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Total Carbon Data, Analysis, and Insights (2023)

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说明

In this session, you'll (re)join Autodesk Product Development and Impact team members, plus customers and partners, to learn about recent and future developments in total carbon data, analysis, and insights. This is a follow-up to our Autodesk University 2022 class of the same title (link below). It will provide an in-depth look at new capabilities from across the Autodesk ecosystem. Total Carbon Data, Analysis, and Insights (2022): https://www.Autodesk.com/Autodesk-university/class/Total-Carbon-Data-Analysis-and-Insights-2022

主要学习内容

  • Update your knowledge on what total carbon is, why it matters, and how it can be analyzed as part of a more integrated BIM.
  • Learn about the latest developments in tools and workflows that provide total carbon analysis from Autodesk Forma to Revit, and more.
  • Discover new resources for learning and applying these tools and workflows from the earliest stages of design.
  • Hear from Autodesk customers who've been helping to shape the development and application of these tools and workflows.

讲师

  • Ian Molloy 的头像
    Ian Molloy
    Ian Molloy is Senior Product Line Manager for MEP and Building Performance Analysis with the Autodesk Building Group. Ian has over 25 years experience in the AEC industry and the development and application of software for the Design, Analysis and Optimization of Building Systems from Design to Operation. Ian has BSc. Eng (Hons) in Building Science and Mechanical Engineering and a degree in Mathematics. Ian is also a LEED Accredited Professional and Certified Scrum Product Owner. Based in Boston, Ian works globally with Autodesk business and software development teams, customers and partners all working to help make the built environment in better, more sustainable ways.
  • Corina Marinescu 的头像
    Corina Marinescu
    Corina Marinescu is a Senior Product Owner at Autodesk, where she coordinates the development of the Next Generation Insight with a focus on Total Carbon. Corina is a member of the Order of Architects in Romania (OAR) and a certified BIM Manager with almost 10 years of experience in designing complex building projects in Romania and internationally. She has successfully graduated with a second Master’s degree in Global BIM Management at the University of Barcelona IL-3. Corina is also a founding member of TEC Cluster in Romania.
  • Marta Bouchard
    Marta leads the sustainability strategy for Architecture, Engineering and Construction (AEC) within Autodesk's ESG & Impact team. Within this role, Marta seeks to position and extend Autodesk and Autodesk's technology to transform the AEC industry to realize more sustainable outcomes. Prior to joining Autodesk, Marta practiced over 15 years in the Architectural Design and Planning industry, providing sustainability consulting and design analysis for the built environment.
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Transcript

MARTA BOUCHARD: Hello, and welcome to our class Total Carbon Data Analysis and Insights. This is the Revit Insight product update for 2023. We're excited to share the latest developments with you today. The short version of the Safe Harbor Statement is that we will be making forward-looking statements today about planned or future development efforts.

These statements are not intended to be a promise or guarantee of future delivery of products, services, or features but merely reflect our current plans, which may change. Therefore, any purchasing decisions should be made with what the offering is today. Let's quickly introduce our speakers today. My name is Marta Bouchard. I lead Autodesk's AEC sustainability strategy.

IAN MOLLOY: Hi, everyone. My name is Ian Molloy. I'm product manager for Insight and MEP.

CORINA MARINESCU: And I'm Corina Marinescu, the product owner of Insight where I coordinate the development.

MARTA BOUCHARD: So here we are one year later with some new developments to share. This is the reintroduction and Insight product update called Total Carbon Data Analysis and Insights. There's no doubt about the AEC industry's impact on climate change. Architecture 2030 published updated figures on the total annual global CO2 emissions attributed to the built environment. The clock is ticking, and we need to accelerate our ability to address it.

To account for carbon emissions in the AEC life cycle, it's useful to visualize where these emissions originate. Embodied carbon is greatest in the upfront emissions first created from the extraction, manufacturing, transportation, and installation of building materials, plus future activities associated with maintenance, replacement, and materials disposal.

Operational carbon refers to the emissions that are a byproduct of all energy sources consumed by year-over-year operations throughout the life of the building. And total carbon is the combination of both embodied and operational carbon. Whole life carbon accounts for the cumulative life cycle emissions, including end-of-life activities such as demolition or reuse.

For those who joined the session last year, you may recall this view on the landscape of carbon analysis tools illustrated from simple to complex. But has it changed? Embodied carbon tools still range from manual calculations and spreadsheets to customized solutions and full life cycle assessment software. Operational carbon tools range from building typology proxies and references to detailed whole building predictive simulations.

Together today, this still evidences a varied, disconnected tool scape used at different points in the design process. Let's take a closer look at how Building Information Modeling, or BIM, fits into this ecosystem and it's position to address total carbon. Here's a high-level view of how to assess total carbon with BIM.

