Comprehensive Scenario Research Articles

Projecting the response of ecological risk to land use/land cover change in ecologically fragile regions

Anthropogenic activities have dramatically altered land use/land cover (LULC), leading to ecosystem service (ES) degradation and further ecological risks. Ecological risks are particularly serious in ecologically fragile regions because trade-offs between economic development and ecological protection are prominent. Thus, ways in which to assess the response of ecological risks to LULC change under each development scenario in ecologically fragile regions remain challenging. In this study, future LUCC and its impact on ESs under four development scenarios in 2040 in western Jilin Province were predicted using a patch-generating land use simulation model and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. Ecological risk was assessed based on future LUCC possibilities, and potential ES degradation and potential drivers of ecological risks were explored using a geographic detector. The results showed that the cropland development scenario (CDS) would experience large-scale urbanization and cropland expansion. Carbon storage (CS), habitat quality (HQ), and water purification (WP) degraded the most under the CDS, and grain yield (GY) and water yield (WY) degraded the most under the ecological protection scenario (EPS). The LUCC probability under the CDS (14.37 %) was the highest, while the LUCC probability under the comprehensive development scenario (CPDS) (8.68 %) was the lowest. The risk of WP degradation was greatest under the CDS, but the risk of soil retention (SR) degradation was greatest under the natural development scenario (NDS), EPS, and CPDS. Ecological risk coverage was the largest (98.04 %), and ecological risks were the highest (0.21) under the CDS, while those under the EPS were the opposite. Distance to roads and population density had a higher impact on ecological risks than other drivers. Further attention should be given to the ecological networks and pattern establishment in urbanized regions. This study will contribute to risk prevention and sustainable urban and agricultural development.

Uncovering the lifecycle carbon emissions and its reduction pathways: A case study of petroleum refining enterprise

The petroleum refining industry is considered energy-intensive and responsible for significant carbon emissions. This study applied both life cycle assessment and system dynamics to comprehensively analyze and simulate the factors that influence corporate carbon emissions. This analysis elucidates the complex interplay of mechanisms that can reduce corporate carbon emissions. This study also provides a deeper understanding of how these mechanisms can reduce emissions over time. The results show that the fuel combustion phase has the highest carbon intensity (0.25 tons CO2e/ton), followed by the industrial production phase (0.09 tons CO2e/ton) and the torch combustion phase (3.31E-04 tons CO2e/ton) throughout the life cycle of the sample enterprise. Notably, carbon emissions from the net purchased thermal energy are “negative,” meaning that steam thermal production from industrial processes may provide environmental benefits through the carbon cycle. Multi-scenario analysis shows that adjusting a single parameter will not achieve the “carbon neutrality” emissions reduction target for the sample enterprise. Instead, adjusting the energy structure and optimizing efficiency represents the first steps toward such a target. According to the comprehensive scenario, the sample enterprise will reach peak carbon emissions by 2025 and eventually achieve “carbon neutrality” by 2058.

Development of an Effective Corruption-Related Scenario-Based Testing Approach for Robustness Verification and Enhancement of Perception Systems in Autonomous Driving.

Given that sensor-based perception systems are utilized in autonomous vehicle applications, it is essential to validate such systems to ensure their robustness before they are deployed. In this study, we propose a comprehensive simulation-based process to verify and enhance the robustness of sensor-based perception systems in relation to corruption. Firstly, we introduce a methodology and scenario-based corruption generation tool for creating a variety of simulated test scenarios. These scenarios can effectively mimic real-world traffic environments, with a focus on corruption types that are related to safety concerns. An effective corruption similarity filtering algorithm is then proposed to eliminate corruption types with high similarity and identify representative corruption types that encompass all considered corruption types. As a result, we can create efficient test scenarios for corruption-related robustness with reduced testing time and comprehensive scenario coverage. Subsequently, we conduct vulnerability analysis on object detection models to identify weaknesses and create an effective training dataset for enhancing model vulnerability. This improves the object detection models' tolerance to weather and noise-related corruptions, ultimately enhancing the robustness of the perception system. We use case studies to demonstrate the feasibility and effectiveness of the proposed procedures for verifying and enhancing robustness. Furthermore, we investigate the impact of various "similarity overlap threshold" parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

A minimal kinetic model for the interpretation of complex catalysis in single enzyme molecules.

