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Land scarcity is increasing over time, driven by complex multisector dynamics. The impacts of land scarcity on the economy and environment are multi-faceted and regional, so any action to convert land will contain inherent tradeoffs. These impacts are complicated by the deeply uncertain evolution of the various sectors influencing land scarcity. A need therefore exists to provide multi-metric and multi-sector assessments that are robust to myriad uncertainties. Land conservation effectively limits the supply of productive land, while biofuel consumption increases the demand and competition for that land, and how these dynamics individually and jointly propagate to economic and environmental impacts is an important open question. To address this, we adopt the Global Change Analysis Model (GCAM) that has representations of various important systems including the climate, macroeconomic, energy, agriculture and land, and water resources systems. Various scenarios of increased land demand (from biofuels) and decreased land supply (from conservation) under various socioeconomic scenarios drawn from the SSPs were simulated using GCAM. We find that while biofuel consumption and land conservation reduce carbon emissions, this comes at the cost of higher food prices, reduced crop production, and increased water withdrawals. Additionally, some regions experience these tradeoffs more severely than others and are more heavily impacted from the same biofuel mandate or by an additional percent of protected land. These and other findings highlight the importance of multisector modeling frameworks that capture many cross-sector linkages, and acknowledge the important uncertainties confronting the human-Earth system when making any analysis of land scarcity impacts.

Global climate goals require a transition to a deeply decarbonized energy system. Meeting the objectives of the Paris Agreement through countries' nationally determined contributions and long‐term strategies represents a complex problem with consequences across multiple systems shrouded by deep uncertainty. Robust, large‐ensemble methods and analyses mapping a wide range of possible future states of the world are needed to help policymakers design effective strategies to meet emissions reduction goals. This study contributes a scenario discovery analysis applied to a large ensemble of 5,760 model realizations generated using the Global Change Analysis Model. Eleven energy‐related uncertainties are systematically varied, representing national mitigation pledges, institutional factors, and techno‐economic parameters, among others. The resulting ensemble maps how uncertainties impact common energy system metrics used to characterize national and global pathways toward deep decarbonization. Results show globally consistent but regionally variable energy transitions as measured by multiple metrics, including electricity costs and stranded assets. Larger economies and developing regions experience more severe economic outcomes across a broad sampling of uncertainty. The scale of CO2 removal globally determines how much the energy system can continue to emit, but the relative role of different CO2 removal options in meeting decarbonization goals varies across regions. Previous studies characterizing uncertainty have typically focused on a few scenarios, and other large‐ensemble work has not (to our knowledge) combined this framework with national emissions pledges or institutional factors. Our results underscore the value of large‐ensemble scenario discovery for decision support as countries begin to design strategies to meet their goals. Plain Language Summary: Most countries have pledged to significantly reduce greenhouse gas emissions over the next few decades. These emissions primarily come from burning fossil fuels for electricity, heat, energy for industrial processes, and transportation fuel. Converting to cleaner forms of energy requires transforming the energy system. However, decision makers must consider the countless, unpredictable ways the future could unfold. Modelers address this "deep uncertainty" by running computer simulations many times and computing how impactful various inputs are on the outcome. We explore different ways countries may meet emissions reduction goals and how impacts vary regionally, considering 11 sources of uncertainty with 5,760 simulations. We find larger economies and developing regions experience the most severe economic outcomes consistently across our wide range of inputs. Further, removing carbon dioxide from the air through engineered and natural solutions allows some flexibility to continue emitting during the transition, but the role of different options varies regionally, and is subject to future costs and the way emissions are priced. Previous work has typically focused on representative scenarios, rather than a "large ensemble," and has not combined this framework with modeling national emissions pledges. These findings are helpful for decisionmakers as countries design strategies to meet their goals. Key Points: Energy transition costs can, by multiple metrics, unevenly impact larger economies, and developing regions under a wide range of futuresRegional investment risk has global implications for mitigation pathways, robust to broad uncertainties and with strong relative impactsThe relative role of different carbon dioxide removal options in meeting decarbonization goals varies across regions and scenario pathways [ABSTRACT FROM AUTHOR]

