The aim of this document is to outline the preliminary requirements and steps needed to fully establish frameworks for certification systems across Europe, specifically to support and incentivize the restoration of peatlands and to provide a framework for reducing GHG emissions from degraded and mismanaged peatlands on a large scale. This will ensure that peatlands across Europe fulfil their potential to become a net carbon sink by 2050, while optimizing ecosystem service provision in a way that is fully consistent with all the relevant European policies. This report covers the following topics: - Analysis of current Carbon Credit systems and other incentives to support wet peatlands. - Economic land use analysis relating to peatlands. - Outline of a framework to support rewetting and peatland restoration. - Recommendations for an Eco-Credit system across Europe.
MULTIFILE
The aim of this research is to assess the potential impact of the CO2 Performance Ladder on CO2 emission reduction. The CO2 Performance Ladder is a new green procurement scheme that has been adopted by several public authorities in the Netherlands; it is a staged certification scheme for energy and CO2 management. The achieved certification level gives companies a certain competitive advantage in contract awarding procedures. While the scheme has been widely adopted by companies in the construction industry, other types of companies in the supply chain of the commissioning parties also participate. Currently, more than 190 companies participate in the scheme. The aggregate CO2 emissions covered by the scheme are around 1.7 Mtonnes, which corresponds to almost 1 % of national greenhouse gas emissions in the Netherlands. Since the introduction of the scheme the total CO2 emissions have decreased substantially. Nevertheless, these emission reductions should be interpreted with caution since emission reductions are dominated by a few companies and are affected to a large extent by economic activity. Companies participating in the scheme have set different types of CO2 emission reduction targets with varying ambition levels. The projected impact of these targets on CO2 emissions is in the range of a 0.5 %-1.3 % absolute emission reduction per year, with a most likely value of 1.1 %. The CO2 Performance Ladder can therefore make a substantial contribution to achieving the CO2 emission reductions for non-ETS sectors in the Netherlands up to 2020.
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
The automobile industry is presently going through a rapid transformation towards autonomous driving. Nearly all vehicle manufacturers (such as Mercedes Benz, Tesla, BMW) have commercial products, promising some level of vehicle automation. Even though the safe and reliable introduction of technology depends on the quality standards and certification process, but the focus is primarily on the introduction of (uncertified) technology and not on developing knowledge for certification. Both industry and governments see the lack of knowledge about certification, which can ensure the safety of autonomous technology and thus will guarantee the safety of the driver, passenger, and environment. HAN-AR recognized the lack of knowledge and the need for novel certification methodology for emerging vehicle technology and initiated the PRAUTOCOL project together with its SME partners. The PRAUTOCOL project investigated certification methodology for two use-cases: certification for automated highway overtaking pilot; and certification for automatic valet parking. The PRAUTOCOL research is conducted in two parallel streams: certification of the driver by human factors experts and certification of vehicle by technology experts. The results from both streams are published and presented in respective but limited target groups. Also, an overview of the PRAUTOCOL certification methodology is missing, which can enable its translation to different use-cases of automated technology (other than the used ones). Therefore, to realize a better pass-through of PRAUTOCOL's results to a broader audience, the top-up is required. Firstly, to write a (peer-reviewed) Open Access article, focusing on the application and translation of PRAUTOCOL's methodology to other automated technology use-cases. Secondly, to write a journal article, focusing on the validation of automatic highway overtaking system using naturalistic driving data. Thirdly, to organize a workshop to present PRAUTOCOL's results (valorization) to industrial, research, and government representatives and to discuss a follow-up initiative.
Our country contains a very dense and challenging transport and mobility system. National research agendas and roadmaps of multiple sectors such as HTSM, Logistics and Agri&food, promote vehicle automation as a means to increase transport safety and efficiency. SMEs applying vehicle automation require compliance to application/sector specific standards and legislation. A key aspect is the safety of the automated vehicle within its design domain, to be proven by manufacturers and assessed by authorities. The various standards and procedures show many similarities but also lead to significant differences in application experience and available safety related solutions. For example: Industrial AGVs (Automated Guided Vehicles) have been around for many years, while autonomous road vehicles are only found in limited testing environments and pilots. Companies are confronted with an increasing need to cover multiple application environments, such restricted areas and public roads, leading to complex technical choices and parallel certification/homologation procedures. SafeCLAI addresses this challenge by developing a framework for a generic safety layer in the control of autonomous vehicles that can be re-used in different applications across sectors. This is done by extensive consolidation and application of cross-sectoral knowledge and experience – including analysis of related standards and procedures. The framework promises shorter development times and enables more efficient assessment procedures. SafeCLAI will focus on low-speed applications since they are most wanted and technically best feasible. Nevertheless, higher speed aspects will be considered to allow for future extension. SafeCLAI will practically validate (parts) of the foreseen safety layer and publish the foreseen framework as a baseline for future R&D, allowing coverage of broader design domains. SafeCLAI will disseminate the results in the Dutch arena of autonomous vehicle development and application, and also integrate the project learnings into educational modules.