Multi-exponential waiting-time distribution and randomness parameter greater than unity ascribe dynamic disorder in single-enzyme catalysis corroborated to the interplay of transforming conformers [English et al., Nat. Chem. Biol., 2006, 2, 87]. The associated multi-state model of enzymatic turnovers with statically heterogeneous catalytic rates misdescribes the non-linear uprising of the randomness parameter from unity in relation to the attributes of the fall-offs of the waiting-time distribution at different substrate concentrations. To resolve this crucial issue, we first employ a comprehensive stochastic reaction scenario and further rationalize and work out the minimal indispensable dynamic-disorder model that ensures the foregoing relationship upon comparison with the data. We elucidate that specific disregard for the transition rate coefficients in the multi-state model on account of the especially slow conformational transitions is the underlying reason for not achieving interrelation between the observables.

A review of existing model-based scenarios achieving SDGs: progress and challenges

Abstract Non-Technical Summary In 2015, the United Nations articulated the ambition to move toward a prosperous, socially inclusive, and environmentally sustainable future for all by adopting the Sustainable Development Goals (SDGs). However, little is known about the pathways that could lead to their concurrent achievement. We provide an overview of the current literature on quantitative pathways toward the SDGs, indicate the commonly used methods and indicators, and identify the most comprehensive pathways that have been published to date. Our results indicate that there is a need for more scenarios toward the full set of SDGs, using a wider range of underlying narratives. Technical Summary Quantitative goal-seeking scenario studies could help to explore the needed systems' transformations to implement the 2030 Agenda for Sustainable Development by identifying enabling conditions and accounting for the synergies and trade-offs between the SDGs. Given that the SDGs were adopted some time ago, here, we review the existing global scenario literature to determine what it can offer in this context. We found only a few scenarios that address a large set of SDGs, while many more deal with specific clusters of 2–6 SDGs. We identified the most frequent clusters and compared the results of the most comprehensive sustainable development scenarios. The latter is complicated because of the diversity of methods, indicators, and assumptions used. Therefore, we suggest that an effort is needed to develop a wider set of scenarios that would achieve multiple SDGs, using a more standardized framework of targets and indicators. Social Media Summary This study reviews the current global pathways toward the SDGs and shows the need for a broader set of SDG scenarios.

Extension of an agent-based simulation for the optimized allocation of freight requests to differently structured supply chains

The paper deals with the extension of the logistics simulation within an agent-based simulation framework to be able to consider the delivery across several supply chains. The logistics simulation is placed in the context of the underlying freight transportation simulation. The use of multiple possible logistics chains addresses a key limitation of previous studies. Logistics chains represent the various transportation options of a Logistic Service Provider (LSP) and are made up of resources such as depots, logistics hubs and carriers that the logistics service provider can commission. When implementing the new functionality, the existing flow of the simulation process must be considered. In particular, changes need to be made to the assignment of freight requests to logistics chains, the iterative optimization of this assignment and scheduling. Appropriate assignment methods and rescheduling strategies are developed and presented. These innovations reveal compatibility problems within the existing simulation framework. The functionality developed is presented in constructed scenarios, highlighting the advantages of using multiple logistics chains. In addition, the applicability is demonstrated in a comprehensive real scenario that includes the delivery of supermarkets.

Research on reinforcement learning based on PPO algorithm for human-machine intervention in autonomous driving

<abstract> <p>Given the current limitations in intelligence and processing capabilities, machine learning systems are yet unable to fully tackle diverse scenarios, thereby restricting their potential to completely substitute for human roles in practical applications. Recognizing the robustness and adaptability demonstrated by human drivers in complex environments, autonomous driving training has incorporated driving intervention mechanisms. By integrating these interventions into Proximal Policy Optimization (PPO) algorithms, it becomes possible for drivers to intervene and rectify vehicles' irrational behaviors when necessary, during the training process, thereby significantly accelerating the enhancement of model performance. A human-centric experiential replay mechanism has been developed to increase the efficiency of utilizing driving intervention data. To evaluate the impact of driving intervention on the performance of intelligent agents, experiments were conducted across four distinct intervention frequencies within scenarios involving lane changes and navigation through congested roads. The results demonstrate that the bespoke intervention mechanism markedly improves the model's performance in the initial stages of training, enabling it to overcome local optima through timely driving interventions. Although an increase in intervention frequency typically results in improved model performance, an excessively high intervention rate can detrimentally affect the model's efficiency. To assess the practical applicability of the algorithm, a comprehensive testing scenario that includes lane changes, traffic signals, and congested road sections was devised. The performance of the trained model was evaluated under various traffic conditions. The outcomes reveal that the model can adapt to different traffic flows, successfully and safely navigate the testing segment, and maintain speeds close to the target. These findings highlight the model's robustness and its potential for real-world application, emphasizing the critical role of human intervention in enhancing the safety and reliability of autonomous driving systems.</p> </abstract>

