publications

2023

1. Recognizing political influences in participatory social-ecological systems modeling

   Lim, Theodore C., Glynn, Pierre D., Shenk, Gary W., Bitterman, Patrick, Guillaume, Joseph H.W., Little, John C, and D.G., Webster

   Socio-Environmental Systems Modelling 2023

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   Stakeholder participation in social-ecological systems (SES) modeling is increasingly considered a desirable way to elicit diverse sources of knowledge about SES behavior and to promote inclusive decision-making in SES. Understanding how participatory modeling processes function in the context of long-term adaptive management of SES may allow for better design of participatory processes to achieve the intended outcomes of inclusionary knowledge, representativeness, and social learning, while avoiding unintended outcomes. Long-term adaptive management contexts often include political influences – attempts to shift or preserve power structures and authority, and efforts to represent the political and economic interests of stakeholders – in the computer models that are used to shape policy making and implementation. In this research, we examine a period that included a major transition in the watershed model used for management of the Chesapeake Bay in the United States. The Chesapeake Bay watershed model has been in development since the 1980s, and is considered by many to be an exemplary case of participatory modeling. We use documentary analysis and interviews with participants involved in the model application and development transition to reveal a variety of ways in which participatory modeling may be subject to different kinds of political influences, some of which resulted in unintended outcomes, including: perceptions of difficulty updating the model in substantive ways, “gaming” of the model/participatory process by stakeholders, and increasing resistance against considering uncertainty in the system not captured by the model. This research suggests unintended or negative outcomes may be associated with both participatory decision-making and stakeholder learning even though they are so often touted as the benefits of participatory modeling. We end with a hypothesis that further development of a theory of computer model governance to bridge model impact and broader theories of environmental governance at the science-policy interface may result in improved SES modeling outcomes.

2. Comparison of different spatial temperature data sources and resolutions for use in understanding intra-urban heat variation

   Kianmehr, Ayda, Lim, Theodore C., and Li, Xiaojiang

   Sustainable Cities and Society 2023

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   In this study, we investigate the compatibility of specific vulnerability indicators and heat exposure data and the suitability of spatial temperature-related data at a range of resolutions, to represent spatial temperature variations within cities using data from Atlanta, Georgia. For this purpose, we include various types of known and theoretically based vulnerability indicators such as specific street-level landscape features and urban form metrics, population-based and zone-based variables as predictors, and different measures of temperature, including air temperature (as vector-based data), land surface temperature (at resolution ranges from 30 m to 305 m), and mean radiant temperature (at resolution ranges from 1 m to 39 m) as dependent variables. Using regression analysis, we examine how different sets of predictors and spatial resolutions can explain spatial heat variation. Our findings suggest that the lower resolution of land surface temperature data, up to 152 m, and mean radiant temperature data, up to 15 m, may still satisfactorily represent spatial urban temperature variation caused by landscape elements. The results of this study have important implications for heat-related policies and planning by providing insights into the appropriate sets of data and relevant resolution of temperature measurements for representing spatial urban heat variations.

2022

1. Community-engaged heat resilience planning: Lessons from a youth smart city STEM program

   Lim, Theodore C., Wilson, Bev, Grohs, Jacob R., and J., Pingel Thomas

   Landscape and Urban Planning 2022

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   While recognition of the dangers of extreme heat in cities continues to grow, heat resilience remains a relatively new area of urban planning. One barrier to the creation and successful implementation of neighborhood-scale heat resilience plans has been a lack of reliable strategies for resident engagement. In this research, the authors designed a two-week summer STEM module for youth ages 12 to 14 in Roanoke, Virginia in the Southeastern United States. Participants collected and analyzed temperature and thermal comfort data of varying types, including from infrared thermal cameras and point sensors, handheld weather sensors, drones, and satellites, vehicle traverses, and student peer interviews. Based on primary data gathered during the program, we offer insights that may assist planners seeking to engage residents in neighborhood-scale heat resilience planning efforts. These lessons include recognizing: (1) the problem of heat in neighborhoods and the social justice aspects of heat distribution may not be immediately apparent to residents; (2) a need to shift perceived responsibility of heat exposure from the personal and home-based to include the social and landscape-based; (3) the inextricability of solutions for thermal comfort from general issues of safety and comfort in neighborhoods; and (4) that smart city technologies and high resolution data are helpful “hooks” to engagement, but may be insufficient for shifting perception of heat as something that can be mitigated through decisions about the built environment.

