Category: 3. Business

As the world continues seeking a treaty to end plastic pollution, Africa is not waiting to adapt but planning ahead.

Beyond short-term bans and clean-ups, the continent is working to revamp laws, markets and supply chains to steer economies towards non-plastic alternatives and substitutes as a new source of industrial growth.

Ghana: Plastic policy for industrial shift

In Ghana, a five-year implementation plan is underway to reduce plastic packaging and make sustainable alternatives more commercially viable.

The blueprint, a first in West Africa, was developed with support from the UK-funded Sustainable Manufacturing and Environmental Pollution (SMEP) Programme of UN Trade and Development (UNCTAD).

It aligns economic incentives, public procurement and key performance indicators with broader industrial strategy, currently being piloted to target the country’s most waste-heavy sectors such as plastic mulch film, sachet water packaging and carrier bags.

“This is a development strategy, not a waste strategy,” Ebenezer Laryea, a project director for SMEP on the ground, told UNCTAD ahead of this year’s Africa Industrialization Week running through 21 November.

 “We’re using plastics policy to drive broader industrial shift, rethinking how we trade and consume so that sustainability and the bioeconomy shape our path to economic growth.”

The blueprint links to forthcoming Extended Producer Responsibility (EPR) rules, as the country aims for a competitive foothold in the fast-emerging global circular bioeconomy.

“Ghana’s transition provides an opportunity for both enhanced environmental protection and economic advancement, positioning the country as a hub for trade in plastic alternatives and natural substitutes,” said Director Larry Kottoe of Ghana’s Environmental Protection Authority.

East Africa: Plastic policy for green industrialization

Supported by UNCTAD’s SMEP programme, countries in East Africa also accelerate progress towards a circular and regenerative economy.

The East African Community is considering a draft bill on a regionally binding roadmap to phase out harmful single-use plastics.

The work seeks to close cross-border loopholes that often hamper national action on single-use plastics, harmonising regulations across Burundi, the Democratic Republic of the Congo, Kenya, Rwanda, Somalia, South Sudan, Uganda and the United Republic of Tanzania.

Beyond banning plastics, the draft bill mandates EPR rules, incentivizes sustainable materials and protects informal workers to formalize the waste economy.

“Plastic controls must be paired with business-enabling measures, supportive policy frameworks, sustainable finance and skills development,” concluded Abraham Korir Sing’Oei, a principal secretary at Kenya’s Ministry of Foreign and Diaspora Affairs.

Prepared by Aurora Abbondanza, Marianna Caccavaio, Valentina Gattinoni and Oana Maria Georgescu

Published as part of the Macroprudential Bulletin 32, November 2025.

As authorities across the euro area work towards including climate risks into regular stress-testing frameworks, this article offers a starting point for assessing bank resilience to climate risks that materialise under a short-term horizon. This is relevant since acute physical risks and abrupt policy change can also materialise at short notice and affect the balance sheet of financial institutions. The analysis uses an adverse macroeconomic backdrop that combines the EBA’s adverse scenario with the Network for Greening the Financial System’s Nationally Determined Contributions (NGFS NDCs) scenarios. It extends the EU-wide 2025 stress test results by incorporating both transition and acute physical climate risks into the credit risk assessment for non-financial corporations by means of top-down models. Transition risks driven by green investments to reduce emissions amplify credit losses and reduce banks’ Common Equity Tier 1 (CET1) capital, particularly in high energy-intensive sectors. Similarly, acute physical risks, such as extreme flood events, reduce CET1 capital through direct damage, local disruptions, and macroeconomic spillovers. While the magnitude of impacts varies across banks, the analysis shows that both types of climate risk can have a moderate but consequential effect on capital ratios. Notably, the banks most exposed to climate-related losses may differ from those identified as the most vulnerable in the broader EU-wide assessment. These findings underscore the importance of incorporating both types of climate risk into regular financial stability assessments.

1 Introduction

In recent years integrating short and long-term climate risks into stress testing has emerged as a key priority for financial regulators globally. Forward-looking assessment methods have become a crucial instrument for quantifying and assessing the potential impacts of climate change on economies and financial systems.^[1] While much attention has been placed on the long-term nature of these risks, their short-term implications are equally important. Acute weather events, abrupt policy shifts and rapid market repricing driven by climate developments can occur at short notice, causing immediate and significant impacts on financial institutions’ balance sheets and the broader economy.

Financial regulators in Europe will be adding climate risk monitoring to their regular stress testing of the financial sector. The European Supervisory Authorities (the European Banking Authority (EBA), the European Insurance and Occupational Pensions Authority (EIOPA) and the European Securities and Markets Authority (ESMA) − collectively referred to as the ESAs) recently issued a Joint Consultation Paper on draft guidelines on the stress testing of environmental, social and governance (ESG) risks. This started with the environmental component, focusing on climate and other nature-related risks such as biodiversity, deforestation, etc. Financial institutions face challenges in modelling future climate pathways, including fragmented tools and a lack of modelling consensus; these draft guidelines aim to harmonise methodologies and practices among banking, insurance and securities supervisors to ensure proportionality and enhance the effectiveness and efficiency of stress tests. The consultation process concluded on 19 September 2025, and the final guidance is expected to be published by the ESAs in the first few months of 2026.

With regards to the banking system, the EBA is working to integrate climate risks into its EU-wide stress-testing framework. Based on the strategy outlined in the EBA’s Annual Report 2024 and in line with its mandate,^[2] the incorporation of climate risks into the EU-wide stress-testing framework will be gradual. Partial integration of climate risks, referred to as a combined approach, will start in 2027, with additional related elements introduced in subsequent stress tests. Particular emphasis will be placed on ensuring comprehensive coverage and assessment of both physical risks (including their acute dimensions) and transition risks, supported by the development of tailored scenarios.

The EBA’s proposed framework for climate stress testing will ensure that the principles of proportionality and simplification are observed while leveraging the existing EU-wide stress test infrastructure. The proportionality principle means the framework will be tailored to the size, risk profile and climate risk exposure of individual institutions. Moreover, the climate stress-testing module will be aligned with the EU-wide stress test in terms of data definitions, reporting processes, scenarios and methodological design, significantly reducing complexity and easing the implementation burden for institutions. Finally, utilising the existing EU-wide stress test infrastructure will enhance consistency and efficiency, while also paving the way for the gradual and comprehensive integration of climate risks into the EU-wide framework over the longer term.

This article provides additional insights into the EU-wide stress test by incorporating climate risk into banks’ credit risk projections via a top-down approach. The analysis extends the 2025 EU-wide stress test results by incorporating both transition and acute physical climate risks into the credit risk assessment for non-financial corporations (NFCs) by means of top-down models. The focus on credit risk is justified as (1) it is a significant risk driver in supervisory stress test exercises; and (2) the transmission channels from climate shock to credit risk are better understood and, to varying degrees, better reflected in banks’ credit risk and stress test models compared with other risk drivers such as market or profitability risks. Transition risks stemming from green investments to reduce emissions increase default probabilities, particularly in high energy-intensive sectors. This leads to amplified credit losses, lowering banks’ CET1 capital by a moderate 74 basis points. Extreme flood events further exacerbate credit risks through direct, local and macroeconomic transmission channels, resulting in an additional 77 basis point decrease in the CET1 ratio (see Box 1). Interestingly, the exercise identifies undetected pockets of risk, as the banks most exposed to climate-related losses may differ from those identified as the most vulnerable in the broader EU-wide assessment (see Rodriguez d’Acri and Shaw, 2025).

