Tzu-Hsin Karen Chen, Mark E. Kincey, Nick J. Rosser, Karen C. Seto, Identifying recurrent and persistent landslides using satellite imagery and deep learning: A 30-year analysis of the Himalaya, Science of The Total Environment, Volume 922, 2024, 171161, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2024.171161.
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Abstract
This paper presents a remote sensing-based method to efficiently generate multi-temporal landslide inventories and identify recurrent and persistent landslides. We used free data from Landsat, nighttime lights, digital elevation models, and a convolutional neural network model to develop the first multi-decadal inventory of landslides across the Himalaya, spanning from 1992 to 2021. The model successfully delineated >265,000 landslides, accurately identifying 83 % of manually mapped landslide areas and 94 % of reported landslide events in the region. Surprisingly, only 14 % of landslide areas each year were first occurrences, 55–83 % of landslide areas were persistent and 3–24 % had reactivated. On average, a landslide-affected pixel persisted for 4.7 years before recovery, a duration shorter than findings from small-scale studies following a major earthquake event. Among the recovered areas, 50 % of them experienced recurrent landslides after an average of five years. In fact, 22 % of landslide areas in the Himalaya experienced at least three episodes of landslides within 30 years. Disparities in landslide persistence across the Himalaya were pronounced, with an average recovery time of 6 years for Western India and Nepal, compared to 3 years for Bhutan and Eastern India. Slope and elevation emerged as significant controls of persistent and recurrent landslides. Road construction, afforestation policies, and seismic and monsoon activities were related to changes in landslide patterns in the Himalaya.
Keywords
Landslide inventory; Landslide evolution; Vegetation recovery; Multi-temporalSpatiotemporal analysis; Machine learning
Ashayeri, M., & Abbasabadi, N. (2024). Unraveling energy justice in NYC urban buildings through social media sentiment analysis and transformer deep learning. Energy and Buildings, 306, 113914-. https://doi.org/10.1016/j.enbuild.2024.113914
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Abstract
This study explores the intricate relationship between human sentiment on social media data, herein tweet posts on X platform, urban building characteristics, and the socio-spatial dynamics of New York City (NYC) boroughs. Leveraging Natural Language Processing (NLP) techniques, particularly sentiment analysis, augmented by the capabilities of transformer deep learning models, RoBERTa, the study places particular emphasis on the term ‘Stay-at-Home’ to encapsulate the pronounced shift in building occupancy during the pandemic's inaugural year. This focus intertwines with pivotal terms like ‘Energy Bill’ and ‘HVAC’, shedding light on their interconnected implications. The sentiment analysis leverages data from New York City's PLUTO and the Department of Energy's LEAD databases to emotional disparities connected to urban building characteristics as well as demographic and socioeconomic factors. This analytical approach unravels prevailing public emotions and extends the discussion to include energy justice concerns, viewing them through the lens of the city's built infrastructure. The research uncovers profound disparities in the built environment and the allocation of resources in NYC, highlighting the critical need to embrace a spatial justice framework for a sustainable future. This research can aid designers, planners, and policymakers in their efforts to promote equitable and inclusive urban development.