For embodied carbon, we first annotate BIM elements and materials, quantify those to create a bill of materials, assign factors by material type, and then convert materials to carbon with carbon coefficients. And to assess operational carbon, similarly, we start by converting BIM this time to an analytical model for energy simulation, simulate annual building energy precise to location and climate and the operation schedule, and calculate operational energy by use and fuel type. This then gets converted energy to carbon with carbon coefficients. BIM, for total carbon, enables a variety of processes and workflows from a common starting point and engaging many stakeholders.

Now, here are Autodesk BIM solutions that enable AEC professionals to measure and reduce carbon emissions across the building life cycle. Embodied carbon can be analyzed from early massing all the way through procurement phases. Operational carbon can be optimized by analyzing energy efficiency during planning and design phases and also managed during operations. Together, these enable design iteration and insights to decarbonize across the life cycle.

In addition to talking to our customers, learning about the industry needs, and shaping our product development, I actually want to share with you some of our team's own exploration using many of these tools shown here and how we assess total carbon with BIM. Let me tell you a little bit about that. I want to highlight this real-worked example that demonstrates how applying different carbon analysis tools can result in different outcomes.

So Autodesk is partnering with a company called Factory OS, a builder of high quality, sustainable, affordable, modular housing products in California. Project Phoenix is a demonstration project to explore, develop, and design modular construction methods towards a net zero carbon housing unit. So experimenting with different building modules during design, the Phoenix Project team applied Autodesk and analysis tools all available today for analyzing both embodied and operational carbon to explore total carbon and other decarbonization strategies to design a path to net zero.

Details of this project case study are available in another AU 2023 session. So I'll mention only the carbon analysis tools and BIM process at a high level. For the Phoenix Project, our teams used Revit architectural models to test four different building scenarios. The goal was to optimize the building's construction materials for lower embodied carbon and the building energy and ventilation systems to optimize operational carbon and, ultimately, augment the design with further carbon reduction strategies.

First, we used Revit Insights new carbon Insights as it offered the ease and simplicity of moving from the Revit file to an embodied carbon analysis in which to analyze various exterior wall constructions. Because of the detailed modular nature of the Phoenix Project, we needed to analyze specific construction materials, which led us to use [? Calicut, ?] a free third party Revit plugin.

And for operational energy, we use Revit Systems Analysis with custom openstudio workflows as a tool to mitigate operational carbon. And then we processed the work. We compared the data using spreadsheets and graphs and aggregated the analysis results to provide the end user Factory OS total carbon insights on the four building options.

This real project aptly illustrates how design decisions that rely on building information models of construction materials and building systems require quite a bit of decision making. Running the analysis was only part of the work as the most important part is actually integrating and aggregating all the data and analysis into meaningful insights.

So last year, we presented the vision of Autodesk Insight, a total carbon solution for building design in Revit. Underpinning the vision and seeing here is a mental model, an interconnected process of three things-- data, analysis, insights-- which connect expertise with everyday AEC professionals that need to interact with it. So our goal is to make this journey in both directions as frictionless and flexible as possible. But let me unpack this a little bit.

So from the left, this is how an expert expects the process to work. Establish some data, run some analysis, maybe create some new data in which to create Insights that answer a specific question. But for most people, we actually start from the right. This reflects the way we actually use software and consume the information.

We look for an Insight and start with a question, do a bit of analysis to get a first answer, then we look into the data behind it so we can trust the whole process and trust the Insight, which we then use to make an actual design decision. So, ideally, the process is a seamless connection of data, analysis, and Insights between expert and human, right?

I'm going to answer this question by going back to our real worked example with Project Phoenix. Ideally, we want an Insight. For Project Phoenix, we want to know the Total Carbon of a building with a lifespan of 50 years. We start with our model, which is full of data. We run some analysis to get our Insight, right?

In practice, we start with what we think is a clear process and way forward. But it's easy to get lost in the tools. Each tool can generate a myriad of outcomes. It can take an expert user to work through all the inputs and the outputs. And go back into the data to review the analysis that, hopefully, answers the question. And often we realize we may need or want a different Insight altogether.

In practice, we want an Insight. But what if we want to further explore the Insight and measure it differently? Maybe we want to know a different carbon metric or an energy metric or a cost or a materials' metric. So metrics measure an outcome. We can measure outcomes with a variety of metrics. And each is a different Insight.

If we want to understand what effects our Insight, we need to change design factors to further explore the Insight. Maybe we want to know a different architectural factor or program or MEP or structural factor. Factors effect an outcome. So if we want to understand what effects our Insight it requires adjusting all those factors.

In practice, we want multiple Insights as they're affected by different design decisions and measured by different outcomes. This requires variability of two key things, factors and metrics. So one last time, let's get back to our worked example, how can people get Total Carbon and other Insights like that of Project Phoenix?

