The rising carbon dioxide emissions from the MENA region constitute a severe danger to the environment, public health, and the execution of the United Nations SDGs. Substantial steps are required to solve this problem and maintain the region’s sustainable future. Hence, the current study focused on distinct factors, including renewable energy, energy intensity, green innovation, GDP, and CO₂ emissions from 1990 to 2021. The research determines the multifarious variables in various quantiles, including the novel Method of Moments Quantile Regression (MMQR) approach, Fully Modified Ordinary Least Square (FM-OLS), Dynamic Ordinary Least Square (D-OLS) and Driscoll-Kraay Standard Errors (DKS) applied. The findings reveal that renewable energy significantly reduces carbon emissions in all quantiles, while energy intensity, green innovation, and GDP lead to carbon emissions in lower, middle, and upper quantiles. For robust outcome confirmed by FM-OLS, D-OLS, and DKS methods. Also, Granger heterogeneous causality applied that confirmed the bidirectional causality among the variables. The study’s findings imply that authorities should emphasize the emergence of renewable energy and green innovation while adopting energy-efficient technologies to minimize carbon emissions and accomplish SDGs 7, 9, and 13 to secure the MENA region.

This paper presents a modified formulation for the wind-battery-thermal unit commitment problem that combines battery energy storage systems with thermal units to compensate for the power dispatch gap caused by the intermittency of wind power generation. The uncertainty of wind power is described by a chance constraint to escape the probabilistic infeasibility generated by classical approximations of wind power. Furthermore, a mixed-integer linear programming algorithm was applied to solve the unit commitment problem. The uncertainty of wind power was classified as a sub-problem and separately computed from the master problem of the mixed-integer linear programming. The master problem tracked and minimized the overall operation cost of the entire model. To ensure a feasible and efficient solution, the formulation of the wind-battery-thermal unit commitment problem was designed to gather all system operating constraints. The solution to the optimization problem was procured on a personal computer using a general algebraic modeling system. To assess the performance of the proposed model, a simulation study based on the ten-unit power system test was applied. The effects of battery energy storage and wind power were deeply explored and investigated throughout various case studies.