• The framework utilizes a Fundamental and Econometric Analysis (FEA) approach, integrating econometric methodologies with fundamental drivers like policy, technology, and geopolitics for comprehensive commodity price benchmarks across short-, mid-, and long-term horizons, ensuring adaptability to evolving market dynamics.
• We are implementing a scenario-based benchmarking framework to analyze commodity price trajectories, anchored in three scenarios: CURP, reflecting minimal policy changes; CMIT, aligned with current energy and climate pledges; and GRNZ, envisioning a shift to net-zero emissions.
• The framework uses probability distributions, Bayesian tuning, and mean-reversion dynamics to simulate price trajectories, enabling trend analysis, risk assessment, and volatility evaluation across bearish, neutral, and bullish outcomes for short-, mid-, and long-term scenarios.
• The framework ensures accuracy and robustness by leveraging comprehensive driver selection, scenario-specific category weighting, and vetted historical data to maintain scientific rigor and deliver actionable insights for long-term commodity benchmarking and strategic planning in evolving energy markets.
• This methodology presents a rigorous, scenario-based methodology for benchmarking commodity prices within global energy markets.
• Developed to support strategic planning and critical decision-making, the probabilistic framework employs advanced techniques such as Bayesian inference, hyperparameter optimization, and mean-reversion modeling to deliver robust projections of price trajectories across diverse market conditions.
• Utilizing over 20 years of historical data and meticulously structured probability distributions, the model provides benchmarks over three distinct timeframes: short-term (1–5 years), mid-term (5–10 years), and long-term (10–35 years).
• Designed to meet the standards of econometric and financial modeling, this framework combines scientific rigor with practical applicability.
• While this document focuses on the conceptual and methodological underpinnings, supplemental technical documentation provides detailed statistical models, computational scripts, and data-handling protocols.

Two core modeling approaches were considered for this framework:

• Pure Econometric Analysis (PEA): Relies exclusively on historical data trends and econometric techniques without integrating fundamental market insights. While effective for short-term projections, PEA lacks the capacity to account for structural shifts driven by policy or technology.
• Fundamental and Econometric Analysis (FEA):  Combines econometric methodologies with fundamental market analysis, incorporating drivers such as policy developments, technological trends, and geopolitical shifts. FEA offers a holistic perspective, making it more suitable for long-term benchmarking.

The chosen approach, FEA, ensures that the benchmarks reflect both quantitative trends and qualitative insights. By integrating fundamental drivers with econometric rigor, FEA provides a balanced, adaptable framework that accounts for evolving market dynamics. This benchmark leverages the FEA methodology to deliver comprehensive and actionable insights.

The proposed framework adopts a systematic, multi-step approach that integrates data-driven probabilistic analysis with clearly defined market scenarios. Each scenario embodies a plausible future trajectory informed by geopolitical developments, policy dynamics, and technological trends.

The model defines three key market scenarios: Current Pathway (CURP), Commitment Trajectory (CMIT), and Green NetZero (GRNZ).

These scenarios encapsulate the nuanced drivers of energy market shifts, offering stakeholders a comprehensive understanding of potential outcomes, price trends, and underlying dynamics.

To ensure a robust representation of market conditions, the model establishes three foundational scenarios:

• Current Pathway (CURP): Assumes minimal policy changes and a sustained reliance on fossil fuels. Reflecting economic and geopolitical stability, this scenario aligns with a conservative outlook.
  
  
• Commitment Trajectory (CMIT): Envisions nations achieving current energy and climate pledges within established timelines. This scenario highlights incremental progress in renewable energy adoption and emissions reduction.
• Green NetZero (GRNZ): Projects an ambitious pathway toward net-zero emissions by mid-century, driven by widespread renewable energy integration, technological innovation, and structural transformation.

The selection of drivers for these scenarios is based on expert reviews and insights from organizations such as the International Energy Agency (IEA) and the U.S. Energy Information Administration (EIA), ensuring a credible and comprehensive analysis of market conditions. The analysis includes:

• Geopolitical drivers (e.g., trade alliances, political stability) dominate in CURP due to the reliance on traditional energy sources.
• Macroeconomic drivers (e.g., GDP growth, inflation) are pivotal in CMIT and GRNZ, where policy-driven investments reshape markets.
• Environmental drivers (e.g., emissions targets, renewable adoption) are most impactful in GRNZ, reflecting the accelerated shift toward decarbonization.

The model employs probability distributions to define bearish, neutral, and bullish outcomes across the short, mid, and long-term timeframes. Probabilities are calculated using established financial modeling principles, ensuring alignment with time-sensitive impacts and structural market shifts. Linear smoothing technique ensure seamless transitions between timeframes, preventing abrupt changes in scenario probabilities and aligning with methodologies in non-linear time series analysis.

To enhance accuracy, the model integrates Bayesian inference, enabling dynamic refinement of probabilities based on historical data and scenario assumptions. Key features include:

• Hyperparameter Optimization: Fine-tunes parameters such as volatility, sensitivity to external shocks, and mean-reversion strength.
• Mean-Reversion Dynamics: Captures cyclical tendencies in commodity markets, reflecting tendencies to revert to long-term averages.

For example, CURP exhibits pronounced mean reversion due to stable policies, while GRNZ shows weaker reversion dynamics, driven by transformative changes in technology and energy systems. Bayesian methodologies further enable the model to adapt to emerging trends, maintaining its relevance over time.

The framework simulates yearly price trajectories for each scenario by applying Bayesian-calibrated probabilities and mean-reversion adjustments. This process generates multiple outcomes (bearish, neutral, bullish), allowing for:

• Trend Analysis: Identifies high-probability trajectories within each scenario.
• Risk Assessment: Analyzes volatility and sensitivity to external shocks.

This stochastic approach mirrors techniques in financial risk management and offers stakeholders a nuanced understanding of potential market pathways.

Accuracy and Robustness

The model’s accuracy depends on comprehensive driver selection, category weighting, and historical data integrity. To achieve this:

• Driver Selection: Incorporates high-impact drivers identified through expert reviews from organizations such as the IEA and EIA, ensuring relevance and balance.
• Category Weighting: Adjusts weights to reflect scenario-specific dynamics, e.g., increased geopolitical weighting in CURP and environmental weighting in GRNZ.
• Data Integrity: Leverages over 20 years of vetted historical data from credible sources such as the International Energy Agency (IEA), World Bank, Intergovernmental Panel on Climate Change (IPCC)

By adhering to best practices in econometrics and ensuring a balanced structure, the model maintains scientific rigor and strategic applicability.

This probabilistic framework provides a scientifically validated tool for long-term commodity benchmarking in global energy markets. By integrating scenario definitions, Bayesian inference, and robust driver analysis, the model delivers actionable insights for strategic planning. Its balanced focus on accuracy, depth, and adaptability ensures a reliable foundation for navigating the complexities of an evolving energy landscape.