Nature-based solutions (NbS) are fast becoming the norm for multifunctional climate adaptation to the combined challenges of increased sea-level rise, coastal population densities, and erosion of... Show moreNature-based solutions (NbS) are fast becoming the norm for multifunctional climate adaptation to the combined challenges of increased sea-level rise, coastal population densities, and erosion of sandy shores worldwide, delivering functions such as flood prevention, recreation, and biodiversity benefits. However, it remains a challenge to the research field to inform decision-makers well on the outcomes and trade-offs of designing, planning, and managing the multifunctional NbS. This study set out to identify the information requirements by decision-makers on NbS for coastal climate adaptation. Using the Sand Motor in The Netherlands as a case study, we applied a policy science framework to distinguish four stages of decision-making to quantitatively analyse the content of functions and indicators utilized per stage in public policy documents. These stages are the ambition, political, bureaucratic, and provisioning processes. This study is the first comprehensive empirical investigation distinguishing these crucial stages of decision-making to analyse NbS information requirements. Our results show, most notably, that as the project developed through the decision-making stages, the content of the functions and indicators changed from abstract to concrete. And, with it, the content of the information required shifted significantly. These results suggest that it is crucial for academic researchers to recognize the decision-making process their information will be used in and adapt its content and level of abstraction accordingly to increase its uptake in decision-making. This study lays the groundwork for future research into the multiple dimensions of NbS decision-making and for the increased understanding of the information requirements on evaluation and trade-offs in planning, designing, and managing NbS, to increase the ability of NbS to deliver multifunctional coastal climate adaptation for sandy shores worldwide. Show less
The workshop “How companies improve critical raw materials criticality” was co-organized by the International Round Table on Materials Criticality in its current project IRTC-Business. After IRTC... Show moreThe workshop “How companies improve critical raw materials criticality” was co-organized by the International Round Table on Materials Criticality in its current project IRTC-Business. After IRTC had investigated the potential of circular strategy to mitigate criticality of critical raw materials in earlier events, discussions and publications, the workshop aimed at understanding concrete applications of circular strategies, in order to identify their drivers and hurdles. For this, a variety of companies were invited to present their business models. Show less
Numerical models of chemical transport have been used to simulate the complex processes involved in the formation and transport of air pollutants. Although these models can predict the... Show moreNumerical models of chemical transport have been used to simulate the complex processes involved in the formation and transport of air pollutants. Although these models can predict the spatiotemporal variability of a variety of chemical species, the accuracy of these models is often limited. Therefore, in the past two decades, data assimilation methods have been applied to use the available measurements for improving the forecast. Nowadays, machine learning techniques provide new opportunities for improving the air quality forecast. A case study on PM10 concentrations during a dust storm is performed. It is known that the PM10 concentrations are caused by multiple emission sources, e.g., dust from the desert and anthropogenic emissions. Accurate modeling of the PM10 concentration levels owing to the local anthropogenic emissions is essential for an adequate evaluation of the dust level. However, real-time measurement of local emissions is not possible, so no direct data is available. Actually, the lack of in-time emission inventories is one of the main reasons that current numerical chemical transport models cannot produce accurate anthropogenic PM10 simulations. Using machine learning techniques to generate local emissions based on past observations is a promising approach. We report how it can be combined with data assimilation to improve the accuracy of air quality forecast considerably. Show less
Rossum, A.C.; Lin, H.X.; Dubbeldam, J.; Herik, H.J. van den 2016
