The decoupling states of CO 2 emissions in the Chinese transport sector from 1994 to 2012: A perspective on fuel types

Abstract

This paper analyzes the decoupling states between CO₂ emissions and transport development in China from 1994 to 2012. The results indicate that, at the aggregate level, the Chinese transport sector is far from reaching the decoupling state. Negative decoupling or non-decoupling years account for 72.2% of the study period. At the disaggregated level, the decoupling states between CO₂ emissions and eight primary fuels are as follows: raw coal and coke are in the absolute decoupling state; crude oil, gasoline and diesel are in the weak negative state; and the other three types (kerosene, heavy fuel oil, and natural gas) are in the strong negative decoupling state. Policy implications underneath the identified decoupling states are also revealed to help China build a more sustainable transportation system.

Keywords

decoupling sustainable transportation energy policy China

Introduction

China faces the dynamic of rapid economic development that drives ever-increasing energy use and, consequently, increasing CO₂ emissions.¹ As its economy increased at an impressive rate over the past two decades, energy-related CO₂ emissions grew by 50% between, 1990 and, 2000 and doubled from, 2000 to 2010, reaching 7 billion tons of CO₂ in, 2010.² However, China has committed to peak CO₂ emissions around 2030 while striving to peak early and boost the share of nonfossil fuel energy to around 20%.³ In this context, the transport sector plays an essential role in addressing this challenge since it consumes nearly half of all global oil and contributes one quarter of total fossil fuel combustion related CO₂ emissions through road, rail, air, and marine transportation.⁴ Additionally, mitigating emissions from the transport sector are also particularly attractive in yielding both rapid and long-term climate benefits, such as improving urban air quality and tropospheric ozone production.

To help the government adopting specific actions that are likely to promote the delinking of environmental harm from transport growth, researchers and practitioners are working toward a better understanding of the key challenges in achieving the sustainability goal of raising transportation growth with a minimum negative impact on the environment and society. Among all the existing methods, the decoupling index is a proper technique to determine the link between environmental pressure and transport growth. The concept of “decoupling indicator” was first proposed by Organization for Economic Co-operation and Development (OECD),⁵ which divided the decoupling into relative decoupling and absolute decoupling. In this decoupling conceptual framing, relative decoupling occurs when both transport growth and environmental pressure change in the same direction, but their relationship has weakened (or the ratio of the latter to the former is reduced). Absolute decoupling relates to the situation when transport growth and environmental pressure change in different directions with the former increasing and the latter either staying at the same level or falling. Tapio⁶ further redefined decoupling indicators to qualify situations into eight types of decoupling states in total, which reflect the advantages of OECD indicators. Due to the rational decoupling positions with eight possible combinations, the Taipo decoupling method has been widely used by many researchers.⁷

At first, decoupling research was primarily conducted in Europe, especially with respect to CO₂ emissions. These studies have focused on income,⁸ economic growth⁹ energy and transport sectors,^6,10 transport and economic growth.^11–14 Since Zhang¹⁵ firstly utilized the decoupling concept to explore the relationship between China’s energy-related CO₂ emission and economic growth, extensive studies have been carried out to decouple CO₂ emissions from rapid economic development and energy consumption in China at the national level^16–18 or the provincial level.^7,19 The literature focused on decoupling carbon emissions from the industrial production sector,²⁰ the tourism sector,²¹ the power sector,^22,23 and the transport sector at the national level^24–29 and the regional and provincial level (Guangdong Province;³⁰ Jiangsu Province;^31,32 Xijiang Province³³; and other provinces.^34–37

However, to the best of our knowledge, most of these studies have put their attentions on studying the decoupling effects of CO₂ emissions at a macroeconomic level, e.g., gross domestic product (GDP), population, income, economic development, etc. There are very few studies attempting to explain the decoupling effects at a microscopic level, particularly from a perspective on fuel type. In this regard, this study aims to fill this gap and reveal the policy implications underneath the identified decoupling states that help China build a more sustainable transportation system. This paper provides at least three major contributions: (1) developing a customized decoupling methodology for the transport sector in China; (2) performing decoupling analyses for China’s transport sector from, 1994 to, 2012 at the aggregate and disaggregated levels; and (3) revealing more targeted energy policy implications on a fuel-by-fuel basis.

The remainder of this paper is organized as follows. Methodology section develops a customized methodology for analyzing the decoupling states of CO₂ emissions in Chinese transport sector. Data consolidation section collects and processes the historical data of travel activities and emission intensities in Chinese transport sector during the given study period (from 1994 to 2012). The CO₂ emissions data are calculated based on the collected data and further decomposed by fuel types and transportation modes. Decoupling analyses section performs decoupling analyses using the proposed methodology in Methodology section. The identified decoupling states and major findings are also reported in this section. Policy implications section reveals policy implications behind the identified decoupling effects to build a more sustainable transport system in China. Finally, Conclusions section concludes this paper. The main study framework of this paper is shown in Figure 1.

Figure 1.

The main study framework of this paper.

Methodology

The term “decoupling” refers to breaking the link between “environmental bads” and “economic goods” or breaking the relationship between environmental pressures and economic performance. The decoupling theory was first established by the OECD in 2001, which aimed at decoupling environmental pressures from economic growth. According to the OECD’s definition, decoupling occurs when the growth rate of an environmental pressure is less than that of its economic driving force (e.g., GDP) over a given period. The Degree of Decoupling (DD) can be either absolute or relative, depending on the relationship between the growth rates of the environmentally relevant variable and the economic variable. This relationship can be measured by Decoupling Elasticity (DE), as shown in equation (1).⁵

D E = Δ E P_{t_{0} \to t_{1}} / Δ D F_{t_{0} \to t_{1}}

(1)

where EP = environmental pressures, e.g., CO₂ emissions in this study; DF = driving forces that cause environmental pressures, e.g., transport turnover volume in this study; Δ denotes the changes of EP and DF during the study period; t₀ and t₁ denote the base year and the target year of a study period.

