In May 2026, EarthLab announced a new cohort of Incubator and Rapid Response Grants. Two CBE PIs were selected in this cohort.
Research Theme: Climate & Energy
Scholarship on climate change mitigation and adaptation, as well as energy efficiency
Just transition, double bind or both? Climate change action by North American community land trusts
Jason Simpson Spicer, Ruoniu (Vince) Wang, Just transition, double bind or both? Climate change action by North American community land trusts, Cambridge Journal of Regions, Economy and Society, 2026;, rsag013, https://doi.org/10.1093/cjres/rsag013
Abstract
Can socially or community-owned enterprise models systematically enable equitable climate action, as has been previously suggested by case study research? To empirically measure and test this proposition, we systematically surveyed all North American community land trusts (CLTs), which primarily serve lower-income households. We found that a majority of CLTs engage in climate action, with mitigation efforts more prevalent than adaptation efforts. Statistical analyses confirm CLTs’ rate of action cannot solely be explained by their climate hazards exposure. This suggests they can concurrently address economic precarity whilst enabling climate action, thereby enabling a “just transition”. Nonetheless, CLTs fail to engage in climate action as often as they would like, reflecting financial constraints shaped by policy choices. This suggests the simultaneous existence of a climate “double bind”, whereby CLTs must, to a degree, choose between economic security and climate goals. We conclude by identifying potential policies to address this double bind.
Keywords
community land trusts; social enterprise; community entrepreneurship; collective housing tenure; climate equity; climate justice
UW Solar student RSO and the UW Solar Canopy in E18
The UW Solar interdisciplinary RSO visited the new solar canopy install in the E18 parking lot. This RSO is advised by Urban Design and Planning Associate Professor Jan Whittington. The successful solar canopy pilot will lead to future canopy install across other parking lots on campus. Read more at the full story here: https://facilities.uw.edu/blog/posts/2026/02/09/solar-canopy-uws-biggest-parking-lot-paves-way-brighter-future
Matt Grosser
Matt is a designer and researcher hailing from the Great South Bay of Long Island, New York. He is an interdisciplinary PhD student, and University of Washington MLA Graduate, whose practice has ranged from helping to oversee the design and implementation of New York City’s green infrastructure program to shoreline design and permitting throughout the Pacific Northwest.
Matt’s research focuses on how ecosystem-based infrastructural adaptation strategies can serve as a mechanism for stemming the impacts of climate change, with a specific interest in the role that critical ecologies and keystone species can play in resilience/adaptation design and planning. His work investigates how traditional and emerging ecologically-based infrastructural techniques could be improved and more broadly applied, as well as how the resultant socio-ecological bonds formed by the implementation of these strategies may elevate the role of ecological systems thinking for design and planning practices within the built environment.
When not pondering relationality within the web of life Matt can be found paddling around the waters of Seattle with his shiba inu Hideki.
Professor Lee and team begin Port of Seattle funded project “Taxi and Transportation Network Company (TTNC) Electrification Policy Guidance”
Professor Chris Lee and team are beginning a project entitled “Taxi and Transportation Network Company (TTNC) Electrification Policy Guidance,” funded by the Port of Seattle. This project aims to support the Port of Seattle—including Seattle-Tacoma International Airport and the Maritime Division—in developing strategies to reduce carbon emissions from passenger ground transportation. Drawing on outreach to taxi and transportation network company (TNC) drivers (e.g., Uber, Lyft), the project will identify key barriers and opportunities for electrifying commercial ground transportation serving key…
Partial Least Squares Structural Equation Modeling of Challenges to Existing Residential Building Net Zero Carbon Retrofitting
Abstract
Residential buildings play a significant role in global energy usage and carbon emissions. In Hong Kong, they contribute to 27% of energy usage and associated carbon emissions. Retrofitting residential buildings to net zero carbon (NZC) is an efficient way to lower carbon emissions and prevent climate change. However, the widespread adoption of NZC retrofitting in the industry has been limited by several challenges, which have rarely been addressed in research. This study intended to evaluate the linkages among various challenges while assessing their impacts on NZC retrofitting of existing residential buildings. Data were collected through a questionnaire survey with 123 residential building occupants in Hong Kong, whose perspectives are largely ignored in existing building retrofit research. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data. Outcomes indicated that technical and social challenges have a considerable negative effect on residential building NZC retrofitting. In addition, this study highlights the connections between challenge categories and presents a predictive model illustrating the relationships between challenges and residential building NZC retrofitting. Theoretically, the outcomes of this study provide new insights into the relationships between residential building NZC retrofitting challenges and their interactive effects, revealing that the challenges influence one another and do not exist in isolation. Practically, the findings could be useful to policymakers and practitioners seeking to promote NZC retrofitting by enabling the development of effective policies and strategies to mitigate the identified challenges.
Urban landscape heterogeneity disaggregates the legacy of redlining on land surface temperature
Abstract
The lingering effects of redlining are linked to contemporary heat inequities observed across US cities. Residential security maps created by the Home Owners’ Loan Corporation (HOLC) have been widely used to analyze neighborhood-level disparities in land surface temperatures. However, the use of aggregated spatial units often fails to capture internal landscape heterogeneity and the heat vulnerabilities associated with redlining. In this study, we introduced urban landscape heterogeneity by incorporating granular development levels captured at different resolutions within HOLC-graded neighborhoods. This approach combined Landsat-based National Land Cover Database (NLCD) data, Sentinel-based WorldCover land cover data, and HOLC map layers. We examined the role of urban landscape heterogeneity in revealing additional patterns of heat inequities beyond those explained by redlining-based macro spatial units, using grouped boxplots and mixed-effects models across three major cities in the Northeastern US: Boston, Massachusetts; New York, New York; and Philadelphia, Pennsylvania. By accounting for urban landscape heterogeneity, our findings revealed that: (1) the well-documented trend of higher land surface temperatures in lower HOLC grades becomes systematically fragmented, (2) statistical models show improved performance in estimating land surface temperature, and (3) the cooling effect of tree canopy exhibits a varying, non-linear threshold pattern. These results highlight the need to consider micro-scale landscape dimensions to better understand the persistent, unequal distribution of temperatures associated with redlining. Municipal and community-led tree planting initiatives should consider comprehensive landscape characteristics to develop spatially targeted heat mitigation strategies and promote equitable climate outcomes.