2. The prospects of artificial intelligence in urban planning

   Sanchez, Tom, Shumway, Hannah, Gordner, Trey, and Lim, Theodore

   International Journal of Urban Sciences 2022

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   Over the past several decades, urban planning has considered a variety of advanced analysis methods with greater and lesser degrees of adoption. Geographic Information Systems (GIS) is probably the most notable, with others such as database management systems (DBMS), decision support systems (DSS), planning support systems (PSS), and expert systems (ES), having mixed levels of recognition and acceptance (Kontokosta, C. E. (2021). Urban informatics in the science and practice of planning. Journal of Planning Education and Research, 41(4), 382–395. doi:10.1177/0739456X18793716; Yigitcanlar, T., Desouza, K. C., Butler, L., & Roozkhosh, F. (2020). Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6), 1473). Advances in information technologies have moved very slowly in the field of urban planning, more recently concerning ‘smart city’ technologies while revolutionizing other domains, such as consumer goods and services. Baidu, Amazon, Netflix, Google, and many others are using these technologies to gain insights into consumer behaviour and characteristics and improve supply chains and logistics. This is an opportune time for urban planners to consider the application of AI-related techniques given vast increases in data availability, increased processing speeds, and increased popularity and development of planning related applications. Research on these topics by urban planning scholars has increased over the past few years, but there is little evidence to suggest that the results are making it into the hands of professional planners (Batty, M. (2018). Artificial intelligence and smart cities. Environment and Planning B: Urban Analytics and City Science, 45(1), 3–6; Batty, M. (2021). Planning education in the digital age. Environment and Planning B: Urban Analytics and City Science, 48(2), 207–211). Others encourage planners to leverage the ubiquity of data and advances in computing to enhance redistributive justice in information resources and procedural justice in decision-making among marginalized communities (Boeing, G., Besbris, M., Schachter, A., & Kuk, J. (2020). Housing search in the Age of Big data: Smarter cities or the same Old blind spots? Housing Policy Debate, 31(1), 112–126; Goodspeed, R. (2015). Smart cities: Moving beyond urban cybernetics to tackle wicked problems. Cambridge journal of regions, Economy and Society, 8(1), 79–92). This article highlights findings from a recent literature review on AI in planning and discusses the results of a national survey of urban planners about their perspectives on AI adoption and concerns they have expressed about its broader use in the profession. Currently, the outlook is mixed, matching how urban planners initially viewed the early stages of computer adoption within the profession. And yet today, personal computers are essential to any job.

3. Quantifying Interactive Cooling Effects of Morphological Parameters and Vegetation-Related Landscape Features during an Extreme Heat Event

   Kianmehr, Ayda, and Lim, Theodore

   Climate 2022

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   In this study, we apply the ENVI-met model to evaluate the effects of combinations of morphological and vegetation-related landscape features on urban temperatures and thermal comfort. We simulated the thermal conditions of 126 scenarios, varying the aspect ratios of street canyons, vegetation cover and density, surface materials, and orientations toward the prevalent winds under an extreme heat situation. Our results show how the effects of physical and vegetation parameters interact and moderate each other. We also demonstrate how sensitive thermal comfort indices such as temperature and relative humidity are to the built environment parameters during different hours of a day. This study’s findings highlight the necessity of prioritizing heat mitigation interventions based on the site’s physical characteristics and landscape features and avoiding generic strategies for all types of urban environments.

