Hi, everyone. Thank you for attending this talk that will be about securing Python project supply chain. So a few words about ourselves. My name is Maya. I'm a software engineer at Red Hat in the emerging technology security team. And hi. My name is Fridolin. I used to work at Red Hat. I used to work at Datadoc. I'm an entrepreneur now. And you can find more information about us on Twitter, Mastodon, or GitHub. OK. So let us start this talk with a simple question, which is, why protecting your supply chain actually matters? So if you follow open source security or maybe Python news, you might have seen this information pass, which is that not too long ago, PyPI maintainers decided to temporarily deactivate user registration on the index and also the upload of new packages, of new projects. Because they got overwhelmed with the volume of malicious packages that got uploaded on PyPI. So they couldn't handle it. Everything was very overwhelming for them. So they had to suspend all those uploads. And maybe a little more or less recently in January, you might have seen that PyTorch, the famous machine learning library, was compromised. Some nightly bit of PyTorch was a victim of dependency confusion attack. So we'll see what a dependency confusion attack is later in this talk. But this is also a supply chain attack. And this is not a coincidence if you have seen all those articles passed recently, because a study showed that year to year, for the past three years, supply chain attacks have increased by more than 700%, which is very high growth rates. Supply chain attacks can cause a lot of damage to an organization, or a project can be both financial and also reputational. And if you have a weak supply chain, this gets you into a lot of legal and compliance issues as well. So some new regulations were put into place to try to secure software supply chains. And one of the most famous ones was issued in 2021 by President Biden. And this was the executive order 1421. It was called Improving the Nation's Cyber Security. And it was issued after the pretty infamous SolarWinds attack, which affected a lot of big organizations and some branches of the United States federal government. And this executive order basically tells corporation that collaborate with the US government that they should be more strict about supply chain standards they adopt. So for example, it pushes organization to adopt secure authentication to servers or maybe strict protocols and these kind of things if they want to sell software to the government. And this includes things like software bills of materials that we'll cover later, which are basically like a list of the ingredients that compose your software. OK, so now let's take a look at supply chain threats and vulnerabilities. Maya already mentioned infamous SolarWinds attack. It was an attack on SolarWinds Orion platform that is quite well used. It's a network performance monitoring platform. And what was done here, attackers basically uploaded malicious DLL file that was subsequently pulled by a build system, which produced software artifacts that were consumed by customers. These software artifacts were properly signed, so customers were not aware that there are some malicious behavior. And the effect of this attack was quite large. So more than 18,000 customers were affected. And more than 400 of US Fortune 500 companies were affected by this attack. You can see, for example, the White House or Pentagon or State Department or National Security Agency that was affected by this attack. What happened? SolarWinds stock price went down. That's not the worst thing, but attackers were able to access confidential information of customers. What could SolarWinds do better? They could follow Salsa framework. Salsa framework is quite recent. Quite recently, it went to version 1. Salsa stands for supply chain levels for software artifacts. So it's not a source. It's not a dense. And Salsa defines four levels, starting from level 1, where there are basically no requirements on the build platform, up to level 3, which is a properly hardened build platform. Salsa introduces this image. So you can see there's a producer that produces source code and the source code is stored in a source repository. Then build platform pulls sources, pulls dependencies, and creates a package or the resulting artifact that is subsequently consumed by consumers. Salsa defines threats in each step, so what can go wrong, but also defines how to prevent from these threats. So how to, for example, prevent from submitting unauthorized code or make sure that the source repository is not compromised. Now let's take a look at some toolbox to protect your Python projects. So the very first toolbox thing we will talk about is TAF. It's the update framework. It's called TAF because it solves TAF problem, and it is basically securing updates and preventing temporal attacks, rollback attacks, or key compromise attacks. The reference implementation is based on Tandy. That's an updater that was used in Tor. TAF is, let's say, more generic, but borrows many ideas from Tandy. There's also AppTain that is similar project to TAF that is used in automotive industry. It's used by companies that populate updates to cars. The reference implementation of TAF is in Python. You can find it under Python TAF. And one company, Datadoc, used TAF to secure agent integrations, the software that is shipped to customers, and uses TAF, and in total that we will talk about later, to securely ship software to customers. There are also efforts like PEP 458 and PEP 480, PEP meaning Python NNS Proposals, to secure PyPI itself. And we will talk also about it. TAF is also used in Zigstore to securely download public keys for instances of Zigstore. I already mentioned Intoto. So Intoto is a framework to secure supply chain. What it does, it basically defines what each step in a pipeline should do. So if a pipeline should, for example, write something or package something, then there is created attestation. So you are sure that each step in a pipeline performed desired task and is properly signed. So users who consume resulting artifacts can verify that each step in the pipeline, each step in the chain did its job properly. Now. OK. So now let's go to an important part of every supply chain, which is code signing. And for this part, I would like to introduce quite new projects when it comes to the space of code signing, which is Zigstore. So Zigstore was started a few years ago by different institutions and companies, like Google, Red Hat, and Purdue University, to make software signing more accessible and simpler. So it provides a very secure and simple interface to sign in kind of codes and containers as well. And to use it, you don't need any specific cryptography knowledge, which is kind of an improvement if you compare it to other signing standards like PGP, where sometimes a configuration can be a bit complex and you might need to know about the underlying cryptographic protocols that the tool uses to sign your software, which is not the case here. So one nice feature of Zigstore is that it uses OpenIDConnect to sign software instead of self-managed private keys. So OpenIDConnect is an authentication protocol and what it allows you to do is to bind your email address or any kind of identity like, for example, GitHub workflow run to your signature. So instead of having a permanent public key, bind to it, you can have something more identifiable for your end users like your email address and more personal. Zigstore has a client implementation in Python. It's called Zigstore Python and you can check it on GitHub. So it's a pretty good tool. It has a lot of integrations like, for example, with GitHub CI runs, you can use it as a GitHub action, you can use it as well as a CLI and I put an example on this side of what it looks like to sign with Zigstore Python. So it's very simple as you can see. If you want to sign a package, let's say a Python package in this case, the only thing you need to do is enter Zigstore sign your package and then it will redirect you to an IDC session. So basically this is a web browser page that opens up and you need to enter your credentials to an identity provider which is, for example, Google or GitHub which are currently supported, enter your password to authenticate and then it will validate your identity and bind it to the signature of your artifact. Here's the package. So you sign your artifacts and the second step is for your end users to verify the signature. So here again, it's quite simple. This is the second command. You just need to run Zigstore Verify here identity and you need to pass the email address of the signer which can be found on the signer certificates which is provided by Zigstore and you need to pass as well the URL of the IDC provider. So here for example, the signer identified with GitHub accounts so you will need to pass the corresponding URL and just what we call a bundle file which basically is some kind of verification file that contains all the materials you need to verify a signature with Zigstore. Okay, so before we skip to the next part, I would like to make a quick reminder about the difference between what is malicious and vulnerable. So a vulnerability in software is some kind of flow in a computer system that can weaken the overall security of the system but the thing with vulnerability is that they can be exploited but they are not always exploitable and actually some study found that less than 10% of vulnerability are actually exploitable and less than 1% of them are actually exploited. On the other hand, malicious software or malware is any kind of software that is intentionally