Analysis of User Acceptance in Fire Extinguisher Simulation Applications with Virtual Reality Technology

This article discusses the use of Virtual Reality (VR) applications to provide comprehensive training scenarios for emergency services such as fire extinguishers. This study presents an initial prototype with 3 levels of variations of fire models that help extinguish virtual fire as a VR training scenario. The Fire Extinguishing VR Application that has been developed is based on literacy technical provisions for fire management in urban areas by the State Minister for Public Works. The prototype provides a VR experience to application users based on the ease of use and learning of fire fighting through questionnaires. The results confirm the value of using VR for training procedures that are difficult to implement in the real world can be realized with 3 levels of fire simulation. A score with a percentage of 92% stated that the application was easy to recognize and use and a score of 89% stated that the application could be used to learn how to extinguish every variation of the fire level scenario that occurred.

Comprehensive Scenario Analyses for Coal exit and Renewable Energy Development Planning of Kazakhstan using PyPSA-KZ

Comprehensive Scenario Analyses for Coal exit and Renewable Energy Development Planning of Kazakhstan using PyPSA-KZ

Advancing coupling coordination simulation in the social-human-ecological system of the Three Gorges Reservoir Area: A multi-scenario system dynamics approach

Due to the lack of scientific and systematic thinking in the early stages of high-quality urbanisation, the interaction and stress between the social-human-ecological (SHE)11SHE social-human-ecological. system intensified, resulting in a series of problems related to the low level of ecological environment and the imbalanced socio-economic development. It is crucial to enhance the safety, health, and well-being of city dwellers, as well as to promote the sustainable development of the region. Based on complex systems science, using the Three Gorges Reservoir Area (TGRA)22TGRA Three Gorges Reservoir Area. as an instance, this study develops a multidimensional coupling conceptual model of the SHE system, employs the system dynamics (SD)33SD system dynamics. method to clarify the SHE system’s causal feedback mechanism, and simulates the coupling coordination degree (CCD)44CCD coupling coordination degree. from 2010 to 2030. Simultaneously, eight scenarios are created to improve the system’s CCD by modifying factors. According to historical and sensitivity test findings, the SD model used in this work can effectively represent the SHE system’s dynamic coupling and interaction connection. Furthermore, under the comprehensive development scenario that takes into account the protection of the ecological environment, human intelligent regulation, and socio-economic development, the TGRA has the biggest growth in CCD. As a result, raising the policy intensity of a single subsystem is insufficient. Balancing policy implementation for each subsystem is advantageous to improving the coupling coordination of the SHE system in the TGRA. The study is of significant importance for achieving high-quality socio-economic development and sustainable ecological management in the TGRA region.

A comprehensive evolutionary scenario for the origin and neofunctionalization of the Drosophila speciation gene Odysseus (OdsH).

Odysseus (OdsH) was the first speciation gene described in Drosophila related to hybrid sterility in offspring of mating between Drosophila mauritiana and Drosophila simulans. Its origin is attributed to the duplication of the gene unc-4 in the subgenus Sophophora. By using a much larger sample of Drosophilidae species, we showed that contrary to what has been previously proposed, OdsH origin occurred 62 MYA. Evolutionary rates, expression, and transcription factor-binding sites of OdsH evidence that it may have rapidly experienced neofunctionalization in male sexual functions. Furthermore, the analysis of the OdsH peptide allowed the identification of mutations of D. mauritiana that could result in incompatibility in hybrids. In order to find if OdsH could be related to hybrid sterility, beyond Sophophora, we explored the expression of OdsH in Drosophila arizonae and Drosophila mojavensis, a pair of sister species with incomplete reproductive isolation. Our data indicated that OdsH expression is not atypical in their male-sterile hybrids. In conclusion, we have proposed that the origin of OdsH occurred earlier than previously proposed, followed by neofunctionalization. Our results also suggested that its role as a speciation gene might be restricted to D. mauritiana and D. simulans.

