Search for: All records

In the 2021 Infrastructure Investment and Jobs Act (IIJA) and the 2022 Inflation Reduction Act (IRA), the United States (U.S.) Congress placed a major bet on the importance of household actions, and the incentives for these actions may yield disproportionately large emissions reductions. Modeling estimates from Rapid Energy Policy Evaluation and Analysis Toolkit (REPEAT) suggest that the IRA's $331 billion investment can reduce carbon emissions by as much as 4% below a 2005 baseline by 2030, assuming a low-friction economic environment. To evaluate the role of household actions, we use a two-part method: 1) Policy analyses of the IRA and IIJA to identify household incentives; 2) Secondary data analysis of REPEAT's policy models to identify the potential for emissions reductions associated with household action. We find that $39 billion, or 12% of climate and energy funds in the IRA and $4.3 billion or 5.7% of clean energy and power funds in the IIJA, target voluntary household actions, and that these actions contribute 40% of the cumulative emissions reductions under the IRA and IIJA, assuming a mid-range scenario for uptake. The importance of household actions to achieving IRA and IIJA's emissions reduction goals suggests that actual impacts will likely vary by behavioral plasticity, and that program design should reflect social and behavioral science insights. 

Abstract As researchers collect large amounts of data in the social sciences through household surveys, challenges may arise in how best to analyze such datasets, especially where motivating theories are unclear or conflicting. New analytical methods may be necessary to extract information from these datasets. Machine learning techniques are promising methods for identifying patterns in large datasets, but have not yet been widely used to identify important variables in social surveys with many questions. To demonstrate the potential of machine learning to analyze large social datasets, we apply machine learning techniques to the study of migration in Bangladesh. The complexity of migration decisions makes them suitable for analysis with machine learning techniques, which enable pattern identification in large datasets with many covariates. In this paper, we apply random forest methods to analyzing a large survey which captures approximately 2000 variables from approximately 1700 households in southwestern Bangladesh. Our analysis ranked the covariates in the dataset in terms of their predictive power for migration decisions. The results identified the most important covariates, but there exists a tradeoff between predictive ability and interpretability. To address this tradeoff, random forests and other machine learning algorithms may be especially useful in combination with more traditional regression methods. To develop insights into how the important variables identified by the random forest algorithm impact migration, we performed a survival analysis of household time to first migration. With this combined analysis, we found that variables related to wealth and household composition are important predictors of migration. Such multi-methods approaches may help to shed light on factors contributing to migration and non-migration. 

Environmental change interacts with population migration in complex ways that depend on interactions between impacts on individual households and on communities. These coupled individual-collective dynamics make agent-based simulations useful for studying environmental migration. We present an original agent-based model that simulates environment-migration dynamics in terms of the impacts of natural hazards on labor markets in rural communities, with households deciding whether to migrate based on maximizing their expected income. We use a pattern-oriented approach that seeks to reproduce observed patterns of environmentally-driven migration in Bangladesh. The model is parameterized with empirical data and unknown parameters are calibrated to reproduce the observed patterns. This model can reproduce these patterns, but only for a narrow range of parameters. Future work will compare income-maximizing decisions to psychologically complex decision heuristics that include non-economic considerations. 

Abstract Hazards and disasters arise from interactions between environmental and social processes, so interdisciplinary research is crucial in understanding and effectively managing them. Despite support and encouragement from funding agencies, universities, and journals and growing interest from researchers, interdisciplinary disaster research teams face significant obstacles, such as the difficulty of establishing effective communication and understanding across disciplines. Better understanding of interdisciplinary teamwork can also have important practical benefits for operational disaster planning and response. Social studies of science distinguish different kinds of expertise and different modes of communication. Understanding these differences can help interdisciplinary research teams communicate more clearly and work together more effectively. The primary role of a researcher is incontributory expertise(the ability to make original contributions to a discipline); butinteractional expertisein other disciplines (the ability to understand their literature and communicate with their practitioners) can play an important role in interdisciplinary collaborations. Developing interactional expertise requires time and effort, which can be challenging for a busy researcher, and also requires a foundation of trust and communication among team members. Three distinct aspects of communication play important roles in effective interdisciplinary communication:dialects,metaphors, andarticulation. There are different ways to develop interactional expertise and effective communication, so researchers can pursue approaches that suit their circumstances. It will be important for future research on interdisciplinary disaster research to identify best practices for building trust, facilitating communication, and developing interactional expertise. 

Abstract The principal nature-based solution for offsetting relative sea-level rise in the Ganges-Brahmaputra delta is the unabated delivery, dispersal, and deposition of the rivers’ ~1 billion-tonne annual sediment load. Recent hydrological transport modeling suggests that strengthening monsoon precipitation in the 21st century could increase this sediment delivery 34-60%; yet other studies demonstrate that sediment could decline 15-80% if planned dams and river diversions are fully implemented. We validate these modeled ranges by developing a comprehensive field-based sediment budget that quantifies the supply of Ganges-Brahmaputra river sediment under varying Holocene climate conditions. Our data reveal natural responses in sediment supply comparable to previously modeled results and suggest that increased sediment delivery may be capable of offsetting accelerated sea-level rise. This prospect for a naturally sustained Ganges-Brahmaputra delta presents possibilities beyond the dystopian future often posed for this system, but the implementation of currently proposed dams and diversions would preclude such opportunities. 

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.