4. Interdisciplinary Inquiry and Spatial Green Stormwater Infrastructure Research

   Huang, Lechuan, Lim, Theodore, and Misra, Shalini

   Sustainability 2022

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   The use of vegetation and infiltration into soils to manage stormwater and water quality—called green stormwater infrastructure (GSI)—is now widely recognized as a viable alternative or supplement to the pipes and pumps of conventional, or “gray”, drainage infrastructure. Over the years, much research has emerged regarding spatial aspects of GSI implemented at large scales, including where it is located, where it should be located, and what metrics best represent the benefits it brings to different locations. Research in these areas involves expertise from multiple academic disciplines, but it is unclear whether and how researchers from different disciplines identify and approach questions related to the spatiality of GSI. By adopting the explanatory sequential mixed method design, we identified four categories of spatial GSI studies through a literature review of over 120 research papers: empirical, ecological, decision support systems, and optimization. Here, we present representative examples of these categories of spatial GSI studies, as well as associations between the academic disciplines represented in these categories of spatial GSI papers. Then, we conducted semi-structured interviews with a sample of GSI researchers which revealed the value of interdisciplinary training and knowledge. Finally, in this paper, we identify several gaps that could be addressed to improve interdisciplinary research on GSI implementation, and sustainability transitions in general.

5. Comparison of machine learning algorithms for emulation of a gridded hydrological model given spatially explicit inputs

   Lim, Theodore, and Wang, Kaidi

   Computers & Geosciences 2022

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   This study compares the performance of several machine learning algorithms in reproducing the spatial and temporal outputs of the process-based, hydrological model, ParFlow.CLM. Emulators or surrogate models are often used to reduce complexity and simulation times of complex models, and have typically been applied to evaluate parameter sensitivity or for model parameter tuning, without explicit treatment of variation resulting from spatially explicit inputs to the model. Here we present a case study in which we evaluate candidate machine learning algorithms for suitability emulating model outputs given spatially explicit inputs. We find that among random forest, gaussian process, k-nearest neighbors, and deep neural networks, the random forest algorithm performs the best on small training sets, is not as sensitive to hyperparameters chosen for the machine learning model, and can be trained quickly. Although deep neural networks were hypothesized to be able to better capture the potential nonlinear interactions in ParFlow.CLM, they also required more training data and much more refined tuning of hyperparameters to achieve the potential benefits of the algorithm.

6. Technoeconomic Analysis of Negative Emissions Bioenergy with Carbon Capture and Storage through Pyrolysis and Bioenergy District Heating Infrastructure

   Lim, Theodore Chao, Cuellar, Amanda, Langseth, Kyle, and Waldon, Jefferson L.

   Environmental Science & Technology 2022

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   Bioenergy with Carbon Capture and Storage (BECCS) has been identified as a cost-effective negative emissions technology that will be necessary to limit global warming to 1.5° C targets. Yet, the study of BECCS deployment has mainly focused on large-scale, centralized facilities and geologic sequestration. In this study, we perform a technoeconomic analysis of BECCS through pyrolysis technology within a district heating system using locally-grown switchgrass. The analysis is based on a unique case study of an existing switchgrass-fueled district heating system in the rural southeastern United States and combines empirical daily energy data with a retrospective analysis of add-on pyrolysis technology with biochar storage. We show that at current heating oil and switchgrass prices, Pyrolysis-Bioenergy (PyBE), and pyrolysis BECCS (PyBECCS) can each reach economic parity with a fossil fuel-based system when the price of carbon is 116/Mg CO2-eq and 51/Mg CO2-eq, respectively. In addition each can reach parity with a direct combustion bioenergy (BE) system when the price of carbon is 264/Mg CO2-eq and 212/Mg CO2-eq, respectively. However, PyBECCS cannot reach economic parity with BE without revenue from carbon sequestration while PyBE can, and in some cases PyBECCS could counterintuitively require more reliance on fossil fuels than both the PyBE case and BE.

2021

1. Patterns in environmental priorities revealed through government open data portals

   Lim, Theodore C

   Telematics and Informatics 2021

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   The ways in which environmental priorities are framed are varied and influenced by political forces. One technological advance–the proliferation of government open data portals (ODPs)–has the potential to improve governance through facilitating access to data. Yet it is also known that the data hosted on ODPs may simply reflect the goals and interests of multiple levels of political power. In this article, I use traditional statistical correlation and regression techniques along with newer natural language processing and machine learning algorithms to analyze the corpus of datasets hosted on government ODPs (total: 49,066) to extract patterns that relate scales of governance and political liberalism/conservatism to the priorities and meaning attached to environmental issues. I find that state-level and municipal-level ODPs host different categories of environmental datasets, with municipal-level ODPs generally hosting more datasets pertaining to services and amenities and state-level ODPs hosting more datasets pertaining to resource protection and extraction. Stronger trends were observed for the influences of political conservatism/liberalism among state-level ODPs than for municipal-level ODPs.

