Dear Colleagues,
Traditional revenue management models optimize yield under assumptions of capacity constraints and relative demand stability. These architectures have no structural mechanism for internalizing non-linear biophysical shocks or crisis-induced demand signal distortion a gap with significant operational consequences when climate disruption hits an environmentally sensitive destination.
The central phenomenon the paper addresses is Phantom Demand: under acute climate anomaly, potential guests engage in hyper-hedging, generating multiple competing reservations across alternative properties to mitigate travel risk under uncertainty. Systems that process this inflated booking pace as genuine intent trigger premature discounting, elevated no-show losses, and long-run rate erosion that persists well beyond the disruption window.
Framework and Theoretical Contribution
The paper introduces the Sustainability-Integrated Revenue Governance (SIRG) framework - a multi-objective decision architecture integrating four sub-models: a structural cost floor formulation (Yield Equilibrium Protocol), a carbon-adjusted minimum rate model (Biophysical Viability and Emissions Framework), a four-state adaptive switching logic driven by a composite Climate Demand Stress Index (Adaptive Revenue Sustainability Protocol), and a phase-structured post-crisis rate restoration model (Algorithmic Crisis Recovery).
The most counterintuitive finding is what the paper terms the Climate-Adjusted Minimum Acceptable Rate Paradox. Under acute stress modeled at 16–22% occupancy with a market ADR of USD 155.00 the biophysically grounded governance floor computes to USD 385.83. Classical yield logic interprets this gap as a signal to discount toward market. The mathematical analysis shows the inverse: at specific biophysical thresholds, volume maximization becomes value-destroying. The adjusted minimum rate functions not as an achievable market target but as a strategic governance signal instructing the system to halt greedy acceptance, filter artificial hedging, and preserve long-run rate integrity.
Selected Simulation Results
A 10,000-iteration stochastic simulation was conducted on a 300-key, five-star resort archetype. Three outcomes are highlighted:
- Phantom Demand Rejection Rate: 8.4% (conventional) vs. 67.3% (SIRG)
- Genuine Reservation Materialization: 62.2% (conventional) vs. 94.1% (SIRG)
- Post-Crisis Payback Horizon: unbounded (conventional) vs. 74 days (SIRG)
Full simulation parameters, mathematical derivations, and adaptive state-switching logic are documented in the preprint, available via the section's library repository.
Open Questions for the Community
- How can the Phantom Demand Coefficient be calibrated dynamically using live multi-source data specifically, booking pace acceleration relative to cancellation gradients?
- What algorithmic fairness criteria are required to safeguard against false-positive phantom demand classifications for genuinely displaced travelers?
- How does portfolio-level network revenue management shift when multiple properties compete for a compressed, post-disruption demand pool?
Methodological critiques and perspectives from colleagues working in stochastic demand modeling, crisis operations, and sustainable systems design are very welcome.
Best regards,
N. P. Gayan Nugawela
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Independent Researcher in Hospitality Revenue Management and Sustainability Governance