Generation Expansion Planning considering environmental impact and sustainable development for an Indian state using the LEAP platform

GEP (Generation Expansion Planning) is one of the most complex tasks in which planning is done to find the best possible distinct solutions to install new generating units to meet future load demands. This paper uses the Low Emission Analysis Platform (LEAP) software environment to analyse the GEP for the AP Power sector from 2021 to 2050. In recent years, the AP power sector has faced several issues, such as frequent service cuts due to power shortages. In this paper, five scenarios such as Business As Usual (BAU), Low Demand Growth (LDG), High Demand Growth (HDG), Least Cost Optimization (LCO) and Climate Mitigation Scenarios, have been analysed with environmental, technical, and economic parameters. This study has estimated the total environmental emissions and total cost of all scenarios. At present, Andhra Pradesh has a total installed capacity of 25.4 GW. The best scenarios can be selected based on environmental impact, cost-effectiveness, efficiency, and reliability. The results show that the LDG scenario will be an optimal choice for AP due to the state's economic crisis, and the government should also focus on energy conservation and limit demand growth to 4.5%. While presenting a general GEP, the study presents comprehensive system performance scenarios from which planners can choose.

Unveiling the importance of VOCs from pesticides applicated in main crops for elevating ozone concentrations in China

Volatile organic compounds (VOCs) are considered as important precursors of ozone in the air, while the contribution of VOCs from pesticide application (PVOCs) to ozone production is unknown. Utilizing data from the Ministry of Agriculture and Rural Affairs of the People's Republic of China and ChinaCropPhen1km, this paper developed PVOC emission inventories with a resolution of 1 km for the main crops (rice, maize, and wheat) from 2012 to 2019 in China. The results revealed that pesticide application is an important VOC emission source in China. Specially, the PVOC emissions from the major grain-producing regions in June accounted for approximately 30% of the annual total PVOC emissions in the local regions. The simulation with the Weather Research and Forecasting Community Multiscale Air Quality model (WRF-CMAQ) indicated that the PVOC emissions increased the mean maximum daily 8-hour average (MDA8) ozone concentration across China by 2.5 ppb in June 2019. During the same period, PVOCs in the parts of North China Plain contributed 10% of the ozone formation. Under the comprehensive emission reduction scenario, it is anticipated that by 2025, the joint implementation of measures including reducing pesticide application, improving pesticide utilization efficiency and promoting solvent substitution will decrease PVOC emissions by 60% compared with 2019, thereby mitigating ozone pollution.

Nature's contributions to people responding to landscape stability in a typical karst region, southwest China

Understanding the response of nature's contributions to people (NCP) to change in landscape stability may constrain and guide human modification of nature. However, the effects of landscape stability on NCP, in the context of active human modification, remain unclear. To address this issue, we chose the Wujiang River Basin in the Yangtze River Economic Belt of China as a study area and quantified landscape stability in 2000, 2010, and 2020 under different scenarios. Environmental protection and comprehensive development scenarios were simulated for 2030 using the patch-generating land use simulation (PLUS) model. For current and future scenarios, NCP were assessed in terms of indicators, which included habitat creation and maintenance (NCP1), regulation of freshwater (NCP6), and protection of soils (NCP8). Under the current status quo, urban expansion was 491.547 km2, and landscape stability showed a decreasing trend, with the mean value decreasing from 0.049 in 2000 to 0.015 in 2020. In future scenarios, landscape stability was highest under the comprehensive development scenario. In addition, in terms of changes in NCP, the high-value contributions declined by 16%–48% from 2000 to 2020. Our results clarified how NCP respond to landscape stability. These results can help conservation managers develop more targeted ecological strategies that will constrain human modification activities to enhance well-being.

Predicting multi-subsequent events and actors in public health emergencies: An event-based knowledge graph approach

Public health emergencies trigger series of chain reactions that have devastating impacts on society. In addition, the subsequent events and actors in public health emergencies represent comprehensive emergency scenarios. Taking this information into account, predicting subsequent events and actors could motivate governments to take necessary and effective countermeasures. Therefore, we develop a model for predicting subsequent events and potential actors, i.e., a subsequent multievent graph convolutional network (SMEGCN), by utilizing the evolutionary information of events. Specifically, we take both relational information and semantic information into consideration to achieve improved prediction performance and simultaneously predict subsequent actors in a convenient manner. Specifically, we collect data from the Sina microblog concerning the COVID-19 pandemic to form five news datasets by employing a Python-based agent to practically test the performance of our model. The agile principle is applied to identify and handle a series of subsequent events and potential actors. The results show that embedding relational information, semantic information, and context inferences into the prediction model can improve the model performance by approximately 20%. Additionally, a comparative analysis indicates that the SMEGCN model is superior to other methods in terms of predicting both subsequent events and actors. From the perspective of an example analysis, social media, especially official media accounts, stimulates interactions between governments and the public and improves the management effectiveness of governments. However, during periods of emerging public health emergencies, the most important events that should be noted diachronically are treatment, daily life guarantees, and acute and chronic disease treatments, whereas at the end of a public health emergency, the key tasks include how to revive commercial activities and improve the vaccination rate. Drawing conclusions from these discussions, the present study not only contributes to the literature on theoretically and methodologically predicting events and actors but also provides practical suggestions for managing public health emergencies.

