Category: 3. Business

We simulate the dynamics of VCG pools using a kinetic simulation that is based on the Gillespie algorithm. In the simulation, oligomers can hybridize to each other to form complexes or dehybridize from an existing complex. Moreover, two oligomers can undergo templated ligation if they are hybridized adjacent to each other on a third oligomer. At each time $t$, the state of the system is determined by a list of all single-stranded oligomers and complexes as well as their respective copy number. We refer to the state of the system at the time $t$ as the ensemble of compounds ${\mathcal{E}}_t$. Given the copy numbers, the rates $r_i$ of all possible chemical reactions $i\in \mathcal{I}$ can be computed. To evolve the system in time, we need to perform two steps: (i) We sample the waiting time until the next reaction, $\tau$, from an exponential distribution with mean $(\sum _{i\in \mathcal{I}}{r}_i{)}^{-1}$, and update the simulation time, $t\to t+\tau$. (ii) We pick which reaction to perform by sampling from a categorical distribution. Here, the probability to pick reaction $i$ equals $r_i/(\sum _{i\in \mathcal{I}}{r}_i)$. The copy numbers are updated according to the sampled reaction, yielding ${\mathcal{E}}_{t+\tau }$. Steps (i) and (ii) are repeated until the simulation time $t$ reaches the desired final time, $t_{\text{final}}$. A more detailed explanation of the kinetic simulation is presented in Göppel et al., 2022; Rosenberger et al., 2021.

Our goal is to compute observables characterizing replication in the VCG scenario based on the full kinetic simulation. In the following derivation, we focus on one particular observable (yield) for clarity. The results for other observables are stated directly, as their derivations follow analogously. Recall the definition of the yield introduced in the Results section,

${\displaystyle {\displaystyle y=\frac{\mathrm{\#}\mathrm{n}\mathrm{u}\mathrm{c}\mathrm{l}\mathrm{e}\mathrm{o}\mathrm{t}\mathrm{i}\mathrm{d}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{V}\mathrm{C}\mathrm{G}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{o}\mathrm{m}\mathrm{e}\mathrm{r}\mathrm{s}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{l}{\tau }_{\text{lig}}}{\mathrm{\#}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{n}\mathrm{u}\mathrm{c}\mathrm{l}\mathrm{e}\mathrm{o}\mathrm{t}\mathrm{i}\mathrm{d}\mathrm{e}\mathrm{s}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{l}{\tau }_{\text{lig}}}.}}$

As we are interested in the initial replication performance of the VCG, we compute the yield based on the ligation events that take place until the characteristic timescale of ligations ${\tau }_{\text{lig}}=k_{\text{lig}}^{-1}\approx {10}^{12}{t}_0$. In principle, we would like to compute the yield based on the templated ligation events that we observe in the simulation. Unfortunately, for reasonable system parameters, it is impossible to simulate the system long enough to observe sufficiently many ligation events to compute $y$ to reasonable accuracy. For example, for a VCG pool containing monomers at a total concentration of $c_{\mathrm{F}}^{\text{tot }}=0.1\mathrm{m}\mathrm{M}$ and VCG oligomers of length $L=8\mathrm{n}\mathrm{t}$ at a total concentration of $c_{\mathrm{V}}^{\mathrm{t}\mathrm{o}\mathrm{t}}=1\text{μ}\mathrm{M}$, it would take about 1700 hr of simulation time to reach $t=5\cdot {10}^{12}{t}_0$ (Figure 8). Multiple such runs would be needed to estimate the mean and the variance of the observables of interest, rendering this approach unfeasible.

Instead, we compute the replication observables based on the copy number of complexes that could potentially perform a templated ligation, that is complexes in which two strands are hybridized adjacent to each other, such that they could form a covalent bond. We can show analytically that the number of productive complexes is a good approximation for the number of incorporated nucleotides: The number of incorporated nucleotides can be computed as the integral over the ligation flux, weighted by the number of nucleotides that are added in each templated ligation reaction,

${\displaystyle {\displaystyle {\displaystyle (\mathrm{\#}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{n}\mathrm{u}\mathrm{c}\mathrm{l}\mathrm{e}\mathrm{o}\mathrm{t}\mathrm{i}\mathrm{d}\mathrm{e}\mathrm{s}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{l}{\tau }_{\text{lig}})={\int }_0^{{\tau }_{\text{lig}}}\mathrm{d}t\sum _{\text{C}\in {\mathcal{E}}_t}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C allows templated ligation}).}}}$