An integrated approach to climate stress tests, when executed well, can help banks perform better, not just meet regulatory expectations. While climate stress-testing exercises have emerged as a key tool for supervisors to assess the impact of climate risks on the banking system, euro area banks themselves are making more and more use of them to inform required disclosures and strategic choices (see ECB report on good practices for climate stress testing). In view of the evolving nature of this topic, banks will have to adapt their practices on an ongoing basis.

2 Extending the 2025 EU-wide stress test by adding a climate risk analysis for NFCs

Banks’ sectoral credit risk exposures to transition risks can be translated into changes in default probabilities and credit losses. The analysis leverages the projections for energy prices, emissions and energy consumption, as well as the trajectories of relevant macroeconomic variables provided by the Network of Central Banks and Supervisors for Greening the Financial System (NGFS) in its Nationally Determined Contributions (NDCs) scenario. By integrating the NGFS NDCs scenario into the EBA’s 2025 adverse scenario, this approach provides insights into how transition risks can amplify the outcomes of the official EU-wide stress test, highlighting their importance for financial stability assessments. The impact of transition risk must be assessed in conjunction with physical risk (see Box 1), as delayed and fragmented transition policies are associated with higher physical risk over the medium term.

2.1 Transition risk scenario

Building on the NGFS NDCs scenario, a short-term transition risk scenario is constructed, reflecting pledged emissions targets and delayed policy action. While climate risk scenarios are typically designed with a long-term horizon, transition risks can also be highly relevant in the short term. This article adopts a short-term perspective on climate transition risk using the 2025-27 path of the NGFS NDCs scenario. This incorporates all pledged emission reduction targets announced by individual countries as of March 2024, even where they have not yet been accompanied by effective policies.^[3] Specifically, the NGFS NDCs scenario foresees a shift in the EU energy mix, marked by a reduction in the consumption of fossil fuels such as gas and coal and growing reliance on renewables and electricity, driven by firm-level green investments. Out to 2027, the share of gas, coal and oil in the energy mix is projected to decrease, while renewables are expected to experience a substantial increase, reflecting the transition towards cleaner energy sources (Chart 1, panel a).

To understand how these changes affect individual firms, the energy mix foreseen by the scenario is downscaled at firm level. This is done by combining data on sectoral energy consumption from Eurostat with the revenue share of each firm within its sector. Furthermore, it is assumed that the technological change needed to support the implied shift in firms’ energy mix is achieved through green investments (Chart 1, panel b), with the magnitude of these directly proportional to the emission intensity of each sector. Green investments affect firms’ leverage and profitability, and hence their probability of default.

Chart 1

The energy mix and green investments implied by the NGFS NDCs scenario

a) Change in the EU aggregate energy mix	b) Cumulative green investments: firm averages
(percentages)	(EUR millions)

2.2 Methodological approach to projecting default probabilities and loan losses under the transition risk scenario

To project corporate default probabilities over the stress test horizon, a fixed effects sector-level regression is employed linking corporate failure rates to leverage and profitability. NFCs’ probabilities of default (PDs) are estimated by leveraging the approach employed by the ECB in the Fit-for-55 scenario analysis, with some adjustments (see Appendix I in ESAs and ECB, 2024). Annual sector-specific PDs are projected by linking sectoral failure rates to projected profitability and leverage, both of which incorporate exogenous climate-related shocks as defined by the NGFS NDCs scenario. The failure rate at sectoral level is calculated as the percentage of failed firms relative to all firms in the sector. Corporate failure is measured by an indicator variable adapted from Gourinchas et al. (2024). This indicator takes a value of 1 if the firm’s interest expenses exceed its cash holdings and its leverage is larger than 1 over two consecutive years, and 0 otherwise. Leverage is defined as the ratio of total liabilities to total assets, and profitability as the ratio of revenue net of operating expenses to total assets.

Profitability is influenced by several interrelated factors, including interest expenses and the amortisation cost associated with green investments. Investments aimed at reducing CO2 emissions are assumed to be paid off and amortised over a ten-year period. Firms’ profitability is reduced by interest expenses on the outstanding amount and the amortisation costs of green investments. Additionally, as firms’ total assets are projected in line with the NGFS scenario path for macroeconomic variables, the transition to a low-carbon economy has an impact on both the numerator and the denominator of leverage, negatively affecting solvency.^[4]

Increased green investments, as outlined in the NGFS scenario, affect firms’ balance sheets through higher indebtedness and lower profitability. The required level of green investment is assumed to depend on the energy intensity and emissions associated with the sector in which a firm operates. Mining and quarrying, manufacturing, electricity generation and transmission and water supply are examples of high energy-intensive sectors. Building, transport and agriculture are considered medium energy-intensive sectors. And some wholesale and trade activities, accommodation and food services, ICT and others are classified as low energy-intensive sectors.

To quantify the effect of climate transition risks on credit losses, projected changes in sectoral PDs are mapped to banks’ sectoral exposures. AnaCredit data are used to map the projected changes in PD at sectoral level to firms’ PD and their creditor bank. Thereafter the change in the PDs of NFCs is aggregated at country level and applied to a bank’s initial PD reported in the EU-wide stress test. Combining the resulting credit losses with those reported under the EBA’s 2025 adverse scenario makes it possible to assess how transition risks can amplify the credit losses in an adverse macroeconomic scenario.

2.3 Transition risk results

The leverage and profitability of high energy-intensive firms react most strongly to the transition risk shocks included in the scenario. The average cumulative increase in leverage relative to the 2024 starting point is most pronounced for high energy-intensive firms. They rise by 4.33 percentage points, compared with an increase of 0.74 percentage points in the medium energy-intensive sectors and a decrease of 0.30 percentage points in the low energy-intensive sectors (Chart 2, panel a). Similarly, profitability declines most in the sectors that are heavily reliant on carbon-intensive energy, with an average cumulative reduction of 8.41 percentage points for high energy-intensity sectors and 1.96 percentage points for the others (Chart 2, panel b). These dynamics are driven by several factors. The pronounced decline in profitability observed in energy-intensive sectors reflects not only the conservative assumptions used to translate the scenario variables into firm-level balance sheet outcomes, but also the added burden of higher amortisation and interest expenses associated with green investments. Funds raised to finance green investments also contribute to a significant increase in leverage, adding further strain to firms’ finances. At the same time, investments allow firms to reduce their consumption of carbon-intensive energy, which lowers their energy costs.

Chart 2

^{Change in leverage and profitability, by energy intensity}

a) Leverage	b) Profitability
(percentage point change)	(percentage point change)

The increase in PD over the three-year horizon is more pronounced for firms operating in high energy-intensive sectors. On average, firms’ PDs rise by 50%, with the largest share of the increase concentrated in the first year of the stressed period (Chart 3, panel a). Sectors with high energy intensity face the most substantial increase, with default probabilities climbing by a median of 91%, while firms in medium energy-intensity sectors show a more moderate median rise of 28% (Chart 3, panel b).