The basic principle of the OECD’s decoupling theory is illustrated in Figure 2(a). In the transport sector, absolute decoupling will occur when transport turnover increases while CO₂ emissions are kept at the same level or even decrease; relative decoupling will occur when transport turnover and CO₂ emissions grow toward the same direction, but transport turnover increases faster than CO₂ emissions. However, the decoupling concepts proposed by the OECD may not describe all possible relationships between environmental pressures and driving forces, e.g., transport turnover may decrease in a specific period. In this regard, Tapio⁶ further developed the decoupling theory to cover all eight-logical coupling or decoupling possibilities of the relationship between environmental pressures and driving forces by introducing the concept of elasticity index. He also redefined decoupling relationships into weak, strong, and expansive/recessive states while laying emphasis on the absolute increase or decrease of elasticity variables, as illustrated in Figure 2(b).⁶

Figure 2.

Decoupling theories developed by the OECD⁵ and Tapio.⁶

In this study, according to the OECD’s and Tapio’s decoupling theories on the environmental cost of economic growth, the relationship between environmental impact and economic growth in transportation sector can be particularly expressed as:

I_{T} = V_{T} \times P_{T}

(2)

where T defines a certain study period, usually in years; I_T, V_T, and P_T denote total environmental impact, total transport turnover volume, and environmental pressure per unit transport turnover volume, respectively, in the study period T.

Assuming the study period is from base year T₀ to target year T_n, the environmental impact in this given period can be expressed as follows:

I_{T_{0}} = V_{T_{0}} \times P_{T_{0}}

(3)

I_{T_{n}} = V_{T_{n}} \times P_{T_{n}}

(4)

V_{T_{n}} = V_{T_{0}} \times {(1 + ρ_{V})}^{(T_{n} - T_{0})} = V_{T_{0}} \times {(1 + ρ_{V})}^{n}

(5)

P_{T_{n}} = P_{T_{0}} \times {(1 - ρ_{P})}^{(T_{n} - T_{0})} = P_{T_{0}} \times {(1 - ρ_{P})}^{n}

(6)

where ρ_v is the increasing rate of transport turnover volume; ρ_p is the decreasing rate of transport environmental pressures. It should be noted that the values of ρ_v and ρ_p are positive by default. In the cases of negative values of ρ_v and ρ_p, transport turnover volumes will decrease, but environmental pressures will increase.

By substituting equations (5) and (6) into equation (4), the environmental impact at target year T_n can be expressed as:

I_{T_{n}} = V_{T_{0}} \times P_{T_{0}} \times {[(1 + ρ_{V}) \times (1 - ρ_{P})]}^{(T_{n} - T_{0})} = V_{T_{0}} \times P_{T_{0}} \times {[(1 + ρ_{V}) \times (1 - ρ_{P})]}^{n}

(7)

Then, the changing rate of environmental impact in any two consecutive years is:

ρ_{I} = \frac{V_{T_{0}} \times P_{T_{0}} \times {(1 + ρ_{V})}^{n} \times {(1 - ρ_{P})}^{n}}{V_{T_{0}} \times P_{T_{0}} \times {(1 + ρ_{V})}^{n - 1} \times {(1 - ρ_{P})}^{n - 1}} - 1 = (1 + ρ_{V}) \times (1 - ρ_{P}) - 1

(8)

According to equation (8), if $ρ_{I}$ is positive (or $(1 + ρ_{V}) \times (1 - ρ_{P}) > 1$ ), the environmental impact will increase; if $ρ_{I}$ is negative (or $(1 + ρ_{V}) \times (1 - ρ_{P}) < 1$ ), the environmental impact will decrease; and if $ρ_{I}$ is zero (or $(1 + ρ_{V}) \times (1 - ρ_{P}) = 1$ ), the environmental impact will be kept at the same level as the previous year, and the critical changing rate of environmental pressures $ρ_{P_C}$ can be defined as:

ρ_{P_C} = \frac{ρ_{V}}{1 + ρ_{V}}

(9)

Finally, in this study, the decoupling factor (D_f) for the transport sector can be quantified by equation (10):

D_{f} = \frac{ρ_{P}}{ρ_{P_C}} = \frac{ρ_{P}}{ρ_{V}} \times (1 + ρ_{V})

(10)

Based on the different decoupling factor values specified in Table 1, the decoupling states between environmental pressures and driving forces in the Chinese transport sector can be grouped into four categories: absolute decoupling, relative decoupling, weak negative decoupling, and strong negative decoupling. For illustration purposes, the four defined decoupling states (shaded in different colors) and decoupling curves (dashed lines) are also presented in Figure 3. The decoupling curves can represent different decoupling factors D_f from equation (10) by substituting various values of ρ_v and ρ _p .

Table 1.

The identified decoupling states in the Chinese transport sector.

Decoupling state	$ρ_{V} > 0$	$ρ_{V} < 0$
Absolute decoupling state	$ρ_{p} > 0, D_{f} \geq 1$	$ρ_{p} > 0, D_{f} \leq - 1$
Relative decoupling state	$ρ_{p} > 0, 0 < D_{f} < 1$	$ρ_{p} > 0, - 1 < D_{f} < 0$
Weak negative decoupling state	$ρ_{p} \leq 0, - 1 < D_{f} \leq 0$	$ρ_{p} \leq 0, 0 \leq D_{f} < 1$
Strong negative decoupling state	$ρ_{p} \leq 0, D_{f} \leq - 1$	$ρ_{p} \leq 0, D_{f} \geq 1$

Figure 3.