2. Model emulators and complexity management at the environmental science-action interface

   Lim, Theodore C.

   Environmental Modelling & Software 2021

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   As our understanding of the interactions present in socio-ecological systems advance, emulation modeling can help reduce the complexity and required computational resources of the models used to represent these systems. While emulation is commonly used in model meta-analyses and parameterization, it has been less explored in the context of environmental management. In this research, I analyze the reflections of a group of watershed modelers on environmental model emulation. I find that decreased simulation run-times are an important motivation because emulators enable stakeholders to interact directly with the model. However, participants also reported that criteria for an emulator in an environmental management context should also assess its capability to act as a platform for learning and to manage stakeholder perceptions of the modeling process. Further, at the science-action interface, stakeholder perceptions play a significant role in the approach to model emulation through determining acceptable levels complexity in model processes and inputs.

2020

1. Examining privilege and power in US urban parks and open space during the double crises of antiblack racism and COVID-19

   Hoover, Fushcia -Ann, and Lim, Theodore C.

   Socio-Ecological Practice Research 2020

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   In this perspective, we argue that creating the positive outcomes socio-ecological researchers and practitioners seek for urban areas requires acknowledging and addressing the interactions of race and systemic racism in parks, open and green spaces. Racial experiences are inseparable from physical landscapes and the processes of designing, managing, or studying them. From COVID-19 to the Black Lives Matter movement and protests, the events of 2020 in the United States underscore how considerations of social justice must extend beyond the conventional distributional focus of environmental justice. It must incorporate an understanding of how the built environment is racialized spatially, but not always readily quantified through the proximity-based measurements frequently used in research and practice. The perspective is organized in three main parts. The first part presents a series of vignettes to frame the ways cities and individuals participate, respond, and interact under COVID-19 with racial segregation as the backdrop. The second part suggests a stepwise approach to building an understanding of racial inequities in socio-ecological systems (SES) research and practice including four entry points: (1) racialized spatial distribution of hazards and amenities, (2) racialized qualities of space, (3) racialized people in space, and (4) racialized creation of space. Finally, the third part proposes actions the SES community can take to enhance our commitment to community recovery, improvement, and thrivability. This perspective cautions practitioners and researchers against opportunistic or quick-fix solutions, and instead challenges our colleagues to be inclusive of disenfranchised voices in shaping socio-ecological goals, now more than ever. The goal of this perspective is to spark engagement with power and privilege in parks and open space using the example of COVID-19 and race in the US.

2. Rocky steps towards adaptive management and adaptive governance in implementing green infrastructure at urban scale in Philadelphia

   Hsu, David, Lim, Theodore Chao, and Meng, Ting

   Urban Forestry & Urban Greening 2020

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   Many scholars have recently looked to adaptive management and adaptive governance as new approaches to implement green infrastructure. Much of the recent scholarship, however, that argues for combining the three concepts of adaptive management, adaptive governance, and green infrastructure is either theoretical, at early planning stages, or based on relatively small experiments. Since green infrastructure is now being implemented in a number of cities at large spatial scales, this paper examines how and whether combining these theories helps to solve anticipated problems compared to earlier work on environmental governance and political implementation. The city of Philadelphia has become known as a national and international leader in using green infrastructure for stormwater management. This paper therefore analyzes Philadelphia’s early experiences in the period from 2009 to 2015 as a test at urban scale of the usefulness of theories of adaptive management and adaptive governance in implementing green infrastructure. The city of Philadelphia found itself rapidly changing its implementation approach multiple times in response to political pressure. The city’s changing responses illustrate challenges in implementing environmental policies among actors with differing levels of political power, economic interests, and participation. This paper describes the rapid changes in programs using mixed methods and data sources including quantitative analysis of the city’s billing and program data, qualitative interpretation of media and public documents, and subsequent interviews with city officials. Understanding how and why Philadelphia rapidly changed its approach will be interesting to policymakers and advocates in other cities who also intend to implement green infrastructure at urban scale.