Chart 3

Firms’ probability of default

a) Change in NFC PD, by year	b) Distribution of change in NFC PD
(percentage change)	(percentage change)

The overall impact of loan losses on banks’ CET1 ratios remains contained; however, banks with higher exposures to energy-intensive sectors face greater losses. Compared with the 2025 EU-wide stress test results, the additional system-level impact of credit risk losses from banks’ NFC portfolios on their CET1 capital ratios under the adverse scenario remains moderate, accounting for 74 basis points over the 2025-27 horizon (Chart 4, panel a). The largest additional credit risk losses arise for banks that are most exposed to high energy-intensive sectors, followed by those with the highest exposures to medium and low energy-intensive sectors (Chart 4, panel b).

Chart 4

^{Distribution of NFC credit risk losses and impact on CET1 capital ratios}

a) NFC loan losses in the EU-wide stress test with additional climate transition risk	b) CET1 impact of additional NFC loan losses due to climate transition risk in the adverse scenario, by bank exposure
(basis points)	(x-axis: basis points, y-axis: density)

Box 1
Flood events in the EU and impact on corporate loan quality

^{Prepared by Aurora Abbondanza, Ugo Albertazzi, Davor Djekic and Aurea Ponte Marques}

This box presents a sensitivity analysis of the 2025 EU-wide stress test results looking at the effects of acute physical risks on corporate loan quality. The EBA’s 2025 adverse scenario captures the main cyclical risks faced by the EU banking sector, but does not account for climate risk, despite its potential to trigger significant and systemic losses.^[5] In this box, bank resilience is therefore assessed against the stress triggered by the financial and the real-economy shocks underpinning the EBA’s adverse scenario in conjunction with the materialisation of acute climate physical risks. The analysis focuses on credit risk, the primary transmission channel through which physical risks affect banks’ financial soundness. It concentrates on corporate loans, considering both data availability and the ubiquity of the business practice of ensuring residential mortgage collateral for physical risk. The analysis is limited to flood events, as these are the best documented in terms of their direct impact on firms’ activity. However, including other physical climate hazards (e.g. wildfires or droughts) would provide a more comprehensive view of country and sector-specific heterogeneity, as exposure to different types of risks varies widely across countries.

The analysis is based on a scenario combining the EBA adverse and the NGFS NDCs scenarios, making it possible to consider the materialisation of physical risk against a backdrop of significant macroeconomic challenges, including a projected cumulative GDP decline of approximately 6.9 percentage points (see Section 2 for a detailed description of the NGFS NDCs scenario).

The materialisation of acute physical risk events may lower the credit quality outlook through three distinct transmission channels, each operating at a different level of aggregation. First, floods may have a direct impact on the solvency of the firms directly affected, due to the disruptions and physical asset damage these events typically entail. Second, they may also produce adverse effects on the credit risk of all firms in the area, even those not directly affected, due to disruption to local transportation or service availability, for example. Third, floods may lead to higher credit risk if they result in macroeconomic deterioration, as is the case in the scenario under consideration. This implies that effective stress testing for acute physical risk requires empirical models that capture the credit risk implications of such events at both the firm and local economy levels. It also demands an approach able to construct a meaningful, granular scenario that clearly identifies the specific local areas and firms affected by the acute event assumed to materialise in the scenario.

A credible assessment of the impact of acute physical risk scenarios should be conducted at a granular level, however, to capture their local nature. Physical risk exposure from river flooding is highly concentrated in specific regions and affects a relatively small but significant subset of firms in the euro area. The physical risk score measures each borrower’s exposure to river flooding for the period 2021-50 under the RCP 4.5 scenario, ranging from 0 (low risk) to 5 (very high risk). As shown in Chart A, panel a), while most firms in the sample exhibit low exposure to physical risk (score 0), a substantial number face moderate to high risk, with more than 22,000 falling into the highest risk category (score 5).

Turning to the empirical evidence, physical climate risks can elevate financial vulnerability across wider regions through macroeconomic channels. Under the EBA’s adverse scenario combined with the NGFS NDCs scenario, degradation of the macroeconomic environment leads to a broad-based deterioration in credit quality. The probability of corporate defaults rises significantly, peaking in 2026 and reaching levels consistently higher than in the EU-wide stress test (Chart A, panel b). The implied increase in default frequency is around 2 percentage points, cumulatively at the end of the projection horizon.

Chart A

^{Probability of default under the EBA’s adverse scenario and NGFS NDCs scenario and firm distribution of exposure to physical risk}

a) Number of firms, by physical risk score	b) Probability of default under the EBA adverse and NGFS NDCs scenarios
(x-axis: physical risk score; y-axis: number of firms)	(percentage, y-axis: probability of default)

A granular physical risk impact assessment, consistent with the combined EBA adverse and NGFS NDCs scenario, can be obtained based on the principle that the affected areas and firms are those displaying the highest levels of physical risk score (Chart A, panel a). This impact assessment assumes materialisation of acute physical risk in the form of widespread floods.^[6] The geographical distribution of floods in the scenario is calibrated by assuming that such events concern areas with some exposure to physical risks, that is all municipalities other than those only populated by firms with the lowest physical risk score.^[7] This approach identifies 2,786 municipalities as affected, representing 36% of loan exposures in the sample (Chart B, panel b). Within each affected municipality, the 6.4% of firms with the highest climate risk score are assumed to be affected (Chart B, panel a). This fraction corresponds to what has been historically observed in the sample used for the RDD analysis (Table A, panel b).

Table A

^{Effect of being in a flooded area (DiD) and effect of being flooded (RDD) on loan quality}

a) Difference-in-difference (DiD) results

(coefficients, standard errors in parenthesis)

	(1)	(2)	(3)
Flooded	-0.002 (0.018)	0.002 (0.023)	0.022 (0.018)
Post	-0.023 (0.023)	-0.011 (0.048)	-0.012 (0.020)
Flooded x Post	0.099 ** (0.042)	0.181 ** (0.074)	0.107 ** (0.053)
Quarters from event	[-2,2]	[-2,2]	[-8,2]
Observations	38,522	33,482	72,900
Number of events	55	25	25

b) Regression discontinuity design (RDD) results

(coefficients, standard errors in parenthesis)

	(1)	(2)	(3)
Flooded	0.720 ** (0.362)	1.967 ** (0.919)	1.885 *** (0.652)
Distance	0.002 *** (0.001)	0.005 *** (0.002)	0.004 *** (0.001)
Flooded x Distance	-0.000 (0.002)	-0.002 (0.004)	-0.003 (0.003)
Quarters from event	(0,1]	(0,1]	(0,2]
Observations	9,705	2,257	4,506
Number of events	6[1]	1 (Valencia)	1 (Valencia)

The impact of physical risk events on corporate loan quality is significant for firms based in affected municipalities, even if they are not directly affected by the event. A difference-in-difference (DiD) exercise assesses the impact of a wide set of events (up to 55) on the loan quality of firms located in affected areas, regardless of whether or not they were directly flooded. The results show that firms in these municipalities also faced a higher likelihood of loan deterioration after the event, with estimated PD increases ranging from 0.1 to 0.2 percentage points compared with unaffected municipalities over the next two quarters, even after considering overall economic conditions (Table A, panel a). These findings are consistent across different timeframes and a wide set of events, confirming the broader regional impact of physical risk, and possibly reflecting disruptions caused to the local economy.