Here, $N(\text{C})$ denotes the copy number of the complex C in the pool ${\mathcal{E}}_t$. $L_{\text{e,1}}$ and $L_{\text{e,2}}$ denote the lengths of the oligomers that undergo ligation, and $\mathbb{1}$ is an indicator function which enforces that only complexes in a ligation-competent configuration contribute to the reaction flux. As only a few ligation events are expected to happen until ${\tau }_{\text{lig}}$, it is reasonable to assume that the ensembles ${\mathcal{E}}_t$ do not change significantly during $t\in [0,{\tau }_{\text{lig}}]$. Therefore, the integration over time may be interpreted as a multiplication by ${\tau }_{\text{lig}}$,

(6)

${\displaystyle {\displaystyle {\displaystyle (\mathrm{\#}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{r}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{n}\mathrm{u}\mathrm{c}\mathrm{l}\mathrm{e}\mathrm{o}\mathrm{t}\mathrm{i}\mathrm{d}\mathrm{e}\mathrm{s}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{l}{\tau }_{\text{lig}})\approx {\tau }_{\text{lig}}⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C allows templated ligation})⟩,}}}$

where $⟨\dots ⟩$ denotes the average over realizations of the ensembles ${\mathcal{E}}_t$ within the time interval $t\in [{\tau }_{\text{eq}},{\tau }_{\text{lig}}]$. This average corresponds to the average number of complexes in a ligation-competent configuration. Note that, at this point, we made the additional assumption that no templated ligations are taking place between $[0,{\tau }_{\text{eq}}]$. This assumption is reasonable, as (i) the equilibration process is very short compared to the characteristic timescale of ligation, and (ii) the number of complexes that might allow for templated ligation during equilibration is lower than in equilibrium (we start the simulation with an ensemble of single-stranded oligomers). Both aspects imply that the rate of templated ligation is negligible during the interval $[0,{\tau }_{\mathrm{e}\mathrm{q}}]$.

In order to compute the average over different realizations of ensembles $\mathcal{E}$ (as required in Equation 6), we need to sample a set of uncorrelated ensembles that have reached the hybridization equilibrium, which can be done using the full kinetic simulation. The simulation starts with a pool containing only single-stranded oligomers and reaches the (de)hybridization equilibrium after a time ${\tau }_{\text{eq}}$. We identify this timescale of equilibration by fitting an exponential function to the total hybridization energy of all complexes in the system, $\mathrm{\Delta }{G}_{\text{tot}}$ (Figure 9A). In the set of ensembles used to evaluate the average in Equation 6, we only include ensembles for time $t>{\tau }_{\text{eq}}$ to ensure that the ensembles have reached (de)hybridization equilibrium. To ensure that the ensembles are uncorrelated, we require that the time between two ensembles that contribute to the average is at least ${\tau }_{\text{corr}}$. The correlation time, ${\tau }_{\text{corr}}$, is determined via an exponential fit to the autocorrelation function of $\mathrm{\Delta }{G}_{\text{tot}}$ (Figure 9B). Besides computing the expectation value (Equation 6), we are also interested in the ‘uncertainty’ of this expectation value, that is in the standard deviation of the sample mean ${\sigma }_{⟨X⟩}$. (We use $X$ as a short-hand notation for $\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\mathrm{C}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n})$). The standard deviation of the sample mean, ${\sigma }_{⟨X⟩}$, is related to the standard deviation of $X$, ${\sigma }_X$, by the number of samples, ${\sigma }_{⟨X⟩}=(N_s{)}^{-1/2}{\sigma }_X$. Moreover, based on the van-Kampen system size expansion, we expect the standard deviation of $X$ to be proportional to $V^{-1/2}$, such that ${\sigma }_{⟨X⟩}\propto (N_{s}V{)}^{-1/2}$.

Using Equation 6 (as well as an analogous expression for the number of nucleotides that are incorporated in VCG oligomers), the yield can be expressed as

${\displaystyle {\displaystyle {\displaystyle y=\frac{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n})\mathbb{1}(L_{\text{e,1}}+L_{\text{e,2}}\ge L_{\mathrm{U}})⟩}{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n})⟩}.}}}$

The additional condition $\mathbb{1}(L_{\text{e,1}}+L_{\text{e,2}}\ge L_{\mathrm{U}})$ in the numerator ensures that the product oligomer is long enough to be counted as a VCG oligomer, that is at least $L_{\mathrm{U}}$ nucleotides long. Analogously, the expression for the fidelity of replication reads

${\displaystyle {\displaystyle {\displaystyle f=\frac{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C allows templated ligation})\mathbb{1}(L_{\text{e,1}}+L_{\text{e,2}}\ge L_{\mathrm{U}})\mathbb{1}(\text{product correct})⟩}{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C allows templated ligation})\mathbb{1}(L_{\text{e,1}}+L_{\text{e,2}}\ge L_{\mathrm{U}})⟩}.}}}$

