Fair played climate b0.2e

More dynamic

The dominant set of quantitative scenario narratives used to project future climate change, i.e., the combination of SSPs and RCPs, deliberately exclude considerations of how human emissions and adaptation behaviors change in response to climate change (van Vuuren et al. 2011, O’Neill et al. 2016, Riahi et al. 2017). Each quantified narrative is locked into a particular future. This approach, although useful for its intended purpose to structure climate change research, risks communicating to decision makers and the public that the future has a limited set of fixed pathways. In reality, greenhouse gas emissions will be driven by dynamic interactions between biophysical and human systems: human emissions drive climate change, altering the occurrence of extreme events, which in turn influences human perceptions of and responses to risk, including future emissions and climate change. These feedback processes are dynamic components of the Earth System that generate multiple alternative climate change futures, but they have been largely absent from climate change scenarios (Beckage et al. 2018). The inclusion of these climate-social feedbacks is crucial for understanding alternative climate change futures, especially the benefits and risks of geoengineering technologies. For instance, workshop participants considered how rapidly accumulating climate damages might affect societies’ willingness to deploy SRM as a quick response to climate change and how SRM might in turn affect societies’ motivation to cut greenhouse gas emissions.

Adding climate-social feedbacks introduces more uncertainty into the diverse set of possible futures, making it even more important that scenarios avoid the appearance of being predictions for decision makers. To explore the range of possible outcomes, we developed scenarios that allow for branching (that is, the future is not pre-determined, but rather that certain events could lead to multiple different end points) and identify key bifurcation points that could lead to very different futures (Wise et al. 2014). With this in mind, this workshop deliberately aimed to break out of the “conceptual flatland” of the 2 X 2 matrix approach (see Curry and Schultz 2009) used to generate the shared socioeconomic pathways. Instead, it produced sets of narrative scenarios that explore a wide range of possible futures that incorporate not just biophysical processes and social processes considered separately, but also social-environmental dynamics and feedbacks.

The main target audience for these narratives is the modeling community and the decision makers that rely on the outputs from these models to inform climate policy. Importantly from a quantitative modeling perspective, the scenario narratives identify key dynamics that cause branching between futures, highlighting a smaller set of processes as influential in determining different futures without trying to predict a particular future. This is important not only for modelers to understand, but also for decision makers to appreciate because they often rely on outcomes of quantitative models for informing climate policy. These and/or similar climate-social dynamics could be identified as priorities for future research funding and can be included into a next generation of IAMs to generate branching points in climate futures as societal responses co-evolve with climate risks (see Beckage et al. 2018, Donges et al. 2020). Such an updated IAM framework would be more in line with the updated Intergovernmental Panel on Climate Change (IPCC) and policy-relevant definition of risk as resulting not just from physical climate impacts, but also from human adaptation and mitigation responses to climate change (such as CDR and SRM).

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Methodological limitations and caveats

Although, when discussing SRM and CDR futures, there is the possibility of falling into particular tropes, such as that SRM will deter mitigation, it was felt that the seeds approach forced the participants to loosen a grip on these tropes, and rather than imposing them from the outset, to test how they might emerge, or not, within the storylines of interacting social, environmental, and political dynamics. For example, one of our storylines (fAIrplay, see Fig. 7) describes how SRM could increase motivation to mitigate greenhouse gas emissions and reduce inequality. That being said, although a futures method can help to avoid certain pitfalls, it is never fail-proof and the contingencies of group dynamics will always affect the outcome of any group process (Hebinck et al. 2018). It must therefore be acknowledged that the final scenarios are indeed constrained by the chosen method, which, although it was able to meet the workshop goals, did result in a particular set of narratives being constructed and these are very much determined by decisions made up front in terms of starting seeds, participants, their imaginations, and their group dynamics. The addition of other methodological innovations, such as wild cards and branching points, were also included to open up certain dynamics in the storyline (such as potentially unforeseen future events or key decisions leading to a bifurcation event). However, these, by nature, also directly affect the final narratives.

