Methodology

The Geopolitical Energy Shock Analyst (GESA) is a structured analytical framework designed to model the transmission of geopolitical energy supply disruptions into service-sector business performance. The model operates through a four-channel transmission mechanism that captures the primary pathways through which an oil price shock propagates from the global energy market into the operating metrics of a specific business.

The framework is scenario-based, constructing three tiers of disruption severity — Baseline, Moderate, and Severe — each calibrated against historical analogues and current market conditions. For each tier, the model computes the impact through four distinct channels, then aggregates the results into projected changes to key performance indicators (KPIs) including gross margin, EBITDA margin, revenue growth, operating expenditure, customer acquisition cost, and weighted average cost of capital (WACC).

The current analysis models the impact of a Strait of Hormuz escalation on US home service providers — a fleet-dependent, energy-intensive sub-sector of the services economy that includes HVAC, plumbing, electrical, landscaping, cleaning, and pest control businesses.

The GESA model decomposes the total impact of an energy shock into four distinct transmission channels, each with its own lag structure, elasticity parameters, and confidence level. This decomposition allows analysts to identify the dominant impact pathway and prioritize mitigation strategies accordingly.

The direct cost channel captures the immediate impact of rising energy prices on a firm's operating expenditure. For home service providers, this channel is dominated by fleet fuel costs — the single largest energy expense for businesses that dispatch 25+ service vehicles daily.

The model estimates fleet fuel consumption as 9% of total operating expenditure, split between diesel (60%) and gasoline (40%). Facility energy (electricity and natural gas for offices and warehouses) accounts for an additional 3% of OpEx. The pass-through rate from crude oil to retail fuel prices is calibrated using EIA retail price elasticity studies: retail gasoline typically reflects 60–65% of crude price changes, while diesel is more volatile at 65–75%.

Under the moderate scenario (+40% oil), fleet fuel costs increase by approximately 30% for diesel and 25% for gasoline, adding $123,840 to annual operating expenditure. Under the severe scenario (+100% oil), these increases reach 65% and 55% respectively, adding $268,320 annually — equivalent to the cost of hiring 3–4 additional technicians.

The supply-chain channel models how upstream energy cost inflation propagates through the input-output network of the economy, eventually reaching the cost of goods and materials purchased by home service providers. This includes HVAC equipment, plumbing fixtures, electrical components, copper wire, PVC pipe, refrigerants, and other materials that account for approximately 22% of revenue.

The model uses a Bureau of Economic Analysis (BEA) input-output multiplier of 1.4x for the services sector, combined with an upstream pass-through rate of 45% estimated from Producer Price Index (PPI) energy sub-index elasticity. This means that a 40% increase in oil prices eventually translates to approximately a 3.8% increase in materials costs, with a 1–4 month lag as manufacturers and distributors adjust their pricing.

The demand channel captures the indirect effect of energy price increases on consumer spending power and, consequently, on demand for home services. Rising fuel and energy costs reduce household real income, which leads consumers to defer or cancel discretionary home improvement projects while maintaining spending on emergency repairs.

The financial conditions channel models the second-order effect of energy-driven inflation on monetary policy and, consequently, on the cost of capital for businesses. When energy prices spike, headline inflation rises, potentially triggering Federal Reserve tightening (or preventing easing), which increases interest rates and the cost of debt.

For a typical home service provider with a debt-to-equity ratio of 0.8 and 40% variable-rate debt exposure, a 50 basis point increase in the federal funds rate (moderate scenario) translates to a 24 basis point increase in WACC. Under the severe scenario, a 125 basis point Fed tightening produces a 60 basis point WACC increase.

This channel has the longest lag (6–12 months) and the smallest immediate impact, but it compounds over time and can significantly affect business valuations, expansion plans, and access to credit — particularly for smaller operators who rely on variable-rate lines of credit.

The three scenario tiers are calibrated against historical oil supply disruptions and current market conditions. The baseline reflects the EIA Short-Term Energy Outlook futures curve as of February 2026. The moderate and severe tiers are constructed by applying supply reduction percentages to the global oil market and estimating the resulting price impact using historical elasticity data.

The sensitivity analysis allows users to explore how changes in key input parameters affect the model's output. Two primary sensitivity dimensions are provided:

Magnitude x Duration Matrix: This heat map shows the EBITDA margin impact (in basis points) for different combinations of oil price shock magnitude (20–120%) and disruption duration (3–18 months). The relationship is non-linear — larger shocks have disproportionately greater impact due to supply-chain amplification effects and demand destruction thresholds.

Pass-Through x Policy Lag Matrix: This heat map shows the revenue impact (in percentage points) for different combinations of upstream cost pass-through elasticity (0.3–0.9) and policy response lag (0–120 days). Faster policy responses (e.g., early SPR release) reduce the duration of peak prices, while lower pass-through rates indicate more effective cost absorption by upstream suppliers.

Every parameter in the GESA model is documented with its value, source, confidence level, and type classification. This transparency allows users to identify which assumptions drive the most uncertainty and where additional data could improve model accuracy.

The GESA model is a simplified representation of complex economic dynamics. Users should be aware of the following limitations:

• 01Static scenario assumption: The model assumes a constant level of disruption over the specified duration. In reality, conflicts escalate and de-escalate dynamically.
• 02Linear channel independence: The four transmission channels are modeled independently. In practice, they interact — e.g., demand compression reduces the pass-through rate as suppliers compete for fewer customers.
• 03Representative firm: The model uses industry-average parameters for a mid-size home service provider ($5M revenue). Individual firms may deviate significantly based on their specific cost structure, geographic location, and service mix.
• 04No hedging or adaptation: The model does not account for firm-level mitigation strategies such as fuel hedging, route optimization, or price surcharges that could reduce the actual impact.
• 05Policy response uncertainty: The timing and magnitude of government policy responses (SPR releases, diplomatic interventions) are highly uncertain and can dramatically alter outcomes.
• 06Regional variation: Energy prices and their impact vary significantly by US region. The model uses national averages, which may not reflect conditions in specific markets.

• [1]U.S. Energy Information Administration (EIA), Short-Term Energy Outlook, February 2026
• [2]International Energy Agency (IEA), Oil Market Report, Q1 2026
• [3]Bureau of Economic Analysis (BEA), Input-Output Accounts Data, 2024 revision
• [4]Bureau of Labor Statistics (BLS), Producer Price Index — Energy Sub-Index, 2020–2026
• [5]EIA, Retail Gasoline and Diesel Price Elasticity Studies, 2023
• [6]Federal Reserve Economic Data (FRED), Federal Funds Rate and Treasury Yields
• [7]IBISWorld, Home Services Industry Reports (HVAC, Plumbing, Electrical), 2025
• [8]Hamilton, J.D. (2003), 'What is an Oil Shock?', Journal of Econometrics
• [9]Kilian, L. (2009), 'Not All Oil Price Shocks Are Alike', American Economic Review
• [10]Baumeister, C. & Kilian, L. (2016), 'Forty Years of Oil Price Fluctuations', Journal of Economic Perspectives