Supporting and evaluation systems of low carbon transport: Study on theory and practice

Abstract

Climate change is an extremely serious international problem, which is concerned by all the communities in the world. As a major sector of energy consumption and the main source of carbon emissions, it is necessary for the Chinese transportation sector to take on a low carbon development path in order to alleviate the energy consumption and environment pollution and take a positive role in these fields. Under such environment, the supporting and evaluation systems of low carbon transport from four aspects, namely, the efficiency, industry, innovation, guide and society, of low carbon are established in this study. An empirical example from Changsha City, Hunan Province in China is provided as numerical example.

Introduction

A commonly accepted scientific opinion is that change in the global climate has been taking place, mainly due to the increase in carbon emissions. In recent years, China has entered into highly developed urbanized period. Highly developed urbanization is facing the increasing expansion of energy consumption and carbon emissions caused by increasing industries and vehicles.

Transport is the major sector for energy consumption and one of the main sources of emissions in China, which would result in more and more political and economic disputes. ¹ Various estimations show that China might become the biggest market country of vehicles in the world in the future.^2–5 It causes various serious problems. Energy consumption in transportation sector has grown significantly over the past decades with an annual rate of 14.7%. It is estimated that there were more than 500 million tons of CO₂ emissions in 2014 which is more than four times of that in 1990 with an annual growth rate of 7.8%. This emission rate is considered relatively high among the developing countries ⁶ and equal to the total volume of the United States and European Union. Therefore, it is very necessary for the Chinese energy and environmental policy makers to investigate energy consumption and carbon emissions in transportation sector and establish supporting and evaluation systems for transportation sector.

Xu ⁷ shows that the construction of low carbon transport is a systematic project. It is necessary for the transport industry to take on a low carbon development path in order to build an eco-friendly society in China. Gui points out that it is urgent for the government to establish a low carbon transport system. However, none of the existing studies provide a complete supporting system or evaluation system mainly for China. ⁸ The objective of this study is to identify a supporting system of low carbon transport for China and provide an evaluation system based on previous studies. An empirical study in Changsha City, Hunan Province is conducted to support the systems referred in this study. Changsha sits at the northeast of Hunan Province in south-central China. Changsha City is the economic and political capital of Hunan Province. It is well known for its famous history and culture.

Supporting systems of low carbon transport

Low carbon transport is a long-term and complex system which is led by the government, followed by the city dwellers, based on the biological system and supported by scientific innovation system. To contribute the sustainable social development, an objective of this study is to provide supporting and evaluation systems of low carbon transport for China. The supporting system of low carbon transport is as follows.

Industrial supporting system

Winston et al. point that low carbon industry is the one based on low energy consumption and low carbon emission. Transportation industry is one of the major components of globalization and makes an important contribution to the society and economy. However, transportation industry is responsible for a large and growing share of emissions that affects global climate change.^9–11 Therefore, the traditional transportation industries of high consumption and carbon emission should carry out low carbon transformation. The auto industry is the first traditional transportation industries that should take actions to reduce energy consumption and CO₂ emission. Various kinds of estimations show that China might probably exceed the United States to become the largest market for autos in the world by the year 2020.^12,13 In the meanwhile, the air pollution and CO₂ emission caused by petroleum consumption used in autos have become more and more serious in China. China has become the second biggest country in the CO₂ emission, which would result in more and more serious international environmental, political and economic disputes. Therefore, industrial supporting system is the first subsystem for the low carbon transport system. The transportation industries should be encouraged to carry out technology transform in order to promote carbon reduction. The idea of ‘low carbon’ should be applied to the transformation of transportation industries to achieve whole industry chain development in the field of transportation as well as the promotion of low carbon transportation supporting system.

Infrastructure supporting system

Transportation infrastructure is the main carrier for urban transportation. Transportation infrastructure provides capacity, in the form of traffic lanes and runways, for various transportation sectors such as highway, railway and airline. ⁹ However, the public transport infrastructure is growing slowly in China. The proportion of investment of urban public transportation only accounts for 22% in the total investment of urban transportation. The slow development of public transport infrastructure restricts the level of public transportation service.^12,13 Therefore, it is urgent for the government to strengthen the construction of urban public transportation infrastructure in order to improve the urban environment and achieve the sustainable social development. Therefore, an infrastructure supporting system is a vital subsystem for low carbon transport system. It can improve the urban public transportation system and greatly decrease the carbon emission from transportation sector.