Zooming further in on the differential effect at firm level reveals a clear and significant impact of floods on the corporate loan quality of firms directly affected. Using a regression discontinuity design (RDD) based on detailed geolocation data covering both firm locations and the areas affected by acute events, the analysis compares firms located in affected areas with neighbouring unaffected ones, focusing on six major flood events in the euro area since 2018.^[8] The results indicate that for loans extended to flooded firms, the probability of becoming non-performing increases by 0.7 percentage points in the next quarter compared with unaffected firms (Table A, panel b). In the specific case of the Valencia flood in 2024, the effect was even stronger − up to 2.0 percentage points in the next two quarters (Table A, panel b, columns 2 and 3). These estimates are statistically significant and suggest a clear causal relationship between direct exposure to physical risk materialisation and worsening loan performance.^[9]

Chart B

^{Share of affected firms by municipality and shares of firms by type of exposure to physical risk}

a) Map of municipalities, gradient coloured by share of affected firms in the granular physical risk scenario	b) Shares of firms by type of exposure to physical risk in the granular physical risk scenario
(percentage, share of affected firms)	(percentages)

Capital depletion from the granular physical risk scenario is obtained by integrating the macroeconomic effect of the combined EBA and NGFS scenarios and the estimated impact on credit risk for loans to unaffected firms in affected municipalities, directly affected firms and other domestic firms, with bank-level information on the size of such portfolios. Building on AnaCredit information, each bank’s portfolio of corporate loans can be divided into the three categories. On aggregate, around 34% of exposures are associated with firms not directly affected by the physical risk scenario but located in affected municipalities. Loans to directly affected firms account for just 2% of banks’ total portfolios, with the remaining exposures corresponding to other domestic firms. This breakdown allows us to compute the losses obtained in the physical risk scenario for each bank, based on the quantification provided above.^[10]

Chart C

Distribution of bank credit risk losses under the EBA adverse and physical risk (NGFS NDCs combined with granular) scenarios

(x-axis: basis points, y-axis: density)

The additional credit risk losses stemming from physical climate risk are relatively contained at the aggregate level, but exhibit significant heterogeneity across banks. Chart D, panel a) presents the aggregate results, showing that the combined impact of the EBA’s adverse scenario and the NGFS NDCs results in an overall capital depletion of 487 basis points – nearly 77 basis points more than the impact of the EBA’s adverse scenario alone. This difference reflects the impact of climate risks and is almost entirely driven by local effects. Interestingly, the largest increases in losses are not concentrated among banks already facing substantial capital depletion under the EBA’s adverse scenario, indicating a weak relation between the two sources of risk. For the median bank, capital depletion under the combined scenario is 496 basis points, compared with 419 basis points under the EBA’s adverse scenario (Chart C). Notably, for 7% of banks, losses exceed 200 basis points, indicating a concentration in exposure to physical climate risk. Finally, as shown in Chart D, this concentration of losses can arise from any of the three transmission channels through which physical risk materialises: losses from directly affected firms, losses from unaffected firms located in affected areas and losses from broader macroeconomic deterioration.

Chart D

Breakdown of system-level credit risk losses and banks’ distribution of climate credit risk losses by type of exposure to physical risk

a) Breakdown of system-level credit risk losses, by type of exposure to physical risk	b) Distribution of climate credit risk losses, by type of exposure to physical risk
(basis points)	(basis points)

3 Conclusions

Analysing transition and physical climate risks reveals a moderate yet consequential impact on bank resilience, highlighting the importance of comprehensive financial risk assessments. As authorities across the euro area work towards including climate risks in regular stress-testing frameworks, this analysis offers a starting point for a sensitivity analysis exploring bank resilience to transition and physical climate risks under an adverse macroeconomic scenario (see EBA, 2024). For transition risk, the analysis shows that the additional impact of transition risk on CET1 capital depletion is moderate on aggregate, at 74 basis points, with the largest part of the impact stemming from exposures to high energy-intensive sectors. The impact of physical risk is assessed in Box 1, looking at a sensitivity analysis where a lack of, or delayed and fragmented transition, policies is associated with higher physical risk over the medium term. The impact of acute physical risk is estimated to increase CET1 capital depletion by 77 basis points beyond the depletion indicated by the EBA’s adverse scenario. Our analysis indicates that transition and physical climate risks can have a moderate but consequential impact on banks’ capital ratios. Additionally, the banks most exposed to climate-related losses may differ from those identified as most vulnerable in the broader EU-wide assessment (see Rodriguez d’Acri and Shaw, 2025). Together, these findings underscore the importance of incorporating both types of climate risk into regular financial stability assessments.

References

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Prepared by Juan Manuel Figueres, Barbara Montero Prieto, Valerio Scalone, James ’t Hooft, Lucas ter Steege and Clarissa Vallotto

Published as part of the Macroprudential Bulletin 32, November 2025.

The severity and the plausibility of stress test scenarios are crucial elements for interpreting the results and ensuring the credibility of stress-testing exercises. This article introduces a comprehensive framework for assessing scenario severity and plausibility in the context of the adverse scenarios used in the EU-wide stress tests. Two families of indicators are developed, characterised by a backward-looking and a forward-looking perspective. Backward-looking indicators compare the scenario with historical regularities, using as key metrics deviations from baseline projections and comparisons with the extreme values of key variables. Forward-looking indicators are drawn from macroeconomic modelling and compare the scenario with projected distributions about future economic developments incorporating the co-movement of variables within a unified analytical framework. These forward-looking metrics enable the severity assessment to account for the prevailing financial conditions and the level of systemic risk in the economy. The analysis presented suggests that the adverse scenarios used in the EU-wide stress tests have become more severe over time, peaking in the 2023 exercise and stabilising in 2025. Taking into account systemic risk, the 2025 scenario appears to be slightly more severe than the 2023 scenario. Overall, the article supports the idea of fostering a more effective definition, monitoring and communication of scenario severity, thereby strengthening the policy relevance and transparency of stress-testing exercises.

1 Introduction

Adverse scenarios for EU-wide stress tests need to be sufficiently severe yet plausible. The two concepts of severity and plausibility are interrelated. Severity is defined as the magnitude of the stress event, measured against a specific reference level (e.g. historical stress episodes, starting points and possible future outcomes). Plausibility refers to the validity (likelihood) of an extreme stress event, which can also be assessed by considering the specific risk environment underlying the scenario narrative. In terms of evaluating these two scenario requirements, backward-looking metrics are informative about the level of severity with respect to historical events, while forward-looking metrics assess both severity and plausibility by explicitly quantifying the tail risks which materialise in the adverse scenario.

A comprehensive assessment of scenario severity and plausibility is important when communicating the underlying assumptions of such scenarios. Understanding the severity of a scenario is essential for interpreting stress test results and for comparing the evolution of capital depletion across different exercises. The macro-financial scenario for the EU-wide banking sector stress test published by the ESRB measures severity in terms of real GDP cumulative growth across the scenario horizon.^[1] The scenario for the 2025 Bank Capital stress test published by Bank of England does not explicitly discuss severity but provides reference historical figures for the global financial crisis along with the scenario paths.^[2] These approaches, while informative, may not provide a complete assessment of scenario severity. By contrast, this article presents a more comprehensive framework for severity and plausibility evaluation which considers the multi-variable, multi-country and systemic risk dimensions of the EU-wide stress test scenario. It does so by leveraging on indicator-based methods and macroeconomic modelling. Such approaches help to ensure that scenarios correctly balance sufficient severity to rigorously test the resilience of the banking sector with plausibility in a realistic macro-financial environment. Overall, this approach aims to enhance the clarity and robustness of stress test scenario evaluation, ensuring that stress test scenarios remain both credible and relevant.

