BMF CP36: Predictors of people’s support for a policy focus on marine and coastal preservation


March 16, 2023

1. Project description

1.1. Main objectives

The current study has three objectives, which are to:

  1. Examine the impacts of socio-demographic factors on people’s support for a policy focus on marine and coastal preservation.
  2. Examine the impact of the national economic background on people’s support for a policy focus on marine and coastal preservation.
  3. Examine the perceived benefits of oceans on people’s support for a policy focus on marine and coastal preservation.

1.2. Materials

The mindsponge theory will be used for conceptual development, and Bayesian Mindsponge Framework (BMF) analytics will be used for statistical analysis on a dataset of 709 people from 42 countries [1-3]. The bayesvl R package, aided by the Markov chain Monte Carlo (MCMC) algorithm, will be employed for statistical analyses [4-6]. For more information on BMF analytics, portal users can refer to the following book [7]. Data and code snippets of this initial analysis were deposited at:

1.3. Main findings

The analysis shows the perceived importance of marine and coastal systems for human well-being and climate change reduction is positively associated with people’s support for ocean protection. People from upper-middle-income and high-income are more likely to support ocean protection than those from lower-middle-income and low-income countries. Other socio-demographic factors also predict people’s support for ocean protection (see Figure 1).

Figure 1. Some predictors’ coefficients

2. Collaboration procedure

Portal users should follow these steps for registering to participate in this research project:

  1. Create an account on the website (preferably using an institution email).
  2. Comment your name, affiliation, and your desired role in the project below this post.
  3. Patiently wait for the formal agreement on the project from the AISDL mentor.

If you have further inquiries, please contact us at

If you have been invited to join the project by an AISDL member, you are still encouraged to follow the above formal steps.

All the resources for conducting and writing the research manuscript will be distributed upon project participation.

AISDL mentor for this project: Minh-Hoang Nguyen.

AISDL members who have joined this project: Viet-Phuong La, Tam-Tri Le, Quan-Hoang Vuong.

The research project strictly adheres to scientific integrity standards, including authorship rights and obligations [8], without incurring an economic burden at participants’ expenses [9].


[1] Nguyen MH, La VP, Le TT, Vuong QH. (2022). Introduction to Bayesian Mindsponge Framework analytics: An innovative method for social and psychological research. MethodsX, 9, 101808.

[2] Vuong QH. (2023). Mindsponge Theory. De Gruyter.

[3] Fonseca C, et al. (2023). Survey data of public awareness on climate change and the value of marine and coastal ecosystems. Data in Brief, 47, 108924.

[4] Van Huu N, Hoang VQ, Ngoc TM. (2005). Central Limit Theorem for Functional of Jump Markov Processes. Vietnam Journal of Mathematics, 33(4), 443-461.

[5] Van Huu N, Hoang VQ. (2007). On the martingale representation theorem and on approximate hedging a contingent claim in the minimum deviation square criterion. In: R Jeltsch, TT Li, IH Sloan (Eds). Some Topics in Industrial and Applied Mathematics (pp. 134-151). Singapore: World Scientific.

[6] La VP, Vuong QH. (2019). bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with ‘Stan’. The Comprehensive R Archive Network.

[7] Vuong QH, Nguyen MH, La VP. (2022). The mindsponge and BMF analytics for innovative thinking in social sciences and humanities. De Gruyter.

[8] Vuong QH. (2018). The (ir)rational consideration of the cost of science in transition economies. Nature Human Behaviour, 2, 5.

[9] Vuong QH. (2020). Reform retractions to make them more transparent. Nature, 582, 149.