Life supporting system

City dwellers are the major participants in the construction of low carbon transport. Their life style and travel mode influence the urban transportation system greatly. Walking and cycling are ideal modes for the construction of low carbon transport system. However, these modes are shrinking mainly because of increasing trip distances in expanding cities, increasing use of private cars and motorcycles as well as worsening of walking and cycling facilities.^14–16 Therefore, the life supporting system is an important subsystem in the low carbon transport system. All the city dwellers have the responsibilities for the life supporting system building. The urban travel behaviour mode should be shifted from ‘high emission’ to ‘low emission’ and even from ‘low emission’ to ‘zero emission’. City dweller’s mode should be modified to establish low carbon transport life supporting system. Chinese dwellers are conscious of the importance of low carbon travel mode gradually (see Table 1).

Table 1.

Comparison among the travel modes of cities in the world. ¹⁷

City	Walk/bicycle	Public transport	Private car	Total
China	35.5	38.3	26.2	100.0
US	8.1	3.4	88.5	100.0
Australia	15.8	5.1	79.1	100.0
Europe	31.3	19.0	49.7	100.0
Asian countries with high income	28.5	29.9	41.6	100.0
Asian countries with low income	32.4	31.8	35.9	100.0

Policy supporting system

Meeting the policy goals of energy consumption and reducing emissions from transportation sector is a complex task indeed. Some polices on low carbon transport seem radical at the present, but the political context of Chinese low carbon transport does not meet targets set by the Chinese government. Reducing carbon emissions and energy consumption from transportation sector is a heavy and complex task. Government plays an important role in this system since the government is the guide and participant in the construction of low carbon transport.^18,19 Therefore, the policy supporting system is also an important subsystem in the low carbon transport system.

Chinese government has made great efforts to promote low carbon transport system. For example, bus rapid transit (BRT) system is being built or expanded in several cities, which can greatly improve the service level of public transport at relatively low cost.^16,20 It is extremely urgent for the government to continue strengthen the low carbon transport policies and establish more effective supporting and evaluation systems for low carbon transport.

Evaluation systems of low carbon transport

The supporting system provides a framework for the evaluation system. Therefore, the evaluation system should include the indices from four fundamental aspects: industrial, infrastructure, life and policy.

Four aspects, namely, low carbon efficiency, low carbon innovation, low carbon guide and low carbon society, based on the above supporting system, clearly shows 10 urban low carbon transport by means of 32 indices (see Table 2). All the aspects in the supporting system include the four fundamental aspects, and the 32 indices covering four fundamental aspects, industry, infrastructure, life and policy, are based on the supporting system. An empirical study from Changsha City, Hunan Province is taken as an example.

Table 2.

Evaluation system of urban low carbon transport.

Overall layer	State layer	Index layer	Unit	Source
Low carbon efficiency (B1)	Emission efficiency C1	Urban transport carbon emission (D1)	Ton	Institute of Low Carbon Economy and Climate Change, Renmin University of China 21
		Urban transport carbon emission per person (D2)	Ton per person
		Growth rate of urban transport carbon emission (D3)	%
	Energy control C2	Urban transport energy consumption (D4)	Tons of standard coal
		Urban transport energy consumption per person (D5)	Tons of standard coal per person
Low carbon innovation (B2)	Clean energy C3	Transport clean energy percentage (D6)	%
		Transport clean energy per person (D7)	Tons of standard coal per person
	New technology C4	Number of trading contracts and patents for new transport energy techniques (D8)	Piece
		Total amount of investment in R&D in transport energy saving techniques (D9)	10,000 yuan
Low carbon guide (B3)	Infrastructure C5	Total amount of investment in urban public transport infrastructure construction (D10)	10,000 yuan
		Improvement in information platform for low carbon transport (D11)	Qualitative
	Government guide C6	Investment index of urban environment protection (D12)	%	Chen, 22 Wang 23 and Long et al. 24
		Perfection of urban low carbon transport policy (D13)	Qualitative
		Percentage of bonus fund of low carbon transport items in expenditure (D14)	%
		Low carbon strategy in transportation industry (D15)	Qualitative
		Low carbon transport technology (D16)	Qualitative
	Industry guide C7	Low carbon strategy in transportation industry (D17)	Qualitative	Institute of Low Carbon Economy and Climate Change, Renmin University of China 21
		Low carbon transport technology (D18)	Qualitative
		Investment index of low carbon transport (D19)	%
		Percentage of green transportation industry (D20)	%
		Number of contracts of international low carbon transport technology (D21)	Piece
	Life guide C8	Level of low carbon transportation industry from citizen recognition (D22)	%	Newton and Meyer 14 and Wang 15
		Satisfaction level of urban low carbon transportation industry from citizen recognition (D23)	Qualitative
		Satisfaction level of urban low carbon environment from citizen recognition (D24)	Qualitative
		Consciousness and behaviour of low carbon travel mode (D25)	Qualitative
Low carbon society (B4)	Low carbon life C9	Turnover volume of passenger traffic (D26)	10,000 km	Wang 15
		Transit share rate (D27)	%
		Bus trip distance (D28)	Kilometre
		Walking and bicycle travel percentage (D29)	%
		Private car quantity (D30)	10,000 quantifier for land vehicles
	Low carbon environment C10	Afforested area of urban main road (D31)	m2	Long et al. 24
		Carbon emission reduction from low carbon transport (D32)	Ton CO2