Jung, M. C., Yost, M. G., Dannenberg, A. L., Dyson, K., & Alberti, M. (2024). Legacies of redlining lead to unequal cooling effects of urban tree canopy. Landscape and Urban Planning, 246. https://doi.org/10.1016/j.landurbplan.2024.105028
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Abstract
Redlining—a racially discriminatory policy of systematic disinvestment established by the Home Owners’ Loan Corporation (HOLC) in the 1930s and continued until the late 1960s—still influences the contemporary landscape of cities in the US. While the heterogeneous distribution of land surface temperature and tree canopy cover between neighborhoods with different HOLC grades have been recently examined, the development of long-term and city-specific heat management strategies is still limited. Here, we explored the effect of redlining in Portland, Oregon, and Philadelphia, Pennsylvania, to assess its contemporary impact on climate equity. We performed a change analysis of land surface temperature and tree canopy area over the past and introduced mixed-effects models to test the intra- and inter-city differences in canopy cooling effects between the different HOLC grades. We found that (1) persistent temporal patterns of lower land surface temperatures and larger tree canopy areas are observed in higher HOLC grades, (2) greater green equity was achieved through contrasting temporal changes in tree canopy areas across HOLC grades in Portland and Philadelphia, and (3) opposite patterns exist between these cities, with stronger canopy cooling effects in neighborhoods with a Low HOLC grade in Portland and those with a High HOLC grade in Philadelphia. Differences in tree canopy change between the two cities over the past decade highlight potential influences of city-specific tree planting practices. Local planners should back tree planting initiatives to equitably mitigate urban heat exposure, considering historical redlining contexts and contemporary landscape features.
Keywords
Redlining; HOLC grade; Tree canopy; Land surface temperature; Tree equity
Min, Y., & Ko, I. (2023). Causal effects of place, people, and process on rooftop solar adoption through Bayesian inference. Energy (Oxford), 285, 129510-. https://doi.org/10.1016/j.energy.2023.129510.
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Abstract
While previous studies have established correlations between rooftop solar adoption and various factors, a comprehensive understanding of the underlying causal mechanisms has been limited by the intricate interrelationships among these variables. To address this gap, we propose a Bayesian causal inference approach that examines the interplay of various factors influencing rooftop solar adoption across multiple cities. By employing post-phenomenology, we uncover latent variables encompassing place, people, and process, shedding light on how they shape public responses to emerging energy technologies. We analyze the causal effects of these factors and highlight the significance of housing and built environment attributes in determining energy expenditure and rooftop solar adoption, emphasizing the need for policies that target energy equity. Additionally, we reveal the influence of neighborhood spillovers on adoption, indicating the role of social norms and information diffusion. The observed city-level variability underscores the importance of local contexts and location-specific factors in the adoption process. Furthermore, we highlight the need to consider causal relationships and the indirect effects of people-related attributes mediated through place-related attributes. Overall, these findings contribute to a deeper understanding of the factors shaping rooftop solar adoption via causal modeling and underscore the importance of tailored policies to promote adoption.
Keywords
Spillover effects; Energy equity; Post-phenomenology; Ignorability; Factor analysis; Clean energy; Photovoltaic systems; Overcoming barriers; Technology adoption; Decision-making; Energy justice; United-States; Vulnerability; Diffusion; Deployment; Responses
Population Health Initiative awarded a Climate Change Pilot Grant to two teams that includes CBE researchers. Projects will begin January 2024, and were awarded $50,000. Read the full story here. Project title: “Sustainable metamaterials for insulation applications.” Project team: Eleftheria Roumeli, Materials Science & Engineering Tomás Méndez Echenagucia, Architecture Project abstract: Amidst an urgent global shift towards a circular economy, the demand for sustainable materials has reached a critical juncture. This transformation requires materials sourced from renewable sources, processed via…
CBE Researchers and Staff attended an event hosted by Population Health Initiative and EarthLab, fostering connections and discussions surrounding climate change. The topics of discussion included adaptation to climate extremes in the Pacific Northwest, carbon sequestration, carbon offsets, coastal adaptation, behavior change and the centering of community in research. The event allowed individuals from different departments and disciplines to connect, discuss, and foster collaborations for future work. Similar events are expected in the future. Read the full story and register…
CBE Assistant Professor of Landscape Architecture Celina Balderas Guzmán was featured in a New Faculty Spotlight story on the UW Research website highlighting her work. “Dr. Balderas Guzmán’s research spans environmental planning, design, and science and focuses on climate adaptation to sea level rise on the coast and urban stormwater inland.” Read the full story here.
Celina Balderas Guzmán, Kevin J. Buffington, Karen M. Thorne, Glenn R. Guntenspergen, Michelle A. Hummel, Mark T. Stacey (2023). Future Marsh Evolution Due To Tidal Changes Induced by Human Adaptation to Sea Level Rise. Earth’s Future. 11(9):e2023EF003518.