Decoupling state chart for China’s transport sector.

Data consolidation

Transport activities data

The transport system in China consists of five essential modes: road, railway, water, air, and pipeline transport. The raw data of transport turnover by mode for the study period (from 1994 to 2012) were collected from the Yearbooks of China Transportation and Communications (CTCY³⁸). It should be noted that the turnover data are measured by ton kilometers (ton-km). In this regard, the turnover of passenger trips that is measured by person-kilometers should be converted to the data in ton-km. The converted turnover of passenger and freight traffic is equal to the turnover of passenger traffic divided by a conversion coefficient, plus the turnover of freight traffic.²⁵ The conversion coefficient is determined by comparing revenues and expenditures per person-km (or moving one person 1 km) with those of moving one ton of goods 1 km, based on the Transportation Enterprise Cost Management Method issued by the Chinese Ministry of Finance and the Chinese Ministry of Transportation. The conversion coefficients are summarized in Table 2, and the converted transport turnover data by mode are presented in Table 3.

Table 2.

Transport turnover conversion coefficients by mode in China.

Transport mode	Road	Railway	Air	Water
Conversion coefficient (passenger × km/ton × km)	0.1	1	0.072	0.33

Table 3.

Transport turnover by mode in China from 1994 to 2012.

	Total transport turnover			Railway transport			Road transport
Year	Passenger turnover (10⁹ p × km)	Freight turnover (10⁹ t × km)	Converted turnover in total (10⁹ t × km)	Passenger turnover (10⁹ p × km)	Freight turnover (10⁹ t × km)	Converted turnover in total (10⁹ t × km)	Passenger turnover (10⁹ p × km)	Freight turnover (10⁹ t × km)	Converted turnover in total (10⁹ t × km)
1994	8591	33,435	37,597	3636.040	12,632.000	16,268.040	4220.300	4486.300	4908.330
1995	9002	35,909	40,025	3545.700	13,049.500	16,595.200	4603.100	4694.900	5155.210
1996	9165	36,590	40,539	3347.600	13,106.200	16,453.800	4908.790	5011.200	5502.079
1997	10,056	38,385	42,635	3584.860	13,269.900	16,854.760	5541.400	5271.500	5825.640
1998	10,637	38,089	42,558	3773.420	12,560.100	16,333.520	5942.810	5483.380	6077.661
1999	11,300	40,568	45,425	4135.940	12,910.300	17,046.240	6199.240	5724.300	6344.224
2000	12,261	44,321	49,627	4532.590	13,770.500	18,303.090	6657.420	6129.400	6795.142
2001	13,155	47,710	53,312	4766.820	14,694.100	19,460.920	7207.080	6330.400	7051.108
2002	14,126	50,686	56,561	4969.400	15,658.400	20,627.800	7805.800	6782.500	7563.080
2003	13,811	53,859	59,535	4788.610	17,246.700	22,035.310	7695.600	7099.500	7869.060
2004	16,309	69,445	76,191	5712.200	19,288.800	25,001.000	8748.400	7840.900	8715.740
2005	17,467	80,258	87,429	6061.960	20,726.000	26,787.960	9292.080	8693.200	9622.408
2006	19,197	88,840	96,682	6622.120	21,954.410	28,576.530	10,130.850	9754.250	10767.335
2007	21,593	101,419	110,026	7216.310	23,797.000	31,013.310	11,506.770	11,354.690	12,505.367
2008	23,197	110,300	119,568	7778.600	25,106.280	32,884.880	12,476.110	32,868.190	34,115.801
2009	24,835	122,133	131,646	7878.890	25,239.170	33,118.060	13,511.440	37,188.820	38,539.964
2010	27,894	141,837	152,436	8762.180	27,644.130	36,406.310	15,020.810	43,389.670	44,891.751
2011	30,984	159,324	170,986	9612.290	29,465.790	39,078.080	16,760.250	51,374.740	53,050.765
2012	33,383	173,771	185,842	9812.330	29,187.090	38,999.420	18,467.550	59,534.860	61,381.615

	Water transport			Air transport			Pipeline transport
Year	Passenger turnover (10⁹ p × km)	Freight turnover (10⁹ t × km)	Converted turnover in total (10⁹ t × km)	Passenger turnover (10⁹ p × km)	Freight turnover (10⁹ t × km)	Converted turnover in total (10⁹ t × km)	Freight turnover (10⁹ t × km)	Converted turnover in total (10⁹ t × km)
1994	183.500	15,686.600	15,747.767	551.580	18.580	61.009	612.000	612.000
1995	171.800	17,552.200	17,609.467	681.300	22.300	74.708	590.000	590.000
1996	160.570	17,862.500	17,916.023	747.840	24.930	82.456	585.000	585.000
1997	155.700	19,235.000	19,286.900	773.520	29.100	88.602	579.000	579.000
1998	120.270	19,405.800	19,445.890	800.240	33.450	95.007	606.000	606.000
1999	107.280	21,263.000	21,298.760	857.300	42.340	108.286	627.930	627.930
2000	100.540	23,734.200	23,767.713	970.540	50.270	124.927	636.000	636.000
2001	89.880	25,988.900	26,018.860	1091.350	43.720	127.670	653.000	653.000
2002	81.800	27,510.600	27,537.867	1268.700	51.550	149.142	683.000	683.000
2003	63.100	28,715.800	28,736.833	1263.190	57.900	155.068	739.000	739.000
2004	66.250	41,428.700	41,450.783	1782.300	71.800	208.900	815.000	815.000
2005	67.770	49,672.300	49,694.890	2044.930	78.900	236.202	1088.000	1088.000
2006	73.580	55,485.750	55,510.277	2370.660	94.280	276.638	1551.170	1551.170
2007	77.780	64,284.850	64,310.777	2791.730	116.390	331.138	1865.890	1865.890
2008	59.180	50,262.700	50,282.427	2882.800	119.600	341.354	1944.030	1944.030
2009	69.380	57,556.670	57,579.797	3375.240	126.230	385.864	2022.420	2022.420
2010	72.270	68,427.530	68,451.620	4039.000	178.900	489.592	2197.190	2197.190
2011	74.530	75,423.840	75,448.683	4536.960	173.910	522.907	2885.440	2885.440
2012	77.480	81,707.580	81,733.407	5025.740	163.890	550.485	3177.290	3177.290