Drawing on the futures cone (Voros 2017), which emphasizes that the future does not only consist of projected, probable, or plausible outcomes, which tend to be captured in conventional environmental models, we were guided by the need to explore possible, preferable, or even preposterous futures to develop anticipatory capacities. By design, the method aimed to capture as diverse a set of narratives as possible, i.e., edging more toward the possible, preferable, and maybe even the preposterous, and so although the final narratives represent a particular slice of potential futures, the slice is hardly a narrow one. Rather, the final narratives do considerably open up the space of possible futures from existing scenarios exploring CDR and SRM in global climate models or integrated assessment models, such as the suite of GeoMIP, GLENS, and CDRMIP scenarios developed for computer simulations and described solely in geophysical terms (Kravitz et al. 2011, Keller et al. 2018, Tilmes et al. 2018). In particular, the scenarios explore a wide range of environmental, social/political, and technological aspects of climate futures based on the diverse set of seeds and wild cards that were selected to represent a mix of these domains. This allowed more holistic storylines to emerge, rather than focusing on just one of these dimensions as previous quantitative modeling has done (Kravitz et al. 2011, Ricke et al. 2013, Keller et al. 2018) or focussing narratives only on dimensions determining challenges to climate mitigation and adaptation, as with the SSPs (O’Neill et al. 2017), without also considering how climate impacts may feedback to change social systems. However, the storylines are not intended to be all-encompassing in any particular dimension. Rather, the results are intended to open up the conversation on the diversity of potential climate futures and especially branching points within these futures that could emerge from the use (or not) of SRM or CDR approaches, in conjunction with other changing social dynamics and choices made in response to climate impacts. There are important learnings and reflections that come from using this innovative method, including important implications for how to go about undertaking quantitative modeling and other awareness-raising and decision-making processes.

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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.

Building Future Wheels and mapping interactions

Once the groups had settled on an imagined mature condition for their seeds, they explored the implications of those mature conditions using a graphical method known as a Future Wheel. The Future Wheels, depicted in Figure 3, helped participants collectively brainstorm the direct impacts of their mature seeds on the world, followed by secondary and tertiary effects of those primary impacts. To think through the various potential impacts in a structured manner, participants were prompted to consider social, technological, economic, environmental, political, and value impacts, based on the STEEP-V framework (Schultz 2015). For example, the SRM group envisioned massive unemployment as a primary impact of mature artificial intelligence, the secondary impacts of which included universal basic income and “more room for human expression,” but also social unrest.

Sometimes groups identified mutually incompatible possible impacts from a single seed. In those cases, they often added two contradictory implications to the Future Wheel with the intention of choosing one of them later in the process. For instance, the CDR group imagined that large-scale afforestation and reforestation could manifest as either regenerated wild forests or as highly managed plantations. Both possibilities appear in that group’s Future Wheel as secondary implications of CDR, though only one ended up appearing in the group’s final narrative.

Once the groups had completed the Future Wheel for each seed, they discussed and mapped out the connections between different impacts of the three seeds, including interactions between different implications of the same seed and interactions between implications of different seeds. The groups were instructed to identify the impacts of the seeds in their mature conditions that they found particularly interesting, and to note specific examples. For instance, participants identified interesting interactions between mature, climate-adapted belt and road infrastructure coupled with a strong voice for global South countries that emerged during their discussions of SRM deployment. It was also important to identify contradictions, surprising possibilities, and counterintuitive outcomes, such as the replacement of nation states with cyber-states. Because some of the Future Wheels were unwieldy, instead of drawing on the figures, teams used wool yarn to highlight these connections (Fig. 4). They found this to be a useful adaptation that allowed for connections to be made more easily as well as allowing for changes as discussions about the emerging relationships continued.

To further deepen participants’ understanding of the potential interactions between seeds, each group also completed a cross-impact matrix to identify ways in which one seed could impact another, and vice versa (Fig. 5). As a final step on the first day, which had involved several hours of in-depth dialogue, each group was asked to stand back, contemplate the rich material they had generated, and start looking for emerging narratives and storylines of a future vision for the development and deployment (or a turning away from) SRM and CDR. After getting a sense of the emerging story, each group had to come up with some news headlines representing their future vision and a statistic. The groups used these to present their “scenario skeletons” to each other at the end of the day.

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Imagining seeds in mature condition

The initial three seeds that are used as starting points for the visioning process in each group are central to the kinds of stories that emerge. As the storylines in each group emerge, new dynamics come into play that are not necessarily based on the starting seeds, but the seeds nevertheless contribute significantly to how the final narratives emerge. Because we wanted to be as participatory as possible, in the month prior to the workshop, the core organizing team invited participants to suggest potential seeds for the process. They were asked to suggest interesting social-environmental or economic-political seeds that were underway in the present that could play out in interesting ways in the future, especially when they clashed with SRM and/or CDR technologies. Because an aim of the workshop was to increase creativity and diversity in futures (that is a core specialty of the Manoa method), subject to the constraint that each group would need at least one category of SRM or CDR as a seed, the organizing team chose seeds from the list generated by participants and allocated them across the three groups to ensure that each group had three seeds that would interact in diversely interesting ways (Table 2). For ease of reference, each group will be referred to as the SRM group, the CDR group, and the SRM and CDR group that used a combination of both interventions (see Fig. 2).

