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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.

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]

AbstractHeavy tails (HT) arise in many applications and their presence can disrupt statistical inference, yet the HT statistical literature requires a theoretical background most practicing statisticians lack. We provide an overview of the influence of HT on the performance of basic statistical methods and useful theorems aimed at the practitioner encountering HT in an applied setting. Higher or even lower product moments (i.e., variance, skewness, etc.) can be infinite for some HT populations, yet all L-moments are always finite, given that the mean exists, thus, the theory of L-moments is uniquely suited to all HT distributions and data. We document how L-kurtosis, (a kurtosis measure based on the fourth L-moment) provides a general and practical heaviness index for contrasting tail heaviness across distributions and datasets and how a single L-moment diagram can document both the prevalence and impact of HT distributions and data across disciplines and datasets. Surprisingly, the theory of L-moments, an extension and evolution of probability weighted moments, has been largely overlooked by the literature on HT distributions that exhibit infinite moments. Experiments reveal L-kurtosis ranges under which various HT distributions result in mild to severe disruption to the bootstrap, the central limit theorem (CLT), and the law of large numbers, even for distributions which exhibit finite product moments. [ABSTRACT FROM AUTHOR]