designed to cause disruption in your system. So that includes for instance ransomware or Trojan horses or viruses, for instance. So to found out about vulnerabilities that exist in software libraries, for instance, you can use vulnerability databases. So here we chose two examples. The first one is OSV. It's a distributed vulnerability database for open source projects and what it does is that it aggregates vulnerability databases from different ecosystems like Golang, Rust, or of course, Python with PyPI data and it makes them available in a format called the OpenSSF Vulnerability Format. The second example we picked is GUAC. So it stands for Graph for Enlisting Artifact Composition and GUAC is a graph database that aggregates all kind of software metadata about security, like for instance, artifacts, identities, like S-bounds we'll talk about later and it stores the relationships between those artifacts and metadata inside the edges of the graph database. So GUAC is quite useful if you want to prevent supply chain attacks because it allows you to understand better the relationship between the different components in your software and how they are used together, for instance. If you try to apply this to the Python ecosystem, there is no direct support for vulnerabilities in PIP, the Python package installer. Nevertheless, there is a tool called PIP audit. It uses the OSV database that Maya mentioned and what it does, it audits already installed Python environments so you can issue PIP audit and it will show you vulnerabilities but also packages that introduce these vulnerabilities to your environments. There was also an experiment called PIP Cuddle. It basically resolves application dependencies without vulnerabilities or only with vulnerabilities that are acceptable ones. So it accepts a configuration file. In this file you state which vulnerabilities you are fine to have in your application and then PIP Cuddle resolves application dependencies and then you can install all the dependencies including transitive ones. There's also a project called security constraints. What it does, it consumes security recommendations by GitHub, so you need to provide GitHub token and it can generate security, it can generate constraints for your application so the resolution process then checks these constraints and resolves application dependencies without vulnerabilities. The PyPI is quite, let's say, bad when it comes to a number of malicious packages published each day. So PyPI maintainers claim that there are roughly 40 malware packages introduced each day and they need to be taken down manually. There's a data set called malicious software packages data set and it aggregates packages that were published on PyPI but were taken down because they were malicious. So if you want to experiment with malicious code you can do so, just be careful. There was also an effort which is an open source tool called GuardDoc, it scans Python source code and tries to find patterns in the source code that can be malicious. GuardDoc uses SEMGREP rules to statically analyze the source code and give you information whether the given package is malicious or not. GuardDoc is not used on PyPI but you can use it on your own or you can plug it into your system. Maya already mentioned SBOM. SBOM was mentioned also in the executive order issued by the US president. SBOM stands for Software Bill of Materials. What it does, it basically states all the software that was used to create or assemble some application. So in this listing you can find all the dependencies, their version and there are two formats that are used in the industry, Cyclone DX or SPDX, there are more but these are, let's say, the most used ones. If you have a software bill of material for your application, you can also use VEX that stands for Vulnerability Exploitability Exchange and VEX states whether the given vulnerability that is present if you are using it and vulnerability that is present in your application is actually exploitable. So if you have a vulnerability in your application it doesn't mean that an attacker can exploit your application because, for example, that vulnerability doesn't need to be on the call path or the application configuration prevents from exploiting the given vulnerability or you deploy your application into an environment in which that vulnerability is not exploitable. There were two efforts, one was OSVDEV, a force to, let's say, standardize VEX in the industry. They introduce a file that you can maintain in your Git repository and that file states information about libraries that you have, information about vulnerability and whether that the given vulnerability is maintainable or not. OSVDEV also proposed a way how to, let's say, manage multiple VEX files across repositories so you can check multiple files when you are consuming multiple libraries. There's also open VEX standard in the industry that was pushed by ChainGuard, ChainGuard the company and it proposes a standard in the industry to describe VEX for your application. So here is an example. It basically states vulnerabilities and also their status and what introduced the given vulnerability. If you want to run your Python applications, you can use Python container images. So Red Hat produces some Python container images, UBI or Federa-based source-to-i images. The main benefit of these images is that it's large RPM ecosystem with Wetted and very well-maintained software. You can use MicropyPen in these container images and on the other hand, there's also ChainGuard's Python image. It's based on Wolfi, they maintain their own ecosystem for packages and it uses multi-stage builds. So you have one container image that is used for building your application and then another one that is very minimal just with Python runtime to actually run your application. They try to minimize number of CVS present in the containerized environments. Another thing you might want to do if you want to check for potential vulnerabilities in your source code is use static source code analysis and we picked an example here of such a tool which is called Bended. Bended was started by the OpenStack security team at Red Hat and what it does is that it scans the files in your Python projects and then from this file, it generates ASTs, abstract syntax trees and it uses plugins to analyze the risk for potential vulnerabilities but you can choose which plugin you use. So for example, you can choose if you want to detect things like hard-to-do passwords, shell injections or crypto mining for instance. Okay, so now let's go over some initiatives that the Python community has taken recently to secure the ecosystem supply chain. There are a bunch of them but we chose a few important ones. So the first one is mandatory 2FA for maintainers of critical packages. So it is a list of packages that are widely used by the Python community and by developers and so PyPI maintainers chose to give away for free with the sponsor security keys so that the maintainers of those critical packages can securely authenticate to PyPI and upload packages in a more secure way. And more recently, they announced that in 2023, so this year, 2FA will be mandatory for every package maintainer on PyPI. They also have another initiative which is Trusted Publisher, quite recent as well. Trusted Publisher uses the OpenID Connect protocol again for users, maintainers of Python packages to use an OpenID Connect identity to get a temporary identity token which allows them to get a temporary access key to PyPI instead of the normally API key would store permanently for example and reuse in CI workflows to publish packages which is a bit more insecure. And one last measure is pretty recent. They chose to drop support for PGP signatures from PyPI. So a Python community member made an audit of how PGP signatures were generated and used in PyPI and found that they weren't that useful and were actually quite hard to maintain so they chose to just drop support for it. So now we'll go over more initiatives to come from the Python community. Okay, so let's take a look at improvements. We will talk about PEPs. PEP stands for Python Enhanced Proposals. That's basically a way how to describe what you want to do in the Python ecosystem and then the community decides whether it's good or not. The first one is PEP 458. It's about securing PyPI downloads with signed repository metadata. This one was accepted and it uses stuff, the framework that we discussed before and it basically secures downloads of Python distribution so you can be sure that you are downloading the right software from PyPI if you're a PyPI consumer. It's still work in progress. Then there is PEP 488. It's about surviving a compromise of PyPI so imagine someone compromised PyPI and uploads packages there on packages. How do you want to check that? This PEP describes a way how to do it. It's based on PEP 458 and it adds developer keys to warehouse or PyPI. Currently it's in a draft state. Also there might be a new PEP in few days so stay tuned. Now let's talk about dependency confusion attack. At the beginning of presentation we described one dependency confusion that happened in the PyTorch ecosystem. So if you are a user and you install Flask and Torch or PyTorch from two indices, let's say PyPI and PyTorch, you want to consume PyTorch from PyTorch because there are let's say special builds that you would like to use. In the Python ecosystem these indices are treated as mirrors so it doesn't really matter for PEP or potentially other installers which index is used to consume a package. So in your example, you would like to install Flask and Torch and also transitive dependencies of these