Keywords
Redlining; Land cover; Spatial resolution; Land surface temperature; Tree canopy cooling; Heat inequity
Beyond unintentionality: considering climate maladaptation as cyclical
Shah, S.H., Haverkamp, J.A., Guzmán, C.B. et al. Beyond unintentionality: considering climate maladaptation as cyclical. Climatic Change 178, 77 (2025). https://doi.org/10.1007/s10584-025-03922-7
Abstract
Climate adaptation is imperative; however, instances of maladaptation are increasingly documented in sectors and locations around the world. Despite the prevalence of maladaptation, researchers and intergovernmental actors, including the Intergovernmental Panel on Climate Change, consistently frame it as “unintentional.” Drawing from environmental injustice, critical development studies, critical race theory, and coloniality scholarship, we argue the impossibility of characterizing maladaptation—now a global-scale phenomenon—as an unintended consequence of well-intentioned adaptation planning. This paper reframes the (re)production of climate maladaptation as a foreseeable result of the unequal systems of colonial racial capitalism through which adaptation is implemented and refracted. Systems-level change that confronts uneven relations of power, rather than incremental institutional reform, can address the prevalence of maladaptation. Treated as such, tackling climate maladaptation becomes a “political project”— not merely a “planning project.”
Keywords
Climate maladaptation; climate vulnerability; transformative adaptation; Longue durée; colonialism, injustice
Optimizing Urban Greenspace Landscapes to Mitigate Population Exposure to Extreme Heat in 21st Century Chinese Cities
Feng, R., Li, G., Alberti, M., Wang, F., Liu, S., & Yu, G. (2025). Optimizing Urban Greenspace Landscapes to Mitigate Population Exposure to Extreme Heat in 21st Century Chinese Cities. Environmental Science & Technology, 59(11), 5510–5520. doi:10.1021/acs.est.4c11345
Abstract
Urban greenspace (UGS) is a crucial nature-based solution for mitigating increasing human exposure to extreme heat, but its long-term potential has been poorly quantified. We used high spatial-temporal resolution data sets of urban land cover and population grid in combination with an urban climate model, machine learning, and land use simulation model to assess the impact of UGS on population exposure to extreme (high-heat exposure, HHE) and its potential spatial optimization strategies. Results showed that the UGS and HHE have a strong spatiotemporal dynamic coupling in 21st century Chinese cities. Moreover, UGS shrinkage increased the HHE by 0.58–1.15 °C, while UGS expansion mitigated it by 0.72–1.26 °C, both stronger in the SSP3–7.0 and SSP5–8.5 scenarios. Different from common impressions, spatial relationships, rather than quantities of UGS, are more influential (1.3–1.8 times) on HHE. Our solutions suggest that simply enhancing the spatial dynamic connectivity between patches can mitigate HHE by 9.1–21.1%, especially for the eastern and central cities. Our results provide an example of how to improve climate adaptation in urban ecological space designs and strongly promote research on optimal spatial patterns for future robust urban heat mitigation.
Keywords
Urban greenspace; extreme heat exposure; mitigation effects; optimization solution; future projection
Can large language models replace human experts? Effectiveness and limitations in building energy retrofit challenges assessment
Linyan Chen, Amos Darko, Fan Zhang, Albert P.C. Chan, Qiang Yang,Can large language models replace human experts? Effectiveness and limitations in building energy retrofit challenges assessment,Building and Environment,Volume 276,2025,112891, ISSN 0360-1323,
https://doi.org/10.1016/j.buildenv.2025.112891.
Abstract
Retrofitting existing buildings is essential to improve energy efficiency and achieve carbon neutrality in the fight against global climate change. Large language models (LLMs) have recently attracted significant attention for their ability to process data efficiently. While LLMs have emerged as useful tools for various tasks, their potential to replace human experts in assessing building energy retrofit challenges remains unexplored. This research explores the potential of replacing human experts with LLMs by evaluating four mainstream LLM chatbots and comparing their performance against a human expert benchmark through semantic similarity and text correlation metrics. It answers the research question: can LLMs replace human experts in assessing the challenges to building energy retrofits? Prompt engineering techniques, including zero-shot and chain-of-thought (CoT) prompting, were employed to guide LLM responses. Results show that LLMs perform well in identifying challenges but are less reliable in ranking them. CoT prompting improves challenge ranking accuracy but does not enhance challenge identification. Incorporating domain-specific knowledge in prompts significantly enhances LLM performance, whereas prompts designed to simulate experts have notable limitations in improving LLM performance. Furthermore, there are no significant performance differences among LLMs, including their advanced versions. While LLMs can streamline the initial identification of building energy retrofit challenges, they cannot fully replace expert judgment in ranking challenges due to their lack of tacit knowledge. This research provides valuable insight into the capabilities and limitations of LLMs in the challenge assessment, offering practical guidance for industry practitioners seeking to integrate LLMs into their building energy efficiency practices.
Keywords
Large language model; Building energy retrofit; Challenges assessment; Prompt engineering; Generative artificial intelligence