 So shall we start? My name is, so first of all, I hope you can hear me well. So my name is Mario Ciancarini. I work in a HINEL energy company in the group strategy function. Today I will try to use those 10 minutes giving you before a quick overview of what we do and why we need, we use climate data to deliver adaptation plan. And then after I will quickly give you some insight of what's our main point of attention, things we are trying to improve or we would like to have a support from the scientific community and from other stakeholders. I agree what was mentioned before, the adaptation for us and in general is not a kind of one time action, it's a process. This is why what you are seeing now, it's our main steps to deliver our adaptation plan. And starting from the climate assessment, here there is a point interesting point because we are into a long collaboration with ICTP. We are working in gathering both data from, and understanding data from global climate model and regional climate model to do both analysis at low resolution value resolution. So for us the first step of course is to understand which kind of hazards we will face, which kind of hazards are important to us. So we have developed matrixes in which we have mapped our technologies. So we are talking about power plant for power productions, power grids. We have both renewable and thermal power plants. And then we mapped all the phenomena important for us. So skipping for a moment on the right side of these slides, for us are important both chronic changes because those affect for example power demand, the temperature impact on power demand, but also for example the effect on power production because the availability of resources can change of course renewable productions or also the efficiency of thermal power plants. And of course we are affected by a wide range of acute phenomena that impact us through business interruption or damages. So for us it's very important to develop a doc matrix that can fit in our vulnerability modeling. This is why one of the important points for us is to find opportunities to collaborate in developing the sectorial metrics because for example right now we have our metrics but it would be very useful to have metrics shared at the sectorial level to set some standards and make you an example to assess the impacts of it waves. On our underground cables we correlated the relationship with the temperature, rain and other variables with our fault and we get the definition of it wave that is correlated with our fault. For us this is really important because this lead us to the other point that to deliver adaptation we need local analysis. The second step for us is to assess vulnerability. As I said we have several kinds of assets. The most difficult one for us, the most challenging one are the distributed assets because are heterogeneous, let's think about power grids, different level of quality, materials, solutions around the world. We have a footprint quite wide, we are in Italy, in Spain and also in some countries of Europe but also South America, for example Brazil, Chile, Colombia but also North America with our wind power plants, renewable power plants and storage. And we have also business development in Asia, in Africa. So this is why for us it's important that we are doing it at cover, a global coverage of our data. And so this is why we work both on global analysis. So we deliver indexes using global climate models, a low resolution to have an overview of the risk. Prioritize the areas where some phenomena are more important than others in terms of changes and potential impact. Then we need to downscale, to log a level. And we go to one of the, I would say a classic of the points that we rise to, we need data with higher resolution. Of course we need data with higher resolution, but I really would like to highlight what has been already mentioned. Of course, the higher resolution is not always the solution because we face with uncertainties and sometimes we find that sometimes you can have better information from a high level view and then you have to be able to interpret what's coming from higher resolution data. This is why we would like to have always an understanding of the uncertainties. What I'm seeing in the market, for example, there are a lot of private companies selling services, climate services, like a black boxes. So to me, for example, it's important to have the support for us and for the older stakeholders to have a kind of guideline so now to deal with this kind of uncertainties. For example, what I'm trying to do internally, is to translate the language of the uncertainties analysis also in a taxonomy, helping decision-maker understanding. This is a directional insight. Now you can save the positive, negative, you cannot save the magnitude. This is something good for decision-maker. This is not useful. But for me to try to give a guideline, a set of guidelines would help really in delivery adaptation because some companies now are kind of confused on how to use data and finish to spend money for data that are not really useful. Another point interesting for us, since I talked about renewable production, for example, is, okay, understand the average changes in a year, but we are also really interested in understanding how we'll change the internal variability, how seasons will change. So as you can imagine, if the water is concentrated, the water availability in one month instead of 12 could make a lot of difference. We are trying to study understanding which metrics to use to really understand what's going on could be very interesting for us. And coming back to the point of uncertainty, I would quote the selection of an ensemble. We're working with ICTP to understand the best way to select our ensemble of models. Sometimes it's hard to find, for example, a good selection all over the world for RCP 4.5. We would try and the companies we will need to reach as much as possible the information also on those kind of scenarios. Because when we go to adaptation, we would like to give as much information on alternative scenarios. Because then we divide our possible options of adaptation between the opportunity to increase resiliency or to leverage on response management. As been mentioned before, of course, sometimes the solutions or the measures of adaptation are measured at need times, required times. So sometimes we have to decide if we want to push more on the response or we have to push both on resiliency and response. So having more information on what's going to happen in alternative scenarios and also, this is another point I would like to highlight, in the next 10 years, 15 years, it's very important. What I'm saying, for example, that sometimes and other things, people are going to take a decision only when faced, the odds are, and if you tell them we are going to face something in the next 30 years, maybe they will not follow you. There are other priorities, pandemic, financial constraints. Well, for us, understanding and working on attribution, understanding what's going to happen, the recent past, in the next 10 years will really help companies and drive their efforts. For example, combining insight from the model and climate models. Because this is something that our management is asking a lot. We would like to understand better, because when you do a plan, you have to invest now for the next 10 years, and sometimes we face a lot of challenging in trimming a good cost-benefit analysis and make them understand that there are some priorities that must be faced now. So for us, for example, sheds and lights in those short periods would be very interesting. And then I would conclude, I guess, I'm out of time, with some ideas that could be interesting on an adaptation side. For example, it would be helpful to me to include in those kinds of studies for adaptation, also some case studies developed with the stakeholder, demonstrating how to apply concrete in a practical way, those kinds of insights. And, for example, how to develop solutions. I made the last example since we mentioned wildfire risk. For wildfire risk, we overlapped layers of land cover with fire weather indexes and layer of our assets. This is because understanding the vulnerability of the territory would be a plus for us. Another idea that came in my mind, and I will conclude, for a company, for example, would be useful to direct priorities to have also an overview of the vulnerability of a territory. Let's think about if we are able to make an atlas of climate change also an atlas of vulnerability. So maybe, and every rain could impact differently for flood or for other for other hazards in different regions. So this would be helpful in direct when we have a lot of uncertainty. Let's go practical on the meet. So thank you all and sorry for... Thank you.