Data centers are becoming flexible prosumers in integrated electricity–heat systems (IEHSs) because of their large energy demand, workload flexibility, and waste-heat recovery potential. Coordinating these resources can improve energy utilization and carbon performance while supporting sustainable operation without curtailing computing services. This paper develops a trading decision-making approach for a data center-based energy hub (DCEH) participating in coupled electricity, heat, and carbon markets while accounting for independently operated power and district heating networks. The DCEH strategically bids electricity and heat prices and quantities and acts as a price taker in a ladder-type carbon market. A detailed DCEH model coordinates workload shifting, server operating states, thermal dynamics, waste-heat recovery, combined heat and power, a gas turbine, and energy storage. A bilevel model is then formulated in which the DCEH optimizes its bids and operation at the upper level, while the lower level clears the electricity and heat markets subject to network constraints. The bilevel problem is reformulated as a single-level mixed-integer linear program using KKT conditions, big-M linearization, and binary expansion. Case studies on a PDN33-DCEH-DHN32 system show that coupled energy–carbon trading improves the DCEH’s private financial outcome and reduces emissions, although the carbon-aware strategy does not necessarily minimize total physical system resource cost. Flexible DCEH operation further reduces the DCEH cost and emissions across different carbon mechanisms and grid-price scenarios, supporting more sustainable DCEH operation.

In recent years, payment schemes in healthcare have garnered attention for their potential impact on service delivery and cost management. This paper explores the impact of the bundled payment scheme (BP) on hospital outpatient services, focusing on the cost–speed tradeoff. Specifically, a higher service rate increases patient demand but also raises medical costs. We consider a queueing-game theoretical model to analyze servers’ service rate behaviors under different payment schemes (fee-for-service and BP) and the payer’s optimal payment scheme setting. Our study shows that achieving the first-best outcome under centralized decision making using the BP requires specific conditions. When the medical budget is sufficiently high, the payer can guide hospitals toward the first-best decision by setting an optimal price under the BP. However, when the budget is at an intermediate level, hospitals may set slower equilibrium service rates to control costs. To address this issue, the payer can implement service level regulation based on the BP scheme to achieve the first-best outcome. This scheme encourages hospitals to choose higher service rates by limiting expected waiting times. When the budget is too low, hospitals may be unwilling to provide service due to unprofitability. Moreover, as competition between hospitals intensifies, it becomes easier to maximize social welfare under the BP scheme.