libraries but which index should be used? So imagine that in this case you are consuming Torch from the PyTorch index and a dependency of Torch called Torch3tron also from the PyTorch index. But if you are an attacker and you upload a package to PyPI with the same name as the one on PyTorch index, then it can cause troubles because these indices are mirrors, right? So the Torch3tron can be malicious and you can consume malicious package. If you would like to detect possible dependency confusion in your Python applications, you can use a tool that is called Yorkshire, so a cute name, right? Then there is another PEP called for extending the repository API to mitigate dependency confusion attacks. That's basically the PEP that's addressing these dependency confusion attacks. It's still in rough state but what it introduces, it introduces a way how to create a contract between indices. So imagine PyPI says that project Torch3tron is trusted on PyTorch and PyTorch index says Torch3tron is trusted on PyPI. So there is a contract between these indices and consumers or installers of packages can verify that these packages are or trust each other so they can pull from PyPI or PyTorch. If there is no these tracks information then the installer can fail and notify about possible dependency confusion. Another PEP called recording the provenance of installed packages, that's PEP 7.10. It's based on PEP 6.10 recording the direct URL of installed distributions. So if you install a Python application and you install it using a URL, so let's say you use GitHub to download an archive of PEP, then PEP and other installers create a special file called direct URL JSON in the metadata directory called this info and track information that you installed PEP from GitHub. And this is the URL, this is the hash of file. Nevertheless, there was no way how to find out what you actually installed if you issued just PIP install, PIP or flask or whatever application or whatever library. So PEP 7.10 introduces a new file called provenance URL JSON that states what file was downloaded, what were the hashes, when you installed packages using their name and optionally their version. It also tracks information about indices. It's still in draft state but if you are interested in it feel free to check it. And now we will have an opportunity to win something. So for those who are listening to us there can be something good. So the rules are, we will ask a question. I will try to check who raises hand the first and then we will give something. Does it sound okay? Yes? Okay, let's do it. So the first question, which project mentioned in this presentation does this photo relate to? Yeah? Sorry? Salsa, yes. So we have a winner. And so that's right, you have like mild salsa deep together with HHS. Okay, so another. To which project mentioned in this presentation does this photo relate to? Wow, yes. Yes. Can you guess also the price? We actually borrowed this idea from Guac people, so developers behind Guac. Yes, so Guac, grab for understanding artifact composition. Okay, to which project mentioned in this presentation does this photo relate to? Anyone? Yes. Into the ring, into the ring. Wait. Okay, so the price is, yes, so salsa is the correct answer. And you get a ticket to salsa lessons so you can choose between Maya or me. Okay, okay, there is also plan B, so. Yes. So hot salsa it is. Okay, to which project mentioned in this presentation? Yes, we have. Yes, it's Yorkshire, so. It could be also Gardock. So what do you want? It's a lollipop. Okay, and now the tough one, to which project mentioned in this presentation does this photo relate to? No. Yes. No, no, that's not it. Okay, a hint. You win chocolate. We wanted to ask for signatures of zig store guys, but maybe next time. Stickers, oh, that's a good idea. And I think we have some space for questions, so if you have any questions, feel free to ask. So I'll repeat the question. So I mentioned that there probably be some pep, like new pep. So I collaborated with one PIPI maintainer, it's Donald Stuffed, and there is something written, so let's see if it will be public or not. There are also other engineers involved, so for example, Trisha Angu is behind Tough, and other people, so let's see if it go public. Yes? Okay, so I think if you want a complete answer, William Woodruff published an article about it, which is pretty explicit, and he gives details about the whole audit he did on PIPI about GPG signatures. I think it's called something like GPG signatures worse than useless, so it's pretty explicit as well, so I encourage you to check it if you want a really complete answer on why GPG signatures were not worse, maintaining anymore. Yes? Oh, yeah, sorry, I just repeat the question for, so the question was, why exactly are GPG signatures not considered worse, maintaining anymore by PIPI? Sorry, yes? Okay, so the question was, if I understood, if we consider chat GPT as a tool to prevent malware, right, and there is a tool called Package Hunter that can help detect vulnerabilities, is that correct? Okay, runtime vulnerabilities, okay. I have not considered chat GPT personally as a tool for this talk, at least we didn't provide any example. I'm sure some tools use it now, but honestly, I didn't search into it. I don't know if you did. Okay, no, we haven't considered it yet. Okay, thank you.


Hello and welcome to the session.  Our co-planer also find the equation of the plane containing both these lines. Consider the two lines vector r equal to vector a1 plus lambda into vector b1 and vector r equal to vector a2 plus mu into vector b2. Now these two lines are co-planer if and only if we have vector a2 minus vector a1 dot vector b1 cross vector b2 is equal to 0 and we have the equation of the plane containing both the lines is given by vector r minus vector a1 this whole dot vector b1 cross vector b2 equal to 0 or vector r minus vector a2 this whole dot vector b1 cross vector b2 is equal to 0. Now this is the key idea that we use for this question.  The other equation of the line is vector r equal to b minus c i cap plus b j cap plus b plus c k cap plus mu into beta minus gamma i cap plus beta j cap plus beta plus gamma k cap. So we take this as equation one and this equation as equation two. So these are the two lines we have to show that these two lines are co-planar.  Now from equation two we have vector a2 is equal to this that is b minus c i cap plus b j cap plus b plus c k cap and vector b2 is equal to this that is beta minus gamma i cap plus beta j cap plus beta plus gamma k cap. Now we have the condition for the two lines to be co-planar. This is the condition that is condition for two lines to be co-planar is vector a2 minus vector a1 this whole dot vector b1 cross vector b2 is equal to 0. First of all let's find out vector a2 minus vector a1 this is equal to b minus c into i cap plus b j cap plus b plus c into k cap. This is vector a2 minus vector a1 which is a minus d i cap plus a j cap plus a plus d into k cap. As we get vector a2 minus vector a1 is equal to b minus c minus a plus d into i cap plus b minus a into j cap plus b plus c minus a minus d into k cap this is vector a2 minus vector a1. Next we find vector b1 cross vector b2 this is equal to determinant i cap j cap k cap. Now we write the scalar components of vector b1 which are alpha minus delta alpha and alpha plus delta then in the third row we write the scalar components of vector b2 which are beta minus gamma beta and beta plus gamma.  So here alpha beta cancels with minus alpha beta then here also alpha beta cancels with minus alpha beta then delta gamma cancels with minus delta gamma here alpha beta cancels with minus alpha beta. So we get this is equal to i cap into alpha gamma minus delta beta minus j cap into 2 alpha gamma minus 2 delta beta plus k cap into alpha gamma minus delta beta or you can say this is equal to alpha gamma minus delta beta this whole into i cap minus 2 j cap plus k cap this is vector b1 cross vector b2. Now let's find out what is vector a2 minus vector a1 this whole dot vector b1 cross vector b2. If this is equal to 0 then we can say that the two lines are and so this is equal to this is vector a2 minus vector a1 so we write here this is equal to b minus c minus a plus d into i cap plus b minus a into j cap plus b plus c minus a minus d into k cap this is vector a2 minus vector a1 this whole dot vector b1 cross vector b2 which is alpha gamma minus delta beta into i cap minus 2 j cap plus k cap. So we find the dot product of these two so this is equal to alpha gamma minus delta beta into b minus c minus a plus d minus 2b plus 2a plus b plus c minus a minus d here we have this 2b cancels with minus 2b c cancels with minus c then d cancels with minus d 2a cancels with minus 2a so we get this is equal to alpha gamma minus delta beta into 0 which would be equal to 0. So therefore we get vector a2 minus vector a1 this whole dot vector b1 cross vector b2 is equal to 0 hence we say that the two lines 1 and 2 are coplanar. Now the equation of the plane containing the lines 1 and 2 is given by vector r minus vector a1 this whole dot vector b1 cross vector b2 equal to 0 as given in the key idea we can have either of the two equations. So we will find out this equation now putting the values for vector a1 and vector b1 cross vector b2 we get vector r minus a minus d i cap plus aj cap plus a plus d k cap and this whole dot vector b1 cross vector b2 which is alpha gamma minus beta delta into i cap minus 2j cap plus k cap is equal to 0.   So this is the required equation of the plane containing both the lines. So this completes the session, hope you have understood the solution of this question.


