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Preliminary descriptions of scenarios

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The following is the established format for referencing this article: Pereira, L. M., D. R. Morrow, V. Aquila, B. Beckage, S. Beckbesinger, L. Beukes, H. J. Buck, C. J. Carlson, O. Geden, A. P. Jones, D. P. Keller, K. J. Mach, M. Mashigo, J. B. Moreno-Cruz, D. Visioni, S. Nicholson, and C. H. Trisos. 2021. From fAIrplay to climate wars: making climate change scenarios more dynamic, creative, and integrative. Ecology and Society 26(4):30. https://doi.org/10.5751/ES-12856-260430 Research

THE ADAPTED MANOA MASH-UP METHOD

To generate integrative, dynamic, and creative scenarios for thinking about climate change and any potential role for SRM and CDR in the structuring of climate change responses, workshop facilitators implemented an adaptation of the Manoa Mash-Up method for scenario generation in a five-day, participatory workshop. Additional methodological details can be found in the Supplementary Material.

The Manoa Mash-up method was initially developed for the Seeds of Good Anthropocenes project (Pereira et al. 2018) and subsequently used by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) scenarios and models expert group as part of their innovative approach for new desirable futures for nature (Pereira et al. 2020). In the Manoa Mash-Up method of scenario generation, participants begin from short descriptions of various “seeds” of future states of the world. Each seed is something that plays a relatively small role in the world today, but which could grow to play a major role in the far future. For instance, artificial intelligence is less widespread now, but it could grow to become as important in the future as the internet is in the present. Starting from these seeds and working in small groups, the participants:

1. Imagine each seed in a “mature condition” by briefly describing the role each seed might play in the distant future;
2. Build a Future Wheel for each seed that describes primary, secondary, and tertiary impacts of the seed across multiple sectors when the seed is in its imagined mature condition;
3. Connect and clash the Future Wheels by identifying mutually reinforcing or contradictory interactions between the elements of each Future Wheel;
4. Develop a bare-bones story of this emergent future that connects the various Future Wheels into a coherent narrative;
5. Develop pathways to this future by thinking about what would have to change to get from the present to that future and what would have to happen along the way for such changes to occur.

The Seeds project is underpinned by an appreciation that “the unknowable future cannot be grasped from the point of view of the search for probable futures” (Miller 2013:107) and that this requires new methods. It extends the Manoa method (Schultz 2015), which uses horizon scanning as a starting point to imagine far futures, by engaging the potentially transformative power of storytelling (Milkoreit 2016, Evans 2017). The Manoa Mash-Up method, as described here, lends itself to creating scenarios that satisfy two of the three criteria outlined above: given an interdisciplinary group, it naturally allows participants to integrate natural and social scientific knowledge, and it produces richly detailed narratives to engage stakeholders and communicate key aspects of possible futures.

To ensure more dynamic scenarios, we extended the Manoa Mash-Up method in two main ways. First, we allowed the narratives to branch at key decision points, highlighting how different responses to social or environmental events lead to very different futures. Second, at unpredictable intervals during the process, the facilitator (literally) threw “wild cards” at the groups, i.e., surprise social or environmental events that the participants could incorporate into their timelines or narratives. These two additions pushed participants to think about how future societies might react to events, rather than simply extrapolating from current trends.

Motivation to mitigate due to climate shocks:

In many of the scenarios, climate impacts, including but not limited to dramatic physical impacts with large, second-order social impacts, increase motivation to take climate action, either among elites, the general public, or both. Existing social scientific studies of this dynamic have returned mixed results (Hazlett and Mildenberger 2020), but at least some recent studies find that climate impacts can alter publics’ and politicians’ attitudes and voting behavior (Konisky et al. 2016, Gagliarducci et al. 2019, Baccini and Leemann 2020, Hazlett and Mildenberger 2020). The groups in this scenario exercise assumed that such dynamics would become stronger in the presence of harsher climate impacts, with the dynamic only coming to play a large role in climate politics later in the century. They typically envisioned this as motivating at least emissions abatement and adaptation. In most cases, it also involved SRM and/or CDR. In a few storylines, CDR or SRM undermined efforts to cut emissions. The scenarios in which this dynamic plays an important role, the groups typically envisioned the impact to be long-lasting because of path-dependent effects involving changes in technology (e.g., falling costs of renewables), power structures (e.g., the displacement of nation-states by city-states or corporations), or social values (e.g., the success of environmental movements or the widespread adoption of intrusive governance mechanisms). By contrast, many existing scenarios, including those in the shared socioeconomic pathways-representative concentration pathways (SSP-RCP) matrix currently used by climate modelers, omit such feedbacks so that climate policies and social structures evolve independently of the magnitude of climate change and climate impacts. However, such feedbacks have been shown to make a significant difference in modeled climate outcomes (Beckage et al. 2018).