Evaluating the cost utility of estradiol plus dydrogesterone for the treatment of menopausal women in China

AimEvaluate the cost utility of menopausal hormone therapy for women in China.Materials and methodsA bespoke Markov cost utility model was developed to evaluate a cohort of symptomatic perimenopausal women (>45 years) with intact uterus in China in accordance with China’s Pharmacoeconomic guideline. Short (5-year) and long (10-year) treatment durations were evaluated over a lifetime model time horizon with 12-month cycle duration. Societal and healthcare payer perspectives were evaluated in the context of a primary care provider/prescriber, outpatient setting with inpatient care for patients with chronic conditions. Disease risk and mortality parameters were derived from focused literature searches, and China Diagnosis-related Group cost data was included. Comprehensive scenario, univariate and probabilistic sensitivity analysis were undertaken along with independent validation. This is the first model to include MHT-related disease risks.ResultsAccording to base case results, the total cost for MHT was 22,516$ (150,106¥) and total quality adjusted life years 12.32 versus total cost of no MHT 30,824$ (205,495¥) and total quality adjusted life years 11.16 resulting in a dominant incremental cost effectiveness ratio of −7,184$ (−47,898¥) per QALY. Results hold true over a range of univariate deterministic sensitivity and scenario analyses. Probabilistic analysis showed a 91% probability of being cost effective at a willingness to pay threshold of three times Gross Domestic Product per capita in China.ConclusionContingent on the structure and assumptions of the model, combination of estradiol plus dydrogesterone MHT is potentially cost saving in symptomatic women over the age of 45 years in China.

A Review of Research on the Impact Mechanisms of Green Development in the Transportation Industry

Green development in the transportation industry is a new type of development. As the huge energy consumption and carbon emissions generated by the transportation industry have caused many environmental problems, the healthy and environmentally friendly mode of industrial development has received more and more attention. However, the quantification of green development in the transportation industry varies in terms of boundaries, scope, and methods. Due to digital empowerment, the degree of influence and direction of the factors affecting the green development are not fixed. The prediction of future development prospects is relatively single-minded, lacking a comprehensive simulation scenario setting from multiple perspectives. This paper systematically reviews the research progress of green development of the transportation industry from three aspects: development performance assessment, influence mechanism analysis, and development path exploration. After a critical analysis, this study concludes that (1) a clear methodology is needed to assess the direct and indirect non-desired output results of the transportation industry; (2) considering the endogenization of the level of technology, the influence of the interaction between the influencing factors, etc., on the degree and direction of the role of the factors, a more scientific econometric model should be established for in-depth discussion; (3) resident travel options are an important factor affecting environmental issues in transportation. Carbon emission projections and analyses of emission reduction scenarios should integrate the multiple possibilities of residential preferences and policy incentives. The findings of this paper provide valuable references to the energy saving and emission reduction goals of the transportation industry, and the coordinated development of the industry and the economy.

(Path)ways to sustainable living: The impact of the SLIM scenarios on long-term emissions

Sustainable lifestyles and behaviour changes can be vital in climate change mitigation. Various disciplines analyse the potential for such changes – but without much interaction. Qualitative studies look into the change process (e.g. social practice theory), while quantitative studies often focus on their impact in stylised cases (e.g. energy modelling). A more holistic approach can provide insightful scenarios with diverse lifestyle changes based on informed narratives for quantifying long-term impacts. This research explores how comprehensive sustainable lifestyle scenarios, coined SLIM (Sustainable Living in Models) scenarios, could contribute to transport and residential emission reductions. By translating and quantifying lifestyle scenario narratives through engagements with advisors and policymakers, we modelled two distinct lifestyle scenarios which differ in their degree of access to structural support. In one scenario, governments, corporations and cities leverage existing values and market systems toshape citizen and consumer preferences and everyday practices. In the other scenario, people adopt ambitious sustainable lifestyle behaviours and practices through peer-to-peer interaction and digital technology. We quantified the scenarios based on motivations, contextual factors, extent, and speed of lifestyle adoptions with regional differentiation. Furthermore, we applied heterogenous adopter groups to determine the model inputs. We present the resulting pathways in per capita emissions and more detailed changes in total emissions via decomposition analyses. We conclude that regional differentiation of the scenario narratives and modelling of intra-regional differences allows accounting for equity in lifestyle changes to a certain extent. Furthermore, new technologies are more important for enabling lifestyle change in a scenario with than a scenario without strong structural support. With strong structural support, lifestyle changes reduce transport and residential emissions to a larger degree (about 39% for Global North and 27% for Global South overall in 2050 relative to a “Middle-of-the-Road” SSP2 reference scenario in 2050). Thus, lifestyle changes in larger systems change are essential for effective climate change mitigation.

