The Impact of the Transportation Efficiency on the Tourism

Eco-efficiency based on PVAR Model: A Case Study of the Yellow

River Basin

Qingsheng Zhang

School of Economics and Management, Beijing Jiaotong University, Beijing, China

Keywords: Tourism Eco-efficiency, Transportation Efficiency, PVAR Model, Yellow River Basin.

Abstract: Transportation and tourism are closely related, and transportation is an important factor that affects the tourism

eco-efficiency. Based on the transportation data and the tourism data of 9 provinces in the Yellow River Basin

from 2007 to 2019, we use the Super-SBM model and the Super-SBM model with undesirable output (Un-

Super-SBM model) to measure the transportation efficiency and the tourism eco-efficiency. We use the PVAR

model to analyse the panel data of the transportation efficiency and the tourism eco-efficiency, and discuss

the impact of the transportation efficiency on the tourism eco-efficiency. As a result, the transportation

efficiency and the tourism eco-efficiency of the whole Yellow River Basin are relatively high, but the

transportation efficiency and the tourism eco-efficiency of Inner Mongolia need to be improved. The

transportation efficiency has a positive impact on the tourism eco-efficiency, and the impact can reach its peak

in the short term, but the impact is long-term. The impact of the tourism eco-efficiency on the transportation

efficiency is not significant.

1 INTRODUCTION

In recent years, China’s tourism industry has

developed rapidly. According to the data from the

National Bureau of Statistics, the number of tourists

reached 6.072 billion in 2019, which has increased by

more than 6 times in 18 years. Although affected by

COVID-19 in 2020, the number of tourists has

reached 2.88 billion. With the large-scale

development of tourism activities, environmental

pollution and resource consumption caused by

tourism activities have also received extensive

attention (Azam, et al., 2018). How to realize the

coordinated development of ecological environment

protection and tourism has become a research hot

spot. Therefore, the concept of the tourism eco-

efficiency has gradually formed. The tourism eco-

efficiency’s focus is the integration of tourism,

ecology, and efficiency. It not only considers resource

energy consumption and environmental pollution, but

also measures the importance of the tourism

economic output. The tourism eco-efficiency is often

described as a variable in the relationship between the

https://orcid.org/0000-0001-5640-0570

tourism input and output. While the economic output

of tourism and the value of services increase, the

carbon emissions are reduced during the tourism

process. There are many measurement methods of the

tourism eco-efficiency, mainly including the ratio

method, the index system method, and the data

envelopment analysis (DEA) method. In recent years,

the DEA method have been widely used in the

measurement of the tourism eco-efficiency. The Un-

Super-SBM model is one of the most common DEA

models used by researchers.

There are many factors influencing tourism eco-

efficiency, and many researchers have conducted a lot

of discussions on both the macro and micro

perspectives. Looking at the existing literature on

tourism eco-efficiency research, it is found that

transportation has always been an important factor

influencing the tourism eco-efficiency. For example,

Gossling and Yao believe that the mode of

transportation is one of the main factors affecting the

tourism eco-efficiency (Gossling, et al., 2005), (Yao,

Chen, 2015). However, there are few papers that can

clearly explain the specific extent of the impact of

Zhang, Q.

The Impact of the Transportation Efﬁciency on the Tourism Eco-efﬁciency based on PVAR Model: A Case Study of the Yellow River Basin.

DOI: 10.5220/0011149400003437

In Proceedings of the 1st International Conference on Public Management and Big Data Analysis (PMBDA 2021), pages 16-21

ISBN: 978-989-758-589-0

transportation on the tourism eco-efficiency. The

transportation efficiency is an index to evaluate the

comprehensive transportation system of a region, and

the most common method to measure the

transportation efficiency is the DEA method.

The Yellow River Basin is a key area for

ecological protection in China. In 2019, President Xi

proposed a major national strategy for ecological

protection and high-quality development in the

Yellow River Basin. The green and high-quality

development of the Yellow River Basin is one of

China's important tasks in the future. A planning for

the construction of the Yellow River National

Cultural Park in 2020 is proposed. With the

advancement of the construction of the National

Cultural Park, the tourism industry in the Yellow

River Basin will develop rapidly. The transportation

infrastructure of the Yellow River Basin has been

greatly improved in recent years. Therefore, this

paper takes 9 provinces in the Yellow River Basin as

case sites, and calculates the transportation efficiency

and the tourism eco-efficiency based on the

transportation data and the tourism data from 2007 to

2019 to form the panel data, and uses the panel vector

autoregression (PVAR) model to study the impact of

the transportation efficiency on the tourism eco-

efficiency.