We need connected data, analysis, and Insight in order to bring what's relevant into focus. We need a seamless flow of data between the expertise and designers and engineers. To be able to answer multiple questions, we need rigorous yet flexible analysis. To explore analysis, we want adjustable factors and metrics.

And to ensure we trust the results, we need to be able to edit the inputs and the outputs. The climate change clock is ticking. And we really must accelerate our impact. So I just highlighted a BIM example to illustrate that building models, defining the materials, the building systems and running analysis is only part of the work. An equally important, but challenging part, is integrating it into meaningful Insights.

Especially for use by the broader project team and client, it's important to clearly communicate all the key inputs and assumptions in order to build trust, set clear targets, assess trade offs between different design options, and track overall progress towards our decarbonization goals. So while existing tools excel at the raw analysis, they tend to leave the job of creating and communicating Insights to the analyst which can be very challenging and can require a lot of subject matter expertise.

So to address this and to help democratize sustainability analysis to broader audiences, we're developing the next generation of Insights currently available as a technology preview. The vision is to create a simple, collaborative workspace which project teams can use to target, track, and trade off key design performance metrics and factors in a very open and flexible way.

So I'm going to hand over to Ian now who will walk you through where the development is at today and the key functionality we're developing to support Total Carbon data analysis and Insights.

IAN MOLLOY: Thanks, Marta.

All right, so I'm going to start with a high level summary of where we were at and how we got here. Here you can see all the various pieces and parts that are going into Next Gen Insight. We've covered this in more detail in last year's class. So this is just an overview and a recap.

Essentially after Insight was first released in 2016, 2017, we learned a lot about what worked well and what its limitations were. And since then we've essentially been pushing forward on three main parts. First was expanding capabilities for more detailed energy analysis. This exists today as a feature in Revit known as systems analysis.

Systems analysis is essentially an open extensible integration between Revit and Energyplus via Open Studio. And we've been continuing to strengthen this in particular developing Open Studio measures that can analyze specific parts under aspects relating to total carbon. For example, surface hot water heating, code base lines, all electric HVAC, PV batteries, and tariff analysis.

For more information on this, check out the AU class Design and Analysis of Building Electrification with Revit Systems Analysis. While Systems Analysis runs Energyplus Open Studio on the desktop, Next Gen Insight runs it in the cloud to essentially provide the same information what's needed for total carbon.

The second part was essentially expanding Insights' scope to calculate embodied carbon. This is what we shared last year and formed the basis of the Insight Tech Preview we released earlier this year. Essentially, it uses the Revit Energy Analytical Model as a starting point for total carbon analysis. It uses data from EC3 and allows you to define your own material carbon coefficients.

It then computes the embodied carbon by material element type and provides various visualizations. Finally, then perhaps the most exciting part, we've been moving some of the core concepts of original Insight that made it very visual interactive, moving them forward into Next Gen Insight. So pieces like metrics, factors, benchmarks, and scenarios, the things that support a more outcome driven approach and more data analysis and Insight driven approach.

This last piece is mainly what I'm going to talk about now. And that's ultimately what I'm going to show is all powered by all the data analysis that already exists with Insight and Systems Analysis.

So we believe the best way to really understand what Next Gen insight is and what can be meant by total carbon is to go through a worked example. This example provides some insight into what total carbon is in the simplest possible terms. But its main purpose is really to help you understand how Insight works and how you can shape it to meet different needs.

Everything I'm about to show you is what we call a real data paper prototype. This is effectively the design the development team is working to, and it uses real data actually taken from Project Phoenix. Once I'm done, Corina is going to show you a video of actual working software, all of which you can then use to judge the overall value and progress for yourself. Either way, we'd love to hear from you, hear your feedback, ideally get you involved in the technical preview. Corina is going to provide information on how to do that later.

So just before we jump into the worked example, we want to share the simple breakdown of key problems that people typically encounter with carbon and many other types of analysis. This is far from an exhaustive list and probably nothing you haven't heard before. But things like, it's very complex, or it's a black box, and I'm not sure where numbers come from and if I can trust it. And it's only for specialists. And it's very precise, but it doesn't really show me direction. And it's only applicable to certain design stages or certain scope of design.

Marta touched on a lot of this earlier on the challenges of the current tool landscape. I only mention them here because in response to these problems, we've established a set of key values or principles that we've really made integral to the workflow and the user experience. These are simple, open, extensible, accurate, and collaborative.

And these might seem a little vague or unclear at the moment. But the purpose of them here is to help break down the worked example I'm going to go through into very clear sections and help connect the dots between how the tool works and the problems and principles that we're solving for.

OK, so the first part of the worked example, this roughly connects to the principle of making things as simple as they possibly can be. We'll explain Insights' overall structure and its main parts. Then we look at what's in this specific Insight and what can be learned with minimal interaction.

