Risk Analysis

Abstract: 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.

Abstract: 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.

Abstract: Energy-economic and coupled human-natural system models are often used to explore potential energy futures and their implications for climate. There are many uncertain assumptions in the human system models that drive those futures, and in previous work we used a traditional Monte Carlo approach to explore socio-economic uncertainties in a multi-sector, multi-region energy-economic-emissions model of the global economy and generate probabilistic ensembles. The amount of data and information generated from these large ensembles is immense and it can be difficult to sort through and extract relevant insights. The goal of this work is to apply a variety of scenario discovery techniques to the probabilistic ensembles in order to extract insights related to energy futures, with a particular focus on the penetration of renewable energy. We apply Classification and Regression Trees (CART) with Random Forest Classifier (RFC) and Time Series Clustering (TSC) to explore key input drivers of the share of renewable generation, how those drivers can vary over time and across regions, different types of pathways for renewables, and relationships among model outputs. We find that the key drivers of renewables can vary significantly based on the policy scenario, region and time period. In particular, the time series clustering revealed interesting dynamics that are missed by looking at individual years. Through this work we demonstrate the value of scenario discovery techniques in drawing insights from large ensembles of energy-projecting models by facilitating the identification of drivers, relationships among variables and areas of the uncertainty space that are particularly interesting or relevant.

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