Search Results (36)

Background: Hypotension following epidural labor analgesia (ELA) is its most common complication, affecting approximately 20% of patients and posing risks to both maternal and fetal health. As digital tools and predictive analytics increasingly shape perioperative and obstetric anesthesia practices, real-world implementation data are needed to guide their integration into clinical care. Current monitoring practices rely on intermittent non-invasive blood pressure (NIBP) measurements, which may delay recognition and treatment of hypotension. The Hypotension Prediction Index (HPI) algorithm uses continuous arterial waveform monitoring to predict hypotension for potentially earlier intervention. This clinical trial evaluated the feasibility, acceptability, and efficacy of continuous HPI-guided treatment in reducing time-to-treatment for ELA-associated hypotension and improving maternal hemodynamics. Methods: This was a prospective randomized controlled trial design involving healthy pregnant individuals receiving ELA. Participants were randomized into two groups: Group CM (conventional monitoring with NIBP) and Group HPI (continuous noninvasive blood pressure monitoring). In Group HPI, hypotension treatment was guided by HPI output; in Group CM, treatment was based on NIBP readings. Feasibility, appropriateness, and acceptability outcomes were assessed among subjects and their bedside nurse using the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM) instruments. The primary efficacy outcome was time-to-treatment of hypotension, defined as the duration between onset of hypotension and administration of a vasopressor or fluid therapy. This outcome was chosen to evaluate the clinical responsiveness enabled by HPI monitoring. Hypotension is defined as a mean arterial pressure (MAP) < 65 mmHg for more than 1 min in Group CM and an HPI threshold < 75 for more than 1 min in Group HPI. Secondary outcomes included total time in hypotension, vasopressor doses, and hemodynamic parameters. Results: There were 30 patients (Group HPI, n = 16; Group CM, n = 14) included in the final analysis. Subjects and clinicians alike rated the acceptability, appropriateness, and feasibility of the continuous monitoring device highly, with median scores ≥ 4 across all domains, indicating favorable perceptions of the intervention. The cumulative probability of time-to-treatment of hypotension was lower by 75 min after ELA initiation in Group HPI (65%) than Group CM (71%), although this difference was not statistically significant (log-rank p = 0.66). Mixed models indicated trends that Group HPI had higher cardiac output (β = 0.58, 95% confidence interval −0.18 to 1.34, p = 0.13) and lower systemic vascular resistance (β = −97.22, 95% confidence interval −200.84 to 6.40, p = 0.07) throughout the monitoring period. No differences were found in total vasopressor use or intravenous fluid administration. Conclusions: Continuous monitoring and precision hypotension treatment is feasible, appropriate, and acceptable to both patients and clinicians in a labor and delivery setting. These hypothesis-generating results support that HPI-guided treatment may be associated with hemodynamic trends that warrant further investigation to determine definitive efficacy in labor analgesia contexts. Full article

When predicting the health impacts of PM_2.5 from future air quality changes, it is crucial to consider both air quality improvements and population aging. This study divided future emission scenarios into a base and control scenario to project air quality from 2015 to 2030 and assess health outcomes. The GUIDE model, an Integrated Assessment Model (IAM), was used to estimate future emissions, while the CMAQ (Chemical Transport Model) and BenMAP (Health Impact Model) evaluated health impacts resulting from changes in air quality in Korea. The study focused on the impact of population aging on future health outcomes. Both scenarios showed improved PM_2.5 concentrations, with the control scenario showing more substantial improvements due to stronger policy measures. When applying current age patterns, health impacts decreased as PM_2.5 concentrations decreased. However, when considering future population aging, health impacts increased despite improved air quality. The results excluding aging show that the number of premature deaths due to cardiovascular disease and all other causes caused by PM_2.5 is 18,413 in the base year, while in the future control scenario, the number decreases to 11,729. In contrast, when aging is taken into account, the number of premature deaths increases to 23,037. This finding suggests that, although PM_2.5 concentrations are expected to decline, the increasing proportion of elderly individuals will exacerbate health risks. Therefore, accounting for aging population trends is essential when studying the health impacts of future air quality changes. Full article