2019

1. Use of the McHargian LUSA in agricultural research and decision-making in the age of non-stationarity and big earth observation data

   Lim, Theodore C

   Socio-Ecological Practice Research 2019

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   In the past 50 years, there have been two major changes that are of methodological and consequential importance to the McHargian land-use suitability analysis (LUSA): increasing evidence of non-stationarity of global and regional ecological conditions and increasing availability of high-resolution spatial–temporal earth observation data. For 50 years, the McHargian LUSA has been an important analysis tool for designers and planners for both regional conservation planning and development. McHarg’s LUSA is a decision support tool that reduces the dimensions of spatial–temporal data. This makes the technique relevant beyond decision support to spatial identification and prediction of areas of socio-ecological opportunity, risk, and priority. In this article, I use a set of recent studies relating to agricultural LUSA to reveal relationships between the traditional McHargian LUSA and related spatial–temporal research methods that are adapting to more data and non-stationary ecological conditions. Using a classification based on descriptive, predictive, and prescriptive research activities, I organize these related methods and illustrate how linkages between research activities can be used to assimilate more kinds of spatial “big data,” address non-stationarity in socio-ecological systems, and suggest ways to enhance decision-making and collaboration between planners and other sciences.

2018

1. Revitalizing urban neighborhoods by adopting green infrastructure— the case of Washington D.C. (In Chinese)

   Lim, Theodore

   Urban Planning International 2018

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   The concept of Green Infrastructure (GI), or using the natural processes of evapotranspiration and infiltration to manage stormwater runoff close to where rain falls is a popular concept among urbanists. In addition to providing the ecosystem services of flood management, GI realizes other goals of increasing urban livability, through mitigating urban heat island effect, providing community amenity, purifying air, and even reducing crime. At the same time, GI has been shown to be primarily driven by federal-level stormwater management regulations to make expensive improvements to aging infrastructure. GI is one way that cities may achieve this goal more efficiently. In this paper, I trace the history of stormwater infrastructure regulation and urban sustainability in the US, how this national context influenced local policy in Washington DC neighborhoods. In addition to the popular narrative that GI can spur neighborhood revitalization, I identify the market-driven urban processes that determine GI locations in already revitalizing neighborhoods. Using an overlay analysis of these factors—centrally-driven planning processes, distributed voluntary participation and distributed development patterns—I show how different neighborhoods throughout the District are likely to have different distributions of Green Infrastructure adoption rates, with areas experiencing high re-investment showing the highest levels of probable GI adoption.

2. Assessing Variability and Uncertainty in Green Infrastructure Planning Using a High-Resolution Surface-Subsurface Hydrological Model and Site-Monitored Flow Data

   Lim, Theodore C., and Welty, Claire

   Frontiers in Built Environment 2018

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   Green infrastructure (GI) is increasingly being used in urban areas to supplement the function of conventional drainage infrastructure. GI relies on the “natural” hydrological processes of infiltration and evapotranspiration to treat surface runoff close to where it is generated, alleviating loading on the conventional infrastructure systems. This research addresses growing interest in identification and quantification of uncertainties with distributed, infiltration-based stormwater control measures, retrofitted on private and public properties and in right-of-ways in existing urban areas. We identify four major sources of variability and uncertainty in cumulative performance of systems that rely on extensive implementation of distributed GI: non-additive effects of individual best management practices (BMPs) at the catchment scale; the spatial configuration of fine-scale land use and land cover changes; performance changes due to climate change; and noise levels present in urban flow monitoring programs. Using a three-dimensional coupled surface-subsurface hydrological model of a residential sewershed in Washington DC, we find that prolonged, large-magnitude rain events affect various spatial configurations of GI networks differently. Runoff peaks and volumes can both be influenced by the spatial permutations of infiltration opportunities in addition to the absolute magnitude of treated area. However, the magnitude of the last source of uncertainty—noise levels in urban flow monitoring programs—may be larger than sources of variability associated with spatial changes in fine-scale land use and land cover. Changes associated with climate change– more frequent and larger rainfall events– will likely intensify performance differences between spatial configurations of GI but also increase noise levels in urban flow monitoring programs.