In the context of banking sector stress tests, the severity of the scenario must be kept separate from banks’ capital depletion. Bank losses depend not only on the severity of the scenario but also on the sector’s exposure to specific risks and its structural imbalances.^[3] Additionally, scenario severity does not inherently account for cases where shocks, while negative for the broader economy, might have a positive impact on banks, such as rising interest rates which can improve net interest margins. These nuances highlight the need to carefully distinguish between the assessment of scenario severity from a macroeconomic perspective and the subsequent assessment of its impact on financial stability and bank solvency.

This article presents a framework to assess scenario severity and plausibility by comparing macro-financial dynamics with past observed dynamics or future developments. In the backward-looking approach, scenario severity is assessed in terms of the absolute past levels of key macroeconomic variables (e.g. GDP, unemployment, inflation) or of their deviation from a pre-defined baseline. This notion of severity focuses on the extent to which the scenario simulates a challenging economic environment, capturing the magnitude of potential stress on the economy as a whole and comparing with past stress events. The forward-looking approach maps the adverse scenario in the distribution of future events, assessing how far the adverse scenario extends in the tail of forecast distribution of potential outcomes, thus reflecting the probability of the shocks considered to generate the scenario itself. Importantly, in the forward-looking approach, systemic risks can affect the forecast distribution and the assessment of the severity in terms of tail event.^[[4]],[[5]]

As risks and financial conditions shape the distribution of macroeconomic dynamics, they should also inform forward-looking severity assessments. Financial stability risks affect macroeconomic dynamics and, hence, the forecast distribution,^[6] influencing where the adverse scenario stands in terms of the tail of said distribution. By leveraging non-linear macroeconomic models, this article presents severity measures which consider the evolution of financial conditions and systemic risks, complementing the forward-looking severity assessment with a risk-adjusted perspective.

The framework suggests that the severity of the EBA’s stress test scenarios has increased over time, with the 2023 and 2025 scenarios standing out as the most severe. Notably, when using non-linear techniques to account for systemic risk and financial conditions at the starting point of the scenario, the 2025 scenario emerges as the most severe.

2 A backward-looking perspective: measuring severity with respect to past events

An indicator-based approach is a straightforward way to assess the severity of scenarios. Originally proposed by Durdu, Edge and Schwindt (2017) in the context of Federal Reserve scenarios, this approach can be adapted to the case of the euro area. Compared with the original methodology, which focuses on a small set of variables, the adapted framework incorporates a broader range of indicators and, in one version, explicitly considers the impact on capital depletion.

Historical episodes of economic stress, such as recessions, serve as benchmarks to define severe adverse scenarios and build severity indices. Examining the evolution of key variables allows the average of mild episodes to be assigned a score of 0, while the most severe episode can receive a score of 100.^[7] This scoring system establishes a scale against which the variables in an adverse scenario can be indexed.^[8] Based on this scoring system, severity indicators can be computed for single variables or as averages across a set of variables in the scenario.

The results reveal a clear trend of increasing scenario severity over time. When the indices of individual scenario variables are examined, the two most recent stress-testing exercises rank as the years with the most severe scenarios (Chart 1). This result is confirmed when considering indices built by averaging across a broader set of variables, whether measured by the deviation of the scenario from its baseline or by the extreme values observed within the scenario (Chart 2). Severity also increases over time when averaging across variables to account for each variable’s sensitivity to capital depletion in banks (Chart 2, panel b). For instance, an equivalent change in GDP may have a substantially different impact on overall capital depletion than a similar change in residential real estate prices. This variability can be addressed by constructing a weighted average severity index, where the weights are based on each variable’s estimated sensitivity to capital depletion.

The severity index varies depending on the combination of variables and the choice of reference region but confirms a trend increase in severity. Region-specific severity indices display particularly high dispersion, reflecting differences in the historical episodes of economic stress experienced by each region.

Chart 1

Severity scores for key macro-financial variables

a) GDP	b) Unemployment	c) Stock prices
(severity score per exercise year, EA12)	(severity score per exercise year, EA12)	(severity score per exercise year, EA12)

Chart 2

Overall severity index

a) Deviation from baseline score	b) Extreme value score	c) Capital-weighted severity index
(severity score per exercise year, EA12)	(severity score per exercise year, EA12)	(severity score per exercise year, EA12)

3 A forward-looking perspective: using macro-econometric models to define severity measures

3.1 Leveraging joint probability concepts to assess scenario severity and plausibility

Scenario severity and plausibility can be assessed by using joint probability models of the relevant scenario variables. The severity and plausibility of a given scenario can be judged within a joint framework to properly account for the dependency structure between variables. To this end, a vector autoregressive (VAR) model for several variables of the EBA’s stress test scenarios is used to (i) estimate co-movements among variables in a consistent framework, and (ii) provide an estimate of the joint probability of the variables.^[9],[10]

Scenario plausibility can be judged by the changes to the model’s shock distribution needed to match the adverse scenario paths. From the viewpoint of a VAR model, scenarios are ultimately conditions on some or all variables of the system (at several points in time) that the model ought to match by adjusting the model’s shock distribution. This adjustment can be measured using the approach set out by Antolín-Díaz et al. (2021) that is termed the divergence metric (DM), given its close connection to the Kullback-Leibler divergence. Intuitively, it can be interpreted as judging a scenario as the outcome of a biased coin flip. A metric value of 0.5 indicates no bias, such that the adjusted shock distribution is identical to the initial one. A value of 1, by contrast, indicates that one needs to push the shock distribution very far from its unconstrained counterpart. As such, high values of this metric indicate a low degree of plausibility as the shocks that implement the adverse scenario are then unlikely under the baseline distribution. Within the context of stress test scenarios, however, this can be interpreted interchangeably with scenario severity as the adverse is typically located in the lower tail of the baseline distribution.

In simple terms, the DM measures the effort required to move the baseline scenario to recreate the variable paths of the adverse scenario. The initial shock distribution is defined to implement the annual baseline scenario paths reported in the official stress test documents.^[11] The deviations from this distribution, illustrated in Chart 3, are then interpreted as a measure of scenario plausibility.^[12] For the baseline scenario in Chart 3, panel a, the method yields a shock distribution (yellow curve) with a slightly higher mean but close to the unconditional one (blue curve), which follows a standard normal distribution. In a second step shown in Chart 3, panel b, the shock distribution is shifted to the left to generate the shock distribution for the adverse scenario (red curve). The DM then computes the distance between the baseline and the adverse shock distribution. The final measure is computed across euro area countries, periods and variables.