While a flurry of studies and Integrated Assessment Models (IAMs) have independently investigated the impacts of switching mitigation policies in response to different climate scenarios, little is understood about the feedback effect of how human risk perceptions of climate change could contribute to switching climate mitigation policies. This study presents a novel machine learning approach, utilizing a probabilistic structural equation model (PSEM), for understanding complex interactions among climate risk perceptions, beliefs about climate science, political ideology, demographic factors, and their combined effects on support for mitigation policies. We use machine learning-based PSEM to identify the latent variables and quantify their complex interaction effects on support for climate policy. As opposed to a priori clustering of manifest variables into latent variables that is implemented in traditional SEMs, the novel PSEM presented in this study uses unsupervised algorithms to identify data-driven clustering of manifest variables into latent variables. Further, information theoretic metrics are used to estimate both the structural relationships among latent variables and the optimal number of classes within each latent variable. The PSEM yields an R² of 92.2% derived from the “Climate Change in the American Mind” dataset (2008–2018 [N = 22,416]), which is a substantial improvement over a traditional regression analysis-based study applied to the CCAM dataset that identified five manifest variables to account for 51% of the variance in policy support. The PSEM uncovers a previously unidentified class of “lukewarm supporters” (~59% of the US population), different from strong supporters (27%) and opposers (13%). These lukewarm supporters represent a wide swath of the US population, but their support may be capricious and sensitive to the details of the policy and how it is implemented. Individual survey items clustered into latent variables reveal that the public does not respond to “climate risk perceptions” as a single construct in their minds. Instead, PSEM path analysis supports dual processing theory: analytical and affective (emotional) risk perceptions are identified as separate, unique factors, which, along with climate beliefs, political ideology, and race, explain much of the variability in the American public’s support for climate policy. The machine learning approach demonstrates that complex interaction effects of belief states combined with analytical and affective risk perceptions; as well as political ideology, party, and race, will need to be considered for informing the design of feedback loops in IAMs that endogenously feedback the impacts of global climate change on the evolution of climate mitigation policies. Full article

As financial institutions navigate an increasingly complex cyber threat landscape and regulatory ecosystem, there is a pressing need for a robust and adaptive security architecture. This paper introduces a comprehensive, Zero Trust model-based framework specifically tailored for the finance industry. It encompasses identity and access management (IAM), data protection, and device and network security and introduces trust through blockchain technology. This study provides a literature review of existing Zero Trust paradigms and contrasts them with cybersecurity solutions currently relevant to financial settings. The research adopts a mixed methods approach, combining extensive qualitative analysis through a literature review and assessment of security assumptions, threat modelling, and implementation strategies with quantitative evaluation using a prototype banking application for vulnerability scanning, security testing, and performance testing. The IAM component ensures robust authentication and authorisation processes, while device and network security measures protect against both internal and external threats. Data protection mechanisms maintain the confidentiality and integrity of sensitive information. Additionally, the blockchain-based trust component serves as an innovative layer to enhance security measures, offering both tamper-proof verification and increased integrity. Through analysis of potential threats and experimental evaluation of the Zero Trust model’s performance, the proposed framework offers financial institutions a comprehensive security architecture capable of effectively mitigating cyber threats and fostering enhanced consumer trust. Full article

Achieving ambitious climate targets, such as the 1.5 °C goal, demands significant financial commitment. While technical feasibility exists, the economic implications of delayed action and differing scenarios remain unclear. This study addresses this gap by analyzing the investment attractiveness and economic risks/benefits of different climate scenarios through a novel emissions cost budgeting model. A simplified model is developed using five global scenarios: announced policies (type 1 and 2), 2.0 °C, and 1.5 °C. A unit marginal abatement cost estimated the monetary value of avoided and unavoided emissions costs for each scenario. Net present value (NPV) and cost–benefit index (BI) were then calculated to compare the scenario attractiveness of the global emission budgets. The model was further applied to emissions budgets for China, the USA, India, and the European Union (EU). Increasing discount rates and gross domestic product (GDP) led to emission increases across all scenarios. The 1.5 °C scenario achieved the lowest emissions, while the baseline scenario showed the highest potential emissions growth (between 139.48% and 146.5%). Therefore, emphasis on the need for further financial commitment becomes important as the emissions’ abatement cost used as best case was estimated at USD 2.4 trillion per unit of 1 Gtons CO₂ equivalent (eq.). Policy delays significantly impacted NPV and BI values, showcasing the time value of investment decisions. The model’s behavior aligns with real-world observations, including GDP growth influencing inflation and project costs. The simplified model could be coupled to existing integrated assessment frameworks or models (IAMs) as none offer cost–benefit analysis of climate scenarios to the best of our knowledge. Also, the model may be used to examine the economic attractiveness of carbon reduction programs in various nations, cities, and organizations. Thus, the model and analytical approach presented in this work indicate promising applications. Full article