Then as we go through each section, we'll go deeper under the hood. And by the end, you should have a reasonable sense of how we're approaching the problems and what you'll be able to do with Insight.

OK. So here is Insight or more specifically an Insight. And we'll get into what this one does specifically in a minute. For now, I'll just outline three key parts and its overall structure. Just watch out for the yellow highlighting to follow along.

First thing to note is that an Insight contains dashboards. Dashboards behave much like tabs, and they can be used to organize various data analysis to serve different purposes or preferences.

Next, dashboards contain cards. There's various types of cards, each with different purposes and behaviors. And then up on the top left, you'll see scenarios, basically, a way to manage different design options or different assumptions and see, do comparisons. These are really key to doing comparisons and trade offs.

And note that we're just starting here with just a single scenario. And we'll show how more can be added later. Then one level down from this, we have these other key cards that we want to highlight here. First of them is metrics.

Metrics are essentially key performance indicators that you want to target, track, and trade off. They're shown in a fixed position here. You can actually move them around. We're going to keep it nice and simple in one place. Factors then are the things that impact metrics. So the design decisions like maybe wall type. Or in this case here, key assumptions that impact the actual metrics and outcomes.

Then we have the ability to edit the dashboard. This enables you to look under the hood and change the definition of metrics and factors. You can also change the layout. Like I said, we're not going to get into that. You'll see that in the demo. Then manage, that defines the data analysis behind the metrics and the factors and the charts and graphs that are shown.

So we're going to use all of those tools here now to show you how Insight actually works and what you can do with it. So let's take a look at what this Insight is actually about. So to understand that, we should actually use what's here is this little, About This Insight. Now it's a little small here. So I'll read it for you.

But it basically says, Autodesk Insight provides a simple, collaborative workspace to explore and explain key design performance decisions using open and extensible data analysis. This Insight provides a simple answer to the question, what is Total Carbon but more importantly helps illustrate the main parts of how Insight works.

It contains a single dashboard, contains three key metrics, Total Carbon, embodied carbon, operational carbon, one chart, and three factors, building lifespan, grid carbon coefficient, and construction waste. Start by playing with the factor sliders to see their impact on key metrics then use the Manage Edit Dashboard buttons to see how each metric factor is defined and adjust them to suit your needs, provide detailed breakdowns, and do side by side comparisons.

So, basically, that's just providing some information about what the purpose of this Insight is and what the user should do with it. And these kinds of notes are totally customizable and can be attached in various places basically with the idea of building guidance in an explanation throughout Insight.

Now let's take a closer look at what's actually in here. So first you see a high level summary of things like building type, location, floor area. You see a view of the analytical model. You see some key metrics for Total Carbon, embodied carbon, operational carbon, and a breakdown of that.

Taken as is, what insight can you really get from this? Not a whole lot. You can kind of see what the Total Carbon number is. You can see that the breakdown is heavily weighted towards operational carbon more than operational carbon. The key question is why?

That is taking just as it is as a static dashboard, it might answer some questions. But actually in many cases, it just raises more. So now what we want to do is look into it further. So one of the key features in here that we took from original Insight and we've resurrected here is this idea of looking at factors and having to play with them.

So take building lifespan. It could be anything from one to 100 years. It's currently set to 50 because this project is built essentially to last for a long time. But very easily, I can just click on that, and you can see if, I brought it down to one year, you know, first of all, notice the real time feedback. And now you'll see the embodied carbon is much more significant.

So as you can see here, the Insight now is building lifespan plays a very significant role in defining what the Total Carbon is where savings can be made. Let me just reset that. Now let's look at grid carbon coefficient. This basically depends on your location and the fuel mix of your electrical utilities. 0.18 is the statewide average for California, where this project is located.

But of course, the grid is decarbonizing. So what might it be in the future? Say it was 0.05. You'll see that actually reduces the amount of operational carbon, puts the shift back on to a little bit more onto embodied carbon. But not a lot, which, again, just shows the tremendous effect that the building lifespan has.

And then, finally, construction waste. This one is kind of specific to projects Project Phoenix. But this provides a rough approximation to enable comparison of traditional on site construction where waste is often in the region of 30% versus what Project Phoenix is using, which is modular off site prefab, much more efficient. Where it's waste is much lower like 2%.

And in that case there, you can see the embodied carbon is greatly reduced. So the point of factors is that they provide real time feedback on the different factors that impact the overall metrics and outcomes. OK. So that's our first pass through what Insight is and what we mean by simple.

So what we're going to show now is how Insight is open. And by that we mean there's no black box, nothing is hidden. We're trying to make everything as transparent as it can be. So now we're going to start to take a look under the hood of what's in this Insight.

So now this isn't quite under the hood yet. It's just showing more of how we can attach some explanatory notes and other information to help explain what things are and where data comes from. But if you click on these little eyes beside the metrics or the factors, you'll see this little explanation of what building lifespan is.