In order to optimize the evaluation system, several opinions were accepted from experts to improve this system. The original evaluation system has been first sent to 45 specialists, including transport experts, professors in environment, PhD students in energy economy and policy. The arithmetic mean was used for agree coefficient (R), and the variable coefficient was taken as the degree of deviation from experts $(V)$ . Larger agree coefficient and lower degree of deviation from experts indicate better reliability and validity. The following equations are used to choose the indices of evaluation system.

$X_{\mathrm{ij}}$ are the scores of index j provided by expert i, the number of experts is n. Hence, the agree coefficient index j is

$$R_j=\frac{1}{n}\sum _{i=1}^n{X}_{\mathrm{ij}}$$

(1)

The degree of deviation of index j is

$$V_j=\frac{\sqrt{\frac{1}{n-1}\sum _{i=1}^n{(X_{\mathrm{ij}}-R_j)}^2}}{R_j}$$

(2)

According to the statistics results, all the indices indicate good reliability and validity $(R>3)$ and $(V<0.5)$ .

Empirical study from China

An empirical study in Changsha City is provided in this study. The data are collected from its statistical yearbooks.

Determining weight

The modified Delphi method is used for determining weights

$$R_{\mathrm{hg}}=1-{\left(\frac{1}{m}{\sum _{i=1}^m(W_{\mathrm{hi}}-W_{\mathrm{gi}})}^2\right)}^{\frac{1}{2}}$$

(3)

where $W_{\mathrm{ij}}$ is the weight expert h gives for index i. $R_{\mathrm{hg}}$ is the similarity of the score for index i between expert h and g.

From the similarity coefficient, the similar matrix ${(R_{\mathrm{hg}})}_{n\times n}$ is obtained

$$P_h=\sum R_{\mathrm{hg}}$$

(4)

$P_i$ indicates the deviation between the weight from experts and the weight from the whole expert team.

From $P_h$ , $P={(p_1,p_2,\dots ,p_n)}^T$ is obtained. Deviation coefficient equals to

$$D_h=\frac{p_{max}-p_h}{p_{max}}\times 100\%$$

(5)

Setting restrictions on $D_0$ . If $D_h>D_0$ , the score of weight from expert h will be deleted. The weights of indices from the rest of experts will be used for evaluation and weight vector is obtained by normalization

$$A=\{a_1,a_2,\dots ,a_m\}\mathrm{and}\sum _{i=1}^{m}a=1$$

(6)

Similarly, weight set for each index $u_i$ is obtained

$$\begin{array}{c}\multicolumn{1}{c}{{\mathrm{A}}_i=\{a_{i1},a_{i2},\dots ,a_{i{m}_i}\}}\\ \multicolumn{1}{c}{\mathrm{and}\sum _{j=1}^{m_i}{a}_{\mathrm{ij}}=1(i=1,2,\dots ,m)}\end{array}$$

(7)

The indices are divided into five levels from very large to very small and assigned the scores of 9, 7, 5, 3 and 1, respectively.