2 DATA AND METHODOLOGY

2.1 The Transportation Efficiency and

the Tourism Eco-efficiency

In order to study the impact of the transportation

efficiency on the tourism eco-efficiency in the Yellow

River Basin, we choose 9 provinces’ data in the

Yellow River Basin from 2007 to 2019, and calculate

the transportation efficiency and the tourism eco-

efficiency of each province by the SBM model. The

data in this article mainly come from the official

website of the National Bureau of Statistics

(http://www.stats.gov.cn/), Year Book of China

Transportation & Communication, Yearbook of

China Tourism Statistic, Yearbook of China Tourism,

and the yearbooks of 9 provinces in the Yellow River

Basin.

The measurement of the transportation efficiency

has not yet formed a unified index system. The impact

of transportation on tourism is mainly reflected in the

passenger transportation. So we mainly consider four

transportation modes: railway, highway, waterway,

and aviation. Therefore, we choose the railway

operating mileage, the highway line mileage, the

inland waterway mileage, the aircraft take-off and

landing sorties, and the number of employees in the

four modes as input indicators. And the passenger

volume and passenger turnover of the four modes of

transportation as output indicators. We use the Super-

SMB model to measure the transportation efficiency.

The SBM model is a data envelopment analysis

(DEA) method proposed by Tone, which overcomes

the shortcomings of ordinary DEA models that cannot

effectively deal with slack variables (Tone, 2001).

Tone put forward the Super-SMB model based on the

SBM model to deal with the effective decision-

making unit (DMU) in 2002 (Tone, 2002). We use X

to represent the input vector and Y to represent the

output vector. m and s represent the number of input

variables and output variables, respectively. We

consider n DMUs and define the matrices X, Y as

follows:

[

]

, ,...,

Xxx x R

=∈

(1)

[

]

, ,...,

Yyy y R

=∈

(2)

The equation of the Super-SBM model to

calculate DMU

y（

is as follows:

1, 0

1/ ( / )

min

1/ ( / )

. . ,

,0 , 0

ijj

kjj

ii k kj

mxx

st x x

xx y y

=≠

≥

≤

≥≤≤ ≥



(3)

where

is the intensity vector and

is the

efficiency value of the DMU.

At present, researchers prefer to use the Un-

Super-SBM model to measure the tourism eco-

efficiency. Based on previous papers, we take the

number of tourism companies (including the number

of star-rated hotels, the number of travel agencies, and

the number of A-level scenic spots), the number of

employees in the tourism industry, and the investment

value of fixed assets in the tourism industry as input

indicators. The number of tourists and the tourism

income are used as desirable output indicators. The

tourism carbon emissions are used as undesired

output. And the tourism carbon emissions refer to the

measurement method of Zha et al. (Zha, et al., 2020)

The Un-Super-SBM model adds undesired output

based on the Super-SBM model. We use X (1), Y (2)

and Z (4) to represent the input vector, the desirable

The Impact of the Transportation Efﬁciency on the Tourism Eco-efﬁciency based on PVAR Model: A Case Study of the Yellow River Basin

output vector, and the undesirable output vector. The

equation of the Un-Super-SBM model to calculate

DMU

000

,,)

yz（

is (5):

( , ,..., )

zz z R

=∈

(4)

1, 0

*= min

1( )

. . ,

0, 0, 0, 0

ijji

kjjk

ljjl

xyz

ikl j

st x x s

yys

zzs

sss

=≠

−+

≥−

≤+

≥−

≥≥≥≥







(5)

where m, s, and w are the number of input variables,

desirable outputs variables, and undesirable output

variables. And

is the efficiency value of the

DMU.

, and

represent the slacks in input,

desirable output, and undesirable output.

We use the software called MaxDEA Ultra to

calculate the Super-SBM model and the Un-Super-

SBM model. In the calculation process below, the

transportation efficiency is abbreviated as TRE and

the tourism eco-efficiency is abbreviated as TOEE.

2.2 Panel Vector Autoregression Model

In order to explore the impact of the transportation

efficiency on the tourism eco-efficiency in the Yellow

River Basin, the panel vector autoregression (PVAR)

model established in this paper is as follows:

,1,2,,

it j it j i t it

YY iT

λγ αβε

−

=+ +++ =



…

(6)

(, )

it it it

Y TRE TOEE=

(7)

where

is a two-dimensional column vector. TRE

and TOEE respectively represent the transportation

efficiency and the tourism eco-efficiency. The k

represents the lag order.

and

respectively

represent fixed effect and time effect.

represents

the matrix of lag period coefficients to be estimated.

represents the 2×1 order intercept term vector.

represents the random disturbance term vector. To

avoid heteroscedasticity, we take the logarithm of

both TRE and TOEE, namely lnTRE and lnTOEE.