It represents the timescale over which the Total Carbon is being assessed. Gives you some typical values for it. And you can either set it to a period or maybe if you're assessing Total Carbon by a certain period of date, you can set in the number of years that you want. For the purposes of this project and to maximize Total Carbon savings, we're assuming a long building life of 50 years.

So that's basically explaining that this is an important factor, and this is our assumption, being totally upfront and transparent about that. Then similarly, if we look at things like grid carbon coefficient, you'll see this gives a more detailed explanation of what that is. And it also provides the source that we got that information from which is the Energy Information Administration's website that provides statewide averages for grid carbon intensity.

Then, finally, construction waste. Again, just another little note, explaining what I mentioned earlier, again, it's providing context on what this setting is, the role it plays, and the different scenarios that we can look at for this project. So that's all super high level stuff. Now let's really look under the hood as to what is behind this Insight.

All we're going to do here is first click on the little Edit Dashboard button. What you'll see immediately, all of the cards become, essentially, editable, the little pencil icon indicates that. And if we dig under what's behind each of these, we want to think, well, we're ultimately trying to save total carbon. And we know that is comprised of embodied and operational.

Let's look at how each of these pieces are defined and how they relate to one another. So if we look at the little Edit button for the metric total carbon, we manage that metric. You'll see it opens up the Manage metrics dialog. And you'll see that the Total Carbon, the metric, has a name, it has a description, but then it has a sort of a formula or a definition.

And it's basically, in this case, it is the sum of two other metrics, embodied carbon and operational carbon. It's a very simple one. This is why we started with this one so that you can see clearly how it works. And there's a nice kind of color coding so that later on as we start to do more complex equations combining, not only computed metrics, but also analysis data or model data or factors, you can see how it's all combined more clearly. All using very simple math operators to do that.

So next we're going to look at what's behind embodied carbon. And, again, just in simplest terms, embodied carbon is the embodied carbon of the walls plus the embodied carbon of the windows, embodied carbon of the floors, plus some embodied carbon of the roofs, they are all analysis results computed from the embodied carbon analysis. And it's multiplied by this blue thing which is a factor called construction waste.

So what is construction waste, and what is a factor? We can then go into the Manage menu, pick out Factors, select the construction waste factor, and see, again, it gives a description of what it is. It gives the table of values, the range of values it could be. It could be zero, it's unlikely. Offsite modular prefab is 2%, industry average for in situ construction is 30%.

So this gives us the range of values over which that factor can vary and a user can explore the cause and effect impact of very easily. These are entirely adjustable. You can add to that table. You can add descriptions. You can add values. Makes it very flexible and extensible.

Then, finally, we're going to look at what's behind operational carbon. And this one's a little bit more complicated, but you'll still see it's still relatively simple. This is combining a number of analysis results like annual electrical energy use, multiplying it by the grid carbon coefficient factor, and then adding that to the annual fuel use times a fixed value, which is a conversion. You'll see the little note talks about a fixed rate conversion for the combustion of natural gas.

So the fuel in this case is assumed to be natural gas. And both of those are added together and multiplied by the building lifespan factor. So unequally we can say, OK, if that's what that is, what is grid carbon coefficient? We can look at go in, find the factor again, see how it's defined, see the range of values that we can consider equally for building lifespan.

So, again, if we wanted to assume, account for 100 years, we could add in a row for 100 years, add that in, and then that would appear on the scale, and we could move it up and down to see what that impact is. And it would compute in real time. So very, very simple.

Trying to be really upfront about how everything is computed and where the data comes from and given the user that trust and confidence in what's there. OK. So the next section then is to talk about extensibility. And this is really the fun part now.

Now we're going to show you how Insights can be customized to meet a range of different needs. For this example, we're basically going to add some new metrics and some new factors. And that, basically, captured the value of adding photovoltaic energy into the mix.

So first thing I do is I select metrics. I create a new metric, and I'm going to call it photovoltaic offset. Now there will be different ways to compute this. This is the beauty of this. I can do the different levels of sophistication here. But what I'm basically doing here is I'm taking an analysis result, which is annual roof solar radiation, and multiplying it by a piece of model data, which is the roof area. And then I'm multiplying it by two constants, 50% for the assumed percentage of roof area and 20% for the overall efficiency of the photovoltaic panels.

That's now going to compute a new metric called photovoltaic offset. We're also multiplying it by grid carbon coefficient of building lifespan to turn it into effectively the analysis of operational carbon provided by PV. And notice, the dashboard hasn't changed yet. Because it's been computed in the background. We're not showing it yet. And we haven't changed anywhere else to respond to that.