The water did geographically differentiate crop yield effects from climate change. So we can look at long-term scenarios at this very detail level of what happens to the yield of all the major crops. Those kinds of yield changes are then fed into our economic model, the agriculture sector. That's the impact model that's been developed at Yipri. And from that we can take these biophysical impacts and climate change and get estimates of food supply in demand, world food prices and trade because the Philippines here is embedded in the global system and food security and malnutrition impacts. We then also now, and I think this is an important step, just to look not just at the agriculture sector, but what happens in the broader economy due to economic climate change impacts in agriculture. This is very important because this can give you reach beyond your agriculture to the next economic development authority, the finance ministries, which are much more interested in what is the broad impacts on the economy due to climate change, not just what happens to the rice or what happens to the sugar. Just one sample, I think we've had outside. Is there a breeze available? Where are they available? Yes, there's a booth outside that has a much more detail on the results. But it's particularly important to note that here we focus on the economic impacts. We also have a great detail. We find that by 2050, I'm sorry, the average between 2010 and 2050 is an annual cost due to climate change of 186 billion Philippines pesos. That's about three billion US dollars per year due to climate change, including the human cost of malnutrition, about 41 billion, 145 billion in these economy-wide losses. And this figure, this is average by 2050. The number is closer to 300 billion, or sorry, 400 billion per year. So it's accelerating the cost for the economy due to climate change. Climate change reduces property activity growth, increases food prices, and therefore reduces food security. The negative impacts on the rest of the economy are driven both by increased commodity prices, which causes the terms of trade and real exchange rate losses and reduces growth in the industrial and service sectors. So agriculture has powerful impacts beyond the agriculture sector. We then looked at what kinds of adaptation policies can help reduce those changes. And here we're basically modeling three different adaptation policies that increase rice productivity in this area and agriculture tariff reduction, reducing the taxes on imports and exports. We do it under two cases, the National Food Administration in the Philippines maintains various kinds of distorting policies to basically subsidize farmers and to reduce the openness of the economy to trade. So we look at the impact of these policies on with and without the so-called NFA subsidy that distorts the enterprise sector. And you can see here, the first thing to see is the very different impacts. So by freeing up the economy from these NFA subsidies, you get much bigger impacts from the different adaptation policies. Rice productivity, you get $128 million benefits for climate adaptation. You get $118 million for the new taxes, tariff reduction of $81 million in taxes. Much lower progress for climate change adaptation with the distortions in the economy. So you can see here the very important interaction between agriculture sector and the rest of the economy and how they feed together. The other brief example on this show is from Newark we're doing, which uses the impact model and the general equilibrium model. We talked about this impact model including things such as the biophysical model and the water resource model. In this work we've added a model on energy. The model is the energy sector in great detail so that we can look at how the energy sector and this water energy food nexus work together in terms of trade-offs and synergies and so forth. So we can look at policy and investments in energy sector, ag and non-ag again. So looking at that and getting outcomes not only as I showed before in the dry economy, using services, but also on food security and on changes in the energy sector, and mass gas emissions and so forth. Let me just go again very briefly. I want to go into details on this one. This shows different energy scenarios for the Philippines. This is a reference scenario and these are all up to 2040. The reference scenario embeds various assumptions in the current Philippine energy plan. As you can see, this shows the mix of energy supply from different sources. Red is coal, brown is shallow, is gas, geothermal is the blue. That's a very important factor in the Philippines. And green is the amount of energy supply for vector city in the Philippines from wind. This is a very small amount. You can see the reference plan in the Philippines straightforward in saying this relies on coal. Significant increases in importation of coal to meet energy needs. That's because it is relatively low cost. Some of the other sources have been slowing down. You can see the result of that. It has a very heavy reliance on coal and therefore much higher greenhouse gas emissions. We then look at several different alternatives. One, it combines various policies to focus on CO2 mitigation. One is a straight carbon tax. Then you have various renewable target EN and a so-called coal-extraternality scenario. That's the various kinds of policies to reduce the impact of coal on greenhouse gas emissions in the environment. You notice that each one of these does achieve significant improvement in reducing coal. For a climate change perspective, it does accomplish positive things. But we also see the yellow line is embedded in each of these groups. This is the additional cost in percentage terms you can see on the right-hand side compared to that reference scenario. It does show the kind of trade-offs. To do these other ones, you do get additional costs to the Philippine economy anywhere from 5% to up to 20%. It shows the kinds of decisions that have to be made by policy makers in terms of whether that kind of cost can justify the benefits in terms of reduced greenhouse gas emissions through coal. Some of the things we find here, for example, the CN2 mitigation target scenario combines results in significant reductions in coal use and greenhouse gas emissions, 47% drop in imported fuel, which is mainly coal, and it has relatively low additional system costs of up to 5%. It therefore could improve energy security and maintain a lower carbon approach. Again, at a cost relative to the current Philippine reference plan. So what are some conclusions here? We've got a couple of slides here. Mali, is a very important tool to influence or strengthen the understanding of climate change and food security policies. It does look at the very systematically of trade-offs that occur as well as in wind solutions. The integrated approach gives a more successful look in many cases at what the trade-offs are and can generate the kinds of outputs that can be used not only in agriculture but in other parts of the sector, such as the use of finance and energy. Downscaling global models to micro-levels also helps to target results in policies below the national level. We do have regional results from this work, including the Philippines. I'm not showing here. We're also going to benefit user-friendly models that were in the process of transferring to Nevada and the Philippines and to do for others. It's important once you have the outputs to target those outputs, to the rice scapholders to enhance policy. I think one thing that most native analysts forget about is how do you get a nice report and show results of what happens next. Briefly, some of the things we have to do is get the right boundary partners who will actually take up and use these results, what influence do they have and what do they need in order to make a difference. That's what we try to do in the Philippines and elsewhere in the work we do. The partners have to be involved right from the start. You know, just bringing them in when you start cranking the throttle is not going to get behind it. Again, it's very important to, as I said a number of times, cut across the departmental side with the financing and culture, energy and water. You're often going to get lost in the mix. For each pathway, this is something I think economists are mad at. Probably I didn't do it perfectly well here either. Getting away from the economic jargon, trying to get to language that is more accountable for the stakeholders you're dealing with. Communications, that needs to be a very important part in terms of, again, identifying how to convey the message whatever and differently. And finally, moving to advocacy coalitions. If we generally do not direct advocacy but work with partners who are, who may be able to do that in a given location, we don't come in and make strong advocacy in a place where we're not directly going to be affected by those decisions in a sense. So we will show these what-if results from the modeling, but then need to look at what kind of coalitions putting in environmental NGOs, government, downstream farmers, irrigation systems, how they can bring this use to a policy-making perspective. So thanks very much. Thank you. In the meantime, discussion at the end. Yeah, we'll do a discussion at the end. Thanks, Emile. Thanks, Mark. I'm going to step back now from the country level and talk a little bit about modeling at the global level, which provides a context to the table at the country level and also a multiple-scale analysis that are all part of the chain in moving from research to action. So I'm going to start with a slide, and I might have reconsidered this following immediately after my boss, but it's something that we like to keep in mind because it helps us think about what models are, how they're used, and what they're for. This actually wasn't said specifically about the type of simulation model when we're talking about it today. These were a couple of statisticians who were talking about the types of models and things, but it applies more generally. It makes us think about, you know, model is a deliberate simplification for a particular purpose. And we've discard some of the detail, but hopefully we keep the key features that are useful for the purpose that we have in hand. So some of you may have used a map before coming to Maradesha, before coming to the GLF this morning to help get a sense of where it was, where you need to go, and hopefully that's sort of the purpose all of you who are here obviously made it. Likewise, in the type of modeling that we're talking about today, simulation models help us look far into the future. The landscape is much more complicated. The terrain itself is shifting underneath our feet as we look ahead 10, 20, 30 years, and climate change is just one of many important drivers that affect the results. So if this is by way of a