Source: The Yearbooks of China Transportation and Communications.³⁸

CO₂ emissions data

Generally, CO₂ emissions from the transport sector can be calculated by the distance-based method or the fuel-based method.²⁴ Using the distance-based method, CO₂ emissions are equal to distance or travel activity data multiplied by emission intensity. The transport system can be considered as a whole (“the aggregate approach”) or be split into individual sub-modes (“the disaggregated approach”). While using the fuel-based method, CO₂ emissions are estimated either by considering the components of the transport system that affect CO₂ emissions (“the bottom-up approach”),³⁹ or based on the total amount of fuel consumption or fuel sales (“the top-down approach”). Due to the limitation of available data, “the top-down approach” is more widely used in China, in which fuel consumption is multiplied by a CO₂ emission factor for each fuel type, respectively, and then is summed up to obtain the overall CO₂ emissions.^24,40 In this study, the “top-down approach” is selected to calculate CO₂ emissions from the Chinese transport sector. The CO₂ emissions of a fuel type in year t are obtained by multiplying the fuel consumption of that fuel type and its corresponding emission factor. Next, the CO₂ emissions from individual fuel types are added to obtain the total CO₂ emissions in year t, as shown in equation (11):

C_{t} = \sum_{j} E_{j} f_{j}

(11)

where C_t denotes the CO₂ emissions in year t; E_j denotes the energy consumption of fuel type j; f_j denotes the emission factor of fuel type j, as summarized in Table 4. j = 1 (raw coal), 2 (coke), 3 (crude oil), 4 (gasoline), 5 (kerosene), 6 (diesel), 7 (heavy fuel oil), 8 (natural gas).

Table 4.

Emission factors of major fuel types.⁴¹

Fuel type	Raw coal	Coke	Crude oil	Gasoline	Kerosene	Diesel	Heavy fuel oil	Natural gas
Emission factor (t/tce)	0.7559	0.855	0.5857	0.5538	0.5960	0.5921	0.6185	0.4483

The energy consumption data by fuel type in the Chinese transport sector were collected from the China Energy Statistical Yearbooks (1995–2013). For the purpose of comparative analysis, the consumption of a fuel type needs to be converted into the equivalent amount of standard coal by its corresponding conversion coefficient. The conversion coefficients are summarized in Table 5. Finally, based on the collected energy data and emission factors, the CO₂ emissions by fuel type from China’s transport sector from, 1994 to, 2012 can be calculated, as summarized in Table 6.

Table 5.

Fuel conversion coefficients to standard coal.⁴¹

Fuel type	Raw coal (ton)	Coke (ton)	Crude oil (ton)	Gasoline (ton)	Kerosene(ton)	Diesel(ton)	Heavy fuel oil (ton)	Natural gas (10⁴ m³)
Conversion coefficient (tce)	0.7143	0.9714	1.4286	1.4714	1.4667	1.4571	1.4286	12.143

Note: 1 tce = 29.3 GJ.

Table 6.

CO₂ emissions by fuel type in China’s transport sector (1994–2012).

Year	Raw coal (10⁴ ton)	Coke (10⁴ ton)	Crude oil (10⁴ ton)	Gasoline (10⁴ ton)	Kerosene (10⁴ ton)	Diesel (10⁴ ton)	Heavy fuel oil (10⁴ ton)	Natural gas (10⁴ ton)	Total emission (10⁴ ton)
1994	794.121	6.071	20.140	733.506	174.839	860.929	201.131	4.246	2794.984
1995	665.761	8.389	7.526	777.155	218.547	1086.023	204.982	3.647	2972.030
1996	843.094	5.515	8.291	1258.987	261.249	1969.872	851.289	6.641	5204.938
1997	794.791	5.374	5.719	1252.531	367.232	1846.568	975.323	6.587	5254.124
1998	634.634	8.555	7.317	1227.570	371.943	2276.426	762.032	12.303	5300.780
1999	488.996	8.422	6.614	1192.688	459.474	2588.938	757.022	20.632	5522.785
2000	464.607	9.335	6.717	1208.716	468.459	2869.620	766.034	21.775	5815.263
2001	444.370	9.701	6.374	1237.665	490.129	2980.430	770.540	33.751	5972.959
2002	450.822	9.501	5.369	1268.623	626.549	3192.842	767.925	62.929	6384.561
2003	512.122	8.962	4.779	1515.180	648.342	3602.652	847.409	74.851	7214.296
2004	446.470	1.487	0.000	1846.925	803.967	4343.217	1036.801	109.037	8587.905
2005	437.541	0.889	0.000	1922.557	832.564	5131.822	1136.456	167.503	9629.332
2006	388.974	0.706	0.000	2050.967	883.367	5704.133	1334.350	206.970	10569.466
2007	376.791	0.457	0.000	2067.446	987.776	6259.129	1586.096	220.067	11497.762
2008	343.337	0.241	0.000	2445.021	1026.772	6664.197	1029.883	343.825	11853.275
2009	333.002	0.116	0.000	2279.799	1148.856	6875.599	1127.088	443.559	12208.019
2010	330.977	0.100	0.000	2535.608	1399.589	7421.505	1195.599	433.891	13317.270
2011	335.248	0.075	0.000	2668.990	1439.162	8263.657	1212.271	585.852	14505.256
2012	316.782	0.075	0.000	2969.242	1562.190	9345.560	1247.229	650.606	16091.685