The choice of seeds definitely influenced the final narratives, but more in a way that forced the teams to engage with different social, political, and economic realities based on the kinds of perspectives to which each of the seeds alludes. Again, as with the range of participants, although the seeds do have a large effect on the final visions, the breadth of the dynamics that unfolds is the important component. There are a myriad possible storylines that the scenario process could develop, given the multiple dimensions of the process, but the important aspect is to ensure that the design meets the ultimate aim of the workshop. In this case, that was to generate creative and dynamic narratives of climate futures incorporating the use of SRM and/or CDR, while accounting for important interplays between biophysical and social dynamics (that is, being integrative). It is also important to note that, had different seeds been paired with the SRM and CDR response options, potentially very different storylines could have emerged. Because this was the first time this group was undertaking such a scenario process for this topic and there were time constraints, only one configuration of the seeds was possible. However, subsequent work could look at combining the seeds in different configurations to derive a wider set of final narratives that would make comparison more robust. For more critical discussion on how the seeds shape the final narratives in this method, see Pereira et al. 2018, Raudsepp-Hearne et al. 2019, and Rana et al. 2020.

After the introductory sessions, the organizers gave each group short descriptions of their assigned seeds, along with the instruction to describe each seed in its mature condition. The “mature condition” refers to what the group thinks the seed would look like if it were no longer marginal, but the dominant way of doing things (Pereira et al. 2018). Table 2 shows the seeds that each group received and the phrase or statistics that each group chose to characterize the seed’s mature condition. Note that in describing their seeds in their mature condition, neither the SRM nor CDR groups felt it necessary to specify the mature condition of their respective climate change response option as a particular technology, rather saying only that SRM and CDR technologies or approaches had been widely deployed.

Developing narratives

After charting out the pathways from the present to various imagined futures, each group set about putting some flesh on the bones of their narratives to ensure that as much rich detail could be captured and compared across the groups. Using the VERGE or ethnographic futures framework (Lum 2014), each group was asked to fill in a table with some key questions exploring changes in the world (see Tables A2 and A3 in Appendix 1). VERGE prompts participants to consider different domains of human experience: in this figure, how do we define things, relate to one another, connect to each other (and the environment), create, consume, or destroy? In addition to these six VERGE categories, the organizing team had also included some specific questions related to climate and energy. The SRM and CDR group also got excited about trying to plot out a “Choose-your-own adventure” story to capture the discussions they had in developing their Three Horizons diagram and in particular the branching. The group used free software called Twine (https://twinery.org/) to prepare a draft of their narrative and the result was effective in explicitly showing key points of diversion in the narrative.

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.

Carbon dioxide reduction (CDR) group

The CDR group, whose seeds were CDR, multinational corporations, and extinction rebellion, began with a near future in which climate policy remains relatively weak, despite growing social pressure for mitigation. Beginning in the 2030s, a prolonged global recession accelerates the consolidation of economic power as a small number of large multinational corporations outcompete their struggling rivals. In about 2040, the collapse of coral reefs sparks massive migration out of the tropics.

The storyline first bifurcates (Fig. 8) here, depending on how societies cope with massive migration. In the “climate wars scenario,” the recession- and climate-driven migrations prompt a series of civil and international conflicts that devastate the global economy. In the other storylines, powerful multinational corporations use the social upheaval as an opportunity to seize power from states, and increasingly powerful environmental movements use the upheaval as an opportunity to foster a global “ecohumanist” ethos that prioritizes social connections and environmental goods over material consumption. The storyline then bifurcates again at the second branching point (Fig. 8), depending on how these two forces interact.

In the main “ecohumanist revolution scenario,” these two forces combine in constructive ways: environmental movements use their social organizing power to compel multinational corporations to replace their directors and executives with new leaders who embrace ecohumanist principles. These corporations use their economic and political power to effect rapid cuts in emissions, deployment of large-scale CDR, and the dedication of vast areas of land to wilderness. Human populations and activities contract into a series of dense megacities.

In the “suboptimal situation scenario,” however, the ecohumanist ethos exerts less influence. Multinational corporations invest heavily in CDR, but the continued influence of fossil fuel interests undermines efforts to cut emissions. Despite its widespread adoption, CDR cannot keep pace with emissions, leading to significant climate change. Ultimately, in the climate wars scenario, in response to the climate wars’ destruction, societies adopt an ecohumanist ethos. This scenario ends in much the same place as the ecohumanist revolution scenario, but it gets there by a much darker, more destructive route in a typical collapse and reconstruct storyline.

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.

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