caution to say we're not using these models to predict the future, or to say it's going to be this particular way in the year 2050, rather, we're using them as tools to help us explore alternative pathways to get to different possible futures. So ultimately, there will be one future that will be realized when we get to 20, 30, or 20, 40, or 50, but that depends on choices we make today. And so models are a tool that we can use to help explore different ways of getting to where we'd like to be, as opposed to where we could end up without a particular kind of careful reflection on different options. Marcus already talked about some of the features that make models useful, so I'll just say transparency, that's a challenge for us because these models are complicated. That doesn't mean that they can't be transparent in terms of being clear about the assumptions that we make and the relationships that we're assuming between different variables and drivers that affect the outcomes that we're interested in. They have to be credible, which means they have to be built on reasonable assumptions, on reasonable relationships between, say, changes in income and changes in what we choose to be or changes in technology and how much is produced for particular commodities. And critically, they need to be flexible because, as I said, the goal is not to produce a specific answer, but to look at how that answer depends on different choices that we make. And for that engagement with those who will use results from the model to make decisions and those who will be affected by those results is critical. Impact is the name of a modeling system that we use, I'll talk about that very briefly. We'll look a little bit at some of the impacts of climate change on agriculture and the food system and briefly on what that means for thinking about policy. So this is actually a system of models that we use with our partners throughout the CDIAR and elsewhere to look at alternative possible features. It links models of climate change, of water, of crop growth with economic models to explore different relationships at different scales. But the analysis that I'll talk about today is primarily at the global and the regional scale. So here's one example. Making an assumption about how rapidly greenhouse gas emissions will increase, what that means for changes in temperature and precipitation. We can say under one particular scenario, this is the RCB 8.5 so a rapid climate change scenario and one particular climate model from the Hadley Center in the UK, it implies certain patterns of temperature and precipitation in the year 2050. So that's the starting point. Those are inputs then into crop models and in this case this is the example of rain-fed maze yields around the world. As you can see, although you may not be able to read the legend, quite significant impacts on yields in different parts of the world. But that's just part of the story. These aren't the final yields that we'll expect. These are in effect what we would expect if we imposed a future climate on today's production systems. But we know that's going to trigger a whole series of changes. If yields actually fell that far, prices would rise, that means we as consumers would change to some degree that means producers would also change how they produce and perhaps even what they choose to produce. So that's another step. But we continue on and that's where the economic model comes in to look at some of those adaptations and market responses which allows us ultimately to say here's a range of possible outcomes for different assumptions about climate change as well as other variables in different parts of the world. This is just one example of one particular set of assumptions that shows the pathway that we use in this type of model. Stepping back a bit, this summarizes a broader analysis that included not only the different impact model but other modeling groups around the world at Bach, Ningen, and NASA and so forth. For a series of major cops, coarse grains, rice, wheat, oil seeds, and sugar, social economic and climate change scenarios. So you may be familiar with these terms here. This is shared social economic pathways SSG 1, 2, and 3 embodying different assumptions about how rapidly population changes, how rapidly incomes grow, and the different RCPs. But basically, what we see, I don't know if the pointer works here, not surprisingly, yields decline, not so much as we sometimes see because there are these economic adjustments that help to mitigate those effects. But what is perhaps more surprising sometimes is to look at the center and call them their production. Very little change actually in total production project under any of these three scenarios. And it's important to note this is not relative to today's levels but relative to levels in 2050 in the absence of climate change. So production is going to grow and significant between now and 2050. But in terms of aggregate levels, we actually project that climate change won't have a large impact in the total, but rather a very significant impact on where this is produced and how it's produced, what it will cost to produce and what the prices will be in the present. So area increases here about 45% depending on the scenario. That's actually about double the rate of land use or land use change that we project between now and 2050 as a result of climate change. So that's obviously going to have important implications for a variety of environmental indicators. Water quality or biodiversity, greenhouse gas emissions and so forth. Prices column there about 15% increased under the most extreme case over there, which is the SSP3, a fragmented world with RCPA 8.5, which is rather climate change. And that 15%, it's about double the price that we would expect in the absence of climate change. So that's going to have important implications for food security obviously. So I'll just jump to a couple sets of results here. Looking at hunger in 2030, which is the time period for the SDGs. These bars on the left axis here show absolute numbers of people who are at risk of hunger under different scenarios. The blue shows basically where we are today, around 800 million people undernourished. The orange bar, the second one, shows that in the absence of climate change, because of income growth and so forth, we're projecting that the number of people at risk of hunger actually falls to about 500 million. But that's reversed. Some of those losses or some of those reductions are offset by climate change, which is shown in the third bar there. Nearly 100 million people more will be hungry in 2030 as a result of climate change than would have been otherwise. Fortunately there are things that we can do about that. In the fourth bar here, the yellow one shows the results of a scenario that includes various types of investments that improve production and efficiency in agriculture. That includes agricultural research and development. New seed varieties, better tolerance to ground and heat, for example. That includes better management of resources, particularly soil and water. And it includes complementary investments in infrastructure to improve market access, both for inputs and outputs. And so the combination of those investments in this particular scenario allows us to bring that number of hungry people back down below the level that we would have achieved, otherwise either with or without climate change. Those are the bars in the left axis. The little circles represent on the right hand axis the share of population with hunger. And we can see in all regions, with the exception of Africa south of the Sahara, the bet proportion falls to about 5% or less. Africa likes a bit in their particular challenges in terms of the need for R&D and these other types of investments that we have talked about. So these investments, that's one set of policy options that we need to consider. And here's one example of the potential impacts that those could have. Another set of policy options is on the demand side, the consumption side. And these are results just as an example of recent research that we've done with colleagues at Oxford University, looking at what would be possible impacts of pricing food according to the role that they play in increasing greenhouse gas emissions as they are produced. Won't go into detail on this, but just to show on the top map there, one of the reasons this is a sensitive topic is because it reduces, it has the potential to reduce greenhouse gas emissions, but taxing food is obviously a very sensitive topic because we're spending disproportionately high amounts on food. And the map on the top right, you can see there that if we taxed foods according to their contribution to greenhouse gas emissions, that would have adverse impact, particularly in Sub-Saharan Africa and South Asia. So that's clearly a concern. But you can adjust and do a different scenario that says, well let's not tax all foods based strictly on their contribution to greenhouse gas emissions. Let's, for example, we could exempt those foods that are particularly important for the poor. We could use the revenue from those types of taxes to subsidize certain types of food, such as in this case, fruit and vegetables. And doing different scenarios like that in this case allows us to find one where those adverse impacts on health are minimized and in fact we get positive impacts on health as well as reductions in greenhouse gas emissions from a policy approach like this. So this is just to show again, by the way, that there are ways to explore alternative policy scenarios to look at different ways of balancing trade-offs in this very complicated area. So to wrap up some of these echo things that Mark said in his presentation, we see increases in pressure on natural resources and slowing progress and reducing hunger under different scenarios, but it's important to explore those to see how sensitive different types of indicators are to different potential policy responses impacts on diet and health as well. But the result being that a mix of policies and investments that are targeted to critical areas, critical populations, and recognizing trade-offs between different types of goals are critical. And in terms of making these kinds of results useful for policy makers, again our analysis at this global scale, global and regional tends to be supported by and to inform partners such as international donors and our partners in the global research community. But reaching national and sub-national decision makers as an example that Mark presented and that Alex will talk a little about in a moment requires linking to other types of tools. We can't look for all of the answers in this global scale analysis, but this is part of the context that helps inform the picture. And finally that we need to build the links for that type of analysis and that type of engagement into our planning both on