Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2023
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Habtamu AlemSammendrag
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Habtamu AlemSammendrag
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Christian Oliver Ewald Jelena Hadina Erik Haugom Gudbrand Lien Ståle Størdal Muhammad YahyaSammendrag
In this paper we examine how sensitive Value-at-Risk (VaR) forecasts based on simple linear quantile regressions are to the sampling frequency used to calculate realized volatility. We use sampling frequencies from one to 108 min for ICE Brent Crude Oil futures and test the out-of-sample performance of a set of quantile regression models using formal coverage tests. The results show that a one-factor model performs exceptionally well for most sampling frequencies used to calculate realized volatility. In comparison with the well-known Heterogeneous Auto-regressive Model of Realized Volatility (HAR-RV) and a quantile regression version of the HAR model (HAR-QREG), we also find that the one-factor model is much less sensitive to the sampling frequency used to calculate realized volatility.
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The SiEUGreen project was implemented to enhance the EU-China cooperation in promoting urban agriculture (UA) for food security, resource efficiency and smart, resilient cities through the development of showcases in selected European and Chinese urban and peri-urban areas. In the last four years, SiEUGreen project assembled numerous existing and/or unexploited technologies for the first time to facilitate the development of the state-of-the-art UA model. In light of this, there is natural interest in whether SiEUGreen’s efforts resulted in meaningful impacts. Hence, the objective of this report is to determine the multi-dimensional impacts of the showcases developed and implemented by the SiEUGreen project. The analysis of the impact of the technologies or showcases implemented by the SIEUGreen mainly relies on the data obtained from other relevant tasks and deliverables within the project (e.g., showcase deployment, market analysis, and deliverables related to technology deployment). The willingness to pay studies use NIBIO’s existing data from a contingent valuation survey for willingness to pay of Oslo residents towards food produced using the target technologies. The report is presented as follows: • Section 2 gives an overview of the implementation status of the SiEUGreen technologies with the current technology readiness levels (TRLs); • Section 3 discusses the impacts in terms of land use, food security, environmental resilience and resource efficiency, and societal inclusion; • Section 4 focuses on willingness to pay studies for UA-related technologies; • Section 5 discusses the results and impact pathways; and • Section 6 provides the lessons learned and recommendations. Overall, our assessment indicates that SiEUGreen has provided a wide-ranging array of impacts in multiple dimensions: land-use, food security, environmental resilience and resource efficiency, and societal inclusion.
Forfattere
Michael Salka Vicente Guallart Daniel Ibañez Divina Gracia P. Rodriguez Nicolas Picard Jerylee Wilkes-Allemann Evelyn Coleman Brantschen Stefano Boeri Livia Shamir Lucrezia De Marco Sofia Paoli Maria Chiara Pastore Ivana ŽivojinovićSammendrag
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Giovanna Ottaviani Aalmo Silvija Krajter Ostoic Divina Gracia P. Rodriguez Liz O’Brien Constanza ParraSammendrag
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