So the natural place to account for photovoltaic offset is in operational carbon. So I'm going to go back into this definition. And in here, now, I'm going to add in these minus photovoltaic offset, minus the metric photovoltaic offset. After it's computed that, it will deduct that, and it will compute that. So now when we go to the next, you'll now see the total carbon number has actually reduced.

You'll see operational carbon has reduced and as such the total carbon has also reduced. So the next thing we're going to do is actually add the photovoltaic metric to the dashboard. So all we do here is we select Add Card, we select the metric tile, we select the metric that we want to include on the Insight. And it gets added to the Insight.

So now we can see the whole story of where the Total Carbon, embodied operation, and the photovoltaic offset are sort of summing together. Next, what we're going to do is we're going to create a new factor to define the percentage of roof for photovoltaic. So, again, I go into Factors, I create a new factor for called percentage roof PV coverage, I give it a description, and give it a range of values from 0, as if we had no PV, to 50%, the default, to 80%, which was like the highest sort of amount of percentage of the roof that you can practically use for photovoltaic. Because you've likely got other equipment up on the roof.

So, again, very conceptual but, hopefully, communicates the idea. So now what we're going to do is we're going to adjust the photovoltaic metric to include the roof percentage as a factor instead of a fixed number. So, again, I edit the metric. I select the photovoltaic offset. I change the formula to replace the original fixed 50% to it now the metric, sorry, the factor percentage roof PV coverage.

Because the default is still 50%, the numbers haven't changed. But what I can now do is now I can add a metric, sorry, a factor slider so that I can actually interact with this. So, again, if I go Add Card, select Factor Slider, select the factor that I want to put on the slider, you'll then see it gets added to the Insight.

And now I have it's reporting the current value of 50%. And of course, what do we want to do with this? We want to change it, of course. And we want to see what is the potential if we go from 50% to 80%? And as you'll see, as you go from 50% to 80%, photovoltaic offset increases, operational carbon reduces, and overall Total Carbon reduces as well.

So very, very simple example of how the data, the metrics, and the factors can be combined to start to provide interactivity and tell a story on what design decisions need to be explored and can be explored.

Yeah, so that should give you some sense of how metrics and factors work in this very simple, open, and extensible way. Remember, the purpose of this example is mainly to understand how Insight works. It's very simple. It can be a lot more complicated. But because it's extensible, you can add so much more. And so this is where we want to connect to that idea that Marta was talking about. When it comes to Total Carbon, there's more than just embodied and operational carbon and even photovoltaic offset. There are many, many different metrics, many different things to measure, and ways to measure them.

And there are many things to vary or combinations of things to vary. And so this is what we want to be able to enable is for Insights to be able to combine different metrics and factors to answer different design questions at different stages in the process for different stakeholders.

So, finally, the next accurate, now this one is a big word. There's a lot of confusion about it. And to be clear in this context, it's less about being right. It's more about openness and extensibility and where you have the tools to dial the Insight in to meet your needs, use all the right data, et cetera.

Here we mean it more in the context of actually providing direction as opposed to precision which is more about position. What you'll also see, though, is that it does rely a bit on extensibility. So far everything we've done so far has been contained on a single dashboard.

And just like current Insight provides a few very high level key performance metrics, a big part of gaining further insight and sort of trusting that data and analysis is starting to break these down in other metrics. So to do that we're going to add a new dashboard that will provide a simple breakdown of embodied carbon and operational carbon.

So to do that, I just pick to add a dashboard. I can, if I want to, pick a blank one and start completely from scratch and start putting metrics, factors, charts and connect the data. But because a breakdown is a common thing to do, we actually have that as a standard format that people want to use.

You pick the metrics or data that you want to actually plot, and then it'll create a dashboard with that breakdown. So here we can see a breakdown of the embodied carbon and a breakdown of the operational carbon over one year along with, again, another model view. And notice it's on another tab.

This is very useful for QA/QC as well as understanding where the carbon is kind of going. And then next, you know, everything we've talked about so far is a single scenario and a single situation. So, obviously, one of the key things we want to do with an Insight is actually understand differences in deltas and trade offs and so on. So to do that, very helpful tool, is to create a side by side comparison.

Again, this is another standard way that people want to look at information. So to do that we have some preconfigured scenarios here. These are essentially the scenarios that are defined for Project Phoenix. We've got essentially four different Revit models.

And then when we plot them, it will create a side by side comparison. And, again, just like the Overview dashboard, it can be configured to meet different needs. I can hide or remove different metrics or factors. I can change the charts. I can also add other scenarios as well.

And the point of this is I can make different-- I can use these to explore the design myself. Or I can also curate one that I want to present to a client to explain, here are the options we looked at. And here's what we arrived at. So that in that case that Insight can be completely specific to that project.