Calculating difference sequence, range, correlation coefficient and correlation degree

$C_i$ is taken as the research objective; $D_i$ is the most important index and $r_{1j}$ is the importance comparing other factors in $C_i$ with $D_i$ . The difference sequence between evaluation factors and $D_i$ is

$${\Delta }_{1j}^{(h)}=|r_{11}-r_{1j}|(j=1,2,\dots ,m_1;h=1,2,\dots ,n)$$

(8)

The minimum difference value is m, and the maximum difference value is M.

Grey relational coefficient is as follows

$$C_{\mathrm{oij}}^{(h)}=\frac{m+\mu M}{{\Delta }_{1j}+\mu M},\mu =0.5$$

(9)

Grey correlation degree is as follows

$$r_{o1j}^{(h)}=\frac{1}{m_1}\sum _{j=1}^{m_1}{C}_{o1j}^{(p)}$$

(10)

From the grey correlation degree processed by normalization, weight coefficient for each index is obtained. If the weight coefficient for $D_i$ is $a_{\mathrm{ij}}$ , then the weight matrix for $D_i$ is

$$A_i=\{a_{i1},a_{i2},\dots ,a_{i{m}_i}\},(i=1,2,\dots ,m)$$

(11)

If the weight coefficient for $D_i$ is $a_i$ , then the weight matrix for $D_i$ is

$$A=\{a_1,a_2,\dots ,a_m\}$$

According to the standard of evaluation index V, the sample matrix is obtained as follows

$$\begin{array}{c}\multicolumn{1}{c}{D=(d_{\mathrm{ij}}^h)}\\ \multicolumn{1}{c}{(i=1,2,\dots ,m;j=1,2,\dots ,m_i;h=1,2,\dots ,n)}\end{array}$$

(12)

Determining grey degree value

The grey degree values are divided into five degrees from very large to very small. The definite weighted functions are as follows:

• First grey class (e = 1), grey number $\otimes \in [0,5,10]$

  The definite weighted functions

  $$f_1(d_i)=\left\{\begin{array}{c}\hfill 0d\notin [0,10]\hfill \\ \multicolumn{1}{c}{d_i/5d\in [0,5]}\\ \multicolumn{1}{c}{1d\in [5,10]}\end{array}\right\}$$

  (13)

• Second grey class (e = 2), grey number $\otimes \in [0,4,8]$

  The definite weighted functions

  $$f_2(d_i)=\left\{\begin{array}{c}\hfill 0d\notin [0,8]\hfill \\ \multicolumn{1}{c}{d_i/4d\in [0,4]}\\ \multicolumn{1}{c}{(8-d_i)/4d\in [4,8]}\end{array}\right\}$$

  (14)

• Third grey class (e = 3), grey number $\otimes \in [0,3,6]$

  The definite weighted functions

  $$f_3(d_i)=\left\{\begin{array}{c}\hfill 0d\notin [0,6]\hfill \\ \multicolumn{1}{c}{d_i/3d\in [0,3]}\\ \multicolumn{1}{c}{(6-d_i)/3d\in [3,6]}\end{array}\right\}$$

  (15)

• Forth grey class (e = 4), grey number $\otimes \in [0,2,4]$

  The definite weighted functions

  $$f_4(d_i)=\left\{\begin{array}{c}\hfill 0d\notin [0,4]\hfill \\ \multicolumn{1}{c}{d_i/2d\in [0,2]}\\ \multicolumn{1}{c}{(4-d_i)/2d\in [2,4]}\end{array}\right\}$$

  (16)

• Fifth grey class (e = 5), grey number $\otimes \in [0,1,2]$

  The definite weighted functions

  $$f_5(d_i)=\left\{\begin{array}{c}\hfill 0d\notin [0,2]\hfill \\ \multicolumn{1}{c}{1d\in [0,1]}\\ \multicolumn{1}{c}{2-d_{i}d\in [1,2]}\end{array}\right\}$$

  (17)

Grey evaluation coefficient

If the grey evaluation coefficient for $D_i$ is $x_{\mathrm{ije}}$ , then the equation is as follows

$$x_{\mathrm{ije}}=\sum _{h=1}^n{f}_e(d_{\mathrm{ijh}})$$

(18)

The sample evaluation matrix for $D_1$ is $d_1$ . The scores for $D_1$ from the number of h experts are $d_{111},d_{112},\dots ,d_{11p},\dots ,d_{11k}$ . Therefore, the grey coefficient for the first grey class $U_{11}$ is

$$x_{111}=\sum _{h=1}^n{f}_1(d_{11h})$$

(19)