The world is facing a global sustainability crisis affecting environmental systems and society. Addressing these issues requires a multi-dimensional approach that can integrate energy, water, and environment Systems, as well as provide scientific policy advice. In this study, an updated version of an Integrated Assessment Model (IAM) was used, together with new data compatible with Shared Socioeconomic Pathways (SSPs) projections, to significantly improve the work developed before. SSP climate data (temperature, precipitation, and total radiative forcing) and socioeconomic data (population and GDP) were loaded into the IAM, together with different scenario parameters. By analyzing varying socioeconomic scenarios, mitigation efforts, and adaptation strategies, this study assesses their impact on primary energy demand and, consequently, their impact on hydropower potential production. Our results show diverse energy paths, strongly dependent on the future scenario. Energy demand could increase up to 160%; however, several projections foresee a decline in hydropower production to minus 46% due to both climate change and socioeconomic transformation. Our findings highlight the importance of considering a range of potential future scenarios in energy planning and policy development. The varied outcomes across the considered scenarios emphasize the need for flexibility in strategies to accommodate for uncertainties and address the challenges posed by divergent trajectories in hydropower use and renewable energy shares. Full article

In the context of the imperative global shift towards renewable energy to mitigate climate change, Latin America (LATAM) emerges as a region of immense untapped potential. However, there is no formal quantification of the effects of developing this potential. This study analyzes the economic and climate impacts of developing renewable energy in LATAM and the Asia–Pacific region using an integrated economic and climate assessment model (IAM). The key findings are as follows. First, exporting renewable energy from LATAM and the Asia–Pacific region yields economic benefits across all regions. However, this surge in renewable energy exacerbates rather than alleviates global warming. Second, the implementation of policy measures accompanying renewable energy exports, aimed at discouraging the use of polluting energy sources, proves effective in mitigating global warming while sustaining significant economic gains globally. Third, LATAM stands to gain substantially from this development. Fourth, due to the gradual process of capital accumulation, any delays in initiating the development of renewable energy exports not only diminish economic gains during the postponement but also in the years following the commencement of exports. These results are robust to several additional simulations and sensitivity analyses. The results align with the goals of the Paris Agreement. Full article

Climate change mitigation measures are often projected to reduce anthropogenic carbon dioxide concentrations. Yet, it seems there is ample evidence suggesting that we have a limited understanding of the impacts of these measures and their combinations. For example, the Inflation Reduction Act (IRA) enacted in the U.S. in 2022 contains significant provisions, such as the electric vehicle (EV) tax credits, to reduce CO${}_2$ emissions. However, the impact of such provisions is not fully understood across the U.S., particularly in the context of their interactions with other macroeconomic systems. In this paper, we employ an Integrated Assessment Model (IAM), the Global Change Assessment Model (GCAM), to estimate the future CO${}_2$ emissions in the U.S. GCAM is equipped to comprehensively characterize the interactions among different systems, e.g., energy, water, land use, and transportation. Thus, the use of GCAM-USA that has U.S. state-level resolution allows the projection of the impacts and consequences of major provisions in the IRA, i.e., EV tax credits and clean energy incentives. To compare the performance of these incentives and credits, a policy effectiveness index is used to evaluate the strength of the relationship between the achieved total CO${}_2$ emissions and the overarching emission reduction costs. Our results show that the EV tax credits as stipulated in the IRA can only marginally reduce carbon emissions across the U.S. In fact, it may lead to negative impacts in some states. However, simultaneously combining the incentives and tax credits improves performance and outcomes better than the sum of the individual effects of the policies. This demonstrates that the whole is greater than the sum of the parts in this decarbonization approach. Our findings provide insights for policymakers with a recommendation that combining EV tax credits with clean energy incentives magnifies the intended impact of emission reduction. Full article

