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This study aims to assess and decompose the sustainable development using the 17 sustainable development goals (SDGs) in Iran in 2018, for proposing agenda-setting of public policy.

It ranks the SDGs not only in Iran but also in the region and the world to reveal the synergetic effects.

Based on the results, subaltern-populace generally suffers from the hegemonic domination of ruling elite-bourgeois, lack of strong institutions, heterogeneous policy networks and lack of advocacy role of non-governmental organizations, due to no transparency, issues in law or no rule of law, no stringent regulation, rent, suppression and Mafia, all leading to corruption and injustice.

To stop the loop of corruption-injustice, Iran should homogenize the structure of the policy network. Furthermore, the failed SDGs of the three-geographic analysis are the same in a character; all of them propose SDG 3, good health and well-being as a serious failed goal.

In this regard, strong evidence is the pandemic Coronavirus, COVID 19 since 2019, due to its highly-disastrous consequences in early 2020 where the public policymakers could not adopt policies promptly in the glob, particularly in Iran.

In Iran, in addition to this, the malfunction of health is rooted in “subjective well-being” and “traffic deaths,” respectively. Concerning the transportations system in Iran, it is underscored that it is damaging the sustainable development from all the three pillars of sustainable development including, economic, social and environmental.

Destinations to be able to compete with each other need to equip themselves with as many competitive advantages as possible. Tourists' loyalty to a destination is considered as a prominent competitive tool for destinations. Tourists' loyalty manifests itself in recommendation of the destination to others, repeat visit of the destination and willingness to revisit the destination. Although a plethora of studies have tried to define models to show the relation between loyalty and the antecedent factors leading up to it, few of them have tried to integrate these models with mathematical approaches for better understanding of loyalty behavior. The purpose of this paper is to integrate a tourist destination model with Bayesian Network in order to predict the behaviour of destination loyalty and its antecedent factors.

This paper has developed a probability model by the integration of a destination loyalty model with a Bayesian network (BN) which enables to predict and analyze the behavior of loyalty and its influential factors. To demonstrate the application of this framework, Tehran, the capital of Iran, was chosen as a destination case study.

The outcome of this research will assist in identifying the weak key points in the tourist destination area for giving insights to the marketers, businesses and policy makers for making better decisions related to destination loyalty. In the analysis process, the most influential factors were recognized as the travel environment image, natural/historical attractions and, with a lower degree, infrastructure image which help the decision maker to detect and reinforce the weak factors and put more effort in focusing on improving the necessary parts rather than the irrelevant parts.

The research identified all critical factors that have the most influence on destination loyalty while driving the associate uncertainty which is significant for the tourism industry. This resulted in better decision-making which is used to identify the impact of tourism destination loyalty.