powerful impetus to limit energy intensity, make energy-saving measures more specific in sectors, and eventually promote energy efficiency. The rate of reaching the standards should be set as a criterion for the political achievement of the

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local government. Besides, government should increase the research and development

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investments to all sectors to promote the application of advanced-energy-efficient and low-carbon technologies, such as hybrid engine and electric vehicle, carbon capture

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and storage technology, green building, and prefabrication. For residential building, the government should adopt the multistep residential tariff pricing, which benefits

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for the residents who efficiently use the electricity and heat and charges more for those who has wasteful habits. Social communities should disseminate the concept of

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saving energy among residents and local industries. It is also important for

appliances.

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government to provide subsidy to local households who buy energy-efficient

It is imperative to optimize the energy structure towards relying less on fossil fuel (e.g. coal and petroleum). Instead, the government should encourage and enlarge the utilization of clean energy such as solar, wind, nuclear, biomass, and shale gas. Increasing the prices of fossil fuels could trigger the externalities of energy (i.e. the cost for scarcity and environment), in the meantime, reduce the demand of fossil-fuel and augment the competitiveness of clean energy. Government can provide funding for the technologies towards the research and development to use the clean energy to research institutions and energy companies for supporting the change of energy

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Journal Pre-proof structure. For inhibiting the use of fossil fuel, government can collect taxes for those who procure a far larger amount over a normal level. This study has two limitations needing future endeavors. Even though this study predicted the carbon emission peaks and driving forces to reach them using empirical data of the four carbon pillar sectors, responsible for 88% of China’s overall carbon emission amount, the sectors generating the rest 12% carbon emission are not included in the study owing to the difficulties of collecting data, which should be the

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future endeavors. The other limitation is that this study selected three major driving forces for carbon emission for the quantitative analysis, but other factors such as urbanization rate are discussed in a qualitative way only. Future work will include

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more factors in the quantitative analysis of carbon emission driving forces.

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Conflict of interest There is no interest conflict.

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Graphical abstract

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Journal Pre-proof Highlights 1. The peaks of carbon emission at China and four sectors are tested and predicted. 2. The impact factors of carbon emission in four sectors are identified. 3. Economic output is major driving factor of carbon emission increasing.

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4. Energy intensity is major inhibiting factor of carbon emission increasing.

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