Multiplying fidelity and yield results in the efficiency of replication,

${\displaystyle {\displaystyle {\displaystyle \eta =\frac{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\mathrm{C}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n})\mathbb{1}(L_{\text{e,1}}+L_{\text{e,2}}\ge L_{\mathrm{U}})\mathbb{1}(\text{product correct})⟩}{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\mathrm{C}\mathrm{a}\mathrm{l}\mathrm{l}\mathrm{o}\mathrm{w}\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n})⟩}.}}}$

The ligation share of a particular type of templated ligation $s(\text{type})$, that is, the relative contribution of this templated-ligation type to the nucleotide extension flux, can be represented in a similar form as the other observables,

${\displaystyle {\displaystyle s(\text{type})=\frac{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C allows templated ligation of given type)}⟩}{⟨\sum _{\text{C}\in \mathcal{E}}N(\text{C})\text{min}(L_{\text{e,1}},L_{\text{e,2}})\mathbb{1}(\text{C allows templated ligation)}⟩}.}}$

As all observables are expressed as the ratio of two expectation values, $\mathcal{Z}=⟨X⟩/⟨Y⟩$, we can compute the uncertainty of the observables via Gaussian error propagation,

${\displaystyle {\displaystyle {\displaystyle {\sigma }_{\mathcal{Z}}=\sqrt{\frac{{\sigma }_{⟨X⟩}^2}{⟨Y{⟩}^2}+\frac{⟨X{⟩}^2{\sigma }_{⟨Y⟩}^2}{⟨Y{⟩}^4}-\frac{2⟨X⟩{\sigma }_{⟨X⟩,⟨Y⟩}^2}{⟨Y{⟩}^3}}.}}}$

Since the variances, ${\sigma }_{⟨X⟩}^2$ and ${\sigma }_{⟨Y⟩}^2$, as well as the covariance, ${\sigma }_{⟨X⟩,⟨Y⟩}^2$, are proportional to $(N_{s}V{)}^{-1}$, the standard deviation of the observable mean, ${\sigma }_Z$, scales with the inverse square root of the number of samples and the system volume, that is ${\sigma }_Z\propto (N_{s}V{)}^{-1/2}$. Therefore, the variance of the computed observable can be reduced by either increasing the system volume or increasing the number of samples used for averaging. Both approaches incur the same computational cost: (i) Increasing the number of samples, $N_s$, requires running the simulation for a longer duration, with the additional runtime scaling linearly with the number of samples. (ii) Similarly, the additional runtime needed due to increased system volume, $V$, also scales linearly with $V$ (Figure 8). One update step in the simulation always takes roughly the same amount of runtime, but the change in simulation time per update step depends on the total rate of all reactions in the system. The total rate is dominated by the association reactions, and their rate is proportional to the volume. Therefore, the change in simulation time per update step is proportional to $V^{-1}$. The runtime, which is necessary to reach the same simulation time in a system with volume $V$ as in a system with volume 1, is a factor of $V$ longer in the larger system. With this in mind, it makes no difference whether the variance is reduced by increasing the volume or the number of samples. For practical reasons (post-processing of the simulations is less memory- and time-consuming), we opt to choose a moderate number of samples, but slightly higher system volumes to compute the observables of interest. The simulation parameters (length of oligomers, concentrations, hybridization energy, volume, number of samples, characteristic timescales) used to obtain the results presented in Figure 2 are summarized in Table 1.

A French oil company engaged in “misleading commercial practices” about the scope of its environmental commitments, a court has ruled.

TotalEnergies, which this month said it aimed to “ramp up production of gas”, was found on Thursday to have probably misled consumers with claims about its climate policies. The civil court in Paris ordered the company to remove messages from its website that said it wanted to reach carbon neutrality by 2050 and be a big player in the energy transition.

The case, brought by NGOs including Greenpeace France and Friends of the Earth France, is the first time the country’s “greenwashing” laws have been applied to a fossil fuel company. Courts in the Netherlands and Germany have already found that airlines misled consumers with vague environmental claims.

The French court gave TotalEnergies a month to take down the misleading statements or face a fine of €10,000 (£8,700) a day. It was also ordered to post the court’s ruling on its website, with the same penalty for noncompliance, as well as to pay €8,000 to each of the three NGOs and €15,000 for their legal costs.

“The French justice system is finally tackling the impunity of fossil fuel greenwashing that Total has enjoyed until now,” said Justine Ripoll, campaigns manager at Notre Affaire à Tous, one of the NGOs that brought the case. “It sends a clear message: climate disinformation is not an acceptable business strategy.”