Note: The data of CO₂ emissions were calculated by multiplying the fuel consumption data by the corresponding emission factor in Table 4.

Decoupling analyses

In this section, decoupling analyses are performed at two levels for the Chinese transport sector in the given study period (1994–2012). Firstly, the decoupling relationships between transport turnover and CO₂ emissions are investigated at the aggregate level. Secondly, at the disaggregated level, the decoupling relationships between transport turnover and each fuel type are studied herein. The policy implications underneath the identified decoupling relationships are then revealed on a fuel-by-fuel basis in the next section.

The aggregate-level decoupling analysis

By using the methodology in Methodology ection, the changing rates of transport turnover and environmental pressure (ρ_v and ρ_p) are calculated for each year of the study period. Next, recall equation (10) to calculate the decoupling indicator, the decoupling state of each study year can then be determined correspondingly, as summarized in Table 7. The decoupling states are also depicted in Figure 4 for an in-depth understanding. As can be seen from Table 7 and Figure 4, in China, the transport turnover has increased throughout the study period except 1998, while the years in which CO₂ emissions decreased only account for 27.8% of the study years. For this reason, only two years are in the absolute decoupling state: 1997 and 2007, about a 10th of the study years. On the other hand, the years in the negative decoupling state are half of the total period, including 1995, 1996, 1999, 2000, 2004, 2005, 2008, 2009, and 2011. If considering the years in the weak decoupling state (2002, 2003, 2010, and 2012), negative decoupling or non-decoupling years account for 72.2% of the study period. This indicates that China’s transport sector is far from reaching the decoupling state between CO₂ emissions and transport turnover during the study period.

Figure 4.

Decoupling curves for the Chinese transport sector from 1994 to 2012 (overall transport turnover vs. CO₂ emissions). Note: State 1: absolute decoupling; state 2: relative decoupling; state 3: weak negative decoupling; and state 4: strong negative decoupling.

Table 7.

Decoupling states for the Chinese transport sector from 1995 to 2012 (overall transport turnover volume vs. CO₂ emissions).

Year	Environmental pressures $ρ_{P}$	Transport turnover $ρ_{v}$	Decoupling indicators $D_{f}$	Decoupling states
1995	−0.12786	0.064564	−2.10824	Strong negative decoupling
1996	−1.46719	0.012861	−115.544	Strong negative decoupling
1997	0.118105	0.051692	2.40291	Absolute decoupling
1998	0.001442	−0.0018	−0.79898	Relative decoupling
1999	−0.2187	0.067375	−3.46472	Strong negative decoupling
2000	−0.1055	0.092491	−1.24617	Strong negative decoupling
2001	0.045166	0.074248	0.653475	Relative decoupling
2002	−0.00745	0.06095	−0.12969	Weak negative decoupling
2003	−0.01817	0.052587	−0.36371	Weak negative decoupling
2004	−0.27192	0.279769	−1.24387	Strong negative decoupling
2005	−0.27924	0.147497	−2.17246	Strong negative decoupling
2006	0.050313	0.105828	0.525734	Relative decoupling
2007	0.137899	0.138025	1.136983	Absolute decoupling
2008	−2.86463	0.086725	−35.8959	Strong negative decoupling
2009	−0.15525	0.10101	−1.6922	Strong negative decoupling
2010	−0.01172	0.157926	−0.08593	Weak negative decoupling
2011	−0.15552	0.121686	−1.43355	Strong negative decoupling
2012	−0.02752	0.086886	−0.34425	Weak negative decoupling

Based on the above analysis results, at the aggregate level, the Chinese transport sector is far from reaching the decoupling state. Negative decoupling or non-decoupling years account for 72.2% of the study period. Only two years are in the absolute decoupling state: 1997 and 2007, which means that the growth rate of carbon emission is slower than the growth rate of transport outputs. This is mainly attributed to the economic downturn in the 1997 Asian financial crisis and the 2007–2008 global recession. Noted that the Chinese government learned a great deal from the 1997 crisis and substantially strengthened its financial system and built up high levels of international reserves. A US$586 billion stimulus package was adopted by the Chinese government as an attempt to minimize the impact of the 2007–2008 global financial crisis. In addition, China has been experiencing accelerated urbanization and industrialization, which led to booming of transport industry. In this background, more energy was consumed and more carbon emissions were produced, which makes negative decoupling or non-decoupling states appear in most years of the study period.

The disaggregated-level decoupling analysis

In this section, the decoupling states of CO₂ emissions are investigated for eight major energy fuel types in the Chinese transport sector. This in turn allows a deeper understanding of decoupling effects to make more targeted and effective decoupling policies. First, the split trend of CO₂ emissions by fuel type during the study period is investigated. Next, the levels of decoupling effects are evaluated for each fuel according to the predefined decoupling factors (D_f) and states. More targeted and effective policies are proposed on a fuel-by-fuel basis in the next section.