The DM indicates that scenario severity increased over time but declined with the EBA’s 2025 stress test adverse scenario. Chart 4, panel a) compares the model-based scenario DM for the 2020, 2021, 2023 and 2025 exercises for the euro area. For the 2020 adverse scenario, the DM comes out at 0.81. In the following exercise (2021), scenario severity then slightly increased as shown by the rightward shift from the blue to the yellow distribution. The 2023 exercise saw a substantial increase in severity (the red distribution) with a modal metric around 0.93. The latest scenario (2025) features a severity metric close to 0.90, in the ballpark of 2023 but slightly smaller. For reference, Antolín-Díaz et al. (2021) find a scenario with a modal value of 0.83 to be “… unlikely but not completely implausible”. Hence, the DM values obtained here seem reasonable, not least because we are analysing severe stress test scenarios.^[13]

The EBA’s 2025 adverse scenario provides a well-calibrated level of severity across euro area countries. Chart 4, panel b) focuses on the cross-country dispersion of the model-based scenario plausibility metric. The 2021 scenario features a marked increase in dispersion compared with the 2020 vintage, consistent with high uncertainty following the pandemic period shortly before. Both vintages feature plausible, and hence rather mild, country-level scenarios. The 2023 scenario vintage is more severe across the board, as shown by an upward shift and compression of the distribution. The 2025 scenario features somewhat higher dispersion than the 2023 vintage and overall turns out to be more plausible/less severe. However, the 2025 DM is still close to the 2023 vintage DM. The 2025 scenario lies above 0.8 in the cross-section, a value that, as illustrated in the previous paragraph, can be considered a sensible lower bound on a scenario believed to be “severe, but plausible”. As such, the 2025 scenario seems to provide a consistently severe scenario in the cross-section.

Chart 4

Scenario plausibility

a) Posterior distributions of plausibility metrics across scenario vintages	b) Cross-sectional posterior distribution of plausibility metrics across scenario vintages
(densities)	(probability)

3.2 Risk-adjusted forward-looking severity: adjusting severity by considering risks to financial stability

When assessing the severity of a scenario, an economy’s level of financial risk at the start of the scenario horizon also needs to be accounted for. The level of financial risk affects how shocks propagate through the economy. Overall, when risks are higher, economic and financial shocks are amplified and are likely to lead to stronger downturns. This section assesses the severity of the stress test scenarios by using non-linear macro-econometric frameworks designed to evaluate the relationship between the level of risk and the macroeconomic outlook. In this way, the forward-looking severity assessment can be adjusted to take account of financial conditions and the prevailing level of systemic risks.

3.2.1 Combining forward-looking metrics with financial conditions within a growth-at-risk approach

Adverse scenarios are designed to reflect financial stability risks to which the EU banking sector is exposed.^[14] A complementary approach to assessing the severity of these adverse scenarios therefore focuses explicitly on the risks that underpin the design of a specific scenario. This approach builds on the growth-at-risk framework (see Adrian et al., 2019, and Figueres and Jarocisńki, 2020), which entails constructing the predictive distribution of GDP growth conditional on the financial risks embedded in the scenario. The severity of the scenario is then assessed by comparing the projected GDP path under the scenario against the range of potential adverse GDP outcomes implied by the financial risks associated with this scenario.

The level of the financial risk associated with the scenario is measured by a financial stress index (FSI). Using the methodology of the Composite Indicator of Systemic Stress (CISS) presented in Holló et al. (2012),^[15] an FSI is built to match the path of the adverse scenario variables for the euro area (Chart 5). The key advantage of the FSI lies in its ability not only to capture historical events of financial stress (e.g. the 2008 global financial crisis and the 2011 euro area crisis) but also to gauge the level of financial risk embedded in the scenario shocks. As shown in Chart 5, the 2023 adverse scenario starts from a situation of elevated risk in 2022 and exhibits a persistent increase in financial stress levels over the three-year scenario horizon. By contrast, the 2025 adverse scenario starts from a situation of relatively low financial risk in 2024, with a pronounced increase in financial stress that diminishes slightly towards the end of the scenario horizon.

A growth-at-risk measure of severity can be obtained by estimating density forecasts for GDP conditional on the level of financial risk conveyed in the scenario. A set of adverse outcomes can be derived by projecting future GDP conditional on the FSI via quantile regression. In particular, the outcomes corresponding to the predictive lower tail at the 5th, 10th and 20th percentiles are considered.^[16] Chart 6 shows the predictive lower tails corresponding to the 2025 (panel a) and the 2023 (panel b) adverse scenarios.

Chart 6

Scenario growth-at-risk

a) 2025 stress test predictive lower tails	b) 2023 stress test predictive lower tails
(real GDP level, index: 2024 = 100)	(real GDP level, index: 2022 = 100)

From a growth-at-risk perspective, the 2025 adverse scenario appears more severe than the 2023 scenario. Chart 6 provides three main insights. First, the GDP paths for both the 2023 and the 2025 adverse scenarios lie within their respective predictive lower tails, indicating that, while both scenarios depict severe stress conditions, they remain plausible within the context of the underlying risks. Second, the predictive lower tails for the 2023 scenario are wider than those for the 2025 scenario, reflecting the elevated financial risk observed in 2022, which determined the initial conditions of the 2023 scenario as shown in Chart 6. Third, the GDP path for the 2025 scenario extends deeper into its predictive lower tail than the 2023 scenario, pointing to the 2025 adverse scenario having a higher degree of severity.

3.2.2 Combining forward-looking metrics with cyclical systemic risks in a local projection approach

The evolution of indebtedness and systemic risk levels plays a crucial role when assessing the severity of stress test scenarios. The level of cyclical systemic risks can affect the way macroeconomic dynamics propagate in the economy. Several studies highlight that when the level of debt is high, shocks are amplified in the economy, as debt plays the role of financial accelerator (see Bernanke at al., 1999, and Kiyotaki and Moore, 1997). When agents are more indebted, external shocks may affect their debt repayment capacity and force them to reduce spending, further amplifying the initial fluctuations.^[17] Higher systemic risks may affect severity assessments, as in times of higher risks and higher vulnerability, the same scenario would be triggered by relatively smaller shocks than in a period of lower systemic risks.

When systemic risk levels are high, the same shocks tend to generate stronger recessions than in periods of low risk. A non-linear macroeconomic model is first utilised to examine how systemic risks influence the propagation of macroeconomic shocks (see Couaillier and Scalone, 2024). The model assumes that the economy smoothly transitions between periods of high and low levels of cyclical systemic risk, as captured by the systemic risk indicator (SRI) (see Lang and Forletta, 2020). Thanks to its non-linear structure, the SRI generates state-dependent dynamics. Chart 7, panel a) illustrates how under conditions of elevated cyclical systemic risk, the impact of shocks is overall significantly amplified, in line with the presence of a financial accelerator in the economy.

The level of cyclical systemic risk has decreased over recent years, driven by slower credit growth and downward asset price corrections. At the beginning of 2021, the observed SRI was high, at 0.06, explained by significantly expanding debt, falling asset price valuations and increasing private sector indebtedness. The SRI then started to fall from this peak. This downward trend continued during the period of monetary policy tightening in line with a deceleration in credit growth and an asset price correction. At the beginning of 2025, the euro area’s SRI was lower than in 2023 and 2021.