TotalEnergies has been approached for comment.

The company, which aims to achieve 100 gigawatts of renewable power generation by 2030 but has made fossil gas a “cornerstone” of its strategy, has said it was a multi-energy company aiming to “responsibly, cost-effectively and sustainably produce the energy that we all need in our daily lives”.

The ruling is the result of a legal action brought by NGOs in 2022 in response to a campaign when the company changed its name from Total.

The court ordered TotalEnergies to remove statements that said it placed sustainable development at the heart of its strategy and that it “contributed to the wellbeing of populations” in line with the UN’s sustainable development goals.

Judges dismissed a further accusation of greenwashing over the company’s claims about fossil gas and biofuels. The court found that although the statements contained some disputed claims, they were for informational rather than commercial purposes.

Climate activists and green groups have increasingly taken fossil fuel companies to court for environmental claims that do not align with published climate science.

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In a landmark report in 2022, the Intergovernmental Panel on Climate Change found that the world had enough existing and planned fossil fuel infrastructure to blow past the goal of limiting global heating to 1.5C above preindustrial levels. Meanwhile, the International Energy Agency found that “no new oil and gas” exploration was compatible with its key scenario for keeping planetary heating to that level.

Jonathan White, a lawyer for ClientEarth, which supported the NGOs, said TotalEnergies appeared to be continuing with oil and gas projects despite warnings from climate experts.

“This landmark judgment sends a clear warning shot to other oil and gas majors in Europe and beyond,” he said. “Claiming to be part of the transition while backing new fossil fuel projects comes at a tried-and-tested legal price.”

October 23, 2025

This acquisition is focused on the clinical-stage program ICT01 in acute myeloid leukemia (AML) targeting patients who are ineligible for intensive chemotherapy or targeted treatments. ICT01 is a first-in-class monoclonal antibody whose data from an ongoing trial showed a high treatment response, which could make it a new standard of care for acute myeloid leukemia, an aggressive blood cancer affecting older adults.

The transaction is expected to close by the end of Q1 2026, subject to fulfilment of customary closing conditions, including the required regulatory and governmental approvals under French and U.S. regulations.

Marc Castagnède, partner at A&O Shearman, said: “This transaction clearly demonstrates our ability to support clients in executing complex, strategic deals in highly specialized sectors such as life sciences. We are proud to have advised Ipsen on this landmark acquisition, which sits at the heart of biotech innovation and therapeutic advancement.”

The A&O Shearman team is being led by M&A partner Marc Castagnède with support from M&A senior associate Antoine Messent, and associate Fatima Ahamada.

Other members of the Paris team involved in the transaction include partner Olivier Picquerey and senior associate Antoine Tantaro on employment matters; partners Laëtitia Bénard and Charles Tuffreau, associate Manon Perret and consultant Marianne Delassaussé on IP matters; senior associate Clémence d’Almeida on antitrust matters; counsel Luc Lamblin and associate Charles-Hugo Lerebour on regulatory and FDI matters; partner Laurie-Anne Ancenys and associate Thomas Feigean on IT and data aspects; and partner Charles del Valle on tax matters.

Support was also provided by the A&O Shearman US corporate and antitrust teams.

Companies need to do more to mitigate the potential effects of cyber-attacks, the head of GCHQ has said, including making physical, paper copies of crisis plans to use if an attack brings down entire computer systems.

“What are your contingency plans? Because attacks will get through,” said Anne Keast-Butler, who has headed GCHQ, the British government’s cyber and signals intelligence agency, since 2023.

“What happens when that happens to you in a company, have you really tested that?” said Keast-Butler, speaking on Wednesday at a London conference organised by the cybersecurity company Recorded Future. “Your plans … have you got them on paper somewhere in case all your systems really go down? How will you communicate with each other if you’re completely reliant on a system that actually you shut down?”

Last week, the National Cyber Security Centre, which is part of GCHQ, announced figures showing that “highly significant” cyber-attacks have risen by 50% in the past year. Security and intelligence agencies are now dealing with a new attack several times per week, the figures showed.

Keast-Butler said the government and business needed to work together to tackle future attacks and improve defensive systems, as modern technology and artificial intelligence make the threats more diffuse and reduce “the entry level capability” that malicious actors need to do damage. She said work with internet service providers to block malicious websites at source was “blocking millions of potential hits” but said major companies needed to do much more to protect themselves.

On Tuesday, a report by the Cyber Monitoring Centre (CMC) said the hack of Jaguar Land Rover had cost the UK economy an estimated £1.9bn, which could make it the most costly cyber-attack in British history.

