Navigating the path towards successful implementation of the EU HTA Regulation: key takeaways from the 2023 Spring Convention of the European Access Academy.

BACKGROUND: The European Regulation on Health Technology Assessment (EU HTA R), effective since January 2022, aims to harmonize and improve the efficiency of common HTA across Member States (MS), with a phased implementation from January 2025. At "midterms" of the preparation phase for the implementation of the Regulation our aim was to identify and prioritize tangible action points to move forward. METHODS: During the 2023 Spring Convention of the European Access Academy (EAA), participants from different nationalities and stakeholder backgrounds discussed readiness and remaining challenges for the Regulation's implementation and identified and prioritized action points. For this purpose, participants were assigned to four working groups: (i) Health Policy Challenges, (ii) Stakeholder Readiness, (iii) Approach to Uncertainty and (iv) Challenges regarding Methodology. Top four action points for each working group were identified and subsequently ranked by all participants during the final plenary session. RESULTS: Overall "readiness" for the Regulation was perceived as neutral. Prioritized action points included the following: Health Policy, i.e. assess adjustability of MS laws and health policy processes; Stakeholders, i.e. capacity building; Uncertainty, i.e. implement HTA guidelines as living documents; Methodology, i.e. clarify the Population, Intervention, Comparator(s), Outcomes (PICO) identification process. CONCLUSIONS: At "midterms" of the preparation phase, the focus for the months to come is on executing the tangible action points identified at EAA's Spring Convention. All action points centre around three overarching themes: harmonization and standardization, capacity building and collaboration, uncertainty management and robust data. These themes will ultimately determine the success of the EU HTA R in the long run.

Empirical validation of integrated stock assessment models to ensuring risk equivalence: A pathway to resilient fisheries management.

The Precautionary Approach to Fisheries Management requires an assessment of the impact of uncertainty on the risk of achieving management objectives. However, the main quantities, such as spawning stock biomass (SSB) and fish mortality (F), used in management metrics cannot be directly observed. This requires the use of models to provide guidance, for which there are three paradigms: the best assessment, model ensemble, and Management Strategy Evaluation (MSE). It is important to validate the models used to provide advice. In this study, we demonstrate how stock assessment models can be validated using a diagnostic toolbox, with a specific focus on prediction skill. Prediction skill measures the precision of a predicted value, which is unknown to the model, in relation to its observed value. By evaluating the accuracy of model predictions against observed data, prediction skill establishes an objective framework for accepting or rejecting model hypotheses, as well as for assigning weights to models within an ensemble. Our analysis uncovers the limitations of traditional stock assessment methods. Through the quantification of uncertainties and the integration of multiple models, our objective is to improve the reliability of management advice considering the complex interplay of factors that influence the dynamics of fish stocks.

Rapid Assessment of the Need for Evidence: Applying the Principles of Value of Information to Research Prioritisation.

We propose a short-cut heuristic approach to rapidly estimate value of information (VOI) using information commonly reported in a research funding application to make a case for the need for further evaluative research. We develop a "Rapid VOI" approach, which focuses on uncertainty in the primary outcome of clinical effectiveness and uses this to explore the health consequences of decision uncertainty. We develop a freely accessible online tool, Rapid Assessment of the Need for Evidence (RANE), to allow for the efficient computation of the value of research. As a case study, the method was applied to a proposal for research on shoulder pain rehabilitation. The analysis was included as part of a successful application for research funding to the UK National Institute for Health and Care Research. Our approach enables research funders and applicants to rapidly estimate the value of proposed research. Rapid VOI relies on information that is readily available and reported in research funding applications. Rapid VOI supports research prioritisation and commissioning decisions where there is insufficient time and resources available to develop and validate complex decision-analytic models. The method provides a practical means for implementing VOI in practice, thus providing a starting point for deliberation and contributing to the transparency and accountability of research prioritisation decisions.

Making Drug Approval Decisions in the Face of Uncertainty: Cumulative Evidence versus Value of Information.