Table 1: The results of the transportation efficiency.

Year SX IM SD HN SC SH GS QH NX

2007 1.31 1.14 1.85 1.57 1.83 1.76 1.27 1.13 1.82

2008 1.37 1.07 2.17 3.60 1.94 1.64 1.45 1.79 1.91

2009 1.21 1.07 2.12 1.57 1.75 2.09 1.28 1.87 1.90

2010 1.27 1.06 2.07 1.64 1.61 1.90 1.27 1.92 2.10

2011 1.28 1.04 1.89 1.33 1.75 2.59 1.33 1.99 2.06

2012 1.24 1.16 1.88 1.45 1.52 1.90 1.44 2.27 1.96

2013 1.27 1.25 2.03 1.48 1.62 1.95 1.45 3.19 2.31

2014 1.26 1.25 1.60 1.42 1.61 1.84 1.36 1.34 2.28

2015 1.26 1.22 1.58 1.47 1.80 1.93 1.40 2.64 2.34

2016 1.26 1.37 1.61 1.47 1.47 1.86 1.31 1.22 2.48

2017 1.25 1.33 1.62 1.61 1.39 1.78 1.42 1.18 2.50

2018 1.25 1.34 1.60 1.52 1.57 1.68 1.25 1.20 2.33

2019 1.29 1.07 1.63 1.47 1.68 2.13 1.28 2.78 2.40

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Table 2: The results of the tourism efficiency.

Year SX IM SD HN SC SH GS QH NX

2007 1.06 0.54 1.13 1.41 1.12 0.64 0.32 1.12 1.35

2008 1.08 0.52 1.15 1.68 1.05 0.60 0.30 1.48 1.32

2009 1.10 0.58 1.15 1.44 1.07 0.82 0.32 1.70 1.33

2010 1.06 0.53 1.21 1.32 1.16 0.81 0.40 1.64 1.37

2011 1.10 0.53 1.20 1.21 1.15 0.95 0.52 1.58 1.27

2012 1.07 0.45 1.15 1.08 1.23 1.05 0.51 1.60 1.24

2013 1.07 0.44 1.13 1.19 1.19 1.04 0.59 1.66 1.32

2014 1.11 0.50 1.10 1.23 1.28 1.06 0.69 1.52 1.30

2015 1.28 0.41 1.08 1.18 1.30 1.01 0.61 1.33 1.44

2016 1.36 0.48 1.05 1.05 1.30 1.04 0.57 1.23 1.27

2017 1.45 0.38 1.09 1.05 1.13 1.06 0.73 1.14 1.49

2018 1.36 0.41 1.10 1.03 1.07 1.07 0.72 1.06 1.67

2019 1.38 0.44 1.06 1.04 1.06 1.05 1.06 1.13 1.54

3 RESULTS

3.1 The Results of the Transportation

Efficiency and the Tourism

Eco-efficiency

The Super-SBM model and the Un-Super-SBM

model are used to evaluate the transportation

efficiency and the tourism eco-efficiency of 9

provinces in the Yellow River Basin. The results are

shown in Table 1 and Table 2.

It can be found from Table 1 that each

transportation efficiency in the Yellow River Basin is

greater than 1.00, indicating that the overall

transportation efficiency of the Yellow River Basin is

relatively high. The transportation efficiency of

Shandong (SD), Shaanxi (SH), Ningxia (NX) and

Sichuan (SC) is higher than that of other provinces.

Inner Mongolia (IM), Gansu (GS) and Shanxi (SX)

have lower transportation efficiency. Qinghai’s (QH)

transportation efficiency is unstable and fluctuates

greatly.

It can be found from Table 2 that, except for Inner

Mongolia, Shaanxi and Gansu, the tourism eco-

efficiency in other provinces is relatively higher

(greater than 1.00). However, the tourism eco-

efficiency in Shaanxi and Gansu has shown an

upward trend, and has exceeded 1.00 in recent years.

The tourism eco-efficiency in Henan (HN) and Inner

Mongolia has a downward trend.

3.2 Empirical Analysis of PVAR Model

3.2.1 Stationarity Test

In order to prevent the spurious regression caused by

non-stationary variables and ensure the validity of the

estimation results, we use LLC test to test the

stationarity of lnTOEE and lnTRE. The results are

shown in Table 3. The results show that both series

are stationary.

