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Anthropogenic climate change threatens production of essential natural resources, including food, fiber, and fresh water, and provisioning of ecosystem services such as carbon sequestration, increasing the risk of societal collapse. The Human and Nature Dynamics (HANDY) model simulates the effect of resource overexploitation on societal collapse but lacks representation of feedbacks between climate change and resource regeneration in ecological systems. We extend the HANDY model by integrating models of climate change and ecological function to examine the risk of societal collapse. We conducted a sensitivity analysis of our expanded model by systematically varying key parameters to examine the range of plausible socio-ecological conditions and evaluate model uncertainty. We find that lowered greenhouse gas emissions and resilient ecosystems can delay societal collapse by up to approximately 500 years, but that any scenario with greater than net-zero greenhouse gas emissions ultimately leads to societal collapse driven by climate-induced loss of ecosystem function. Reductions in greenhouse gas emissions are the most effective intervention to delay or prevent societal collapse, followed by the conservation and management of resilient ecological systems to sequester atmospheric carbon. 

Opinion dynamics models are increasingly used to understand changes in opinions, behaviors, and policy in the context of climate change. We review recent research that demonstrates how these models enable the linkages between individual, social, institutional, and biophysical factors to explain when and how social change emerges over time and what its impact might be on emissions and the climate system. We focus on applications of opinion dynamics models to climate change and describe how factors interact in those models to create feedback loops that reinforce or dampen change. We demonstrate how these models reveal the dynamics of consensus or polarization in climate opinions, the evolution of sustainability technologies and policies, and when and how interventions or negotiations related to climate change are likely to succeed or fail. 

Despite the growing impacts of climate change worldwide, achieving consensus on climate action remains a challenge partly because of heterogeneity in perceptions of climate risks within and across countries. Lack of consensus has hindered global collective action. We use a system dynamics approach to examine how interactions among cultural, socio-political, psychological, and institutional factors shape public support or opposition for climate mitigation policy. We investigate the conditions under which the dominant public opinion about climate policy can shift within a 20-year time frame. We observed opinion shifts in 20% of simulations, primarily in individualistic cultural contexts with high perceived climate risk. Changing the dominant opinion was especially difficult to achieve in collectivistic cultures, as we observed no shifts in dominant opinion within the parameter ranges examined. Our study underscores the importance of understanding how cultural context mediates the approaches needed to effectively mobilize collective climate action. 

Abstract Solar radiation modification (SRM) is a climate intervention method that would reflect a portion of incoming solar radiation to cool the Earth and could be used to ameliorate the impacts of climate change, but that provokes strong reactions from experts and the public alike. Research has explored both the biophysical and human behavioral aspects of SRM but has not integrated these processes in a single framework. Our expectations for SRM development and deployment will be inaccurate until we integrate the feedbacks between human behavioral and cognitive processes and the biophysical and climate system. We propose a framework for describing these feedbacks and how they may mediate transitions in the development and operationalization of SRM as a climate intervention. We consider components such as public trust in SRM, moral hazard concerns, climate risk perceptions, and societal disruptions, and illustrate how the driving processes could change across the pre-development, post-development, and post-deployment phases of SRM operationalization to affect outcomes around SRM deployment and climate change. Our framework illustrates the importance of feedbacks between climate change, risk perceptions, and the human response and the necessity to integrate such feedbacks in the development of future scenarios for SRM. 

Food system sustainability, and ways of measuring it, are widely explored and discussed in academic literature. Measurement efforts are challenging because food systems are inherently complex and multifaceted, spanning diverse components, indus­tries, sectors, and scales. Several systems of indica­tors and metrics have been proposed to measure sustainability; however, most existing research focuses either on narrow scales (e.g., farm level or within a single supply chain), expansive scales that can gloss over complexity (e.g., national or global assessments), or limited scopes (e.g., only consider­ing environmental factors). A gap in the literature is a holistic local or regional approach to food sys­tem sustainability that integrates components across the system at a regional scale. In this reflec­tive essay, we describe our development of a framework to measure and track sustainability in such systems. We use a tiered framework that includes five sustainability dimensions and a system of indices, indicators, and metrics that allows for the measurement of important food system charac­teristics in a feasible and reproducible way. We employ a collaborative, transdisciplinary, facilitated team science process to first propose, and then refine, a sustainability assessment framework, using the U.S. state of Vermont as a case study. This paper details our process and progress, as well as reflections on challenges and recommendations for other team scientists. We further propose a plan to implement the framework, collect data, and engage with community members. The experiences and findings described here serve as a foundation for our own team’s continued work, as well as a springboard for other similar research efforts. 

Abstract With mounting scientific evidence demonstrating adverse global climate change (GCC) impacts to water quality, water quality policies, such as the Total Maximum Daily Loads (TMDLs) under the U.S. Clean Water Act, have begun accounting for GCC effects in setting nutrient load‐reduction policy targets. These targets generally require nutrient reductions for attaining prescribed water quality standards (WQS) by setting safe levels of nutrient concentrations that curtail potentially harmful cyanobacteria blooms (CyanoHABs). While some governments require WQS to consider climate change, few tools are available to model the complex interactions between climate change and benthic legacy nutrients. We present a novel process‐based integrated assessment model (IAM) that examines the extent to which synergistic relationships between GCC and legacy Phosphorus release could compromise the ability of water quality policies to attain established WQS. The IAM is calibrated for simulating the eutrophic Missisquoi Bay and watershed in Lake Champlain (2001–2050). Water quality impacts of seven P‐reduction scenarios, including the 64.3% reduction specified under the current TMDL, were examined under 17 GCC scenarios. The TMDL WQS of 0.025 mg/L total phosphorus is unlikely to be met by 2035 under the mandated 64.3% reduction for all GCC scenarios. IAM simulations show that the frequency and severity of summer CyanoHABs increased or minimally decreased under most climate and nutrient reduction scenarios. By harnessing IAMs that couple complex process‐based simulation models, the management of water quality in freshwater lakes can become more adaptive through explicit accounting of GCC effects on both the external and internal sources of nutrients.