And then, finally, collaborative. Now this is actually a super simple one. Nope. Put simply reducing carbon is a team sport. And so Insights are meant to be shared. Now the fact that it's web based really lends itself to easy sharing of both the Insights directly, as a web URL. And this will help designers and clients explore and explain decisions as well as reusing Insights on other projects.

Then further in the future, and maybe we're stating the obvious, but we really believe in the promise of unified data across disciplines and design tools. So one of the things we're working towards is unlocking ways to provide the use of Insights' metrics, factors, charts directly in tools like Forma and Revit. And then from Forma and Revit provide a path back to Insight so that the user can see where the data come from, how is it configured, and build that trust and confidence, and make it suit their needs.

So, therefore, the tool isn't just a one size fits all thing. You're not limited by the way we define carbon should be analyzed or anything should be analyzed.

OK. So that was a lot. Now I'm just going to leave you with just a very quick recap of what we just went through there. So like we said, Next Gen Insight is intended as a simple collaborative workspace project teams can use to target, trade off, and track key design performance metrics and factors in a very open and extensible way.

You've seen how Insights contain dashboards and cards. Cards comprise things like metrics, factors, charts, and model views. And all of these are highly open and extensible.

So now that we've pieced it all apart and explained how it works, hopefully, you can see what we're adding and what we're doing with Insight is really starting to tackle some of these key challenges that we talked about, this need to create a seamless connection between the expert and the typical designer, seamless connection between data analysis and insights. The need to connect many different metrics and factors and analyze many different metrics and factors and work in this simple open extensible accurate collaborative way.

These are really the key aspects that we're trying to optimize this tool to serve. And then, finally, we just want to leave you with the point that the idea of Insight containing multiple Insights doesn't just extend to different definitions of breakdowns of carbon or whatever metric that you're tracking but combined with how models are created and combined and specific, sort of project specific challenges and opportunities to save carbon, Insight is well suited to serving these needs in a more consistent and repeatable way.

So to help illustrate this, we've used these other examples. Now check out all these other AU classes, some are technical instructions some are case studies. Now full disclosure, while these case studies did not actually use the Insight you saw today, they'll all admit that they did it the hard way. And working with these customers and partners has really helped us shape the solution that we're developing and presenting here today. And they're very keen to take advantage of this in future projects.

And they've kind of validated this problem that we're solving and solution that we're putting forward. So that's it for me. I'm going to hand you over to Corina now who's going to share the latest demo with you and information about how to provide feedback and get involved in the tech preview.

CORINA MARINESCU: For those tuning in online, please scan the QR code currently visible on your screen to watch the latest demo. Your feedback is highly valued and appreciated. So leave us a comment. If you want to test the tech preview for yourself, as we expand the functionality that was presented, please scan the QR code to get involved and help shape the ongoing development.

So thank you for attending our AU class, Total Carbon Data Analysis and Insights. Here is a list of other sustainability classes we'll think you'll enjoy.