This study provides a quantitative analysis of future energy–climate developments at the global level using two well-established integrated assessment models (IAMs), PROMETHEUS and TIAM-ECN. The research aims to explore the results of these IAMs and identify avenues for improvement to achieve the goals of the Paris Agreement. The study focuses on the effects of varying assumptions for key model drivers, including carbon prices, technology costs, and global energy prices, within the context of stringent decarbonization policies. Diagnostic scenarios are utilized to assess the behavior of the models under varying exogenous assumptions for key drivers, aiming to verify the accuracy and reliability of the models and identify areas for optimization. The findings of this research demonstrate that both PROMETHEUS and TIAM-ECN exhibit similar responses to carbon pricing, with PROMETHEUS being more sensitive to this parameter than TIAM-ECN. The results highlight the importance of carbon pricing as an effective policy tool to drive decarbonization efforts. Additionally, the study reveals that variations in technology costs and global energy prices significantly impact the outcomes of the models. The identified sensitivities and responses of the IAMs to key model drivers offer guidance for policymakers to refine their policy decisions and develop effective strategies aligned with the objectives of the Paris Agreement. By understanding the behavior of the models under different assumptions, policymakers can make informed decisions to optimize decarbonization pathways and enhance the likelihood of meeting global climate goals. Full article

An Integrated Assessment Model (IAM) was employed to develop a Narrative Policy Framework (NPF) and a quantitative model to investigate the changes in land use within the Brazilian Amazon. The process began by creating a theoretical NPF using a ‘systems thinking’ approach. Subsequently, a ‘system dynamic model’ was built based on an extensive review of the literature and on multiple quantitative datasets to simulate the impacts of the NPF, specifically focusing on the conversion of forests into open land for ranching and the implementation of soil management practices as a macro-level policy aimed at preserving soil quality and ranching yields. Various fallow scenarios were tested to simulate their effects on deforestation patterns. The results indicate that implementing fallow practices as a policy measure could reduce deforestation rates while simultaneously ensuring sustainable long-term agricultural productivity, thus diminishing the necessity to clear new forest land. Moreover, when combined with payments for avoided deforestation, such as REDD+ carbon offsets, the opportunity costs associated with ranching land can be utilized to compensate for the loss of gross income resulting from the policy. A sensitivity analysis was conducted to assess the significance of different model variables, revealing that lower cattle prices require resources for REDD+ payments, and vice-versa. The findings indicate that, at the macro level, payments between USD 2.5 and USD 5.0 per MgC ha⁻¹ have the potential to compensate the foregone cattle production from not converting forest into ranching land. This study demonstrates that employing an IAM with a systems approach facilitates the participation of various stakeholders, including farmers and landowners, in policy discussions. It also enables the establishment of effective land use and management policies that mitigate deforestation and soil degradation, making it a robust initiative to address environmental, climate change, and economic sustainability issues. Full article

Carbon capture and storage (CCS) is an essential greenhouse gas removal (GGR) technology used to achieve negative emissions in bioenergy plants using biomass feedstock (Bio-CCS). In this study, the climate mitigation potential of a novel GGR technology consisting in the production of renewable-derived plastics from municipal solid waste (MSW) refuse has been evaluated. This novel GGR technology allows for carbon storage, for variable periods, in stable materials (plastics), and thus overcomes the technical limitations of CCS. A time-dependent carbon cycle assessment has been conducted based on the Absolute Global surface Temperature change Potential (AGTP) metric. This new method to assess carbon emissions is presented against a traditional life cycle assessment (LCA). The production of renewable-derived plastics proves to be an effective GGR technology for both landfill- and incineration-dominant countries in Europe. The results obtained encourage the implementation of renewable-derived plastics in Integrated Assessment Models (IAMs) to assess their global potential in forecasting scenarios to achieve the ambitious climate change targets set in the European Union. Thanks to this study, a novel approach toward a green and sustainable economy has been established. This study will help to fill the gaps between bioenergy and renewable materials production. Full article