the research side and also on the policy side so we can have this type of iteration and explore alternative scenarios as opposed to just publishing set of results that are interpreted as predictions and expecting that in this context. So with that, thanks very much and hand it over to Alex. Okay good morning thank you for coming to this session so you have seen from my colleagues some examples of the use of modeling this is what we do a lot of and if we what I wanted to do in my presentation is actually offer some considerations some thoughts about how a certain type of research the policy oriented research is evolving now and also drawing from some recent experience in terms of our work how we can improve the collaboration among researchers and policy makers so have you seen that a lot of this stuff that we do should be of particular interest and use for policy makers and stakeholders the thing is that the relationship between the research community and policy makers can improve and should improve so of course we have moved a long way from how many of the research was happening in the ivory tower and so that doesn't happen so much anymore however it is clear that there is still a need for a better interaction between the research community and researchers and policy makers and in fact there is a growing call for for scientists to provide more usable research and also call for funds to be a project that actually has that sort of goal as one of the output of the project and so informing policy makers there is also an increased recognition of the importance of a close collaboration among scientists and users and stakeholders and this is particularly true for issues related to climate change which is particularly complicated and there is a lot of information sometimes the information is contradictory but it starts to make sense out of it and the research community can really help with that and of course as policy makers and researchers become more engaged there has to be a better understanding of the various needs and a better understanding of how to collaborate and also how to share resources and then how to provide the data if you look around there is a growing number of successful collaboration which is really important but I think that the point I'm trying to make is still not enough if we can improve on that I was here last year sort of talking about a recent work research that we had done with the government of Colombia and they're heading for their high NEC I was particularly happy because it's particularly successful it doesn't happen very often when your research really gets included in an official document and in particular what appeared useful is this sort of global to local approach in which these exogenous forces need to be taken into consideration when you have to devise long term policies like the ones that have to deal with climate change and then those national plans also have repercussions for local communities and those local communities and their needs and preferences then sort of determine what is actually feasible and then ultimately that feeds back into the global sort of community in terms of how a country can contribute for instance to reduction of greenhouse gas emissions I was here talking about this work and you know it was particularly interesting because it was connecting multiple scales and the work that Keith and Mark were describing is part of that this idea of sort of moving from the global to the national to the local and we have developed these tools but however at this point a year later I see that there is even more work that needs to be done and part of this is that you know those things and co-findings are fantastic but unfortunately is not enough to really have an impact so I've tried to represent here sort of an old fashion of doing research and some people would say that this is a straw man it doesn't really happen like this this is a linear way but I would say that actually too often it gets too close to this model and so you have the research institution that initially has some contact with stakeholders with the donors, gets the data and then goes back in the office and does the work and then provides the results to the end users or the funding agency and there are good reasons for this to happen actually I think first of all researchers are often not most socially oriented people and so we tend to work in our office and feel perfectly comfortable with that but also if you think in terms of what are the the career sort of rewards often for researchers it's the number of published articles and this is a very efficient way of publishing peer review so I can tell you that this happens a little too often however what we know and there is literature known as research on research on projects that tells us that the co-production of knowledge is actually extremely helpful it is shown that it generates information that better supports management decisions and then not only it facilitates the sort of the use of the research results by the end users by the stakeholders but also through that co-production of knowledge it fosters some creative solutions across researchers themselves so you might sort of think about new questions that you hadn't thought of at the very beginning this is through this sort of engagement with the brain and users and so building on that sort of project and work that was telling about the work in Colombia on the IMDC we wrote an article with my collaborators in Colombia and we wrote an article that was looking at the process that we followed in Colombia to have such a heavy impact and we tried to understand what is that we had done not on purpose, not fully on purpose but what is that had done it had contributed to success I've tried to sort of draw the kind of relationships that actually came together to as I said to have a successful sort of use of our research and as you can tell it's very different but this hopefully it makes some sense to you but tries to represent about a two year collaboration and a constant interaction between researchers and stakeholders all sorts of different levels and so you have the research institution that collaborates and continuously is in contact with stakeholders in terms of exchanging data in terms of a changing research question informing what in which direction what numbers and what particular insights will be useful for them all this would have actually happened through what we call a boundary organization which is sort of a trusted broker in that case was the CGR program on climate change that has a very strong presence and a strong collaboration with the end user which in that case in the case of Colombia was the ministry of the environment so but what is important here in this sort of circular motion that this diagram is trying to convey is this continuous interaction this is two year of work that was building another two years of research so long time to actually get it so I'm gonna just go over some take it with essence from our experience so this is stuff that you actually see in the published literature this idea that you have to have sustained stakeholder interaction continuous and you have to be able to talk to them as Mark and Keith have mentioned already the transparency and openness of the engagement process is essential to be inclusive to make sure that all stakeholders at one point have a voice they have to bind into the process and the transparency in the modeling part and the capability of actually including new data when that data becomes available and then this is incredibly taxing but important is the willingness and the capacity to break through the disciplinary and institutional barriers which is very difficult you have to develop this common language across the people involved but also what we found that in our case was extremely important was this idea of having a global institution that was acting as a champion for the project in this case was the minister part of the ministry itself but it continuously supported us supported the idea of the importance of including more research and more knowledge into in this case the INDC document of the country and that was really the deal breaker and also for the donors this idea that there has to be a greater flexibility in the use of funds because sometimes you start with some clear ideas of how you're going to spend the money on a project but it doesn't end up in that way and you have to be able to shift things around as this interaction with stakeholders and also how you assess the project everybody wants to have impact but in the meantime you have to understand exactly how the impact is measured and then finally since I'm also a modeler I wanted to greaterate this idea that actually the importance in our case of the model was this idea that it really provided this objective basis for discussion across stakeholders who had are part of different power structures so there are people who are more powerful and better connected with the ministry than others but at that point you have something you can everybody sort of can start from and that also was incredibly powerful in our case so to conclude as I said we have new modeling tools we can develop all sorts of new insights but what is really useful what is useful for the community that then ultimately will have to implement project will have to use this data and make decisions there and try to go beyond those structure of power that I was talking about what kind of information do policy makers really need to implement their plans and what information can facilitate the dialogue among the implementers and those who then ultimately have to live with the outcomes of those decisions Thank you, good morning to everyone First of all I would like to thank the organizer for inviting the Philippine National Economic and Development Authority for this important part it's a way by which we could really stress how important the linkage with research institutions is for institutions like us Development Planning and Policy Formulation Project Evaluation Policy Prioritization and all those things results of research like this would really help us in our Policy Formulation and Development and developing our upcoming Philippine Development Plan for 2017-2022 So Mark already provided you with the impact model how to describe how the model was the structure of the model and the things that it can do and so just a little background on how we were able to get engaged, the NEDA in particular got engaged with IFP then our secretary was the secretary of the National Economic Development Authority really wanted to have a tool by which we could simulate scenarios we could come up with some magnitudes of ideas that could really influence that could really provide some understanding to our policy makers and how problems like some measures or how we should really respond to problems would be very beneficial to economic development