Optimal Carbon Pricing Considering Human Health Benefits from the Reduction in Air Pollutant Emissions in Urban Energy Systems

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Integrated assessment modeling and carbon pricing design researchers can draw on the bi-level optimization approach and the explicit handling of health co-benefits.

🏢実務担当者:Corporate sustainability teams can use the carbon price range as a signal for long-term carbon cost projections, but the model itself is policy-oriented.

🏛政策担当者:Policymakers designing carbon pricing mechanisms can learn from this framework to internalize air pollution health externalities and calibrate price levels.

Objective: Carbon pricing is a critical tool for achieving environmental objectives while maintaining economic equilibrium, yet determining an optimal carbon price remains a complex challenge. Carbon pricing not only influences the optimal conditions and the technological composition of the energy system but also leads to new emission patterns. The energy system has a lot of external costs, including social, health, ecosystem damage, and resource depletion. It is essential to set taxes in a way that minimizes market disruptions while internalizing externalities. Greenhouse gas (GHG) emissions Penalty (GP) cannot only reduce health costs due to global warming but also, through a co-benefit approach, reduce health costs associated with air pollution. Thus, this study estimates the level of GP with the aim of internalizing the human health costs of global warming and air pollution. The structure of the energy system significantly affects carbon emissions and pricing, necessitating an integrated modeling approach. Method: This study developed a novel bi-level integrated assessment model of emission-health-energy, where the lower level optimizes the energy system and the upper level optimizes the carbon price. The Shared Socioeconomic Pathways (SSP) scenarios depict the socioeconomic status, while the Representative Concentration Pathways (RCP) scenarios represent the climatic conditions. Based on the RCP scenarios, General Circulation Models (GCMs) are applied to determine the climate condition. A data-driven Regional Climate Model (RCM) is developed to estimate climate conditions in the study area. At the upper level, the objective function seeks to minimize the GP annually. In the GP approach, the price should compensate for the health costs stemming from climate change and air pollution. This approach emphasizes that GP has the capacity to offset the damages caused by air pollution, effectively demonstrating a co-benefit between carbon emissions and air pollution in policy-making. In this approach, the discounted GHG emission penalty in each year is equal to discounted health costs. In the first step, the more complex lower-level model was developed. After running the integrated energy-health-emission model at the lower level, the outputs were compared to verify whether the discounted health costs match the discounted GHG emission penalty. If this condition was not satisfied, the GP would be adjusted; otherwise, the optimal pathway for the energy supply chain and the optimal GP were determined. Results: This model was applied to four scenarios within a case study. The model revealed carbon prices ranging from $3.3 to $7.9 per tCO2eq, varying across years and scenarios. The results demonstrated the importance of considering economic benefits in carbon pricing, highlighting significant differences compared to previous studies. Conclusions: The proposed model provides a robust tool for policymakers to align carbon pricing with environmental and climate policy while maintaining economic stability. By linking energy system optimization, health cost internalization, and climate policy, this framework designs efficient carbon policies. Future research could expand its applicability to other regions and externalities, further refining the balance between sustainability and economic growth.

• openalex https://doaj.org/article/97d4285c7ca64b0d916ebc180398619dfirst seen 2026-08-02 18:28:32