2017

1. Effects of spatial configuration of imperviousness and green infrastructure networks on hydrologic response in a residential sewershed

   Lim, Theodore C., and Welty, Claire

   Water Resources Research 2017

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   Green infrastructure (GI) is an approach to stormwater management that promotes natural processes of infiltration and evapotranspiration, reducing surface runoff to conventional stormwater drainage infrastructure. As more urban areas incorporate GI into their stormwater management plans, greater understanding is needed on the effects of spatial configuration of GI networks on hydrological performance, especially in the context of potential subsurface and lateral interactions between distributed facilities. In this research, we apply a three-dimensional, coupled surface-subsurface, land-atmosphere model, ParFlow.CLM, to a residential urban sewershed in Washington DC that was retrofitted with a network of GI installations between 2009 and 2015. The model was used to test nine additional GI and imperviousness spatial network configurations for the site and was compared with monitored pipe-flow data. Results from the simulations show that GI located in higher flow-accumulation areas of the site intercepted more surface runoff, even during wetter and multiday events. However, a comparison of the differences between scenarios and levels of variation and noise in monitored data suggests that the differences would only be detectable between the most and least optimal GI/imperviousness configurations. Plain Language Summary This research evaluates the effectiveness of green stormwater infrastructure management techniques in residential neighborhoods and the dependence of effectiveness on spatial location within the neighborhood. We find that locating green infrastructure on properties with high flow accumulation mitigates more runoff, but that the differences in effectiveness are only expected to be detectable through flow monitors between the most and least optimal configurations.

2. An empirical study of spatial-temporal growth patterns of a voluntary residential green infrastructure program

   Lim, Theodore Chao

   Journal of Environmental Planning and Management 2017

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   Voluntary residential green infrastructure (GI) stormwater management retrofit programs can help cities comply with environmental regulations while also improving quality of life. Previous research has identified influential factors in residents’ willingness to adopt GI, but few have simultaneously studied the spatial and temporal dynamics of GI. I use a six-year record of participation in a voluntary residential GI program in Washington DC to explore how neighborhood characteristics and social influence affect GI adoption over time. Statistical regression and Monte Carlo permutation resampling techniques are used to explain the spatial-temporal patterns of growth of the program. I demonstrate empirical evidence that participation location is increasingly determined by the locations of previous participants. These findings suggest that past participants will increasingly influence spatial clustering of GI in the city.

2016

1. Predictors of urban variable source area: A cross-section analysis of urbanized catchments in the united states

   Lim, TC

   Hydrological Processes 2016

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   Many studies have empirically confirmed the relationship between urbanization and changes to the hydrologic cycle and degraded aquatic habitats. While much of the literature focuses on extent and configuration of impervious area as a causal determinant of degradation, in this article I do not attribute causes of decreased watershed storage on impervious area a priori. Rather, adapting the concept of variable source area (VSA) and its relationship to incremental storage to the particular conditions of urbanized catchments, I develop a statistically-robust linear regression-based methodology to detect evidence of VSA-dominant response. Using the physical and meteorological characteristics of the catchments as explanatory variables, I then use logistic regression to statistically analyze significant predictors of the VSA classification. I find that the strongest predictor of VSA-type response is the percent of undeveloped area in the catchment. Characteristics of developed areas, including total impervious area, percent developed open space, and the type of drainage infrastructure do not add to the explanatory power of undeveloped land in predicting VSA-type response. Within only developed areas, I find that total impervious area (TIA) and percent developed open space both decrease the odds of a catchment exhibiting evidence of VSA-type response and the effect of developed open space is more similar to that of TIA than undeveloped land in predicting VSA response. Different types of stormwater management infrastructure, including combined sewer systems (CSS) and infiltration, retention, and detention infrastructure are not found to have strong statistically significant effects on probability of VSA-type response. VSA-type response is also found to be stronger during the growing season than the dormant season. These findings are consistent across a national cross-section of urbanized watersheds, a higher resolution dataset of Baltimore Metropolitan Area watersheds, and a subsample of watersheds confirmed not to be served by (CSS). This article is protected by copyright. All rights reserved.