I prodotti fitosanitari sono considerati uno dei principali strumenti di difesa contro le più rilevanti avversità che colpiscono la produzione agricola. Per garantire che il loro utilizzo sia realmente basato su principi di sostenibilità, nel 2009 l’Unione Europea ha introdotto la cosiddetta direttiva sull’Uso Sostenibile dei Pesticidi (EU 128/2009/EC) dove lo sviluppo di appropriati indicatori di rischio insieme all’implementazione di una corretta attività di formazione e sensibilizzazione sono da considerare fondamentali per ridurre l’esposizione. Per contribuire in questa direzione, in questo studio sono stati prodotti: a) un toolbox di pratici indicatori di rischio indiretti per essere utilizzati da parte delle autorità nazionali per monitorare le performance; b) un nuovo strumento e-learning (OpenTEA) di formazione e sensibilizzazione per raccogliere e condividere i più efficienti e consistenti materiali a disposizione. Questi contributi sono stati sviluppati utilizzando un approccio pragmatico basato sia su una consultazione degli stakeholders sia su un’analisi completa del rischio (usando dei modelli previsionali di esposizione e svolgendo un’indagine sistematica “sul campo”). Tutto è stato reso possibile grazie al coinvolgimento nelle attività nel centro di ricerca OPERA, un “think tank” che attraverso il suo approccio innovativo basato su costruire reti con gli stakeholders e ponti tra scienza e politica, permette il raggiungimento di soluzioni pragmatiche condivise. / Pesticides are considered one of the principle tools of defence against the most relevant adversity affecting the agricultural production. To ensure that their use is really based on sustainability principle, in 2009 the European Union introduced the so called Directive on Sustainable Use of Pesticides (EU 128/2009/EC) where the establishment of appropriate risk indicators to monitor the performances together with the implementation of appropriate training and awareness raising to improve behaviours are considered fundamentals. To contribute in this direction, in this study were produced: a) a toolbox of practical indirect risk indicators to be used by EU Member States to monitor the performances; b) a new e-learning tool (OpenTEA) for training and awareness raising to collect and share the most efficient and scientifically sound training and communication material. These contributions were developed using a pragmatic approach focusing either on a complete stakeholder consultation process either on a comprehensive analysis of risk (looking at some exposure models and performing a systematic surveys “on the field”). All the process was possible getting involved in the OPERA research centre, a “think tank” that through its innovative approach based on building network among stakeholders and bridges between science and policy, allow the achievement of pragmatic and agreed solutions.

Studies have revealed that securing Information Systems (IS) from intentional misuse is a concern among organizations today. The use of Web-based systems has grown dramatically across industries including e-commerce, e-banking, e-government, and e learning to name a few. Web-based systems provide e-services through a number of diverse activities. The demand for e-learning systems in both academic and non-academic organizations has increased the need to improve security against impersonation fraud. Although there are a number of studies focused on securing Web-based systems from Information Systems (IS) misuse, research has recognized the importance of identifying suitable levels of authenticating strength for various activities. In e-learning systems, it is evident that due to the variation in authentication strength among controls, a ‘one size fits all’ solution is not suitable for securing diverse e-learning activities against impersonation fraud. The main goal of this study was to use the framework of the Task-Technology Fit (TTF) theory to conduct an exploratory research design to empirically investigate what levels of authentication strength users perceive to be most suitable for activities in e-learning systems against impersonation fraud. This study aimed to assess if the ‘one size fits all’ approach mainly used nowadays is valid when it comes to securing e-learning activities from impersonation fraud. Following the development of an initial survey instrument (Phase 1), expert panel feedback was gathered for instrument validity using the Delphi methodology. The initial survey instrument was adjusted according to feedback (Phase 2). The finalized Web-based survey was used to collect quantitative data for final analyses (Phase 3). This study reported on data collected from 1,070 e-learners enrolled at a university. Descriptive statistics was used to identify what e-learning activities perceived by users and what users perceived that their peers would identify to have a high potential for impersonation. The findings determined there are a specific set of e-learning activities that high have potential for impersonation fraud and need a moderate to high level of authentication strength to reduce the threat. Principal Component Analysis was used to identify significant components of authentication strength to be suitable against the threats of impersonation for e-learning activities.