to reaching the development targets that we want for the Philippines he was talking to me then and I knew impact from when I was also involved there in the institution so I told him let's start with Mark Rose Grant because I think impact would be able to do exactly what you want and so what was already discussed a while ago asking the right question knowing what you want to do knowing what you want the model would like you to inform about was exactly make the things the interaction work between research institutions and government agencies so when Mark came over to the Philippines we discussed about it and so that's where it all started and we were very much concerned about climate change how vulnerable the Philippines is everybody probably still remembers the big typhoon high-end inflicted you know distractions in the Visayas islands and that's not the end of it just last October we had another close to a super typhoon 5 that really devastated the rice the rice farms in the northern part of the Philippines and it's not only typhoons even the El Nino which we had a very big typhoon just the first quarter first semester of 2016 it dried up a lot of corn farms bringing rice corn farmers to close to really property and difficulty of really getting themselves back again to do the livelihood activities so we did that and the results in that Mark has already shown to you those numbers and it was really very much appreciated there are estimates but then that already gives sorts of an idea to policy makers to different stakeholders and what it could really mean if we are not going to be doing something so the scenarios that provided us with the different adaptation measures especially for agriculture was really something that was very much appreciated by our senators senator Villar who is the head of the committee on agriculture in our senate was our speaker and I was in one of the policy forum and she really expressed that studies like these are things they would need to aid them in their legislative work so you know they need the numbers even though there are estimates they need those numbers to really provide evidence that indeed if you don't do anything it will probably get into something worse than what we are now and we had also some policy briefs that we were able to distribute policy briefs you know got into congress got into senate and now the office of senator who is championing climate change in our country are using those alternative scenarios adaptation scenarios that was given there to inform even our department of agriculture that look there are indeed adaptation measures that you could really get into to help you you know get that sector really resilient it's just a matter of really trying to determine which one would work and which one would not work but there are alternatives are you the most I think and this has been hard work difficult work that we have been doing in the past there has been several studies with regards to the rice trade policy of the Philippines the NFA subsidy that mark showed lifting our QRs it probably came the results probably came at the right time when we have this new administration wherein they were also critical on those so in one of the cabinet meeting that we had it's a meeting among the economic managers the economic managers are composed of the secretary of finance the secretary of the national economic development authority the secretary of the department of trade and industry the secretary of department of budget and management and we invited department of agriculture they wanted to really assess you know whether extending the QR is going to be still the policy for that we need to continue subsidizing NFA would be still the policy and the results that we had under this project really validated the past results that we have already had and so we strengthened the decision among the economic managers to really to say that really lifting the QR is the best way to go and restructuring NFA so that it just becomes buffer stacking maintaining the buffer stack would be the only way to go and so just before I left the memorandum of circular or memorandum order some kind of instruction to be considered by the president to really start the process of verification you know liberalizing rights so that will go to the office of the president because we really need to do a lot of things to be able to utilize the rice market one we have an agricultural tarification act and rice is exempted from there so we really need to amend that law so that rice should be included also as one of those crops that we've done so there are several things that the processes that we need to do in order to be able to get into better rice market policy so we are now in the we are now in the process of formulating our film development plan and I think a lot of the recommendations that we had in this project particularly on strengthening the resiliency of agriculture will be very valuable inputs to our work we are also in the process of updating our agricultural and fisheries modernization act and again those measures adaptive measures will go as really very valuable inputs there our department of budget and management even without the they already ask for our they already ask for the studies because you know they were as references they do the evaluation they do the prioritization of projects primarily for agriculture that would be a very valuable resource or reference material for them to be able to you know do all these things and so with all with the results also we hope that we will be able to influence the department of agriculture to reallocate more you know a greater part of its budget outside rise to high value crops which is what we think would be a measure by which we could really increase the incomes of farmers and also to afford more funds to activities like research and development and extension the energy simulation that came out this is also the first time that I saw the simulation I think those are going to be very valuable because we are now sort in a dilemma on how we would really position our energy plan to go really you know to shift away from coal based to really low carbon and you know there has been like issues with regards to how can we get that target of industrializing if we really go to this so it's really going to be this will probably provide us some information on how we would really proceed balancing that low low emission based energy policy with regards to the coal based or the fuel based policy and so again you know I think really linking more to research institutions that would provide us with valuable inputs to our policy work to our development formulation of our development plans would be now increasingly being recognized as really very much needed in the Philippines and I think that's also the same in other countries thank you I'm Carol Saint Laurent from my UCN the International Union for Conservation of Nature I just mentioned I'm French Canadian have a tendency to gesticulate wildly as I speak so if the microphone is up over here someone please signal me I'll bring it back so for those of you who may not know IUCN very well I'll just mention really quickly that we're a union of members of state and non-state members we have about 1300 members and we also have expert commissions with more than 16,000 experts that allows us to work from the local it allows us to work on an issue right from having the idea to generating knowledge to influencing policy to implementing the action on the ground through our members which is kind of a special situation to be in I'd like to thank our host today if prefer including a UCN in this event and we look forward to strengthening our collaboration with you I work primarily on the international policy side of things and so I was expecting actually a different perspective but I was struck by how aligned our thinking is after listening to these presentations things like I was going to start with general comments about science policy influence is not a linear process we heard that already we need more than communication of research findings in order to achieve impacts we've heard that so it's an ongoing process also it's not just to take one step leads you to another one you've got your wonderful impact it's a loop all the time at the ongoing policy process even if you've got fantastic findings as people are thinking through how to implement those findings additional challenges will come up and this point about continuous engagement being critical was brought up by a couple of speakers what I'd like to do is just illustrate this a bit with a couple of examples from our UCN's work how we're addressing science policy linkages at different levels first of all at the global level and this will be linked to our work mostly on forest landscape restoration and I'll just mention very quickly by that we mean restoration of degraded lands deforested lands at a landscape scale it's about restoring forward to meet the needs of societies in the future not looking back to some ideal historical state it's about building multifunctional landscapes which is where this ties in very well with addressing food security really at this landscape scale so at the global level we had this knowledge product that we came up with in 2011 a world of opportunities map and this showed that around the world it was based on globally consistent data so obviously not the finest data available but it did show us that told us that around the world there were more than 2 billion hectares where restoration opportunities may be found so at the time of doing this analysis there were other things going on so we had just before that the aichi targets being adopted under the CBD the emergence and strengthening of the plus in red plus we had the Rio Plus 20 event lead to the land degradation neutrality goal putting that together gave a real boost to the global restoration movement the knowledge and the international policy framework and that triggered us with Germany and others to launch the bond challenge which is a global effort to restore 150 million hectares by 2020 and 350 million hectares by 2030 and that's kind of taken off we have 38 pledgers who between them have committed almost 125 million hectares so far we're seeing that showing up in policy arenas as well for example 114 countries in their climate change nationally determined contributions for the land sector included restoration of forest landscapes and many countries are including restoration of forests in their biodiversity strategies and action plans but so far so good but that's not the end of the job and there's some really additional very important audiences that we want to engage and how do we do that by adding an extra layer to the knowledge generation so for example we're doing some work on restoration opportunities in mangroves worldwide with the nature conservancy doing work with a number of partners on the