Saudi Arabia has announced plans to cut greenhouse gas (GHG) emissions by 278 million tons of CO2 equivalent (CO2e). In this regard, this paper contributes a modeling view of the climate-related effects of various energy policies on the Saudi energy system. The baseline entails the continued progression of current domestic policies without energy price reform. We examine two main alternative scenarios: The announced policies scenario (APS) incorporates some of the plans that the Saudi government has announced. In the other scenario, we run the baseline scenario with a cap on total CO2 emissions equal to those displayed in the announced policies.

The prevailing perception is that the transition to renewable energy ­will lead to a decrease in demand for hydrocarbons and petrochemicals. The pre­sent work empirically demonstrates that this common sense may be more imaginary than real: it finds evidence that the renewable energy development may have a positive impact on hydrocarbons through its growing demand for petrochemicals.

Transparent and evidence-based representations of global crude oil refining systems remain limited in the public literature, constraining robust energy systems modeling and policy analysis. This study develops a comprehensive, configuration-based modeling framework for all operating crude oil refineries worldwide using plant-level process unit data. Forty unique refinery configurations are identified through an unsupervised decision tree-based clustering approach that accounts for process unit presence and relative conversion intensity. An extremely randomized trees (ETR) machine learning model is trained on approximately 11,000 refinery-year observations to predict refined product yields as a function of refinery configuration, capacity, and crude oil diet. The model achieves out-of-sample coefficients of determination exceeding 0.90 for all major products and outperforms multiple linear regression and other ensemble methods. The predictive model is integrated with a differential evolution optimization algorithm to enable refinery programming under operational and feedstock constraints. The application of this model to Gulf Cooperation Council (GCC) refineries shows that, under existing technologies, petrochemical feedstock yields are bounded at approximately 37%, significantly below announced long-term diversification targets of 70–85%. Yield improvements of up to 6 percentage points are feasible through operational optimization but are associated with capacity utilization adjustments and product trade-offs. The framework provides a scalable tool for refinery benchmarking, energy transition analysis, and strategic planning across facility, national, and global levels.

The prevailing perception is that the transition to renewable energy will lead to a decrease in demand for hydrocarbons and petrochemicals. The present work empirically demonstrates that this common sense may be more imaginary than real: it finds evidence that renewable energy deployment may have a positive impact on hydrocarbons through its growing demand for petrochemicals. To obtain rigorous results, we employ state-of-the-art panel time-series methods that account for the main issues in panel data (e.g., cross-sectional dependence, non-stationarity, and heterogeneity) and provide insights for individual countries. Our key finding that renewables have a positive impact on petrochemical import demand is robust across various model specifications considered in the empirical analysis. Using the polymer intensities of the renewable energy systems and the estimated elasticities of demand for petrochemical imports, the global petrochemical polymer demand to meet projected renewable energy system penetration in a net-zero emissions scenario by 2050 was quantified to be in the range of 155 to 223 million metric tons (which translates to a growth in equivalent crude oil demand of 0.80 to 1.13 million barrels per day between 2025 and 2050), depending on the energy system design preferences. Import demand, which is an opportunity for petrochemical exporters, arising mainly from growth in the economies of India and China, accounts for between 67 and 96 million metric tons of polymer materials. The insights into policymaking in the petrochemical-importing economies are that their renewable transition will require more petrochemicals to be imported. The development of manufacturing capabilities for renewable energy technologies and high domestic petrochemical prices would further boost this import. Policymaking in petrochemical-exporting economies should consider that the findings may suggest opportunities to expand petrochemicals and related sectors, such as hydrocarbons.

Crude oil refineries produce finished petroleum products through a series of processing steps known as refinery unit operations. The varieties and capacities of process units, along with their operational characteristics, differentiate one refinery from another. Over the years, the refining industry has adopted various metrics to categorize refineries based on their capital investment and upgrading capability. These efforts have resulted in refinery complexity indicators such as the equivalent distillation capacity, the Nelson complexity index and the bottom of the barrel index. Although these metrics have helped quantify the refinery complexity and suitability for various crude oil feed qualities, they fail to provide relevant information for refinery modeling applications.

There is a dearth of public literature on reliable, evidence-based modeling representation of global crude oil refineries, this has led to the lack of transparency and consistency on how business and government policy processes are informed in many energy systems and energy transitions modeling scenarios. This paper uses actual field data to identify that 40 unique refining configurations account for the global refinery landscape. A machine learning algorithm training extremely randomized trees predictors is applied to develop a non-parametric production model of the refineries. To facilitate computational convenience and suitable deployments in diverse use cases, a multivariate linear regression model of the refineries is also presented. For all refined products captured, the machine learning model demonstrate superior predictive performance with coefficients of determination of over 90%, even though both models are useful. Other performance metrics are also assessed for both models.