Our purpose is to advance a reasoned perspective on the scientific validity of computer simulation, using an example—integrated assessment modeling of climate change and its projected impacts—that is itself of great and urgent interest to policy in the real world. The spirited and continuing debate on the scientific status of integrated assessment models (IAMs) of global climate change has been conducted mostly among climate change modelers and users seeking guidance for climate policy. However, it raises a number and variety of issues that have been addressed, with various degrees of success, in other literature. The literature on methodology of simulation was mostly skeptical at the outset but has become more nuanced, casting light on some key issues relating to the validity and evidentiary standing of climate change IAMs (CC-IAMs). We argue that the goal of validation is credence, i.e., confidence or justified belief in model projections, and that validation is a matter of degree: (perfect) validity is best viewed as aspirational and, other things equal, it makes sense to seek more rather than less validation. We offer several conclusions. The literature on computer simulation has become less skeptical and more inclined to recognize that simulations are capable of providing evidence, albeit a different kind of evidence than, say, observation and experiments. CC-IAMs model an enormously complex system of systems and must respond to several challenges that include building more transparent models and addressing deep uncertainty credibly. Drawing on the contributions of philosophers of science and introspective practitioners, we offer guidance for enhancing the credibility of CC-IAMs and computer simulation more generally. Full article

Humans have always wanted to know what the future holds in store for them. In earlier centuries, people often sought clues to the future from sacred texts. Today, more secular approaches are increasingly used, although the older approaches to the future persist. Modern methods for prediction include trend extrapolation, the Delphi method, mathematical modeling, and scenario analysis, including backcasting. Extrapolation was only possible when reliable past data became available. The Delphi method relies on the judgement of experts in the subject matter. Mathematical modeling has been very successful in the physical sciences, and, in the form of integrated assessment models (IAMs), has been applied to problems such as assessing future energy use. Scenario analysis looks at a number of possible futures and develops internally consistent story lines around each. It is often used in conjunction with IAMs. Each of the four methods, including both their strengths and weaknesses, are discussed in turn. Finally, this entry looks at the future of prediction, and concludes that despite progress in each of the four approaches treated, predicting the future, never easy, is now harder than ever. Full article

Integrated assessment modelling (IAM) has been successfully used in the development of international agreements to reduce transboundary pollution in Europe, based on the GAINS model of IIASA. At a national level in the UK, a similar approach has been taken with the UK Integrated Assessment Model, UKIAM, superimposing pollution abatement measures and behavioural change on energy projections designed to meet targets set for the reduction of greenhouse gas emissions and allowing for natural and imported contributions from other countries and shipping. This paper describes how the UKIAM was used in the development of proposed targets for the reduction of fine particulate PM_2.5 in the UK Environment Act, exploring scenarios encompassing different levels of ambition in reducing the emissions of air pollutants up to 2050, with associated health and other environmental benefits. There are two PM_2.5 targets, an annual mean concentration target setting a maximum concentration to be reached by a future year, and a population exposure reduction target with benefits for health across the whole population. The work goes further, also demonstrating links to social deprivation. There is a strong connection between climate measures aimed at reducing net GHG emissions to zero by 2050 and future air quality, which may be positive or negative, as illustrated by sectoral studies for road transport where electrification of the fleet needs to match the evolution of energy production, and for domestic heating, where the use of wood for heating is an air quality issue. The UKIAM has been validated against air pollution measurements and other types of modelling, but there are many uncertainties, including future energy projections. Full article

Cities play a fundamental role in reducing greenhouse gas emissions and advancing the 2030 Agenda for Sustainable Development. In this context, public authorities need tools to help in identifying the best set of available solutions for the urban environment. Here, we developed an approach to help decision makers in evaluating sustainable solutions, considering aspects such as emission rate, economic attractiveness, job creation, and local competitiveness in an intersectoral fashion. To rank the best solutions, we developed a new methodology that links integrated assessment models (IAMs) to the available solutions at the Innovation Observatory for Sustainable Cities (OICS) database and applied it to Brazil. Our results show that the solutions with the greatest impact were often related to new technologies, for example, renewable energy, which depends on institutional and financial arrangements that are beyond the administrative capacity of the vast majority of municipalities. Despite these limitations, Brazilian cities can act as regulators or provide financial incentives and advocacy to promote sustainable solutions in the urban environment. Full article