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In this paper three approaches on transitions pathways are combined to study the role of agricultural nature conservation in the Dutch land use domain for achieving internationally agreed climate and biodiversity targets. The three perspectives used are the Multilevel Perspective (MLP), Initiative Based Learning (IBL) and Integrated Assessment Modelling (IAM). The analysis provides insights in how the combination of different research approaches can lead to more comprehensive policy advice on how agricultural nature conservation could help to achieve internationally agreed sustainability goals related to climate change and biodiversity. IAM shows under which conditions agricultural nature conservation could be consistent with European and global long-term goals regarding food security, biodiversity and climate. MLP provides insight into the extent in which agricultural nature conservation has affected or changed the existing nature and agricultural regimes. IBL, finally, reveals the challenges of encouraging agricultural nature conservation with policy measures. Our analysis shows that a combined perspective provides a deeper understanding of the underlying processes, reasons and motives of agricultural nature conservation, leading to more comprehensive policy recommendations.
Das Zusammenspiel von aufstrebenden Technologiefeldern eröffnet neue Potenziale für die Nachhaltigkeitstransformation. Gleichzeitig erzeugt es komplexe Umweltbelastungen, die bisher kaum sichtbar und noch weniger gestaltbar sind. Für eine nachhaltige Digitalisierung brauchen wir jetzt ein Verständnis für die ökologischen Wechselwirkungen des zukünftigen Digitalsystems. Am Beispiel der Machine Economy und der ihr zugrunde liegenden Technologien Internet of Things, Künstliche Intelligenz und Distributed Ledger Technologie bzw. Blockchain machen wir in diesem Forschungsbericht Umweltwirkungen transparent und Ansatzpunkte greifbar - damit Digitalisierung ganzheitlich ökologisch gestaltbar wird.
Im Zeitalter der Machine Economy ist der maschinelle Dialog allgegenwärtig - das bietet neue Chancen für Nachhaltigkeit, erhöht gleichzeitig aber durch die zugrundeliegenden Technologien auch den Druck auf unsere Umwelt. Internet of Things (IoT), Künstliche Intelligenz (KI) und Distributed Ledger Technology (DLT) sind das technologische Fundament der Machine Economy. Damit verbunden sind Infrastrukturen, Datenströme und Anwendungen, die hohe Energie- sowie Ressourcenaufwände erzeugen. Der derzeitige politische Diskurs sowie die Nachhaltigkeitsforschung fokussieren sich auf Umweltwirkungen durch digitale Infrastrukturen. Daten, Applikationen sowie die Rolle von Akteuren als Treiber der Umweltwirkung werden zu wenig beleuchtet. In diesem Papier sprechen sich die Autorinnen und Autoren für eine "Grüne Governance der Machine Economy" aus. Adressiert werden Annahmen zu systemübergreifenden Treibern von Umweltbelastungen und ihrer Wirkung. Ziel ist es, ein Gesamtsystem nachhaltiger Entscheidungen und ein ökologisches Zusammenspiel aller beteiligten Technologien in der Wertschöpfung zu ermöglichen. Zukünftige Forschung soll die hier vorgestellten Hypothesen weiter ausarbeiten und konkrete Handlungsoptionen für eine Stakeholder übergreifende Roadmap erarbeiten.
CICERONE aims to bring national, regional and local governments together to jointly tackle the circular economy transition needed to reach net-zero carbon emissions and meet the targets set in the Paris Agreement and EU Green Deal. This document represents one of the key outcomes of the project: a Strategic Research & Innovation Agenda (SRIA) for Europe, to support owners and funders of circular economy programmes in aligning priorities and approaching the circular economy transition in a systemic way.
This report provides an overview of the main findings from the different research tasks in the CIRCTER project and delivers selected policy messages with European coverage. The report provides: (Sec. 2) a territorial definition of the circular economy; (Sec. 3) insights into the available statistics on material and waste patterns and flows and their interpretation, alongside new territorial evidence on both aspects; (Sec. 4) a sectoral characterisation of the circular economy at regional level (NUTS-2), including data on turnover and jobs; (Sec. 5) key findings from the CIRCTER case studies; (Sec. 6) a systemic interpretation of the circular economy that works as a knowledge-integration mechanism for the entire report; (Sec. 7 and 8) an analysis of the most relevant circular economy policies and strategies at various territorial levels; (Sec. 8) a subset of policy recommendations focusing in particular on territorial and cohesion policies, and; (Sec. 9) suggestions for further research.
Effectiveness and efficiency of food-waste prevention policies, circular economy, and food industry
(2020)
The study sheds light on the background of the prevention of plastic waste from packaging and disposable products by explaining the need for action, the environmental impacts and risks to human health. Experiences of the members of the PREVENT Waste Alliance and their partners in the prevention of plastic waste by multi-actor partnerships are presented by means of 17 best practice examples. Finally, the study gives recommendations for the reduction of plastic waste and the further work of the PREVENT Waste Alliance. These include success factors for waste prevention, necessary next steps and conclusions regarding the necessary political framework conditions.
Künstliche Intelligenz in der Siedlungsabfallsortierung als Wegbereiter der Kreislaufwirtschaft
(2020)
Artificial intelligence in the sorting of municipal waste as an enabler of the circular economy
(2021)
The recently finalized research project "ZRR for municipal waste" aimed at testing and evaluating the automation of municipal waste sorting plants by supplementing or replacing manual sorting, with sorting by a robot with artificial intelligence (ZRR). The objectives were to increase the current recycling rates and the purity of the recovered materials; to collect additional materials from the current rejected flows; and to improve the working conditions of the workers, who could then concentrate on, among other things, the maintenance of the robots. Based on the empirical results of the project, this paper presents the main results of the training and operation of the robotic sorting system based on artificial intelligence, which, to our knowledge, is the first attempt at an application for the separation of bulky municipal solid waste (MSW) and an installation in a full-scale waste treatment plant. The key questions for the research project included (a) the design of test protocols to assess the quality of the sorting process and (b) the evaluation of the performance quality in the first six months of the training of the underlying artificial intelligence and its database.