Membership calculation and membership matrix determination

By the normalization process of $x_{\mathrm{ije}}$ , the membership $r_{\mathrm{ije}}$ is obtained. Taking $U_{11}$ for example, its grey evaluation coefficient is $x_{111}$ , $x_{112}$ , $x_{113}$ , $x_{114}$ and $x_{115}$ . By the normalization process, five memberships – $r_{111}$ , $r_{112}$ , $r_{113}$ , $r_{114}$ and $r_{115}$ – are obtained. The membership vector for $U_{11}$ is $R_{11}=(r_{111},r_{112,}{r}_{113,}{r}_{114,}{r}_{115})$ .

Similarly, the membership vector for $U_{\mathrm{ij}}$ is $R_{\mathrm{ij}}$ . The membership $R_i$ for $U_i$ is obtained. The matrix is as follows

$$R_i=\left\{\begin{array}{c}\hfill R_{i1}\hfill \\ \multicolumn{1}{c}{R_{i2}}\\ \multicolumn{1}{c}{⋮}\\ \multicolumn{1}{c}{R_{i{m_i}_i}}\end{array}\right\}\left\{\begin{array}{c}\hfill r_{i11}{r}_{i12}\cdots r_{i15}\hfill \\ \multicolumn{1}{c}{r_{i21}{r}_{i22}\cdots r_{i25}}\\ \multicolumn{1}{c}{⋮⋮⋮⋮}\\ \multicolumn{1}{c}{r_{i{m}_{i}1}{r}_{i{m}_{i}2}\cdots r_{i{m}_{i}5}}\end{array}\right\}$$

(20)

Fuzzy comprehensive evaluation

• One-level fuzzy comprehensive assessment model.

After the evaluation for each index, the fuzzy vector $A_i$ on $U_i$ should be transferred into the fuzzy vector $B_i$ on V by fuzzy transformation. $B_i$ is the result of one-level fuzzy comprehensive assessment model. The equation is as follows

$$\begin{array}{c}\multicolumn{1}{c}{B_i=A_i·R_i=(a_{i1},a_{i2},\dots ,a_{i{m}_i})}\\ \multicolumn{1}{c}{\left\{\begin{array}{c}\hfill r_{i11}{r}_{i12}\cdots r_{i1k}\hfill \\ \multicolumn{1}{c}{r_{i21}{r}_{i22}\cdots r_{i2k}}\\ \multicolumn{1}{c}{⋮⋮⋮⋮}\\ \multicolumn{1}{c}{r_{i{m}_{i}1}{r}_{i{m}_{i}2}\cdots r_{i{m_i}_{i}k}}\end{array}\right\}=(b_{i1},b_{i2},\dots ,b_{\mathrm{ik}}),}\\ \multicolumn{1}{c}{(i=1,2,\dots ,m)}\end{array}$$

(21)

• Two-level fuzzy comprehensive assessment model.

One-level fuzzy comprehensive assessment model is the comprehensive evaluation for each index, and two-level fuzzy comprehensive assessment model is the comprehensive evaluation for index set U. The evaluation matrix is

$$R_i=\left\{\begin{array}{c}\hfill B_1\hfill \\ \multicolumn{1}{c}{B_2}\\ \multicolumn{1}{c}{⋮}\\ \multicolumn{1}{c}{B_m}\end{array}\right\}=\left\{\begin{array}{c}\hfill A_1·R_1\hfill \\ \multicolumn{1}{c}{A_2·R_2}\\ \multicolumn{1}{c}{⋮}\\ \multicolumn{1}{c}{A_m·R_m}\end{array}\right\}$$

(22)

As mentioned above, B is the fuzzy comprehensive evaluation set which takes V as discourse domain. $b_p(p=1,2,\dots ,k)$ is the fuzzy comprehensive evaluation index. The equation for B is

$$\begin{array}{c}\multicolumn{1}{c}{B=A·R=(a_1,a{}_2,\dots ,a_m)\left\{\begin{array}{c}\hfill b_{21}\hfill \\ \multicolumn{1}{c}{⋮}\end{array}\begin{array}{c}\hfill b_{22}\hfill \\ \multicolumn{1}{c}{⋮}\end{array}\begin{array}{c}\hfill \cdots \hfill \\ \multicolumn{1}{c}{⋮}\end{array}\begin{array}{c}\hfill b_{2k}\hfill \\ \multicolumn{1}{c}{⋮}\end{array}\right\}}\\ \multicolumn{1}{c}{=(b_1,b_2,\dots ,b_k)}\end{array}$$

(23)

B is the result of one-level fuzzy comprehensive assessment model, $B=\{B1,B2,B3,B4\}$ . There are 10 models in two-level fuzzy comprehensive assessment and 32 models in three-level (see Table 3).