role of restoration of forest landscapes and ecosystem based adaptation disaster risk reduction and we hope to be doing that work with the folks here today on food security and how that links up with forest landscape restoration at the national and sub national level we've generated methodology the restoration opportunities assessment methodology and I'll just briefly touch on this example where this has taken us including a specific example how this links to the policy process in Malawi so we're applying this methodology in about 25 countries currently and the idea is to generate new knowledge including on the various benefits of restoration including for food security to help countries and sub national jurisdictions define restoration targets define where appropriate restoration interventions might be possible so within this you've got things like stakeholder prioritization of restoration interventions cost benefit analysis of the different interventions diagnostic of an assessment of key success factors that sort of thing in this situation I'll just explore that for a minute if you'll allow me to so we piloted a room assessment at a district level at the start rather than national and through that process stakeholders identified priority areas suitable restoration intervention types key success factors enabling conditions that were absolutely essential costs and benefits etc in this case food security was actually the driver of interest in carrying out restoration opportunities assessment it wasn't just another layer that was being factored in it was very firmly the entry point and this involved then generating information about food availability food accessibility, food utilization so based on the outcomes of this district level assessment the department of forestry pledged to do an assessment for the whole country again driven by the food security issue and this kicked off in February of this year and involved different government departments, academia civil society, media, district authorities and I think this aspect of involving all of these different sectors in the knowledge generation policy development process is absolutely essential so they will be looking at the current situation in terms of crop production, poverty drivers of food insecurity and how FLR interventions restoration interventions defined identified by the stakeholders could enhance crop production and make communities more resilient to climate change what it would cost and how it would benefit the average household one contributing factor to helping to move this work forward towards impact in Malawi was that the director of the forest department Dr. Tiemont Chibima became a real leading advocate for the knowledge generation and policy change process and even the preliminary findings that were obtained enabled Malawi to set some restoration targets and so during the UCM progress in Hawaii in September they announced that they would restore 2 million hectares by 2020 and an additional 2.5 million hectares by 2030 for a total of 4.5 million hectares so what are some of the lessons that you can draw from this and other examples as we've heard it's absolutely essential to embed the knowledge generation process in existing institutions and policy processes right from the start so the key agents of change are involved right from the beginning it's important to identify people who can be champions champions for not just for the outcomes once the research is done but champions for the whole process work will be public awareness is a bonus factor in Malawi there is a plant one tree per person per year policy and campaign which helps to create the conditions within which the research can have more more impact so really to tailor research and policy development also to these national priorities is I mean it's evident isn't it but it's the absolutely critical starting point for all of this for Malawi it was food security in other places it's different for China we've worked on water security for megacities that's their entry point for the research they want to see done on policy and policy delivery adaptation based mitigation to reduce flooding in El Salvador and in the United States recovery from wildfire and integration of jobs which may actually be in years to come so just in conclusion just to close off as a summary the effective deployment of new knowledge and insights plugged into policy making processes that respond directly to the national priorities supported by champions and key positions this is essential for scaling out to healthy productive multifunctional landscapes and we look forward to reinforcing our cooperation with partners here today and others to making this happen in landscapes around the world thank you thank you very much I'd like to invite all presenters to the front and we'll open it up for questions yes


This is stock number 20J106A, we are here at Summit Automotive in Fond du Lac, Wisconsin, your new and used car headquarters. Today we are checking out this super clean 2012 Chevy Cruze LT1 with the RS package. This car has the 1.4 liter turbocharged 4-cylinder motor which pumps out 138 horsepower. It has been fully safety and inspected by our service shop has a fresh oil and filter change. All the fluids have been checked and topped off and this car is 100% ready to go. Ice blue metallic is the color we shoot all of our videos in 1080p so if you have HD capabilities on your computer tablet or smartphone device, turn them on right now because it is definitely your best way to ensure the quality and condition of the vehicle before seeing it in person. If you want to check out all the photos of this car in the upper right hand part of your screen there is a link right to our website, it comes with the painted aluminum rims and it has newer unor oiled tiger paw tires. These are 215, 60, R16s and they have I'd say probably about 80% of the tread left on them, maybe even a little bit more. Front fenders in pretty nice condition. The headlight lenses are nice and clear, factory fog lights, front bumper, no major dents, stings or cracks on that and the hood is in really nice shape as well. Passengers side fender is in nice condition too and this passenger side rim other than a little bit of oxidization is in pretty nice condition. As you go down this side of the car, take note of how clean the body is, how reflective and mirror like that paint is. We take these HD videos so if you are far away or even if you're close by and just cannot make the trip down but you're still interested in purchasing the car you can see the car, hear the car and have confidence in what you're looking at before you even get here. That's why we offer the Summit Auto Fast Pass option in the upper right hand part of your screen, a link right to our website where you can apply for financing, get pre-approved, see a payment quote, even appraise a trade all from the comfort of your own home. That way when you get here you'll be all set to go and all you gotta do is drive the car and love it. Back tires have just as much tread as the front tires and this back rim is in really nice shape as well. Coming around the back rear bumper, no major dents, dings or cracks on that. The trunk is in nice shape as well and it does come with a deck lid spoiler, part of that RS package. The trunk area is in nice shape too and this one does come with a spare tire and jack tools. Trunk shuts nice and solidly. Those back seats do fold down which we'll check out in just a little bit. As you go down this side of the car just as clean as that passenger side and for full disclosure this back rim about the same condition as the rest but no major scuffs or scrapes. Down the rest of this side, didn't see any dents or dings on the doors and it does come with power mirrors. Inside the LT1 package gives you the black cloth interior, it does have driver's seat height adjuster, no rips or tears on this seat, side current airbags and this one comes with the factory floor mats. Auto headlamps, tilt and telescopic steering wheel, power windows, power locks and power mirrors and we'll hop inside the car here and turn it on. You can see that all the lights go off except that brake light which they've got the parking brake on and you can see that this one has 74,847 miles. You get a digital speedometer and the instrument cluster is very nice and clean. You get the leather wrapped steering wheel that is in great shape, Bluetooth and audio controls on the right, cruise controls on the left. Your information center controls are right there and it does come with AM, FM and Sirius XM radio capabilities.  This one has the six speed manual transmission, there's your stability track, passenger side floor mats in excellent shape and the passenger side seat no rips or tears on that. Headliner is in nice shape as well and you do get map lights up there. Also on-star capabilities in that review mirror and we'll take a quick look at the back seats and then we will check out under the hood. Back seats are just as clean as the front seats no rips no tears back here it does have the latch child safety system for all your child car seat needs and these seats do fold down like I said we have factory floor mats back here as well and these seats go down pretty good and the seat belts not locked there we go so they go down like that and you can get into the trunk and then there is an armrest with cup holders in there as well. We'll take a quick look under the hood bottoms of the doors all look really good I'd personally like to thank you for checking out the video today and hopefully from this HD video you will have been able to tell just how clean this car is all the way around inside and out under the hood we have a 1.4 liter turbocharged four-cylinder motor engine base very clean runs very smooth this car has been fully safety to and inspected by our service shop has a fresh oil and filter change all the fluids have been checked and tapped off the car has been gone through mechanically 100% is 100% ready to go to see more pictures of this car one of our other 450 new and used cars trucks SUVs minivans Wranglers half tons three-quarter tons one tons you name it we got it go to our website www.summitauto.com full pictures and descriptions of every single vehicle from two locations all at summit auto.com and if you'd like to check out more HD videos you can go to youtube.com slash summit auto remember to like subscribe and share on this video and all the videos that you see there in fact in a second you'll see link to subscribe to our YouTube channel in the left a link to more Chevy Cruze vehicles like this one on your right if you have not been to our website on the bottom link to this vehicle on our website click those check us out and we really look forward to helping with this super clean 2012 Chevy Cruze LT1 with the RS package with the six-speed manual transmission thanks again for checking out the video