BACKGROUND: The COVID-19 pandemic underscored the criticality and complexity of decision making for novel treatment approval and further research. Our study aims to assess potential decision-making methodologies, an evaluation vital for refining future public health crisis responses. METHODS: We compared 4 decision-making approaches to drug approval and research: the Food and Drug Administration's policy decisions, cumulative meta-analysis, a prospective value-of-information (VOI) approach (using information available at the time of decision), and a reference standard (retrospective VOI analysis using information available in hindsight). Possible decisions were to reject, accept, provide emergency use authorization, or allow access to new therapies only in research settings. We used monoclonal antibodies provided to hospitalized COVID-19 patients as a case study, examining the evidence from September 2020 to December 2021 and focusing on each method's capacity to optimize health outcomes and resource allocation. RESULTS: Our findings indicate a notable discrepancy between policy decisions and the reference standard retrospective VOI approach with expected losses up to $269 billion USD, suggesting suboptimal resource use during the wait for emergency use authorization. Relying solely on cumulative meta-analysis for decision making results in the largest expected loss, while the policy approach showed a loss up to $16 billion and the prospective VOI approach presented the least loss (up to $2 billion). CONCLUSION: Our research suggests that incorporating VOI analysis may be particularly useful for research prioritization and treatment implementation decisions during pandemics. While the prospective VOI approach was favored in this case study, further studies should validate the ideal decision-making method across various contexts. This study's findings not only enhance our understanding of decision-making strategies during a health crisis but also provide a potential framework for future pandemic responses. HIGHLIGHTS: This study reviews discrepancies between a reference standard (retrospective VOI, using hindsight information) and 3 conceivable real-time approaches to research-treatment decisions during a pandemic, suggesting suboptimal use of resources.Of all prospective decision-making approaches considered, VOI closely mirrored the reference standard, yielding the least expected value loss across our study timeline.This study illustrates the possible benefit of VOI results and the need for evidence accumulation accompanied by modeling in health technology assessment for emerging therapies.

New government drug repurposing programs: Opportunities and uncertainties.

Drug repurposing can be cheaper and faster than developing new compounds. Yet, it remains underused, partially because of regulatory and intellectual property challenges. Policy-makers in the United States and Europe have created seven drug development programs that aim to overcome these challenges using a variety of different strategies.

Optimal scale sizes and average-profit efficiency under uncertainty: A chance-constrained DEA approach.

When the costs of the inputs and outputs of the units under evaluation are known, the evaluation of the profit efficiency of the units is one of the most significant evaluations that can provide valuable information about them. In this research, first, a new definition of the optimal scale size based on the maximization of the average measure of profit efficiency is presented. The average measure of profit efficiency develops the concept of economic efficiency measure by introducing a more accurate measure of efficiency compared to the measure of comparative and profit efficiency. It has been shown that the average measure of profit efficiency in a convex space is equivalent to the measure of profit efficiency in constant returns to scale technology, and then, some models are presented to calculate profit efficiency in a stochastic environment, to increase the ability of profit models in real examples by considering the calculation errors of inputs and outputs. Finally, the proposed method is used in an empirical example to calculate the average profit efficiency of a set of postal areas in Iran.

Performance and uncertainty assessment of green roofs for urban flood reduction in a high-density catchment in Ahmedabad, India.

Urban flooding poses a significant challenge to the rapidly growing Indian cities. Low-impact development strategies such as green roofs have shown the potential to reduce urban flooding. However, their performance assessment significantly varies across different studies. Therefore, the study's primary objective is to evaluate green roofs in the Indian context. For this evaluation, the green roofs are assessed based on building-level implementation scenarios for a high-density urban area in India for 25%,50%, and 75% application rates and different rainfall intensities (2,3 and 4-h duration and 2,5,10 and 25-year frequencies). Secondly, to probe the variations in the green roof performance across studies, uncertainty contributions to the runoff reduction from different parameters are quantified. The results show that green roofs can reduce up to 62% of flood volume and 24% of runoff. However, they are reasonably effective only beyond 25% application rates. Further, rainfall intensity contributes the most to the uncertainty of runoff reduction from green roofs. This uncertainty assessment implies that localized evaluation of green roofs depending on local rainfall conditions is required for city-wide policy planning. The study has a significant contribution to building confidence in the ability of green roofs to reduce urban floods in the context of developing countries like India.

Bioplastic derived from corn stover: Life cycle assessment and artificial intelligence-based analysis of uncertainty and variability.