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Abstract
With sea level rise threatening coastal development, decision-makers are beginning to act by modifying shorelines. Previous research has shown that hardening or softening shorelines may change the tidal range under future sea level rise. Tidal range can also be changed by natural factors. Coastal marshes, which humans increasingly depend on for shoreline protection, are ecologically sensitive to tidal range. Therefore, it is critical to examine how changes in tidal range could influence marsh processes. A marsh accretion model was used to investigate the ecological response of a San Francisco Bay, California, USA marsh to multiple tidal range scenarios and sea level rise from 2010 to 2100. The scenarios include a baseline scenario with no shoreline modifications in the estuary, a shoreline hardening scenario that amplifies the tidal range, and 14 tidal range scenarios as a sensitivity analysis that span tidal amplification and reduction of the baseline scenario. The modeling results expose key tradeoffs to consider when planning for sea level rise. Compared to the baseline, the hardening scenario shows minor differences. However, further tidal amplification prolongs marsh survival but decreases Sarcocornia pacifica cover, an important species for certain threatened wildlife and an effective attenuator of wave energy. Conversely, tidal reduction precipitates marsh drowning but shows gains in Sarcocornia pacifica cover. These mixed impacts of tidal amplification and reduction shown by the model indicate potential tradeoffs in relation to marsh survival, habitat characteristics, and shoreline protection. This study suggests the need for a cross-sectoral, regional approach to sea level rise adaptation.
Wang, Y., Hu, S., Lee, H. W., Tang, W., Shen, W., & Qiang, M. (2023). To Achieve Goal Alignment by Inter-Organizational Incentives: A Case Study of a Hydropower Project. Buildings (Basel), 13(9), 2258–. https://doi.org/10.3390/buildings13092258
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Abstract
Although the use of incentives has been widely recognized as an effective project management tool, its application still needs specific exploration. Existing research on incentives mainly focuses on intra-organizational incentives, lacking systematic research with empirical evidence from the perspective of the inter-organizational level. To fill this research gap, this study conducted an in-depth investigation into the application and impacts of inter-organizational incentives by studying a typical case of a hydropower project. In this case, a series of innovative inter-organizational incentives, involving a multiple contractual incentive scheme concerning schedule, quality, safety, as well as environmental performance, is applied. Using a mixed methodology that included a document review, a questionnaire survey, and interviews, this case study revealed that inter-organizational incentives could effectively help promote goal alignment, stimulate cooperative inter-organizational relationships, and improve project performance. This research developed a novel classification of inter-organizational incentives and emphasized the importance of non-contractual and informal incentives, which were ignored in previous research. The results further highlight that while incentivized organizations generally value incentives according to their monetary intensity, their prioritization of goals is determined by various factors. Therefore, to achieve project goal alignment, the optimization of incentive schemes should comprehensively consider a variety of influencing factors rather than merely focusing on monetary intensity. These findings will help both academic researchers and industrial practitioners design and execute effective inter-organizational incentives for superior project performance, especially for those projects that pursue high sustainable performance with safety and environmental performance included.
Keywords
inter-organizational incentive; inter-organizational relationship; multiple incentive; motivation; goal alignment; relational contracting; contractual incentive; environment incentive; environment performance; project performance
In March 2021, Population Health Initiative awarded 8 pilot grants. The team below includes CBE researcher Andrew Dannenberg, read more about their final project outcomes. A Collaboratory to Support Equitable and Just Climate Action Investigators Jeremy Hess, Departments of Emergency Medicine, Environmental & Occupational Health Sciences, and Global Health Jason Vogel, Climate Impacts Group Julian Marshall, Department of Civil & Environmental Engineering Sara Curran, Jackson School of International Studies and Department of Sociology Kris Ebi, Departments of Environmental & Occupational…