The split trends of CO₂ emissions by fuel type

Figure 5 shows the CO₂ emissions split for eight primary energy fuels in the Chinese transport sector during the study period. It can be seen that CO₂ emissions were coal dominated in the 1990s, but the share of coal has clearly experienced a downward tendency from 33.35% in, 1992 to only 2.03% in 2002. The researchers believe it is closely associated with technological advancements in the railway sector, in which steam locomotives have been replaced by electric and diesel locomotives. The share change of diesel shows that China’s transport sector is diesel dominant in recent years. This conclusion is reasonable because diesel has been used widely in multiple transport modes in China, especially in road fleet transport. The shares of gasoline and kerosene have been relatively stable in the past two decades. The share of heavy fuel oil increased first to the peak value of 14.87% in, 1997 before decreasing to 7.50%; this pattern is consistent with the decline of water transport. In contrast, the share of natural gas has dramatically increased by almost 25-fold over the past two decades, from 0.23% in, 1994 to 5.16% in 2012.

Figure 5.

CO₂ emissions split by fuel type in China’s transport sector (1994–2012).

Decoupling states by energy fuel type

By following the same procedures with the aggregate analysis, the decoupling factors for each fuel type in each year are calculated and summarized in Table 8. Figures 6 and 7 also depicts these decoupling relationships for the purpose of illustration. Based on the analysis, CO₂ emissions from raw coal and coke were decoupled from transport turnover growth, mainly in the absolute decoupling state. CO₂ emissions from crude oil, gasoline, and heavy fuel oil were generally decoupled from transport development, mainly in the relative decoupling state. However, CO₂ emissions from kerosene and diesel were non-decoupled from transport turnover growth, mostly in either the weak or the strong negative decoupling state. Natural gas, as a type of new clean energy, has seen increasing use in the Chinese transport sector. Its decoupling states were in a loop state of “strong negative decoupling–relative decoupling–weak negative decoupling–strong negative decoupling.”

Table 8.

The decoupling states by fuel type for the Chinese transport sector from 1994 to 2012.

Year	Raw coal		Coke		Crude oil		Gasoline		Kerosene		Diesel		Heavy fuel oil		Natural gas
Year	D_f	State	D_f	State	D_f	State	D_f	State	D_f	State	D_f	State	D_f	State	D_f	State
1995	5.616	A	−4.911	SN	−29.351	SN	−0.413	WN	−2.872	SN	−2.860	SN	1.012	A	−2.217	SN
1996	9.228	A	27.637	A	−0.106	WN	0.286	R	−14.193	SN	0.096	R	2.022	A	−119.841	SN
1997	−3.199	SN	1.495	A	0.308	R	−2.744	SN	−6.848	SN	−0.817	WN	−29.835	SN	2.496	A
1999	2.028	A	1.231	A	0.944	R	0.403	R	−3.377	SN	−1.495	SN	−6.202	SN	−3.517	SN
2000	2.289	A	−0.173	WN	0.647	R	−0.045	WN	0.342	R	−0.568	WN	0.871	R	−1.275	SN
2001	2.052	A	0.473	R	1.401	A	0.694	R	0.390	R	0.327	R	0.921	R	0.652	R
2002	0.936	R	1.337	A	0.214	R	0.033	R	−0.640	WN	−0.805	WN	0.672	R	−0.129	WN
2003	0.777	R	2.080	A	4.167	A	−3.538	SN	0.848	R	−2.338	SN	−0.487	WN	−0.343	WN
2004	1.788	A	3.981	A	1.590	A	0.142	R	−0.139	WN	0.285	R	0.201	R	−1.272	SN
2005	1.137	A	3.727	A	0.833	R	0.525	R	0.481	R	−0.357	WN	0.938	R	−2.203	SN
2006	2.049	A	2.943	A	−1.740	SN	0.035	R	−0.265	WN	−0.370	WN	0.027	R	0.532	R
2007	1.393	A	3.557	A	1.000	A	0.891	R	0.063	R	−0.320	WN	0.381	R	1.147	A
2008	1.337	A	6.451	A	0.859	R	−0.366	WN	0.545	R	−0.451	WN	3.051	A	−36.319	SN
2009	1.365	A	6.121	A	1.731	A	1.669	A	−0.177	WN	0.686	R	0.066	R	−1.700	SN
2010	1.016	A	1.905	A	0.811	R	0.289	R	−0.382	WN	0.497	R	0.615	R	−0.087	WN
2011	0.915	R	3.055	A	3.736	R	0.568	R	0.768	R	0.067	R	0.885	R	−1.438	SN
2012	1.563	A	1.000	A	−0.529	WN	−0.295	WN	0.016	R	−0.507	WN	0.668	R	−0.344	WN

A: absolute decoupling; R: relative decoupling; SN: strong negative decoupling; WN: weak negative decoupling.

Figure 6.

The decoupling states by fuel type for the Chinese transport sector from 1994 to 2012: Number of years in each state.

Figure 7.

The decoupling states by fuel type for the Chinese transport sector from 1994 to 2012: Detailed decoupling factors.

At the disaggregated level, only CO₂ emissions from raw coal and coke were decoupled from the growth of transport turnover, mainly in the absolute decoupling state. It can be seen that CO₂ emissions were coal dominated in the 1990s, but the share of coal has clearly experienced a downward tendency from 33.35% in, 1992 to only 2.03% in 2002. The researchers believe it is closely associated with technological advancements in the railway sector, in which steam locomotives have been replaced by electric and diesel locomotives. Due to the declining proportion of energy consumption and the continuous improvement of electrification, the absolute decoupling state appears for these two types of fuels.