Taking into account cyclical systemic risk levels, the 2025 scenario emerges as more severe than the 2023 and 2021 scenarios. The model’s non-linear dynamics are used to compare the severity of the scenarios across the different years, once the level of cyclical systemic risk is incorporated. First, the non-linear model is used to match the negative cumulative GDP growth of the EBA’s 2025 scenario, including the risk level observed during the scenario calibration period. The same set of shocks required to match the 2025 scenario downturn is then used to compute the hypothetical cumulative GDP growth considering the risk levels for 2023 and 2021. Chart 7, panel b) reports the cumulative GDP growth under the original EBA adverse scenarios over the three-year stress test horizon for the 2021, 2023 and 2025 exercises (blue bars) and the hypothetical cumulative growth for each scenario (yellow bars). If the same set of shocks used in 2025, hence the same severity, had been used in the previous exercises, the cumulative GDP growth would have been 0.9 percentage points higher in 2023 and 3.0 higher in 2021.

Chart 7

Severity assessment with cyclical risk correction

a) Macro dynamics across different risk levels	b) GDP growth across EBA scenarios: unadjusted and corrected by risk level
(percentage points deviation from the starting point)	(percentage points deviation from the starting point)

4 Conclusion

This article introduces a comprehensive framework for assessing the severity and plausibility of the adverse scenarios used in the EU-wide stress tests, incorporating multiple dimensions to ensure a robust evaluation. First, backward-looking indicators are used to assess scenario severity by benchmarking the adverse scenario outcomes against historical crisis episodes. This backward-looking leg focuses on two critical dimensions: (i) the deviation of adverse outcomes in variables such as real GDP, unemployment and inflation from the baseline scenario; and (ii) the levels of these variables in the adverse scenario. These dimensions offer insights into both the relative and the absolute levels of stress embedded in the scenarios. A forward-looking leg assesses the severity of the scenario in terms of the future density forecasts. This leg relies on a model-based severity index constructed by leveraging a macroeconomic VAR model and the concept of scenario plausibility. The resulting plausibility metric evaluates how far an adverse scenario deviates from baseline expectations, providing an intuitive measure of their alignment with historical regularities and hence their realism.

The forward-looking severity assessment can be expanded to consider how systemic risks affect macroeconomic dynamics. To this end, the article presents a methodology for evaluating scenario severity that incorporates prevailing financial conditions and systemic risks. These approaches focus on cumulative GDP growth and give rise to two complementary measures. The first shows how financial conditions can affect severity assessments. A growth-at-risk approach compares the location of the scenario-implied GDP path in the lower tails of predictive distribution, providing insights into the severity of adverse outcomes relative to expected risks. The second measure studies how systemic cyclical risks can affect severity assessments. This approach computes how the cumulative GDP growth of the 2023 and 2021 scenarios would have differed if the same shocks used to generate the 2025 cumulative GDP growth had been used in 2021 and 2023, taking into account the relatively higher levels of systemic risk in those periods.

This framework finds that the adverse scenarios used in the EU-wide stress tests have become more severe over time. According to the indicator-based approach and the model-based plausibility index, scenario severity reached a peak in 2023 before declining slightly in 2025. Taking into account the financial risk environment that prevailed when the scenarios were devised, it turns out that the GDP path can be considered more severe under the 2025 scenario than under the 2023 scenario since it is located further into the tail of its predictive distribution. Moreover, when adjusting for systemic risk, the 2025 scenario appears to be the most severe, even surpassing the 2023 scenario in terms of its risk-weighted severity. This highlights the importance of considering systemic risk levels when evaluating scenario severity.

The proposed methodologies provide a multidimensional, rigorous framework for evaluating the severity and plausibility of stress test scenarios. By benchmarking against historical episodes, employing macroeconomic modelling and applying systemic risk adjustments, this framework helps scenarios to be devised that are both severe and plausible. This makes it easier to assess the resilience of the banking sector to adverse macro-financial developments while improving the clarity, credibility and comparability of stress test results.

References

Adrian, T., Boyarchenko, N. and Giannone, D. (2019), “Vulnerable Growth”, American Economic Review, Vol. 109, No 4, April, pp. 1263-1289.

Antolín-Díaz, J., Petrella, I. and Rubio-Ramírez, J.F. (2021), “Structural scenario analysis with SVARs”, Journal of Monetary Economics, Vol. 117, January, pp. 798-815.

Bernanke, B.S., Gertler, M. and Gilchrist, S. (1999), “The financial accelerator in a quantitative business cycle framework”, Handbook of Macroeconomics, Vol. 1, pp. 1341-1393.

Couaillier, C. and Scalone, V. (2024), “Risk-to-buffer: setting cyclical and structural banks capital requirements through stress tests”, Working Paper Series, No 2966, ECB.

Durdu, B, Edge, R. and Schwindt, D. (2017), “Measuring the Severity of Stress-Test Scenarios”, FEDS Notes, Board of Governors of the Federal Reserve System, 5 May.

Figueres, J.M. and Jarociński, M. (2020), “Vulnerable growth in the euro area: Measuring the financial conditions”, Economics Letters, Vol. 191(C), June.

Geweke, J. (1993), “Bayesian treatment of the independent Student‐t linear model”, Journal of Applied Econometrics, Vol. 8, Supplement, December, pp. S19-S40.

Holló, D., Kremer, M. and Lo Duca, M. (2012), “CISS – a composite indicator of systemic stress in the financial system”, Working Paper Series, No 1426, ECB.

Jarociński, M. (2010), “Responses to monetary policy shocks in the east and the west of Europe: a comparison”, Journal of Applied Econometrics, Vol. 25, Issue 5, pp. 833-868.

Kiyotaki, N. and Moore, J. (1997), “Credit Cycles”, Journal of Political Economy, Vol. 105, No 2, April, pp. 211-248.

Lang, J.H. and Forletta, M. (2020), “Cyclical systemic risk and downside risks to bank profitability”, Working Paper Series, No 2405, ECB.

Ter Steege, L. (2024), “Variational inference for Bayesian panel VAR models”, Working Paper Series, No 2991, ECB.

– New agreement is airline’s fourth 737 MAX purchase and includes options for 75 additional airplanes
– Efficient 737 MAX serves as the backbone of flydubai’s growing fleet

DUBAI, UAE, Nov. 19, 2025 /PRNewswire/ — Boeing [NYSE: BA] and flydubai announced today the airline has signed a Memorandum of Understanding (MoU) for its fourth 737 MAX purchase. The agreement for 75 orders and 75 options will enable flydubai to modernize its fleet and further expand its growing network.

The Dubai-based carrier said the 737 MAX’s fuel efficiency, range and reliability has enabled its network expansion, which now spans over 135 destinations, including new routes Lasi, Nairobi, Riga, Latvia, among other cities.

The new deal allows flydubai to take advantage of the 737 MAX family’s flexibility and commonality, while leveraging the unique size and range of the 737-8, 737-9, and 737-10 models to suit its growing business.

“We are pleased to announce a new aircraft order agreement with Boeing. Looking ahead, proactive fleet planning is essential to ensuring we are well-placed to meet the rising demand for travel, a demand we are confident will continue to grow. Anticipating future needs is a defining factor in the success of any airline and today’s announcement reflects our commitment to that principle,” said His Highness Sheikh Ahmed bin Saeed Al Maktoum, Chairman of flydubai, commenting on the milestone announcement.