Table 3.

Weight results.

Overall layer	State layer	Index layer	Weight
Low carbon efficiency (B1, 0.0460)	Emission efficiency C1 (0.0499)	D1	0.0635
		D2	0.0472
		D3	0.0389
	Energy control C2 (0.0421)	D4	0.0538
		D5	0.0304
Energy innovation (B2, 0.0462)	Clean energy C3 (0.0411)	D6	0.0301
		D7	0.0521
	New technology C4 (0.0512)	D8	0.0442
		D9	0.0582
Low carbon guide (B3, 0.0267)	Infrastructure C5 (0.0359)	D10	0.0289
		D11	0.0429
	Government guide C6 (0.0375)	D12	0.0495
		D13	0.0350
		D14	0.0347
		D15	0.0412
		D16	0.0272
	Industry guide C7 (0.0251)	D17	0.0269
		D18	0.0400
		D19	0.0198
	Life guide C8 (0.0174)	D20	0.0196
		D21	0.0194
		D22	0.0128
		D23	0.0255
		D24	0.0189
		D25	0.0125
Low carbon society (B4, 0.0171)	Low carbon life C9 (0.0194)	D26	0.0124
		D27	0.0123
		D28	0.0243
		D29	0.0241
		D30	0.0239
	Low carbon environment C10 (0.0148)	D31	0.0119
		D32	0.0176

A total of 15 experts were selected from different scientific and practical fields, including the managers of transportation and logistics companies, experts from the local government, specialists in carbon emission, and professors in environment and energy consumption. The selection was in charged by Transport of Changsha Municipality. The measurement items were taken in a 5-point Likert type scale with ‘5’ being ‘Excellent’ and ‘1’ being ‘Very bad’. In order to improve the quality of the investigation, data were collected through the issuance of paper questionnaire investigation from 1 October to 30 October 2014.

The index evaluations from 15 experts are presented in Table 4. The indices are divided into five degrees from excellent to very bad (100, 80, 60, 40, 20; see Table 4).

Table 4.

Results from experts.

Scores	Excellent (100)	Good (80)	Qualified (60)	Bad (40)	Very bad (20)
D1	6	7	2	0	0
D2	4	7	4	0	0
D3	7	8	0	0	0
D4	5	10	0	0	0
D5	7	5	3	0	0
D6	2	5	6	2	0
D7	7	7	1	0	0
D8	6	5	4	0	0
D9	4	11	0	0	0
D10	4	8	3	0	0
D11	5	7	3	0	0
D12	3	6	4	2	0
D13	2	10	2	1	0
D14	5	5	4	1	0
D15	0	9	4	2	0
D16	7	8	0	0	0
D17	6	9	0	0	0
D18	5	9	1	0	0
D19	4	8	3	0	0
D20	2	11	2	0	0
D21	6	4	5	0	0
D22	3	8	4	0	0
D23	5	4	4	2	0
D24	7	6	2	0	0
D25	3	9	2	1	0
D26	7	8	0	0	0
D27	5	8	2	0	0
D28	4	11	0	0	0
D29	7	6	2	0	0
D30	5	8	1	1	0
D31	4	4	5	2	0
D32	7	6	2	0	0

Finally, the membership of low carbon efficiency $(B1)$ is given in Table 5.

Table 5.

Membership of low carbon efficiency (B1).

Scores	Excellent (100)	Good (80)	Qualified (60)	Bad (40)	Very bad (20)
Evaluation	0.314	0.600	0.082	0.012	0

The membership of energy innovation $(B2)$ is given in Table 6.

Table 6.

Membership of energy innovation (B2).