Exploring feasible and renewable alternatives to reduce dependency on traditional fossil-based plastics is critical for sustainable development. These alternatives can be produced from biomass, which may have large uncertainties and variabilities in the feedstock composition and system parameters. This study develops a modeling framework that integrates cradle-to-grave life cycle assessment (LCA) with a rigorous process model and artificial intelligence (AI) models to conduct uncertainty and variability analyses, which are highly time-consuming to conduct using only the process model. This modeling framework examines polylactic acid (PLA) produced from corn stover in the U.S. An analysis of uncertainty and variability was conducted by performing a Monte Carlo simulation to show the detailed result distributions. Our Monte Carlo simulation results show that the mean life-cycle Global Warming Potential (GWP) of 1 kg PLA is 4.3 kgCO2eq (P5-P95 4.1-4.4) for composting PLA with natural gas combusted for the biorefinery, 3.7 kgCO2eq (P5-P95 3.4-3.9) for incinerating PLA for electricity with natural gas combusted for the biorefinery, and 1.9 kgCO2eq (P5-P95 1.6-2.1) for incinerating PLA for electricity with wood pellets combusted for the biorefinery. Tradeoffs for different environmental impact categories were identified. Based on feedstock composition variations, two AI models were trained: random forest and artificial neural networks. Both AI models demonstrated high prediction accuracy; however, the random forest performed slightly better.

The impacts of economy policy uncertainty on peer effects of firms R&D investment: Based on LDA machine learning and regression statistical modeling approach.

The Latent Dirichlet Allocation (LDA) model is used to extract the text themes of newspaper news and construct the Chinese Economic Policy Uncertainty (EPU) Index. On this basis, based on the relevant data of Chinese A-share listed companies from 2008 to 2020, this paper empirically analyzes the impact of EPU on peer effects of firms R&D investment, and finds that EPU will aggravate the peer effects of firms R&D investment. Furthermore, the moderating effect of manager's motivation to maintain reputation on the process of EPU influencing the peer effects of firms R&D investment was tested, and the mechanism of EPU influencing the peer effects of firms R&D investment through financial frictions was verified.

Companies inadvertently fund online misinformation despite consumer backlash.

The financial motivation to earn advertising revenue has been widely conjectured to be pivotal for the production of online misinformation1-4. Research aimed at mitigating misinformation has so far focused on interventions at the user level5-8, with little emphasis on how the supply of misinformation can itself be countered. Here we show how online misinformation is largely financed by advertising, examine how financing misinformation affects the companies involved, and outline interventions for reducing the financing of misinformation. First, we find that advertising on websites that publish misinformation is pervasive for companies across several industries and is amplified by digital advertising platforms that algorithmically distribute advertising across the web. Using an information-provision experiment9, we find that companies that advertise on websites that publish misinformation can face substantial backlash from their consumers. To examine why misinformation continues to be monetized despite the potential backlash for the advertisers involved, we survey decision-makers at companies. We find that most decision-makers are unaware that their companies' advertising appears on misinformation websites but have a strong preference to avoid doing so. Moreover, those who are unaware and uncertain about their company's role in financing misinformation increase their demand for a platform-based solution to reduce monetizing misinformation when informed about how platforms amplify advertising placement on misinformation websites. We identify low-cost, scalable information-based interventions to reduce the financial incentive to misinform and counter the supply of misinformation online.

Regional policy options for carbon peaking in the Yangtze River Delta under uncertainty.

The Yangtze River Delta (YRD) region plays a crucial role in achieving China's carbon peaking goal. However, due to uncertainties surrounding future economic growth, energy consumption, energy structure, and population, the attainment of carbon peaking in this region remains uncertain. To address this issue, this study utilized the generalized Divisia index method to analyze the driving factors of carbon emissions, including economy, energy, investment, and population. Subsequently, Monte Carlo simulations were combined with scenario analysis to dynamically explore the peak path of regional heterogeneity in the YRD from 2022 to 2035 under uncertain conditions. The findings highlighted that economic uncertainty has the most significant impact on carbon emissions. Furthermore, reducing energy intensity and promoting the transformation of the energy consumption structure contribute to carbon reduction. The study also revealed that the carbon peak in the YRD exhibits regional heterogeneity. According to the baseline scenario, carbon emissions in the YRD will not peak before 2035. However, under the low-carbon development scenario, the carbon emissions of Zhejiang and Shanghai will peak before 2030. Moreover, under the enhanced emission reduction (EE) scenario, carbon emissions in Jiangsu, Zhejiang, and Shanghai will peak before 2025, while Anhui will reach its peak before 2030. Collectively, the entire YRD region is forecasted to attain a carbon emissions peak of 2.29 billion tons by 2025 under the EE scenario. This study provides valuable insights into the carbon emission trajectories of the YRD region under uncertain conditions. The findings can be instrumental in formulating carbon peaking policies that account for regional heterogeneity.