In order to study how to reach different climate targets, scientists and policymakers rely on results from computer models known as Integrated Assessment Models (IAMs). These models are used to quantitatively study different ways of achieving warming targets such as the Paris goal of limiting warming to 1.5-2.0 °C, deriving climate mitigation pathways that are optimal in some sense. However, when applied to the Paris goal many IAMs derive pathways that overshoot the temperature target: global temperature temporarily exceeds the warming target for a period of time, before decreasing and stabilizing at the target. Although little is known with certainty about the impacts of overshooting, recent studies indicate that there may be major risks entailed. This thesis explores two different ways of including overshoot risk in a simple IAM by introducing stochastic elements to it. Then, algorithms from Reinforcement Learning (RL) are applied to the model in order to find pathways that take overshoot risk into consideration. In one experiment we apply standard risk-neutral RL to the DICE model extended with a probabilistic damage function and carbon dioxide removal technologies. In the other experiment, the model is further augmented with a probabilistic tipping element model. Using risk-constrained RL we then train an algorithm to optimally control this model, whilst controlling the conditional-value-at-risk of triggering tipping elements below a user-specified threshold. Although some instability and convergence issues are present during training, in both experiments the agents are able to achieve policies that outperform a simple baseline. Furthermore, the risk-constrained agent is also able to (approximately) control the tipping risk metric below a desired threshold in the second experiment. The final policies are analysed for domain insights, indicating that carbon removal via temporal carbon storage solutions could be a sizeable contributor to negative emissions on a time-horizon relevant for overshooting. In the end, recommended next steps for future work are discussed. / För att studera hur globala klimatmål kan nås använder forskare och beslutsfattare resultat från integrerade bedömningsmodeller (IAM:er). Dessa modeller används för att kvantitativt förstå olika vägar till temperaturmål, så som Parisavtalets mål om att begränsa den globala uppvärmningen till 1.5-2.0 °C. Resultaten från dessa modeller är så kallade ”mitigation pathways” som är optimala utifrån något uppsatt kriterium. När sådana modellkörningar görs med Parismålet erhålls dock ofta optimala pathways som överskjuter temperaturmålet tillfälligt: den globala temperaturen överstiger målet i en period innan den sjunker och till slut stabiliseras vid det satta målet. Kunskapen om vilken påverkan en överskjutning har är idag begränsad, men flertalet nyligen gjorda studier indikerar att stora risker potentiellt kan medföras. I denna uppsats utforskas två olika sätt att inkludera överskjutningsrisk i en enkel IAM genom användandet av stokastiska element. Därefter används Förstärkande Inlärning på modellen för att erhålla modellösningar som tar hänsyn till överkjutningsrisk. I ett av experimenten utökas IAM:en med en stokastisk skadefunktion och tekniker för koldioxidinfångning varpå vanlig Förstärkande Inlärning appliceras. I det andra experimentet utökas modellen ytterligare med en stokastisk modell för tröskelpunkter. Med hjälp av risk-begränsad Förstärkande Inlärning tränas därefter en modell för att optimalt kontrollera denna IAM samtidigt som risken att utlösa tröskelpunkter kontrolleras till en nivå satt av användaren. Även om en viss grad av instabilitet och problem med konvergens observeras under inlärningsprocessen så lyckas agenterna i båda experimenten hitta beslutsregler som överträffar en enkel baslinje. Vidare lyckas beslutsregeln som erhålls i det andra experimentet, med den risk-begränsade inlärningen, approximativt kontrollera risken att utlösa tröskelpunkter till det specificerade värdet. Efter träning analyseras de bästa beslutsreglerna i syfte att finna domänmässiga insikter, varav en av dessa insikter är att temporära kollager kan ge betydande bidrag för koldioxidinfångning i en tidshorisont relevant vid överskjutning. Slutligen diskuteras möjliga nästa steg för framtida arbeten inom området.