Shared community scenarios of societal and environmental system changes have underpinned a broad range of research and assessment studies over the past several decades. These scenarios have largely aimed to address specific questions within broad issue areas like climate change or biodiversity and generally provided information on the drivers of change. The consequences of those drivers, such as impacts on society and policy responses, have tended to be left to the research community to investigate, using scenarios of drivers as inputs to their studies, producing projections of a disparate set of relevant output metrics. While this approach has had many benefits, it has fallen short of producing a robust, comparable literature describing outcomes across studies in common metrics. We argue that new scenarios are needed that extend current approaches to be organized around common outcome metrics for the well‐being and resilience of society and ecosystems. We propose an approach that would focus on agreed upon outcomes for well‐being and resilience as well as critical drivers of change, cut across issues and scales in multiple sectors, and draw on new systematic methods of scenario generation and discovery to highlight scenarios that are most critical in understanding societal risks and responding to them. Research derived from this outcome‐based scenario development approach would facilitate improved assessment of risks of and responses to a range of stressors and the multi‐sector interactions they generate. Plain Language Summary: Scenarios are visions of how the world might unfold. They can consist of stories, numerical projections, or both. Typically scenarios describe trends in drivers of change—factors like population and economic growth, how fast technological progress occurs, and changes to the climate system. Historically, small sets of common scenarios have been widely used by the global change research community. Researchers use the scenarios as inputs to project the consequences of the drivers, for example, for agricultural production, water availability, or the costs of decarbonization. We propose that new scenarios are needed that include outcomes (consequences) not just for physical or managed systems, but also for human well‐being and resilience, including health, poverty, and household food, water, and energy security. Further, the scenarios should not only include well‐being outcomes, but be organized around them. That is, scenarios should be designed not necessarily to span a wide range of drivers, but rather to span a wide range of well‐being and resilience outcomes. Designing scenarios around the ultimate outcomes of interest will improve the assessment of risks and responses related to well‐being and resilience. New quantitative methods for generating and identifying scenarios can facilitate this process. Key Points: Community scenarios facilitate research and assessment but have fallen short of producing a literature with comparable outcomesNew scenarios are needed that are organized around outcomes for human well‐being and resilienceWe propose an outcome‐based scenario development approach that would cut across issues, scales, and sectors [ABSTRACT FROM AUTHOR]

Beltaos (2018) presented an analysis of ice-jam flood frequency of the lower Peace River. Beltaos (2018) stated that the 'ecosystem has experienced prolonged dry periods since the late 1960s, coincident [...]

Understanding the long‐term effects of population and GDP changes requires a multisectoral and regional understanding of the coupled human‐Earth system, as the long‐term evolution of this coupled system is influenced by human decisions and the Earth system. This study investigates the impact of compounding economic and population growth uncertainties on long‐term multisectoral outcomes. We use the Global Change Analysis Model (GCAM) to explore the influence of compounding and feedback between future GDP and population growth on four key sectors: final energy consumption, water withdrawal, staple food prices, and CO2 emissions. The results show that uncertainties in GDP and population compound, resulting in a magnification of tail risks for outcomes across sectors and regions. Compounding uncertainties significantly impact metrics such as CO2 emissions and final energy consumption, particularly at the upper tail at both global and regional levels. However, the impact of staple food prices and water withdrawal depends on regional factors. Additionally, an alternative low‐carbon transition scenario could compound uncertainties and increase tail risk, particularly in staple food prices, highlighting the influence of emergent constraints on land availability and food‐energy competition for land use. The findings underscore the importance of considering and adequately accounting for compounding uncertainties in key drivers of multisectoral systems to enhance our comprehensive understanding of the complex nature of multisectoral systems. The paper provides valuable insights into the potential implications of compounding uncertainties. Plain Language Summary: Understanding how future population and economic growth changes could impact our energy, water, food, and environment requires looking across multiple sectors and regions. This is because these systems are all connected. For example, increasing population drives higher food demand but requires more energy and water to grow food. At the same time, reducing greenhouse gas emissions from energy through increased biofuel consumption can compete with food production for land resources. This study explored how uncertainties in future population and GDP growth could compound and interact to affect future energy use, water withdrawal, staple food prices, and carbon dioxide emissions. Using a model that connects all these systems, the study found that combined uncertainties in population and GDP lead to more significant risks of extreme outcomes than individually looking at those uncertainties. There are substantial regional differences in future outcomes, like higher uncertainty in food prices for India and West Africa compared to other regions. We highlight the need to consider multisector and multisystem interactions between uncertainties across different regions. Accounting for these compounding uncertainties provides valuable insights into potential risks, like higher extreme projections for food prices or water use. These insights are essential for long‐term planning in our connected human‐Earth system. Key Points: Long‐term implications of population and GDP changes on the coupled human‐Earth system focusing on multisectoral and regional understandingUncertainties in GDP and population growth can compound, resulting in more extreme outcomes for multisectoral systemsCompounding uncertainties differ by region and metrics; staple food prices and water withdrawal show significant regional variability [ABSTRACT FROM AUTHOR]