Environmentally conscious Research & Development (R&D) is strongly encouraged by Publicly Funded Research Programmes. The European Union's Research and Innovation funding programme for 2007-2013 (FP7) and Horizon 2020 (2014-onwards) calls explicitly require addressing environmental aspects from a life cycle perspective within the innovative products development process. Life Cycle Assessment (LCA) is often used as a mean to ensure such environmentally conscious Research & Development.

In many specific EU research calls, LCA is proposed as a tool to support decisions and the International Reference Life Cycle Data System (ILCD) handbook and ISO 14040 family are suggested to be used as guidance. Such life cycle approach is expected to be continued in future calls, also considering that in the Horizon 2020 - Work Programme 2014-2015 "Nanotechnologies, Advanced Materials and Production", the life cycle perspective to assess the environmental performances of the solutions is explicitly required in several calls. For example, some recent calls on Circular Economy (e.g. CIRC-01-2016-2017) require "A life cycle thinking and assessment, in line with the recommendations and reference data from the European Platform on Life Cycle Assessment when applicable, should be applied", in particular "Data should be disseminated through nodes in the Life Cycle Data Network and studies through the Resource Directory".

Why is it important to publish on the Life Cycle Data Network datasets on innovative products and manufacturing processes relevant for the EU Funded Research Projects?

• The Life Cycle Data Network is a concrete step to increase the availability and interoperability of reliable data (thanks to LCDN Entry-Level quality requirements);
• Promoting and facilitating data publication and sharing can sensibly increase the number of data providers;
• The tools available through the EPLCA (LCDN, ELCD and Resource Directory) could facilitate the results dissemination and LCI data sharing.

Pilot Case study

FP7 HarWin (Harvesting solar energy with multifunctional glass-polymer windows) Project specifically addressed for the development of "Smart Windows" (FP7-2012-NMP-ENV-ENERGY-ICT-EeB, Cooperation call "Energy-efficient Buildings 2012"; 1st September 2012 - 31st August 2015).

One of the major outputs of the HarWin project was one quality-assured LCI dataset on glassflakes production as a result of the final environmental evaluation of HarWin window components. The project aimed at developing laminated glass containing glass-polymer composite interlayers, that are mechanically reinforced materials which enable weight reduction, high visible light transmission, thermal and sound barrier enhanced properties. The integration of datasets into existing LCA database wants to represent a mean to disseminate the results of the Project and also to make available useful data for the scientific and industrial community to further develop highly performing sustainable products.

SASLAB

Sustainability Assessment of Second Life Application of Automotive Batteries (SASLAB) is an exploratory project led by JRC under its own initiative in 2016-2017, aims at assessing the sustainability of re-purposing electric vehicles’ batteries to be used in energy storage applications from technical, environmental and social perspectives. One of the output of the SASLAB project is the development of a LCI dataset of a traction battery cell based on primary data ("Battery cell, LMO/NMC chemistry {JP}") obtained through the dismantling of a LMO/NMC (lithium-manganese-oxide/lithium-nickel-manganese cobalt oxide) battery extracted from a Mitsubishi Outlander Plug-In Hybrid Electric Vehicle. The integration of such a dataset into existing LCA database is a mean to disseminate this result of the project and to make this dataset usable to further develop highly performing sustainable products. The dataset is modelled assuming that the battery cell is manufacture in Japan (JP) according to the available information; more details are published in Cusenza et al (2019). Due to the rapid change of the batteries’ technology, the dataset is provided as disaggregated including the adopted sub-processes in order to ease further updates according to future available inputs.