Table 3: The results of LLC test.

T Statistics P-value

lnTRE -6.6648

***

0.0000

*** stands for the significance level of 1%.

3.2.2 Determining the Optimal Lag Order

We choose the maximum lag order at 3 and use three

information criteria (AIC, BIC and HQIC) to

determine the optimal lag order. The calculation

results of the information criteria are shown in Table

4. According to the results of the three information

criteria, the optimal lag order is selected as 3.

Table 4: The results of the optimal lag order.

Lag AIC BIC HQIC

1 -2.37741 -1.80072 -2.14408

2 -2.87812 -2.15595

-2.5869

3 -2.94468

-2.05785 -2.58887

* stands for the optimal lag order determined by the AIC,

BIC and HQIC information criteria.

The Impact of the Transportation Efﬁciency on the Tourism Eco-efﬁciency based on PVAR Model: A Case Study of the Yellow River Basin

3.2.3 Granger Causality Test

We use the optimal lag order to perform Granger

Causality Test on lnTOEE and lnTRE. The results

shown in Table 5 suggest that lnTRE is the granger

cause of lnTOEE, but lnTOEE is not the granger

cause of lnTRE. It shows that the transportation

efficiency can affect the tourism eco-efficiency.

Table 5: The results of granger causality test.

Equation Excluded Chi2 P-value

h_lnTOEE h_lnTRE 10.2480

0.017

h_lnTRE h_lnTOEE 2.0791 0.556

** stands for the significance level of 5%.

3.2.4 Impulse Response Function

The impulse response function (IRF) can analyse the

impact of an endogenous variable on other variables,

that is, how current value and future value of the

variable will be affected when the model is impacted

or the random error term changes. There are four

response graphs of lnTOEE and lnTRE, including

response graphs of these two variables to itself and

the mutual response graphs of them. According to the

results of the Granger Causality Test, we mainly

analyse the IRF of lnTOEE to lnTRE. Figure 1 is the

graph of IRF of lnTOEE to lnTRE. lnTOEE has a

positive response to the impact of lnTRE. After being

impacted by lnTRE by one standard deviation,

lnTOEE reaches its peak in the first period, and then

gradually decreases. And it lasts a long time.

Figure 1: The impulse response function.

3.2.5 Variance Decomposition

We use variance decomposition to measure the

proportion of lnTOEE impacted by lnTRE (the

variance contribution rate of lnTRE to lnTOEE) to

further explore the impact of the transportation

efficiency on the tourism eco-efficiency. Figure 2 is

the graph of the variance decomposition results for 20

forecast periods. In the first forecast period, lnTOEE

is not affected by lnTRE. In the second prediction

period, the variance contribution rate of lnTRE to

lnTOEE increases rapidly to 9.1%. And then the

growth rate gradually slows down. The variance

contribution rate reaches the maximum value of

13.5% in the fifth period, and remains until the

seventh forecast period, after which the variance

contribution rate falls to 13.4% in the eighth period

and remains unchanged for a long time. It shows that

the transportation efficiency can affect the tourism

eco-efficiency, and this impact will exist for a long

time.

Figure 2: The results of the variance decomposition.

4 CONCLUSIONS

Based on the transportation data and the tourism data

of 9 provinces in the Yellow River Basin from 2007

to 2019, we use Super-SBM model and Un-Super-

SBM model to measure the transportation efficiency

and the tourism eco-efficiency. The PVAR model is

used to explore the impact of the transportation

efficiency on the tourism eco-efficiency.

Except for Inner Mongolia, the transportation

efficiency and the tourism eco-efficiency of the other

provinces in the Yellow River Basin are at a higher

level. The transportation efficiency and the tourism

eco-efficiency of Inner Mongolia need to be

improved.

From 2007 to 2019, the transportation efficiency

of the 9 provinces in the Yellow River Basin has a

positive impact on the tourism eco-efficiency, but the

tourism eco-efficiency has no significant impact on

the transportation efficiency. The impact of the

transportation efficiency on the tourism eco-

efficiency reaches the peak (13.5%) in the fifth

forecast period, but drops to 13.4% after maintaining

three forecast periods and remains unchanged for a

long time. The impact of the transportation efficiency

on the tourism eco-efficiency can be seen in the short

term, but the impact is long-term.

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The Impact of the Transportation Efﬁciency on the Tourism Eco-efﬁciency based on PVAR Model: A Case Study of the Yellow River Basin