Abstract

Abstract

Abstract
The electrification of the transport sector is a critical element in the transition to a low-emissions economy, driven by the widespread adoption of electric vehicles (EV) and the integration of renewable energy sources (RES). However, managing the increasing demand for EV charging infrastructure while meeting carbon emission reduction targets is a significant challenge for charging station operators. This work introduces a novel carbon-aware dynamic pricing framework for EV charging, formulated as a chance-constrained optimization problem to consider forecast uncertainties in RES generation, load, and grid carbon intensity. The model generates day-ahead dynamic tariffs for EV drivers with a certain elastic behavior while optimizing costs and complying with a carbon emissions budget. Different types of budgets for Scope 2 emissions (indirect emissions of purchased electricity consumed by a company) are conceptualized and demonstrate the advantages of a stochastic approach over deterministic models in managing emissions under forecast uncertainty, improving the reduction rate of emissions per feasible day of optimization by 24 %. Additionally, a surrogate machine learning model is proposed to approximate the outcomes of stochastic optimization, enabling the application of state-of-the-art explainability techniques to enhance understanding and communication of dynamic pricing decisions under forecast uncertainty. It was found that lower tariffs are explained, for instance, by periods of higher renewable energy availability and lower market prices and that the most important feature was the hour of the day.

Abstract

Abstract

Abstract
The specialization of Health Human Resources (HHR) is increasingly recognized as essential for addressing evolving healthcare demands. This paper presents a comprehensive policy framework for assisting with the implementation of Clinical Nurse Specialist (CNS) roles at the national or regional level, integrating key dimensions including barriers and enablers, regulation and governance, education and training requirements, career development, workforce planning, and economic analysis. The framework was applied to the implementation of CNS roles in Portugal, resulting in the issuance of a decree-law by the government. Our findings demonstrate that the economic analysis step was critical in addressing concerns from government authorities and health system funders regarding the potential budgetary impact of CNS implementation. By providing evidence-based projections of costs and benefits, the economic analysis facilitated smoother negotiations and consensus-building among stakeholders, including nursing unions. Furthermore, the integration of workforce planning ensured the alignment of educational capacity with workforce needs, thus avoiding potential implementation bottlenecks. The application of the framework also revealed important feedback relationships between its dimensions, highlighting the interdependent nature of the implementation process. This dynamic approach, which adapts to real-time feedback and stakeholder input, underscores the necessity of a holistic and iterative strategy for successful CNS role integration. The insights gained from the Portuguese case underscore the utility of this policy framework in guiding the implementation of advanced nursing roles in diverse healthcare contexts.