We are proud to place another 737 MAX order with Boeing, a trusted partner that has played a key role in growing our network to its current scale. Reliable aircraft availability and timely deliveries are vital to the ongoing growth of our industry, and this agreement ensures we remain well-positioned for future growth, adding to the fleet as well as replacing current aircraft. I want to thank our team for their dedication and hard work. Their efforts, combined with Dubai‘s ambitious vision for the years ahead, fuel our optimism and enthusiasm for what lies ahead, including playing a key role Dubai World Central’s expansion plans.”

flydubai currently operates 96 Boeing 737 airplanes. The new agreement, when finalized, would add to outstanding 737 MAX orders from prior purchases.

“flydubai is one of the world’s first 737 MAX operators and their plan to place yet another order – their fourth order to date – reflects the 737 MAX’s market-leading value and versatility,” said Stephanie Pope, president and CEO of Boeing Commercial Airplanes. “We are proud that Boeing airplanes will continue to serve as the backbone of flydubai’s strategic fleet and growth plans.”

The 737 MAX family delivers better fuel efficiency, improved environmental performance and increased passenger comfort compared to the airplanes they replace. The family is optimized for growth in the Middle East – offering more range and greater seat capacity than previous 737 models.

In 2023, flydubai also placed its first-ever widebody airplane order with a purchase of 30 787 Dreamliner jets.

Over the next 20 years, the Middle Eastern single-aisle fleet is projected to more than double to enhance connectivity within the region and much of Europe, according to Boeing’s 2025 Commercial Market Outlook.

A leading global aerospace company and top U.S. exporter, Boeing develops, manufactures and services commercial airplanes, defense products and space systems for customers in more than 150 countries. Our U.S. and global workforce and supplier base drive innovation, economic opportunity, sustainability and community impact. Boeing is committed to fostering a culture based on our core values of safety, quality and integrity.

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SOURCE Boeing

Here, we presented a simple model to calculate the relative contribution of the direct and EB-dependent end-binding of MCAK (Appendix 1—figure 1).

Based on the model, we had the dissociation constants:

(6)

${\displaystyle {\displaystyle K_0=\frac{\left[MCAK\right]\cdot \left[EB1\right]}{\left[MCAK\cdot EB1\right]}}}$

(7)

${\displaystyle {\displaystyle K_1=\frac{\left[MCAK\right]\cdot \left[MTE\right]}{\left[MCAK-MTE\right]}}}$

(8)

${\displaystyle {\displaystyle K_2=\frac{\left[EB1\right]\cdot \left[MTE\right]}{\left[EB1-MTE\right]}}}$

(9)

${\displaystyle {\displaystyle K_3=\frac{\left[MCAK\cdot EB1\right]\cdot \left[MTE\right]}{\left[MCAK\cdot EB1-MTE\right]}}}$

Then, the relative contribution of the direct and EB-dependent end-binding of MCAK can be expressed as α:

(10)

${\displaystyle {\displaystyle \alpha =\frac{\left[MCAK-MTE\right]}{\left[MCAK\cdot EB1-MTE\right]}=\frac{K_3\cdot \left[MCAK\right]}{K_1\cdot \left[MCAK\cdot EB1\right]}=\frac{K_3\cdot K_0}{K_1\cdot \left[EB1\right]}}}$

Here, we considered two scenarios. In the cytoplasm, both EB1 and MCAK undergo free diffusion and can associate with each other without restrictions. The relative concentrations of MCAK and EB1 are critical parameters, but they may vary across different cell types and remain unknown. We also considered the second scenario in which MCAK is locally enriched at specific cellular localizations through an EB-independent mechanism. For example, EB1 does not affect the localization of MCAK at centromere and centrosome, nor does EB1 significantly affect the function of MCAK there (Domnitz et al., 2012). Here, we assumed that the local concentration of anchored-state MCAK is relatively high, and EB1 remains diffusive and its concentration is nearly constant, as it is continuously replenished in the local space from the vast cytoplasmic pool. In both cases, the ratio of K₃ to K₁ emerges as a key determinant.

K₃ is the end-binding affinity of the MCAK·EB1 complex. Intuitively, it depends on the respective microtubule-binding affinities of MCAK and EB1, as well as the cooperativity, if any, of their microtubule-binding behaviors. K₃ can be expressed as:

(11)

${\displaystyle {\displaystyle \frac{1}{K_3}=a\frac{1}{K_1}+b\frac{1}{K_2}}}$

(12)

${\displaystyle {\displaystyle K_3=\frac{K_2\cdot K_1}{a{K}_2+b{K}_1}}}$

where a and b represent the weighting factors of binding sites or cooperativity factors of the binding behaviors. Therefore, K₃ shows a positively correlated, monotonically increasing dependence on K₁, indicating that the increase in the end-binding affinity of MCAK contributes to that of the MCAK·EB1 complex. Therefore, we think that MCAK’s functional impact at microtubule ends derives not only from its intrinsic end-binding capacity, but also its ability to strengthen the EB1-mediated end association pathway.

In the simplest case, the formation of the MCAK·EB1-MTE complex arises from the binding of either MCAK or EB1 to microtubule ends, and the binding behaviors for MCAK and EB1 are independent (a=1; b=1). Consequently, K₃ can be expressed as:

(13)

${\displaystyle {\displaystyle K_3=\frac{K_2\cdot K_1}{K_2+K_1}}}$

if $K_1\ll K_2$, then

(14)

$K_3\approx K_1$

if $K_1\gg K_2$, then

(15)

$K_3\approx K_2$

If $K_1\approx K_2$, then

(16)

$K_3\approx \frac{K_1}{2}$

In our experiments, we measured the dissociation constants of MCAK to growing microtubule ends is 69 µM (K₁). We also performed similar experiments with EB1 and found that EB1 showed the dissociation constant of 722 µM (K₂) for growing microtubule ends (Appendix 1—figure 2), similar to the value reported in our previous report (Song et al., 2020). Therefore, substituting Equation 14 into Equation 10, we obtained

(17)

$\alpha \approx \frac{K_0}{\left[EB1\right]}$

Here, if we assume that the cytoplasmic concentration of EB1 is twice the value of K₀, then α=0.5, indicating that 50% of MCAK binds to microtubule ends via the direct binding pathway; even if the EB1 concentration reaches ten times the value of K₀, 10% of MCAK still utilizes the direct binding pathway. Overall, as the EB1 concentration increases relative to K₀, α decreases, reflecting a decline in the proportion of MCAK that associates with microtubule ends through the direct binding mechanism.

Polyalert, Switzerland’s national alert and warning system developed by Eviden will leverage the company’s managed services expertise to remain at the forefront of technology

Zurich, Switzerland – Paris, France – November 19, 2025

Eviden, the Atos Group product brand leading in advanced computing, cybersecurity products, mission-critical systems, and Vision AI, today announces that it has been awarded a contract extension by the Swiss Federal Office for Civil Protection (FOCP). The agreement, valid until 2031 and extendable until 2035, covers the maintenance and regular upgrade of Polyalert, Switzerland’s critical information system for alerting the population in the event of major incidents such as extreme weather phenomenon, environmental disasters or industrial accidents. This extension underscores Eviden’s technological expertise and ability to continuously adapt Polyalert to FOCP’s evolving needs.

Designed and developed by Eviden and operational since 2016, Polyalert is the central system enabling rapid, consistent notifications across multiple channels in German, French, Italian and English. It allows the alerting manager to select the most appropriate channels for each situation and forms a key component of Switzerland’s multichannel alerting strategy.

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