From fAIrplay to climate wars: making climate change scenarios more dynamic, creative, and integrative

Laura M. Pereira ^1,2,3,4

, David R. Morrow

⁵

, Valentina Aquila

⁶

, Brian Beckage

^7,8

, Sam Beckbesinger

⁹

, Lauren Beukes

¹⁰

, Holly J. Buck

^11,12

, Colin J. Carlson

¹³

, Oliver Geden

¹⁴

, Andrew P. Jones

¹⁵

, David P. Keller

¹⁶

, Katharine J. Mach

^17,18

, Mohale Mashigo

⁹

, Juan B. Moreno-Cruz

¹⁹

, Daniele Visioni

²⁰

, Simon Nicholson

⁵

and Christopher H. Trisos

^21,22

¹Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden, ²Global Change Institute, University of the Witwatersrand, South Africa, ³Centre for Sustainability Transitions, Stellenbosch, South Africa, ⁴Copernicus Institute of Sustainable Development, Utrecht University, The Netherlands, ⁵Forum for Climate Engineering Assessment, School of International Service, American University, Washington, D.C., USA, ⁶Department of Environmental Science, American University, Washington, D.C., USA, ⁷Department of Plant Biology, University of Vermont, Burlington, Vermont, USA, ⁸Department of Computer Science, University of Vermont, Burlington, Vermont, USA, ⁹Independent science fiction author, South Africa, ¹⁰Independent fiction author, South Africa, ¹¹Department of Environment and Sustainability, University at Buffalo, Buffalo, New York, USA, ¹²Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, California, USA, ¹³Center for Global Health Science and Security, Georgetown University, Washington, D.C., USA, ¹⁴German Institute for International and Security Affairs (SWP), Berlin, Germany, ¹⁵Climate Interactive, Washington D.C., USA, ¹⁶GEOMAR - Helmholtz Centre for Ocean Research, Kiel, Germany, ¹⁷Department of Environmental Science and Policy, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA, ¹⁸Leonard and Jayne Abess Center for Ecosystem Science and Policy, University of Miami, Coral Gables, Florida, USA, ¹⁹School of Environment, Enterprise and Development, University of Waterloo, Waterloo, Ontario, Canada, ²⁰Sibley School for Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York, USA, ²¹African Climate and Development Initiative, University of Cape Town, Cape Town, South Africa, ²²Centre for Statistics in Ecology, the Environment and Conservation, University of Cape Town, Cape Town, South Africa

Performance

Allowing for the creative communication of these scenario narratives is a key component of the method (also known as embodied foresight; Floyd 2012). As a final step in the process, each group performed their stories in ways that showcased not only their visions of the future, but also how different choices at critical moments led to different futures. Each of the three groups presented their visions very differently. The SRM & CDR group used the “choose-your-own adventure” story they had built in Twine to lead an interactive game in which the audience was able to make decisions that led to different futures. The SRM group started with a story technique inspired by one of the workshop ice-breakers (“fortunately and unfortunately” technique: see Appendix 1 for more information) and then went on to present a series of vignettes illustrating how different choices had led to three very different futures (Fig. 7), one of which did not even end up deploying the SRM technology. The CDR group started with a musical number (adapted from Wicked) describing the desirable future to which their narrative led, but which alluded to the turning points in the narrative at which point more problematic outcomes could have arisen (Fig. 8). See Appendix 1, Table A5 for more details about these performances.

The workshop process

The workshop’s success hinged on participants’ willingness to engage with one another and with the subject matter in unorthodox ways. To set the right tone for the workshop, the first half-day alternated between “intellectual scene-setting” through more academic presentations of background material on shared socioeconomic pathways and representative concentration pathways, a presentation on storytelling from the science-fiction writers, and playing a modified version of Decisions for the Decade, a game about responding to climate risks that involved geoengineering in the form of using an electric knife to cut a dice made from sponge to alter the probability of a climate hazard (Jones 2018; see https://vimeo.com/215056621). These exercises aimed to contextualize the workshop and break down disciplinary cliques and personal barriers between participants (see Appendix 1 for more details). The participants were then allocated into their three smaller groups in which they spent most of the rest of the workshop. The overall structure of the workshop is captured in Figure 1.

