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Identifying key-psychological factors influencing the acceptance of yet emerging technologies–A multi-method-approach to inform climate policy [1]

['Julius Fenn', 'Institute Of Psychology', 'University Of Freiburg', 'Freiburg', 'Jessica F. Helm', 'Cluster Of Excellence Livmats', 'Fit Freiburg Center For Interactive Materials', 'Bioinspired Technologies', 'Philipp Höfele', 'Institute Of Philosophy']

Date: 2023-06

The best combination of possible climate policy options (mitigation, adaptation and different climate engineering technologies) to tackle climate change is unknown. Climate policy is facing a hard decision in answering the question whether climate engineering technologies should be researched, limitedly deployed or even deployed at global scale. Such technologies bear large epistemic and ethical uncertainties and their use as well as non-use might have severe consequences. To deal with such uncertainties, the (ethical) assessment of climate engineering technologies should include the perspectives of various stakeholders including laypersons to inform climate policy. To facilitate (ethical) technology assessment, we propose a novel 2-step methodology to collect and analyze data on ethical concerns and the acceptability of climate engineering technologies. Thereby we focus on Stratospheric Aerosol Injection (SAI) as an use case. We propose an innovative combination of newly developed methods consisting of two data collection tools (Cognitive-Affective Mapping and large-scale survey) and two types of data analyses (using graph theory and factor analysis). Applying this multi-method approach we were able to identify (1) central ethical and governance related concerns regarding SAI (by Cognitive-Affective Maps) and (2) to estimate the relative importance of core constructs (positive and negative affect, risk and benefit perception, trust) on the acceptability of SAI (by large-scale survey).

Funding: The authors (J.F., J.H., L.K., A.K.) were supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2193/1 – 390951807 and the grant 2277, Research Training Group “Statistical Modeling in Psychology" (SMiP). One author (P.H.) received support from the PRIME program of the German Academic Exchange Service (DAAD) with funds from the German Federal Ministry of Education and Research (BMBF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability: All the study files and analyses described in the research article are on OSF: https://osf.io/zn7vy/ (Identifier: DOI 10.17605/OSF.IO/ZN7VY ). A detailed explanation of all the uploaded files can be found in the Wiki of the OSF project.

Copyright: © 2023 Fenn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Introduction

At the 21st Conference of the Parties in Paris in 2015, over 190 states decided to limit global warming to well below 2 degrees, at best to 1.5 degrees celsius [1]. However, according to the Intergovernmental Panel on Climate Change (IPCC), the current “Nationally Determined Contributions” to reduce greenhouse gas emissions bear a “greater than 50% likelihood that global warming will reach or exceed 1.5°C in the near-term [2022–2040], even for the very low greenhouse gas emissions scenario” [2 p44]. This indicates that the 2 degrees celsius climate target might not be reachable without Climate Engineering Technologies (CET), which already have been proposed in the 5th IPCC report [cf. 3–6]. There are two different approaches of CET [see reports 7–10]: Carbon Dioxide Removal (CDR) and Solar Radiation Management (SRM). CDR technologies remove carbon dioxide from the atmosphere and thus address the root cause of climate change. SRM aims to reflect a small percentage of the solar radiation back into space before it reaches the earth.

In this article, we focus on one specific SRM technology, which is called Stratospheric Aerosol Injection (SAI). SAI is able to reduce incoming solar radiation, e.g. by releasing sulfur particles into higher regions of the atmosphere (stratosphere), which enhances the reflective properties of the aerosol layer. The technology is mimicking the mechanism of past volcanic eruptions, which produced a cooling effect [cf. 11] by releasing sulfur particles into the atmosphere (see also scenario text in A in the online supplementary; https://osf.io/vb5qe). We decided to focus on SAI because simulation studies predicted a larger effect for this technology compared to other CET in reducing the global mean temperature [e.g. 12, 13]. Furthermore, compared to other CET, the SAI technology is highly affordable and timely [10]. In addition, SAI is one of the most studied CET and seems to be the most likely SRM approach to be implemented [14]. Nevertheless, there is an ongoing debate whether SAI, or more generally, any other CET, should be researched or even deployed.

On the one hand, there are two central ethical arguments to justify the research of CET, especially SRM. First, the “arming-the-future” argument states that we are morally obliged to explore all options in order to provide future generations with an optimal basis for decision-making, especially in the case of a climate emergency. Second, the “buying time” argument promotes the idea that SRM could be a stopgap measure to buy time until an “aggressive” abatement of emissions shows an effect on a global scale [15–17]. On the other hand, the “arming-the-future” argument is highly contested [18], because the characteristics of a climate emergency (like potential tipping points) are hardly predictable [19] and an emergency framing in general can lead to adverse climate policy effects [20, 21]. Several further arguments against climate engineering, like the “moral hazard” argument, which states that the mere prospect of CET will encourage many actors to continue to emit a lot of carbon dioxide, are brought forward [for an overview see 16, 17, 22]. Furthermore, it is highlighted in the IPCC 2022 “Summary for Policymakers”, that SRM approaches “introduce a widespread range of new risks to people and ecosystems, which are not well understood”, and there are “[l] arge uncertainties and knowledge gaps” [23 p19]. In line with these claims, a recent simulation study showed that the potential ecological impacts of using SAI are for the most part unknown [14]. Moreover, two expert surveys did not only identify SAI as the climate technology with the highest temperature-reduction potential, but also as the technology with the highest composite risks [24, 25]. All these uncertainties and possible issues (especially consequences of maladaptation) lead some scientists to argue for an international non-use agreement for SAI [26].

Climate policy is facing a hard decision in answering the question if a technology like SAI should be researched, limitedly deployed or even deployed at global scale [cf. 27]. Decisions under such high uncertainty are characteristic for ‘post-normal’ science and require innovative methodologies [28]. Here, we propose an innovative combination of newly developed methods consisting of two data collections tools (Cognitive-Affective Mapping and large-scale survey) and two types of data analyses (using graph theory and factor analyses). This is intended to assess the acceptability and potential ethical concerns related to SAI.

The article is divided into three parts: in the first part, we will motivate the importance of including laypersons in the process of (ethical) technology assessment (Section: Importance of upstream engagement) and theoretically derive an integrative model to predict the acceptability of SAI. In the second part of the article the study design is described, which includes two central procedures: (a) Step I procedure: “Cognitive-Affective Mapping”, which was conducted as a pre-study to extend the theoretically derived integrative model and (b) Step II procedure, a large-scale survey to measure the acceptability of SAI. These procedures combine different tools of data collection and different types of statistical analyses, which are explained in detail in the respective sections. The final discussion (General Discussion) section aims to summarize the utility of the proposed methodology for future research on CET to inform climate policy.

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[1] Url: https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000207

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