Anticipatory life-cycle assessment for responsible research and innovation

Wender, Ben A.; Foley, Rider W.; Hottle, Troy A.; Sadowski, Jathan; Prado, Valentina; Eisenberg, Daniel A.; Laurin, Lise; Seager, Thomas P.

doi:10.1080/23299460.2014.920121

Cited by 109 publications

(103 citation statements)

References 24 publications

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“…One of the most well-known strategies for attempting to strengthen science-society relationships, and one that is central to articulations of RRI, is engagement, a broad term that includes public engagement (Wilsdon and Willis 2004), stakeholder engagement (Wender et al 2014) and expert engagement (Fisher et al 2015). Engagement practice takes place at different levels and is often organised between universities and different kinds of actors, including business, governments, the voluntary sector and civil society (D'Este and Patel 2007; Hughes and Kitson 2012;Landry et al 2010).…”

Section: The Creation Of Value and Societal Benefits By Public Institmentioning

confidence: 99%

Introducing the dilemma of societal alignment for inclusive and responsible research and innovation

Ribeiro

Bengtsson

Benneworth

et al. 2018

Journal of Responsible Innovation

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In this discussion paper, we outline and reflect on some of the key challenges that influence the development and uptake of more inclusive and responsible forms of research and innovation. Taking these challenges together, we invoke Collingridge's famous dilemma of social control of technology to introduce a complementary dilemma that of 'societal alignment' in the governance of science, technology and innovation. Considerations of social alignment are scattered and overlooked among some communities in the field of science, technology and innovation policy. By starting to unpack this dilemma, we outline an agenda for further consideration of social alignment in the study of responsible research and innovation.ARTICLE HISTORY

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Section: The Creation Of Value and Societal Benefits By Public Institmentioning

confidence: 99%

Introducing the dilemma of societal alignment for inclusive and responsible research and innovation

Ribeiro

Bengtsson

Benneworth

et al. 2018

Journal of Responsible Innovation

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“…Our results suggest that focusing experimental resources on improving data for suspended solids partitioning behavior and multi-species toxicity indicators has the greatest potential to reduce uncertainty of current C60 CF estimates. In this capacity, stochastic evaluation of impact assessment models to identify the most influential parameter uncertainties and inform future research agenda constitutes an example of anticipatory LCA (Wender et al 2014a;Wender et al 2014b). …”

Section: Resultsmentioning

confidence: 99%

Sensitivity-based research prioritization through stochastic characterization modeling

Wender

Prado

Fantke

et al. 2017

Int J Life Cycle Assess

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Purpose Product developers using life cycle toxicity impact assessment models to understand potential impacts of material substitutions face serious challenges related to large data demands and high uncertainty. This motivates greater focus on model sensitivity toward input parameter variability, particularly in the context of emerging contaminants like engineered nanomaterials (ENMs), to guide future efforts in data refinement and design of experiments. This study presents a Monte Carlo tool designed for use with USEtox 1.0 that allows researchers to rapidly prioritize data needs according to influence on characterization factors (CFs).Methods Using Monte Carlo analysis we demonstrate a sensitivity-based approach to prioritize research through a case study comparing aquatic ecotoxicity CFs for the ENM C60 and the vitamin B derivative niacinamide, two antioxidants used in personal care products. We calculate CFs via 10,000 iterations assuming plus-or-minus one order of magnitude variance for fate and exposure-relevant inputs. Spearman Rank Correlation Indices are used for all variable inputs to identify parameters with the largest influence on CFs, which we prioritize for data refinement and future experimental investigation. Based on the importance of aggregate multi-species toxicity (average log EC50) and studies suggesting solvent residues may yield erroneous toxicity estimates, we recalculate C60 CFs omitting all studies using solvents in sample preparation.Results and discussion For emissions to freshwater, the C60 CF is log-normally distributed with a geometric mean of 280 and geometric standard deviation (GSD) of 2.1 PAF m 3 day/kg compared to 2.6 with a GSD=1.8 PAF m 3 day/kg for niacinamide. C60 CFs are most sensitive to varied suspended solids partitioning coefficients (Kpss) and average log EC50, whereas variation of other substance parameters has comparatively little effect on model results. Insufficient experimental evidence hampers to revise assumptions for Kpss, and we suggest prioritizing future experiments that elucidate C60 interactions with suspended solids. Recalculating C60 CFs without toxicity studies that use solvents reduces the geometric mean by more than a factor of ten. This reinforces the importance of thorough characterization of released ENMs, in this case regarding the presence of solvent residues. ConclusionCalculating stochastic CFs allows sensitivity-based prioritization of data needs and future experiments, which is particularly helpful in the context of emerging contaminants like C60. Researchers can conserve resources and address parameter uncertainty by applying our approach when developing new or refining existing CFs for the inventory items that contribute most to toxicity impacts. The Monte Carlo tool can be applied to current toxicity characterization models like USEtox and is freely available at

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“…For example, it may occur that it is more responsible to produce and market an innovation in one region than in another, based on the envisaged scenarios, the regions' energy mix, the waste treatment systems, etc. [60,71]. This is what several authors define as "aiming at glocal sustainability research", for instance [20,72].…”

Section: Life Cycle Perspectivementioning

confidence: 99%

“…For that, teams will have to assess and manage the carbon footprint of their research, i.e., the contribution to CC of all activities of the life cycle of the project [59][60][61]. Below, the tools that can support this aim are discussed.…”

Section: Effective and Efficient Integration In The Core Management Omentioning

confidence: 99%

“…Nevertheless, Wender et al [60] argued that LCA must be adapted to successfully promote RRI. LCA "as usual" may not be effective for two main reasons: (i) approaches to LCA are mostly retrospective, relying greatly on data collected from established activities with existing supply chains; and (ii) LCA only consider stakeholders for supplying information, but not to participate in critical modelling decisions [73].…”

Section: Life Cycle Perspectivementioning

confidence: 99%

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

et al. 2018

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Responsible Research and Innovation (RRI) has only lately included environmental sustainability as a key area for the social desirability of research and innovation. That is one of the reasons why just a few RRI projects and proposals include environmental sustainability, and Climate Change (CC) in particular. CC is one of the grand challenges of our time and, thus, this paper contributes to the operationalization of CC prevention in RRI. To this end, the tools employed against CC were identified. Tools originated in corporate social responsibility and sustainable innovation which help to operationalize strategies against CC in RRI practice. Complementarily, the latest proposals by RRI projects and actors related to CC were reviewed. The findings of the document analysis and the web review were arranged in a framework intended for research and innovation that has an indirect but relevant negative impact due to CC. Thus, four main strategies for CC prevention in RRI were determined: a voluntary integration of the aims, a life cycle perspective, open access databases and key performance indicators, and stakeholder management. The article is finished acknowledging diverse barriers hindering the operationalization of CC prevention in RRI, and we introduce future avenues for research in this area.

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Anticipatory life-cycle assessment for responsible research and innovation

Cited by 109 publications

References 24 publications

Introducing the dilemma of societal alignment for inclusive and responsible research and innovation

Introducing the dilemma of societal alignment for inclusive and responsible research and innovation

Sensitivity-based research prioritization through stochastic characterization modeling

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

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