Moreover, the overall decoupling level for each fuel type can be simply described using the average value of decoupling factors of all study years, as illustrated in Figure 8.

Figure 8.

The overall decoupling states by fuel type for the Chinese transport sector (1994–2012).

Policy implications

According to the above discussions, it is reasonable to select the following four fuels as the major study objects: coal (the fuel most commonly used in China), gasoline and diesel (the dominant energy sources of CO₂ emissions), and natural gas (the strongest negative decoupling fuel). The energy policies targeted at mitigating CO₂ emissions from these four fuels are proposed herein. Other fuels should also be benefited from these or similar energy policies. At the end, we briefly discussed China’s Nationally Determined Contribution (NDC) and its effects on our policy recommendations, focusing on the transport sector only. Noted that on 3 September 2016, China ratified the Paris Agreement and it has policies to reach its NDC goals.

Coal

During the study period, the share of coal in China’s energy consumption declined continuously, and hence its CO₂ emissions were absolutely decoupled from the growth of transport turnover. However, it is still safe to say that China is and will continue to be a coal-power economy, since coal in China can be characterized as a cheap and abundant source of energy. In 2012, China reported over 3 million tons of coal consumption in the transport sector alone.⁴² In this regard, China faces the challenges of reducing and decoupling coal-fired emissions from transport development that could dampen future economic growth. The following proposed measures seek to respond to such challenges:

Most of the coal resources are located in inland Northern provinces in China, e.g., Shanxi, Shaanxi, and Inner Mongolia, which are far away from coastal demand centers, e.g., eastern coastal provinces. Therefore, transporting coal across the county requires a large share of domestic transport capacity. To cope with the difficulties of uneven distribution of coal production and consumption, the policy of increasing the use of coal by wire, whereby energy from coal is extracted by combustion at the mine-to-mouth thermal power plants and then delivered long-distance to demand centers, presents potential for reducing coal transport demands. However, this long-term potential benefit may be offset by the short-term significant current electricity grid construction cost for the construction of power facilities.⁴² In this regard, this attractive policy needs to consider the trade-offs between long-term and imminent benefits.

Another promising policy is to improve coal’s energy efficiency by coal washing. Currently, Chinese thermal coal has a much lower calorific value, about 5000–5500 kcal/ton, than that of thermal coal imported from other countries, e.g., Australia, with a calorific value of 6000 to 6500 kcal/ton. By promoting the coal-washing policy more extensively, the energy efficiency of coal could be increased by more than 20% and thus could reduce total coal consumption and transportation pressure. However, the high water consumption for coal washing, about 4 tons of water per ton of raw coal, has constrained this policy implementation, especially in the water-deficit regions of China. In this regard, implementing this policy needs to consider the uneven water distribution of local regions in China.

Gasoline and diesel

In the rapidly evolving context of urbanization, Chinese cities have been undergoing fundamental changes in terms of simultaneous demographic, technological, and socioeconomic transition, implying a significant expansion in demand for urban infrastructure and transport. In this regard, the transport energy in China is dominated by gasoline and diesel, accounting for about three-fourths of the total energy consumption in 2012. However, the previous analyses indicate that CO₂ emissions from gasoline and diesel are still in the weak negative decoupling state overall, which underlines the necessity of enhancing energy policies to address pressing environmental challenges from gasoline and diesel. The promising policies can include:

Addressing the decoupling issues in the transport sector requires the interplay between socioeconomic development, urban infrastructure, and the environment. An integrated approach that combines spatial, transport and urban planning, land market governance, and environmental policies is particularly compelling.⁴³ The Chinese government must be aware that various land use factors such as density, regional accessibility, and roadway connectivity affect travel behavior, including per capita vehicle travel, modal split, and non-motorized travel. Therefore, relevant transport and urban planning tools can be designed to discourage unnecessary transportation trips and hence reduce the CO₂ emissions from gasoline and diesel.

Given the experiences in developed countries, improvement in public transport services and lower commuting costs (including time value) of public transport effectively reduces the use of private cars and subsequently fuel consumption.⁴⁴ Therefore, the comprehensive policy packages integrating high-quality services of public transport consistency with sustainable urban development strategies have to be put in place. It is necessary to convince car users to make a modal shift in the urban environment.⁴³

Public and fiscal policies are needed to restrict excessive car use in China’s metropolitan areas. Experiences such as those learned from Beijing and Shanghai are a necessity, including: high purchase tax and license registration fees (vehicle quota system); road use restrictions; parking fees and road-pricing systems; and incentives for purchasing and using more energy-efficient and carbon-saving vehicles.

Diesel engines, primarily on-road heavy-duty vehicles and international marine vessels, will increase the CO₂ emissions rapidly in regions where controls lag. Practices in OECD nations could provide lessons for China to address diesel emissions. A diesel vehicle emissions reduction program should be enacted in three areas: new vehicles, fuels, and the in-use fleet. This program could include several strategies, including new vehicle emissions and fuel quality standards, prevention of vehicle overloading, fiscal policies to encourage higher quality fuel supply, and discouraging the use of higher emitting vehicles.⁴⁵