Market liquidity and volatility: Does economic policy uncertainty matter? Evidence from Asian emerging economies.

This research investigates the complex interaction between liquidity and volatility while considering Economic Policy Uncertainty (EPU) as a moderating factor. Using a comprehensive dataset that incorporates various liquidity measures such as market resilience, depth, and breadth, the study examines how changes in liquidity impact volatility in four Asian incipient economies: China, Pakistan, India, and South Korea. By utilizing sophisticated econometric techniques, particularly the System Generalized Method of Moment (GMM), the findings demonstrate a statistically significant inverse relationship between liquidity and volatility. These findings imply that, within the Asian context, lower levels of volatility are correlated with higher market liquidity. By incorporating EPU into the model, the research acknowledges the significant role of economic factors in shaping market dynamics. Stakeholders, decision-makers, and investors can gain valuable insights from this analysis of variables influencing market stability in Asian emerging economies. The study's outcomes can guide policymakers in formulating strategies that promote market stability and improve market microstructure.

The impact of macroeconomic policy uncertainty on micro-enterprise investment efficiency-Empirical evidence from China.

The impact of macroeconomic policy uncertainty (EPU) on micro-level entities has garnered increasing attention in economic circles. This study examines the influence of EPU on the efficiency of investments made by China's A-share listed companies between 2016 and 2021. Using a panel fixed effect model for analysis, the research reveals that EPU has a notable adverse effect on the investment efficiency of enterprises. Furthermore, it suggests that advancements in digital finance, strong ESG performance, and enhanced entrepreneurial confidence can mitigate this negative impact effectively. The study also highlights that enterprises with lower valuation, shareholder control, limited audit reputation, and no bank connections are more vulnerable to the impact of EPU on investment efficiency compared to those with higher valuation, manager control, strong audit reputation, and bank connections. Consequently, future efforts should be directed towards enhancing the stability and relevance of economic policies, promoting digital finance, and enhancing corporate governance structures.

An integrated simulation-optimization approach for combined allocation of water quantity and quality under multiple uncertainties.

Multiple uncertainties such as water quality processes, streamflow randomness affected by climate change, indicators' interrelation, and socio-economic development have brought significant risks in managing water quantity and quality (WQQ) for river basins. This research developed an integrated simulation-optimization modeling approach (ISMA) to tackle multiple uncertainties simultaneously. This approach combined water quality analysis simulation programming, Markov-Chain, generalized likelihood uncertainty estimation, and interval two-stage left-hand-side chance-constrained joint-probabilistic programming into an integration nonlinear modeling framework. A case study of multiple water intake projects in the Downstream and Delta of Dongjiang River Basin was used to demonstrate the proposed model. Results reveal that ISMA helps predict the trend of water quality changes and quantitatively analyze the interaction between WQQ. As the joint probability level increases, under strict water quality scenario system benefits would increase [3.23, 5.90] × 109 Yuan, comprehensive water scarcity based on quantity and quality would decrease [782.24, 945.82] × 106 m3, with an increase in water allocation and a decrease in pollutant generation. Compared to the deterministic and water quantity model, it allocates water efficiently and quantifies more economic losses and water scarcity. Therefore, this research has significant implications for improving water quality in basins, balancing the benefits and risks of water quality violations, and stabilizing socio-economic development.

Application of the adaptive Monte Carlo method for uncertainty evaluation in the determination of total testosterone in human serum by triple isotope dilution mass spectrometry.

The measurement uncertainty is a crucial quantitative parameter for assessing the reliability of the result. The study aimed to propose a new budget for uncertainty evaluation of a reference measurement procedure for the determination of total testosterone in human serum. The adaptive Monte Carlo method (aMCM) was used for the propagation of probability distributions assigned to various input quantities to determine the uncertainty of the testosterone concentration. The basic principles of the propagation and the statistical analysis were described based on the experimental results of the quality control serum sample. The analysis of the number of Monte Carlo trials was discussed. The procedure of validation of the GUM uncertainty framework using the aMCM was also provided. The number of Monte Carlo trials was 2.974 × 106 when the results had stabilized. The total testosterone concentration was 16.02 nmol/L, and the standard uncertainty was 0.30 nmol/L. The coverage interval at coverage probability of 95% was 15.45 to 16.62 nmol/L, while the probability distribution for testosterone concentration was approximately described by a Gaussian distribution. The validation of results was not passed as the expanded uncertainty result obtained by the aMCM was slightly lower, about 7%, than that by the GUM uncertainty framework with consistent results of the concentration.

