Data-driven Bayesian Network Modeling for Measuring Sustainable Development Goals: A Case Study of Iran

نوع: Type: Thesis

مقطع: Segment: masters

عنوان: Title: Data-driven Bayesian Network Modeling for Measuring Sustainable Development Goals: A Case Study of Iran

ارائه دهنده: Provider: mandana tondkar

اساتید راهنما: Supervisors: Mr. Dr. Khodakarami

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Mr. Dr. Behnamian, Mr. Dr. Dezfulian

زمان و تاریخ ارائه: Time and date of presentation: 2026

مکان ارائه: Place of presentation: کلاس سمینار صنایع

چکیده: Abstract: The adoption of the Sustainable Development Goals as an agenda provides a set of comprehensive goals to simultaneously promote economic, social, and environmental dimensions. Since these goals operate in an integrated and interconnected manner, there is a need for frameworks to monitor and evaluate progress in achieving these goals that can combine conventional indicators with approaches to demonstrate the links and interdependencies between indicators. However, the multidimensional, contingent, and heterogeneous nature of the Sustainable Development Goals has made measuring and evaluating progress on these goals a methodological challenge, as their separate or single-dimensional assessment cannot provide an accurate picture of the true state of development. Therefore, in this research, in order to face this challenge, a data-driven approach based on Bayesian networks for measuring sustainable development goals has been presented. In this framework, by utilizing the data-driven Bayesian approach, an attempt has been made to systematically model the interdependencies between the goals and the three dimensions, the non-uniformity between the sub-indicators, and the uncertainty related to sustainability assessment, and finally, a comprehensive framework for measuring sustainable development goals in the form of a composite index, under the title of the Sustainable Development Goals, Composite Index was presented. The findings of this study indicate that the data-based Bayesian network approach has a high capability in managing the specific characteristics of sustainable development indicators and assessing their status at different levels and can be used as an effective tool in monitoring, prioritizing goals, and supporting the policy decision-making process in order to achieve sustainable development. Also, the results of implementing scenarios based on Iranian provincial data on the proposed model provide the possibility of identifying key goals and indicators affecting national sustainability and provide the basis for designing and implementing targeted policies to promote the country's sustainability.