**4. Discussion**

System thinking is an appropriate approach to study energy sustainability policies. Probabilistic and mathematical modeling enables a formal realization of energy sustainability dynamics.

Applying Bayesian Belief Networks to Saudi Arabia's context quantified the probability of improvement, decline, and steadiness of the Business as Usual scenario pertaining to the identified energy sustainability dimensions. A similar method was used by Daim et al. to support policy design in the state of Oregon, USA. It differs from the method in this paper in that the BBNs have been constructed by conversion from Causal Maps, which were developed by consulting experts and energy authorities in Oregon. The step of generating the Causal Maps is analogous to the adoption of the WETI indicators (causes) and dimensions (effects) in this work. The results offered networks with probabilities like those shown in Section 3, Figures 3–6, describing the different states of the correlated factors [29].

The BBN application presented in [28] has sought a higher accuracy in the construction of the BBN by applying an augmented naive model to the quantitative data and k-folds analysis of the Bayesian models. The researchers examined the best scenarios to inform policymaking in Italy and Germany concerning geothermal energy and hydro energy. Another study aimed at computing probabilities of power system states to enable renewable energy integration into smart grids and improve power flow control. The approach addressed a more sophisticated issue of real-time modeling of power systems [31].

The scope of the previous studies was limited to exploring the scenarios of nuclear energy, renewable energy, and investments in different contexts. The contribution of this work is the use of BBN to model the entire energy sustainability system covering energy security, energy equity, and environmental sustainability.

This research was performed before the release of 2020 WETI. Saudi Arabia's 2020 profile shows slight changes compared to 2019 scores: From 55 to 59.9 in energy security, from 98 to 99 in energy equity, and from 35 to 44.3 in environmental sustainability [45]. The change extent supports the findings of the research depicted in Figure 3 and described in Section 3 concerning the energy policy Business as Usual scenario.

For the 2030 policies, the study provided a tool to devise numerous states of each policy and evaluate the propagation of their impact to assist in identifying the critical engagements to achieve energy sustainability. For instance, CO2 per capita and energy intensity are interlinked with the parent variables being energy efficiency and energy equity, and the impact of the latter was mainly due to the fluctuations in the electricity prices. Therefore, the joint impact of energy efficiency and energy prices reform needs equal consideration to that given to energy resources diversification.

BBN is a useful decision support tool that can give insights and an improved understanding of the policy context and allows the examination of several alternatives. The sensitivity analysis that measures the interdependencies between the BBN nodes gives specific information that the policymakers can use to plan the desired adjustments for improved sustainability.

For enhanced accuracy in analyzing the new energy policies, a more complex BBN comprising the 32 indicators of the WETI and their causes is suggested. The most influential indicators identified in the suggested complex BBN can be further investigated to disclose the required finer interventions. Moreover, other system dynamic methods can be applied to attain different perceptions of interdependence.

The projected data can also be reinforced by experts' judgment and stakeholder consultations to assist the policymakers in optimizing the options. This type of qualitative data can be used in BBN following methods like those described in [16,46] (pp. 11–12).

**Author Contributions:** Conceptualization, M.S.H.M., A.A. and A.Y.S.; sata curation, M.S.H.M.; investigation, M.S.H.M.; methodology, M.S.H.M.; project administration, A.A.; supervision, M.S.H.M.; validation, M.S.H.M.; writing—original draft, M.S.H.M.; writing—review & editing, M.S.H.M. and A.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant No. (J: 65-135-1441). The authors, therefore, acknowledge with thanks DSR for technical and financial support.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data set available on request to corresponding authors.

**Conflicts of Interest:** The authors declare no conflict of interest.