JLR had to shut down systems across all its factories and offices after the attack in August, and may not be able to return to normal production capacity until January.

Keast-Butler said “[there are] far, far, far more attacks that get stopped than the ones that we’re focusing in on”, but added that the increased publicity around the JLR and several other major cyber-attacks provided a good moment to ram home the importance of cybersecurity protocols.

She said she spoke regularly to CEOs of major companies and one of her messages to them was that they need to put people who understand cybersecurity on their boards. “Quite often, the way boards are configured, they don’t have people who will know the right questions to be asking. So the interest is there, but the right questions don’t get asked,” she said.

Earlier this year, the Co-op Group suffered a cyber-attack that cost it up to £120m in lost profits and compromised the personal data of some of its members. Shirine Khoury-Haq, the group’s CEO, released an open letter in the aftermath detailing the importance of cybersecurity drills to build strategy on how to deal with an attack.

“The intensity, urgency and unpredictability of a live attack is unlike anything you can rehearse. That said, those drills are invaluable; they build muscle memory, sharpen instincts, and expose vulnerabilities in your systems,” wrote Khoury-Haq.

Keast-Butler encouraged companies to share information on attacks with government agencies, saying that “safe spaces” had been set up to enable them to do so without the risk of giving away commercially sensitive information to competitors.

“I think sometimes people are a bit too reticent to come forward because there’s a sort of personal thing on them or their company as a whole. And that doesn’t help any of us, because then they’re not making the kind of long-term strategic systems changes that we can help out with,” she said.

Dublin, 23 October 2025

It is an honour to receive the Pádraig Ó hUiginn Award. Pádraig Ó hUiginn and the previous recipients of this award (John Bruton, Catherine Day and Michael Noonan) have all contributed to the development of Irish and European policy frameworks for the financial services sector.

A successful financial services sector requires the sound underpinning provided by high-quality public institutions and sustainable public finances. In particular, the financial services industry is characterised by distinct complementarities between the private sector and the public sector: commercial providers of financial services benefit from a public regulatory and supervisory framework that maintains prudential standards, protects consumers and underpins financial stability.^[1]As the severe costs of the Irish banking crisis so painfully demonstrated, it is neither in our collective interest as a society nor in the interest of the financial services sector to be complacent about the essential contribution of financial regulation to the long-term health of the sector.

At the same time, the regulatory and supervisory system should neither deter new entrants nor inhibit the capacity of the financial services sector to innovate and roll out new technologies. In adapting to a rapidly-digitalising financial system, it is in the shared interest of regulators and financial services firms to make sure that this digital transition takes place without putting at risk the essential features of a well-regulated financial system.

In the context of financial digitalisation, let me also highlight that the European Central Bank (ECB) is working hard to make sure that central bank money – which provides the underlying foundations for the entire financial system – is also modernised. In particular, the digital euro would provide a digital version of central bank money, maintaining the essential feature of the monetary system that commercial bank money is fully interchangeable at par with central bank money.^[2]

In terms of wholesale transactions, the ECB has also approved a plan that will enable distributed ledger technology (DLT) transactions to be settled using central bank money.^[3]The initiative follows a two-track approach: the first track “Pontes” provides a short-term offering to the market – including a pilot phase – and the second track “Appia” focuses on a potential long-term solution. The decision is in line with the commitment of the Eurosystem to supporting innovation without compromising on safety and efficiency in financial market infrastructures.

In addition to the digitalisation agenda, let me also highlight another fundamental and, indeed, inter-connected challenge for the financial services sector: it is clear that geopolitical developments have made it all the more urgent to make substantial progress on integrating the European financial system.^[4]In particular, it is critical to complete the savings and investments union and the banking union to an ambitious timetable.

A more integrated and more fully developed European financial system is in our collective interest. An integrated European financial system will: improve market efficiency; offer greater opportunities to European firms to raise both equity and debt financing; and, through the benefits of economies, make it easier for European households to hold diversified financial portfolios, while also lowering transaction costs.^[5] In addition, an integrated financial system will make Europe more attractive for global investors, allowing Europe to gain from an improvement in the financial “terms of trade”. In terms of public policy objectives, an integrated European financial system will also support the large-scale funding needs of the green transition and the scaling up of the European defence industry, including through greater scope for joint funding of pan-European initiatives.^[6]

These topics are high on the agenda for today’s European Council and Euro Summit meetings in Brussels, reflecting the political urgency in building an integrated-but-open European digital-ready financial system that ensures sovereignty and resilience, improving the performance of the European economy and making Europe a more attractive destination for global investors.