Towards an eco-welfare state – how to direct the transition?
We interviewed Mikko Jalas, Creative Sustainability Programme Director and Professor of Practice, about Towards Eco-Welfare State: Orchestrating for Systemic (ORSI) which is a joint research project between Tampere University, Finnish Environment Institute (Suomen ympäristökeskus, SYKE), VTT Technical Research Centre of Finland and Aalto University. The research is funded in 2019-2022/25 by The Strategic Research Council (SRC) at the Academy of Finland. The common research objective is analyzing the concept of eco-welfare state and developing policy and governance tools from the premise of a welfare state which highlights the justice and impartiality of societal transitions in Finland. The project is divided into four themes: public governance and budgeting for sustainability, fair transition in the everyday life of citizens, responsible innovation processes and steering of consumption choices.
This news is from an external site. Read full article: here
- Published:
- Updated:
Read more news
‘Off to a flying start’ – a new research team explores bacteria that thrive in extreme conditions
Jarno Mäkelä joined Aalto’s Department of Neuroscience and Medical Engineering as Assistant Professor of Biophysics in early September, together with research fellow Taras Redchuk, postdoctoral researchers Dan Noel, Eveliny Nery, doctoral researchers Ada Pajari and Aakeel Wagay, and research assistant Chris Hayes. They were accompanied by equipment, funding from the Academy of Finland and an ERC Starting Grant from the European Research Council.
The physical and digital worlds of production and internal logistics meet in a multidisciplinary TwinFlow project
Researchers from Aalto University and the University of Tampere are collaborating with companies to accelerate the data-driven business in the manufacturing industry. The joint three-year project is funded by Business Finland.
Best Student Paper Award for Eloi Moliner – again!
The award-winning paper shows how speech recordings can be improved by removing the room reverberation effect using unsupervised machine learning