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ABSTRACT

Understanding possible climate futures that include carbon dioxide removal (CDR) and solar radiation modification (SRM) requires thinking not just about staying within the remaining carbon budget, but also about politics and people. However, despite growing interest in CDR and SRM, scenarios focused on these potential responses to climate change tend to exclude feedbacks between social and climate systems (a criticism applicable to climate change scenarios more generally). We adapted the Manoa Mash-Up method to generate scenarios for CDR and SRM that were more integrative, creative, and dynamic. The method was modified to identify important branching points in which different choices in how to respond to climate change (feedbacks between climate and social dynamics) lead to a plurality of climate futures. An interdisciplinary group of participants imagined distant futures in which SRM or CDR develop into a major social-environmental force. Groups received other "seeds" of change, such as Universal Basic Income or China's Belt and Road Initiative, and surprises, such as permafrost collapse that grew to influence the course of events to 2100. Groups developed narratives describing pathways to the future and identified bifurcation points to generate families of branching scenarios. Four climate-social dynamics were identified: motivation to mitigate, moral hazard, social unrest, and trust in institutions. These dynamics could orient toward better or worse outcomes with SRM and CDR deployment (and mitigation and adaptation responses more generally) but are typically excluded from existing climate change scenarios. The importance of these dynamics could be tested through the inclusion of social-environmental feedbacks into integrated assessment models (IAM) exploring climate futures. We offer a step-by-step guide to the modified Manoa Mash-up method to generate more integrative, creative, and dynamic scenarios; reflect on broader implications of using this method for generating more dynamic scenarios for climate change research and policy; and provide examples of using the scenarios in climate policy communication, including a choose-your-own adventure game called Survive the Century (https://survivethecentury.net/), which was played by over 15,000 people in the first 2 weeks of launching.

Key words: carbon dioxide removal; climate change; futures; geoengineering scenarios; science fiction; solar radiation management

Solar radiation modification (SRM) group

The SRM group, whose seeds were artificial intelligence (AI), the belt & road initiative, and SRM, began from a near future in which AI drives job losses during the 2020s in the context of continuing trade wars between China and the United States. The SRM group briefly explored a scenario, which deviates from the main storyline at the first branching point (see Fig. 6). In this “social unrest scenario,” countries fail to cope with AI-induced job losses and international cooperation continues to erode. The group did not develop this scenario in detail, but their consideration of it illuminates the non-trivial assumption in their other scenarios that humanity learns to manage the social effects of AI.

In the group’s main scenarios, the job losses and the collapse of tropical coral reefs in the 2030s drive governments, especially in the global South, to provide new social and environmental protections, such as a universal basic income, climate adaptation measures, and the regional testing and moderate regional deployment of SRM, such as marine cloud brightening over reef ecosystems or the use of geotextiles in the Arctic. The storyline continues to a bifurcation point in the 2050s (branching point 2), depending on the success of mitigation (see Fig. 7). In the “eco-autocracy scenario,” greenhouse gas emissions fall sharply for several reasons. Rising investment through the belt & road initiative drives increases in renewables, nuclear energy, and CDR throughout the global South. Artificial intelligence-driven “social credit scores” incentivize climate-friendly choices by individual consumers, first in China and then beyond. Carbon prices rise around the globe, enforced by space-based monitoring of greenhouse gas emissions. Solar radiation modification technologies are developed but never deployed at a global scale due to the success of mitigation and adaptation.

In the other scenarios, greenhouse gas emissions rise after the second branching point because the belt & road initiative drives economic growth, but efforts to reduce the carbon intensity of the economy falter. When the permafrost collapses in the 2060s (a wild card that was introduced for this group), the UN votes to deploy SRM globally through AI-guided stratospheric aerosol injection. The scenario bifurcates again at the third branching point, depending on the strength of the moral hazard effect from SRM (see Fig. 7). Moral hazard in this context describes people perceiving the problem of climate change to be solved by a technological fix and that this then undermines other efforts to mitigate or adapt to climate change (Lin 2013, Morrow 2014, Jebari et al. 2021). In the “stumble & scramble scenario,” a strong moral hazard effect reduces mitigation efforts substantially while SRM is deployed. Twenty years after SRM deployment begins, terrorist attacks on the SRM drones cause the abandonment of global deployment and a rebound of global warming. Various countries scramble to deploy SRM regionally, leading to serious geopolitical strife. In contrast, in the “fAIrplay scenario,” a weak moral hazard effect means that SRM works in tandem with mitigation and adaptation to significantly reduce climate risk. New governance structures emerge to promote the equitable distribution of the benefits of AI.