Assessing the Responsibility Towards Climate Change of Research Projects by Means of Analytic Hierarchy Process

Abstract

In this paper we aim at modeling how to assess the responsibility of research projects. For that, we focus on a specific discipline: Climate Change. We apply Analytic Hierarchy Process as the tool for analyzing the problem and engaging experts into a constructive discussion. We believe our findings will help other researchers to identify key concepts regarding responsible research and innovation, assessing and monitoring the responsibility of research projects, starting processes of dialogue and, eventually, ranking different research proposals from the point of view of responsibility preventing climate change.

Addressing Climate Change in Responsible Research and Innovation: Recommendations for Its Operationalization

“…Therefore, the contributions to CC in those projects normally will be more relevant for the life cycle of the outcomes of the research than for the research activities themselves [40,61,67]. Currently, there are proven tools to guide Life Cycle Assessment (LCA), including the ISO 14040 series of standards [66] and, to lesser extent, the PAS 2050 [64].…”

Addressing Climate Change in Research and Innovation Projects. A tool for Anticipatory Carbon Footprint Calculation

“…- There are several R&I activities aiming to develop a new product or system which later will be industrialized and widely used. Therefore, the contributions to CC in those projects normally will be more relevant for the life cycle of the outcomes of the research than for the research activities themselves (Gómez-Navarro and Ligardo-Herrera, 2016;Owen and Goldberg, 2010;Vermeulen et al, 2016 LCA is very laborious and, thus, for R&I teams, one of its essences is to identify and prioritize the main contributions to CC during the life cycle of the innovation, as rigorously as possible (ISO, 2009). That way, LCA allows us to identify the minimum indicators to add to the research management key performance indicators (KPI) (Capuz-Rizo and Gómez-Navarro, 2002;ISO, 2009;McAloone and Hare, 2015;Schroeder, 2017;Szabo, 2016), and can be re-run later in an abridged manner based on those KPI.…”