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While decarbonisation is the main objective of the RES4BUILD integrated energy solutions, decoupling from fossil fuels contributes to security of supply, and using on-site generated electricity contributes to affordability, moving away from heavily fluctuating market prices. A report has indicated the technical potential of these integrated energy systems in the European market,

A good practice assssment performed by the EU funded RES4BUILD project reveals that accelerating the uptake of Integrated Energy Solutions (IES) increasingly requires consideration of financial and social innovation to address non-technological barriers.

The latest ZERO IN report from the CONSTRAIN project dives into the science set out in the recent Intergovernmental Panel on Climate Change assessments. It investigates what our climate future could look like by 2050, depending on whether we take action in line with the Paris Agreement, or decide to follow current national policies and plans which still largely lack the action and ambition needed to stop global warming.

Maintaining indoor comfort in buildings accounts for more than 30% of total energy use worldwide – smart control of building energy use can contribute greatly towards energy sustainability goals. This is an important part of the RES4BUILD project which is looking at renewables for clean energy buildings in a future power system.

New study on the impacts of wildfire emissions on fine particle air pollution in the western US until the end of the 21st century, under different climate change scenarios.
This work highlights the fact that when committing to a particular level of future warming, we are also committing to a particular level of fine particle air pollution. This has to be taken into account when considering mitigation and adaptation strategies.

EIFFEL H2020 project offers the EO-based community the ground-breaking capacity of exploiting existing GEOSS and external datasets, with minimal new data collection activities. Added-value services interoperable with GEOSS will be designed, using cognitive search and metadata augmentation tools based on Artificial Intelligence (AI), including Natural Language Processing. These tools will leverage advanced cognitive features to extract meaningful information from and enrich GEOSS metadata.