Scores	Excellent (100)	Good (80)	Qualified (60)	Bad (40)	Very bad (20)
Evaluation	0.260	0.614	0.110	0.016	0

The membership of low carbon guide $(B3)$ is given in Table 7.

Table 7.

Membership of low carbon guide (B3).

Scores	Excellent (100)	Good (80)	Qualified (60)	Bad (40)	Very bad (20)
Evaluation	0.282	0.609	0.065	0.043	0

The membership of low carbon society $(B4)$ is given in Table 8.

Table 8.

Membership of low carbon society (B4).

Scores	Excellent (100)	Good (80)	Qualified (60)	Bad (40)	Very bad (20)
Evaluation	0.312	0.576	0.068	0.044	0

The membership of comprehensive evaluation is given in Table 9.

Table 9.

Membership of comprehensive evaluation.

Scores	Excellent (100)	Good (80)	Qualified (60)	Bad (40)	Very bad (20)
Evaluation	0.284	0.614	0.082	0.019	0

Results

The maximum membership is good (80). Under the Two-Oriented Society Strategy, Changsha was devoted much time and effort to low carbon transport construction. Public transport has been greatly improved in Changsha City. However, further improvement is still required. From the results of Tables 5–9, the evaluation scores of low carbon society $(B4)$ are the lowest. It indicates that the low carbon life has not been popularized in Changsha City. Public buses are the main public transport mode in Chinese cities; they are losing their share gradually in the total travel demand for a range of reasons but most importantly, the rapidly growing private cars are slowing them down. From the statistical data of Hunan provincial transportation statistical yearbook, there are nearly 2 million cars in Changsha City. The increase in private cars in Changsha gives great pressure to its low carbon transport system establishment. Under such serious condition, 10 projects have been under construction, namely, station construction, road network construction, urban rail transit construction, BRT, urban conventional bus transportation, intelligent vehicle–highway system, low carbon bus, urban and rural public transport reform, traffic demand management (TDM) and knight service in order to improve the low carbon transport system in Changsha City.

From Tables 4 and 7, the evaluation scores from the low carbon guide $(B_3)$ are good, but the scores for index $D15$ are low. No one gave excellent and two experts gave bad level. It shows that the low carbon strategy in transportation industry is not good. From the view point of supporting system and evaluation system of low carbon transport, the government is the guide and performer in the construction of low carbon transport. The government should promote the industry’s reform for low carbon production. The transportation industry is the performer and participant in the construction of low carbon transport and plays a positive promoting role in the low carbon transport supporting system. The industry should support the technology reform in transportation industry and promote the construction of innovation platform of low carbon technology in the transportation industries. However, the technology reform has not been properly carried out. It is difficult for the traditional transportation industries to accept the low carbon technology and innovation because of the investment in low carbon technology and innovation. It is suggested that the government should carry out low carbon strategy to support the traditional transportation industry in low carbon innovation.

Conclusion

The problems of transportation energy consumption and environment are the major challenges faced globally and are especially serious in China. The future 20 years is the strategic opportunity period of the transition for Chinese transportation energy and carbon emission reduction.

The four supporting systems of low carbon transport, namely, the supporting system of industry supporting, infrastructure, life and policy, are proposed in this study. Moreover, the evaluation system of low carbon transport from four aspects such as the efficiency, innovation, guide and society of low carbon is established. On the basis of the two systems, a numerical study in Changsha City in Hunan Province is provided to support the supporting and evaluation systems.

From the view of the two systems above, the government should increase the public transport investment, promote reform for low carbon industries and modify the low carbon travel mode. Chinese government plays more and more important role in low carbon policy system under the state of environmental degradation. More environmental-transport policies should be carried out by the Chinese government.

Transportation industry is the performer and participant in the construction of low carbon transport and plays more and more important role in the low carbon transportation supporting system. It should support the technique innovation reform in transportation industries, promote the construction of innovation platform of low carbon techniques in the transportation industries and make the idea of ‘low carbon’ practice in transportation area to achieve the development of whole industry chain.

City dwellers are the main participants in the construction of low carbon transport. However, the realistic behaviour of city dwellers is often not well captured by the government. From the numerical example, the evaluation scores of low carbon society (B4) are the lowest. It is advised that their travel behaviour mode should be modified from ‘high carbon’ to ‘low carbon’ or even ‘zero carbon’. In a word, all the people should be involved in the construction and development of low carbon transport system in China.