Natural gas

Natural gas, as a type of relatively clean energy compared to other fossil energies, has witnessed an increasing use in the Chinese transport sector. It has been anticipated that decarbonization is beginning with the replacement of coal and oil with natural gas. This is because natural gas-fired power plants emit 57% less CO₂ per kilowatt-hour (kWh) than coal-fired plants, and are on average 20% more efficient at converting fuel energy to electricity than coal plants.⁴⁶ Especially compared to coal, natural gas generates negligible amounts of particulates, sulfur, and mercury, which particularly benefits China’s eroding air quality. The 13th Five-Year Plan (2016–2020) of the Chinese government calls for the replacement of coal in non-power sectors either with natural gas and/or electricity. As a result, natural gas is China’s fastest growing major fuel, with demand quadrupling in the past decade. it is now about 6–7% of China’s energy demand, doubling the market share in 2007, and China seeks natural gas to be 10% of energy by, 2020.⁴⁷

However, according to our analysis, the decoupling state of natural gas was in a loop state of “strong negative decoupling–relative decoupling–weak negative decoupling–strong negative decoupling” during the study period. If simply considering the average decoupling indices for all study years, natural gas presented the strongest negative decoupling state among eight fuel types. The authors hold that the following reasons may explain this phenomenon:

Natural gas resources in China are unevenly distributed and often remote from demand centers. Transporting natural gas to demand centers would generate a large amount of carbon emissions.

Constrained by geological complexity, shortage of water, land access, and the limitations of infrastructure, the production of natural gas consumes more resources and hence generates more greenhouse emissions.

The output of natural gas is monopolized by some Chinese state-owned giants. Production wise, they are not nimble and waste tons of resources without any innovation.

Although this may be contradictory to the widely accepted fact that increased use of natural gas reduced CO₂ emissions in recent years. We, along with other researchers, found that without strong limits on CO₂ emissions or policies that explicitly encourage renewable electricity, abundant natural gas may actually slow the process of decarbonization, primarily by delaying deployment of renewable energy technologies (e.g., the wind and solar energy that can easily scale up the production in China).⁴⁸ In this regard, China’s current policies of promoting natural gas should be examined critically in the context of a national “cap to trade” scheme that reduce natural gas consumption while enabling China to achieve its climate goals by favoring the deployment of lower-carbon renewable energy sources.

The discussion on China’s NDC goals

As the world’s largest emitter, China’s NDC puts forward a new goal on its carbon emission for 2030: reducing CO₂ emissions per unit of GDP (known as carbon intensity) by 60–65% below, 2005 levels. This new carbon intensity target builds on China’s existing target to reduce intensity 40–45% by 2020, and it is also roughly consistent with scenarios showing China’s CO₂ emissions peaking in 2030. To implement this NDC, China needs to address emissions from its transport sector, which is gaining in importance as China moves to bring industrial emissions under control. In this context, any policies or measures to decouple carbon emissions from the growth of its transport sector are very significant for the implementation of China’s NDC.

As discussed previously, the electrification in transport sector by low-carbon electricity (e.g., the solar and wind power) would greatly reduce the carbon intensity and hence decouple carbon emissions from transport outputs. China also has a huge potential of transport electrification. It is estimated that China will deploy 800–1000 GW in nonfossil capacity, close to the United States total current electricity capacity.³

It should be also noted that our study is focusing on the decoupling policies in the transport sector, while the goals of China’s NDC are much wider and the policies of its implementation evolve many other economic sectors. The discussion of any policies in other sectors may be beyond the scope of our study.

Conclusions

China faces rapid economic development that drives ever-increasing energy use and consequently increases CO₂ emissions. Sustaining rapid economic and urban growth while minimizing the detriment to the environment is a dilemma for the Chinese government. Unlike existing studies, this paper focuses on analyzing the decoupling states between CO₂ emissions and the growth of China’s transport sector from, 1994 to, 2012 at two levels. The major findings and policy implications are summarized as follows:

At the aggregate level, the Chinese transport sector has been far from reaching the decoupling state between CO₂ emissions and the development of transport turnover during the study period. Negative decoupling or non-decoupling years accounted for 72.2% of the total study years.

Based on the decomposed decoupling analyses by eight primary fuels, the decoupling effect of each fuel during the study period can be described as follows: raw coal and coke are in the absolute decoupling state; crude oil, gasoline and diesel are in the weak negative state; and the other three fuel types, kerosene, heavy fuel oil, and natural gas, are in the strong negative decoupling state.

Policy implications underneath the identified decoupling effects are revealed on a fuel-by-fuel basis. Noted that the suggested energy policies need to evaluate the trade-offs between immediate economic costs and long-term environmental benefits.

In summary, addressing the environmental, economic, and social challenges systematically should be the direction taken in the future by the researchers for building a more sustainable transport sector in China.

Footnotes

Acknowledgements

The authors would like to thank all those involved for support and advice on this study.

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: This research was supported by the Natural Science Foundation of China (Project No.: 51278057).

Dayong Wu, PhD, is a senior research associate. Dayong's research interests are Transportation Data Analytics, GIS in Transportation, Intelligent Transportation Systems and Transport Environmental Issues.

Changwei Yuan, PhD, is a Professor and Yuan's research interests are transportation, energy, and environment complex mechanism.

Hongchao Liu, PhD, is a Professor. Liu's research interests are transportation and traffic engineering.

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The decoupling states of CO 2 emissions in the Chinese transport sector from 1994 to 2012: A perspective on fuel types

Abstract

Keywords

Introduction

Methodology

Data consolidation

Transport activities data

CO2 emissions data

Decoupling analyses

The aggregate-level decoupling analysis

The disaggregated-level decoupling analysis

The split trends of CO2 emissions by fuel type

Decoupling states by energy fuel type

Policy implications

Coal

Gasoline and diesel

Natural gas

The discussion on China’s NDC goals

Conclusions

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

References

CO₂ emissions data

The split trends of CO₂ emissions by fuel type