Significance of the sample determination in assessment of radiological examinations frequencies for population doses due to medical exposures.

The radiological examination frequency, i.e. the number of examinations performed annually, is necessary for estimating the collective effective dose of the population from medical exposures with ionizing radiation. Examination frequency surveys usually collect data from a limited number of radiological facilities participating in the survey. The collected data are then extrapolated to the existing radiological facilities in a country/region. Thus, the number of facilities and the specific facilities to participate, as well as, the extrapolation method used, are significant elements when designing the survey sample and methodology for examinations frequency assessments. This work attempted to simulate the situation when examination frequency data are collected from a limited number of facilities by investigating several "virtual sample" designs and two extrapolation methods. Comparisons between the calculated - by extrapolation - and the actual examination frequency in the country were made, for several scenarios and examination type data sets. The uncertainties were estimated and discussed thoroughly. The findings of this work highlighted the need for appropriate registry of the existing facilities in a country/region, the categorization of facilities considering the medical sector pattern in the country/region, the representativity and homogeneity of the samples used for a survey, as well as, the necessity for quality control of the collected examination frequency data. The results showed that when the aforementioned conditions were fulfilled, the examination frequency could be calculated with reasonable accuracy, based on data collected from a limited number of facilities. The paper also provides suggestions and tips for the collection and analysis of examination frequency data.

Unlocking the path to environmental sustainability: navigating economic policy uncertainty, ICT, and environmental taxes for a sustainable future.

The study aims to gauge the impact of economic policy uncertainty, ICT, and environmental tax on environmental sustainability, which is measured by carbon emission and ecological footprint in a panel of 22 nations from 1997 to 2021. The present study has implemented the advanced panel data estimation techniques, including continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC), dynamic seemingly unrelated regressions (DSUR), and nonlinear autoregressive distributed lagged (NARDL) in documenting the elasticities of target variables. Moreover, the directional causality has been tested through the D-H causality test. Study findings documented a positive and statistically significant linkage between EPU and environmental degradation. That is, EPU amplifies the emission of CO2 and ecological instability. The effects of ET and ICT are positively associated with environmental sustainability; that is, ET and ICT control the emission of CO2 and bring ecological improvement. This study contributes to the existing body of literature by conducting a thorough analysis of the relationship between various factors and their impact on environmental degradation. The study emphasizes the significance of every factor in influencing environmental outcomes. It provides policy suggestions to reduce CO2 emissions and promote ecological sustainability. The findings add valuable insights to the ongoing conversation about how to tackle environmental challenges in our constantly evolving world.

Reframing wildlife disease management problems with decision analysis.

Contemporary wildlife disease management is complex because managers need to respond to a wide range of stakeholders, multiple uncertainties, and difficult trade-offs that characterize the interconnected challenges of today. Despite general acknowledgment of these complexities, managing wildlife disease tends to be framed as a scientific problem, in which the major challenge is lack of knowledge. The complex and multifactorial process of decision-making is collapsed into a scientific endeavor to reduce uncertainty. As a result, contemporary decision-making may be oversimplified, rely on simple heuristics, and fail to account for the broader legal, social, and economic context in which the decisions are made. Concurrently, scientific research on wildlife disease may be distant from this decision context, resulting in information that may not be directly relevant to the pertinent management questions. We propose reframing wildlife disease management challenges as decision problems and addressing them with decision analytical tools to divide the complex problems into more cognitively manageable elements. In particular, structured decision-making has the potential to improve the quality, rigor, and transparency of decisions about wildlife disease in a variety of systems. Examples of management of severe acute respiratory syndrome coronavirus 2, white-nose syndrome, avian influenza, and chytridiomycosis illustrate the most common impediments to decision-making, including competing objectives, risks, prediction uncertainty, and limited resources.