Can the implementation of household waste classification mitigate greenhouse gas emissions in Beijing? A comprehensive analysis of recent trends and future scenarios

Household waste contributes significantly to global greenhouse gas (GHG) emissions, and waste classification is crucial for reducing emissions. This study focuses on Beijing and utilizes life cycle assessment (LCA) and material flow analysis (MFA) to calculate GHG emissions in waste management systems and quantify emission reduction potential of different measures. The results show that net emissions from the classification system in 2021 are 116.77 kg CO2-eq/t waste, reducing 61.82 % compared to the traditional mixed collection and transportation system. Waste volume, classification efficiency, and treatment strategies are the primary factors affecting emissions in classification systems. Recycling is identified as effective treatment methods. Three scenarios are designed to explore emission pathway of the system toward 2060. In the business-as-usual (BAU) Scenario, emissions will continue to grow to 108.57 × 104 t CO2-eq/yr in 2060. In the Classification Efficiency Scenario and the Comprehensive Scenario, emissions in 2060 will be cut to −177.26 × 104 t CO2-eq/yr and −702.00 × 104 t CO2-eq/yr, respectively. These results underscore the critical role of waste classification and recycling in mitigating the negative impacts of increasing waste volume. By 2060, combining waste classification with recycling can offset emissions by 803.51 × 104 t CO2-eq/yr, contributing 99 % to emission reduction potential. Improving classification efficiency and recycling ratio are key measures for achieving this reduction goal. Meanwhile, treatment methods and technologies should prioritize classification and recycling. Aiming at carbon neutrality, the study proposes several recommendations to improve classification systems, including enhancing classification efficiency, optimizing treatment facilities and strategies, and establishing recycling and utilization systems, etc.

Cost-Effectiveness Analysis of Trastuzumab Deruxtecan Versus Trastuzumab Emtansine for Patients With Human Epidermal Growth Factor Receptor 2 Positive Metastatic Breast Cancer in the United States

ObjectivesTo assess the cost-effectiveness of trastuzumab deruxtecan compared with trastuzumab emtansine as second-line therapy for patients with human epidermal growth factor receptor 2 positive metastatic breast cancer from a US healthcare sector perspective. MethodsA 3-state partitioned survival model was developed to estimate the cost-effectiveness of trastuzumab deruxtecan compared with trastuzumab emtansine. For both treatments, modeled patients were administered treatment intravenously every 3 weeks indefinitely or until disease progression. Transition parameters were principally derived from the updated DESTINY-Breast03 phase III randomized clinical trial. Costs include drug costs extracted from Centers for Medicare and Medicaid Services average sales price and administrative, adverse event, and third-line therapy costs derived from published literature, measured in 2022 US dollars. Health utilities for health states and disutilities for adverse events were sourced from published literature. Effects were measured in quality-adjusted life years (QALYs). We conducted both probabilistic sensitivity analysis and comprehensive scenario analysis to test model assumptions and robustness, while utilizing a lifetime horizon. ResultsIn our base-case analysis, total costs for trastuzumab deruxtecan were $1 266 945, compared with $820 082 for trastuzumab emtansine. Total QALYs for trastuzumab deruxtecan were 5.09, compared with 3.15 for trastuzumab emtansine. The base-case incremental cost-effectiveness ratio was $230 285/QALY. Probabilistic sensitivity analysis indicated that trastuzumab deruxtecan had an 11.1% probability of being cost-effective at a $100 000 per QALY willingness-to-pay threshold. ConclusionsDespite the higher efficacy of trastuzumab deruxtecan in patients with human epidermal growth factor receptor 2 positive metastatic breast cancer, our findings raise concern regarding its value at current prices.