The empirical analysis of international climate policies for energy decisions

While the Paris Agreement enforces a radical transformation of energy systems to limit global temperature rise to below 1.5°C, market-based economic instruments, like the European Union Emissions Trading System (ETS) and the Carbon Border Adjustment Mechanism (CBAM), accelerates the internalization of carbon emission costs internationally. This pressure demands transparent, feasible, and comparable planning tools to safeguard supply security while controlling costs. However, significant gaps persist: data-intensive general equilibrium models fall short in responsiveness, and machine learning-based prediction models, as "black boxes", lack sufficient auditability and transparency for policymakers. This dissertation, comprising a complementary series of four papers, aims to fill the identified gap by developing a multi-layered hybrid methodology building on the global energy analysis covering 147 countries and extending it to national dynamics of Türkiye and electricity sector in detail. This study integrates computationally efficient partial equilibrium economics, multi-output machine learning, and optimization methods to provide a controllable trade-off between cost, carbon, and energy security under ETS/CBAM conditions across various carbon price scenarios. It proposes an innovative planning perspective that simultaneously tackles the carbon constraints of the Paris Agreement along with the goals of energy security and fiscal efficiency, through four consecutively structured papers spanning global, national, and sectoral scales. In the first paper, the partial equilibrium model replicates the fossil fuel share of 147 countries from 1997–2014 with an average absolute error of 0.28–0.34%, demonstrating that carbon tax-incentive packages align with the 2°C target without disrupting budget balance. The second paper models the governance update period (GUP) using multilevel machine learning, quantitatively determining an optimal policy cycle that further reduces the fossil share by 6 percentage points. The third paper, scaled to the national level, assesses Türkiye's electricity installed capacity for 2030 by comparing a parametric depreciation model with LSTM/GRU-based deep learning estimates, where the deep learning approach significantly reduces the normalized error rate in hydropower generation. The Green Growth scenario increases the renewable energy share from 56% to 79% while lowering carbon intensity to 160 kg CO₂/MWh. In the fourth paper, multiple output (MO) predictions are integrated with set-covering and two-stage grid-search optimization, reducing the total root mean square error (RMSE) to 0.587, boosting renewable energy capacity by 2.2 times compared to 2024, and enabling the grid-search method to achieve the lowest CO₂ emission profile. The collective findings from these four papers underscore the development of a multi-layered hybrid methodology that delivers three unique contributions: (i) it facilitates seamless integration of global modeling outputs with low computational overhead into national-level machine learning models; (ii) it directly models simultaneous relationships among diverse energy sources through its multi-output architecture; (iii) it employs optimization tools in bidirectional interaction with machine learning forecasts, enabling a holistic evaluation of decision-making, cost, and risk dimensions within a unified framework. Findings indicate that robust carbon pricing and data-driven capacity allocation, paired with a long-term planning perspective, can align carbon intensity with Paris Agreement targets while minimizing total system costs. Consequently, methodology developed in this dissertation equips policymakers with a scalable, auditable, and feasible decision-support tool within the ETS–CBAM environment.