Replanteamiento del manejo de problemas por enfermedades de fauna mediante el análisis de decisiones Resumen El manejo actual de las enfermedades de la fauna es complejo debido a que los gestores necesitan responder a una amplia gama de actores, varias incertidumbres y compensaciones difíciles que caracterizan los retos interconectados del día de hoy. A pesar de que en general se reconocen estas complejidades, el manejo de las enfermedades tiende a plantearse como un problema científico en el que el principal obstáculo es la falta de conocimiento. El proceso complejo y multifactorial de la toma decisiones está colapsado dentro de un esfuerzo científico para reducir la incertidumbre. Como resultado de esto, las decisiones contemporáneas pueden estar simplificadas en exceso, depender de métodos heurísticos simples y no considerar el contexto legal, social y económico más amplio en el que se toman las decisiones. De manera paralela, las investigaciones científicas sobre las enfermedades de la fauna pueden estar lejos de este contexto de decisiones, lo que deriva en información que puede no ser directamente relevante para las preguntas pertinentes de manejo. Proponemos replantear los obstáculos para el manejo de enfermedades de fauna como problemas de decisión y abordarlos con herramientas analíticas de decisión para dividir los problemas complejos en elementos más manejables de manera cognitiva. En particular, las decisiones estructuradas tienen el potencial de mejorar la calidad, el rigor y la transparencia de las decisiones sobre las enfermedades de la fauna en una variedad de sistemas. Ejemplos como el manejo del coronavirus del síndrome de respiración agudo tipo 2, el síndrome de nariz blanca, la influenza aviar y la quitridiomicosis ilustran los impedimentos más comunes para la toma de decisiones, incluyendo los objetivos en competencia, riesgos, incertidumbre en las predicciones y recursos limitados.

Uncertainty analysis and economic value prediction of water environmental capacity based on Copula and Bayesian model: A case study of Yitong River, China.

Water environmental capacity (WEC) is an indicator of environment management. The uncertainty analysis of WEC is more closely aligned with the actual conditions of the water body. It is crucial for accurately formulating pollution total emissions control schemes. However, the current WEC uncertainty analysis method ignored the connection between water quality and discharge, and required a large amount of monitoring data. This study analyzed the uncertainty of the WEC and predicted its economic value based on Copula and Bayesian model for the Yitong River in China. The Copula model was employed to calculate joint probabilities of water quality and discharge. And the posterior distribution of WEC with limited data was obtained by the Bayesian formula. The results showed that the WEC-COD in the Yitong River was 9009.67 t/a, while NH3-N had no residual WEC. Wanjinta Highway Bridge-Kaoshan Town reach had the most serious pollution. In order to make it have WEC, the reduction of COD and NH3-N was 5330.47 t and 3017.87 t. The economic value of WEC-COD was 5.97 × 107 CNY, and the treatment cost was 2.04 × 108 CNY to make NH3-N have residual WEC. The economic value distribution of WEC was extremely uneven, which could be utilized by adjusting the sewage outlet. In addition, since the treated water was discharged into the Sihua Bridge-Wanjinta Highway Bridge reach, the WEC-COD and the economic value were 19,488.51 t/a and 8.24 × 107 CNY. Increasing the flow of rivers could effectively improve WEC and economic value. This study provided an evaluation tool for guiding river water environment management.

Financial regulations and sustainability: The role of energy price and climate policy uncertainty.

Russia ranks among the top five countries worldwide in terms of carbon emissions, with the energy, transportation, and manufacturing sectors as the major contributors. This poses a significant threat to both current and future generations. Russia faces challenges in achieving Sustainable Development Goal 13, necessitating the implementation of more innovative policies to promote environmental sustainability. Considering this alarming situation, this study investigates the role of financial regulations, energy price uncertainty, and climate policy uncertainty in reshaping sectoral CO2 emissions in Russia. This study utilizes a time-varying bootstrap rolling-window causality (BRW) approach using quarterly data from 1990 to 2021. The stability test for parameters indicates instability, suggesting that the full sample causality test may yield incorrect inferences. Thus, the BRW approach is employed for valid inferences. Our findings confirm the time-varying negative impact of financial regulations on CO2 emissions from energy, manufacturing, and transportation sectors. Additionally, findings confirm time-varying positive impact of energy prices and climate policy uncertainty on CO2 emissions from the energy, manufacturing, and transportation sectors. Strong financial regulations and stable energy and climate policies are crucial for achieving sustainability, highlighting significant policy implications for policymakers and stakeholders.