This document summarizes a study that used Monte Carlo analytical hierarchy process (MCAHP) to rank factors influencing the environmental carrying capacity of coastal tourism in Dalian District, China. Survey data from 636 respondents was used to determine factor weights through traditional AHP and MCAHP. Both methods identified the top five factors as marine sights, water quality, air quality, crowding, and degree of development. However, MCAHP provided a more objective analysis by accounting for uncertainty in survey responses. The results suggest tourists prioritize natural sights like marine views and water quality for coastal tourism.

1. Application of Monte Carlo AHP in
ranking coastal tourism environmental
carrying capacity factors
Zhu J, Wang E, Sun W (2019)
Anisa Aulia Sabilah
C552190011

3. Outline
Introduction01
Methods02
Results03
Discussion04
Conclusion05

4. Introduction
01
03
05
04
02
Since the Reform and Opening up, the
tourism
industry is burgeoning into a new increase
point of economics in China. According to the
official statistics (NBS, 2015), the total
number of visitors was 400 million and the
total revenue of tourism industry is $60 billion
in 2015.In comparison with other sorts of tourism
resources, coastal tourism comprises
unique
natural features (sea, sand and island) and
creature features (marine plant and
animal), which are relatively more sensitive
and easily affected by human activities
leading to irreversible ecological damage
(Singh, 2015).
Nevertheless, in China there are
still a number of existing issues
in comparison with developed
countries, especially on the
development and improvement of
tourism environmental resources
(Butler, 2017).
Because of the ultimate goal of carrying
capacity management, it is essential to
establish a balanced relationship on
sustainable development between
economic benefits and natural resources
costs (Marsiglio, 2017).
Monte Carlo Simulation
approach is recommended to
compensate for the
shortcomings
of AHP (Yousefian & Monsef,
2011).

5. The propose to add Monte Carlo Simulation in
carrying capacity indicator analysis to form a
MCAHP model in order to conclude more
objective and accurate ranking results.
The aim

6. Study Area
Dalian District,
China.

7. Indicator Indentification

8. Survey schedule
800 respondents as actual ranking survey by random in coastal
parks and seaside scenic spots with the support from local
management department, and 636 questionnaires results are
valid.
A total of 377 (59.3%) respondents are female, and age
distribution is as follows: 35.5% aged 21–30 years old,
38.8% aged 31–40 years old, and others aged in the rest
age groups.
Around 70.3% of tourists hold a bachelor degree, and over
96% had at least a high school and an equivalent
education level. During the pilot survey, only 36.6% of
respondents are local residents.

9. Data analysis
in Table 3 that the overall Cronbach’s alpha coefficient
is 0.911, greater than 0.8. indicating the good reliability
of questionnaire and sample data.

10. Methodology
Traditional AHP Analyses
As mentioned above, the consistency
test of all the judgment matrices is
acceptable.
CR < 0.1

11. Methodology
Monte Carlo AHP
According to the estimations of survey,
it confirms the probability distribution
as the three points of estimations
(maximum, optimum and minimum) to
create triangular distribution of random
variables as follows.

12. Results
AHP

13. Results
Monte Carlo AHP

14. Results
MCAHP

15. Discussio
n
Both traditional AHP and Monte Carlo AHP
techniques retain the top five leading
alternatives regarding coastal tourism carrying
capacity, including Marine, Water, Air, Degree,
Crowding and etc.
The results identify that great attentions are
paid to “crowding”, because not only tourists
are in the pursuit of comfortable recreation
experience, but also the expenses need to be
guaranteed.
Quality of “marine sightseeing” and “seawater”
are the tourists’ prior considerations in the
coastal tourism, and this is in line with the
main purpose of tourists.

16. Conclusion
The analysis presented in this paper successfully
combines MCAHP (Monte Carlo Analytical
Hierarchy Process). Empirical results indicate
the newly developed method provides a better
framework for identifying the significance of the
concerned factors.

17. Thank you