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我们通过 Doubleclick 在 Doubleclick 提供支持的站点上投放数字广告。根据 Doubleclick 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Doubleclick 收集的与您相关的数据相整合。我们利用发送给 Doubleclick 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Doubleclick 隐私政策
HubSpot
我们通过 HubSpot 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。. HubSpot 隐私政策
Twitter
我们通过 Twitter 在 Twitter 提供支持的站点上投放数字广告。根据 Twitter 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Twitter 收集的与您相关的数据相整合。我们利用发送给 Twitter 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Twitter 隐私政策
Facebook
我们通过 Facebook 在 Facebook 提供支持的站点上投放数字广告。根据 Facebook 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Facebook 收集的与您相关的数据相整合。我们利用发送给 Facebook 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Facebook 隐私政策
LinkedIn
我们通过 LinkedIn 在 LinkedIn 提供支持的站点上投放数字广告。根据 LinkedIn 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 LinkedIn 收集的与您相关的数据相整合。我们利用发送给 LinkedIn 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. LinkedIn 隐私政策
Yahoo! Japan
我们通过 Yahoo! Japan 在 Yahoo! Japan 提供支持的站点上投放数字广告。根据 Yahoo! Japan 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Yahoo! Japan 收集的与您相关的数据相整合。我们利用发送给 Yahoo! Japan 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Yahoo! Japan 隐私政策
Naver
我们通过 Naver 在 Naver 提供支持的站点上投放数字广告。根据 Naver 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Naver 收集的与您相关的数据相整合。我们利用发送给 Naver 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Naver 隐私政策
Quantcast
我们通过 Quantcast 在 Quantcast 提供支持的站点上投放数字广告。根据 Quantcast 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Quantcast 收集的与您相关的数据相整合。我们利用发送给 Quantcast 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Quantcast 隐私政策
Call Tracking
我们通过 Call Tracking 为推广活动提供专属的电话号码。从而,使您可以更快地联系我们的支持人员并帮助我们更精确地评估我们的表现。我们可能会通过提供的电话号码收集与您在站点中的活动相关的数据。. Call Tracking 隐私政策
Wunderkind
我们通过 Wunderkind 在 Wunderkind 提供支持的站点上投放数字广告。根据 Wunderkind 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Wunderkind 收集的与您相关的数据相整合。我们利用发送给 Wunderkind 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Wunderkind 隐私政策
ADC Media
我们通过 ADC Media 在 ADC Media 提供支持的站点上投放数字广告。根据 ADC Media 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 ADC Media 收集的与您相关的数据相整合。我们利用发送给 ADC Media 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. ADC Media 隐私政策
AgrantSEM
我们通过 AgrantSEM 在 AgrantSEM 提供支持的站点上投放数字广告。根据 AgrantSEM 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AgrantSEM 收集的与您相关的数据相整合。我们利用发送给 AgrantSEM 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AgrantSEM 隐私政策
Bidtellect
我们通过 Bidtellect 在 Bidtellect 提供支持的站点上投放数字广告。根据 Bidtellect 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bidtellect 收集的与您相关的数据相整合。我们利用发送给 Bidtellect 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bidtellect 隐私政策
Bing
我们通过 Bing 在 Bing 提供支持的站点上投放数字广告。根据 Bing 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bing 收集的与您相关的数据相整合。我们利用发送给 Bing 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bing 隐私政策
G2Crowd
我们通过 G2Crowd 在 G2Crowd 提供支持的站点上投放数字广告。根据 G2Crowd 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 G2Crowd 收集的与您相关的数据相整合。我们利用发送给 G2Crowd 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. G2Crowd 隐私政策
NMPI Display
我们通过 NMPI Display 在 NMPI Display 提供支持的站点上投放数字广告。根据 NMPI Display 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 NMPI Display 收集的与您相关的数据相整合。我们利用发送给 NMPI Display 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. NMPI Display 隐私政策
VK
我们通过 VK 在 VK 提供支持的站点上投放数字广告。根据 VK 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 VK 收集的与您相关的数据相整合。我们利用发送给 VK 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. VK 隐私政策
Adobe Target
我们通过 Adobe Target 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Adobe Target 隐私政策
Google Analytics (Advertising)
我们通过 Google Analytics (Advertising) 在 Google Analytics (Advertising) 提供支持的站点上投放数字广告。根据 Google Analytics (Advertising) 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Google Analytics (Advertising) 收集的与您相关的数据相整合。我们利用发送给 Google Analytics (Advertising) 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Google Analytics (Advertising) 隐私政策
Trendkite
我们通过 Trendkite 在 Trendkite 提供支持的站点上投放数字广告。根据 Trendkite 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Trendkite 收集的与您相关的数据相整合。我们利用发送给 Trendkite 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Trendkite 隐私政策
Hotjar
我们通过 Hotjar 在 Hotjar 提供支持的站点上投放数字广告。根据 Hotjar 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Hotjar 收集的与您相关的数据相整合。我们利用发送给 Hotjar 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Hotjar 隐私政策
6 Sense
我们通过 6 Sense 在 6 Sense 提供支持的站点上投放数字广告。根据 6 Sense 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 6 Sense 收集的与您相关的数据相整合。我们利用发送给 6 Sense 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. 6 Sense 隐私政策
Terminus
我们通过 Terminus 在 Terminus 提供支持的站点上投放数字广告。根据 Terminus 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Terminus 收集的与您相关的数据相整合。我们利用发送给 Terminus 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Terminus 隐私政策
StackAdapt
我们通过 StackAdapt 在 StackAdapt 提供支持的站点上投放数字广告。根据 StackAdapt 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 StackAdapt 收集的与您相关的数据相整合。我们利用发送给 StackAdapt 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. StackAdapt 隐私政策
The Trade Desk
我们通过 The Trade Desk 在 The Trade Desk 提供支持的站点上投放数字广告。根据 The Trade Desk 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 The Trade Desk 收集的与您相关的数据相整合。我们利用发送给 The Trade Desk 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. The Trade Desk 隐私政策
RollWorks
We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

是否确定要简化联机体验?

我们希望您能够从我们这里获得良好体验。对于上一屏幕中的类别,如果选择“是”,我们将收集并使用您的数据以自定义您的体验并为您构建更好的应用程序。您可以访问我们的“隐私声明”,根据需要更改您的设置。

个性化您的体验,选择由您来做。

我们重视隐私权。我们收集的数据可以帮助我们了解您对我们产品的使用情况、您可能感兴趣的信息以及我们可以在哪些方面做出改善以使您与 Autodesk 的沟通更为顺畅。

我们是否可以收集并使用您的数据,从而为您打造个性化的体验?

通过管理您在此站点的隐私设置来了解个性化体验的好处,或访问我们的隐私声明详细了解您的可用选项。