Environmental Indicators and Impact Assessment Commons^™

Detecting Burned Vegetation Areas By Merging Spectral And Texture Features In A Resnet Deep Learning Architecture, Jiahui Fan, Yunjun Yao, Yajie Li, Xueyi Zhang, Jiquan Chen, Joshua B. Fisher, Xiaotong Zhang, Bo Jiang, Lu Liu, Zijing Xie, Luna Zhang, Fei Qiu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Timely and accurate detection of burned areas is crucial for assessing fire damage and contributing to ecosystem recovery efforts. In this study, we propose a framework for detecting fire-affected vegetation anomalies on the basis of a ResNet deep learning (DL) algorithm by merging spectral and textural features (ResNet-IST) and the vegetation abnormal spectral texture index (VASTI). To train the ResNet-IST, a vegetation anomaly dataset was constructed on high-resolution 30 m fire-affected remote sensing images selected from the Global Fire Atlas (GFA) to extract the spectral and textural features. We tested the model to detect fire-affected vegetation in ten study areas …

Frameworks And Life Cycle Assessment For Reinforced Concrete Bridges For Sustainability In Transportation, Yu-Fu Ko, Jessica Gonzalez

Mineta Transportation Institute

Bridge structures are critical components of California’s transportation network, with reinforced concrete (RC) bridges being among the most widely used. Extending the lifespan of these structures can lead to significant reductions in environmental impacts. However, California’s frequent seismic activity has repeatedly exposed the vulnerability of existing RC bridges, highlighting the urgent need for seismic retrofitting and maintenance to improve infrastructure resilience against earthquakes and to increase sustainability. This research used detailed computer simulations known as nonlinear finite element models, which are highly detailed computer models, incorporating section damage indices to predict damage and assess structural deficiencies in RC bridges during …

Unraveling Crop Nitrogen-Water Dynamics With Hyperspectral-Thermal Sensing In Northern Central Valley, California, Shahryar Fazli, Surendra Maharjan, Wenzhao Li, Joshua B. Fisher, Rejoice Thomas, Fernando Romero Galvan, Gabriela Shirkey, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Ensuring global food security in the face of climate change requires optimizing crop water use and nutrient management. This study investigates the relationship between canopy nitrogen (N) and evapotranspiration (ET) across sunflower, rice, walnut, alfalfa, and plum crops using advanced remote sensing technologies. High-resolution hyperspectral data from NASAs Earth Surface Mineral Dust Source Investigation (EMIT) and thermal multispectral data from the Landsat-based OpenET system were analyzed over 1,135 km² in California. Regression analysis revealed strong spatial association between canopy N and ET for sunflower (R² = 0.82), rice (R² = 0.71), and walnut (R² = 0.68), …

Performance Mapping And Weighting For The Evapotranspiration Models Of The Openet Ensemble, M. Reitz, J. M. Volk, T. Ott, M. Anderson, G. B. Senay, F. Melton, A. Kilic, R. Allen, Joshua B. Fisher, A. Ruhoff, A. J. Purdy, J. Huntington

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Evapotranspiration (ET) accounts for the majority of water available from precipitation in the terrestrial water cycle, and improvements to the accuracy, resolution, and coverage of ET data can enhance hydrologic models and assessments. The OpenET collaboration of six remotely sensed ET modeling teams has demonstrated that an ensemble approach to ET estimation generally provides improved accuracy relative to individual ensemble members. The performance of individual models has been shown to vary by land cover type and climate zone, but a thorough study of the variables that influence model performance differences has not yet been conducted. In this paper, we model …

Sustainability: Buzz Word Or Future Of Fashion? Measuring The Feasibility Of Outright Sustainability Among Fast Fashion’S Biggest Agents, Ryan Miller

Apparel Merchandising and Product Development Undergraduate Honors Theses

Abstract

The fashion industry is currently experiencing unsustainable rates of pollution within its supply chains. The rapid increase in demand for short lead times perpetrated by large-scale retailers has led to hazardous practices negatively affecting both the environmental conditions and working conditions of producing countries. With this increased pressure, relationships between brands and suppliers have become untenable. Limited transparency and imbalanced power dynamics at the hand of the industry’s leading retailers require restructuring in order to build more sustainable and equitable supply-chain practices. Further, governmental regulation is currently limited in its capacity to enforce sustainable business practices on a global …

Assessing Coastal Vulnerability And Climate-Driven Migration Risk In West Africa, Wenzhao Li, Surendra Maharjan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

This study presents a GIS-based multi-criteria decision-making framework to assess climate-induced migration risk along the West African coast. We developed a comprehensive risk index that integrates environmental hazards such as flood frequency and socio-economic vulnerability indicators, including poverty levels, population density, and adaptive capacity. By utilizing datasets such as the Geocoded Disasters (GDIS) dataset, Social Vulnerability Index (SVI), Poverty and Adaptive Capacity Index (PACI), and the Population Exposure Index (PEI), the study identifies regions most susceptible to displacement. Results reveal that areas like Benin’s Abomey-Calavi, Cotonou, and Akpo-Misserete are especially vulnerable due to high disaster frequency, substantial population exposure, and …

Insights From Swot Data On Transboundary Upstream-Downstream Impacts In The Nile Basin, Hesham Morgan, Wenzhao Li, Ali Elgendy, Surendra Maharjan, Rejoice Thomas, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

This study utilizes high-resolution data from NASA's Surface Water and Ocean Topography (SWOT) mission to investigate water dynamics and upstream-downstream impacts across key reservoirs in the Nile Basin. Focusing on the Grand Ethiopian Renaissance Dam (GERD), Rosaries Dam, Merowe Dam, and the Aswan High Dam, the analysis spans 15 months (August 2023 to October 2024). By systematically selecting 30 points across each reservoir, monthly boxplots of surface water elevation were generated, revealing significant temporal and spatial variability. The results show that GERD’s filling phase led to a steady increase in water levels (peaking at ~615 meters from June to October …

High Spatial Resolution Crop Type And Land Use Land Cover Classification Without Labels: A Framework Using Multi-Temporal Planetscope Images And Variational Bayesian Gaussian Mixture Model, Minh Tri Le

Mathematics, Physics, and Computer Science Faculty Articles and Research

Previous studies often combined high spatial resolution data (e.g., PlanetScope) with wider spectral range data (e.g., Sentinel-2) and relied on supervised classification methods to produce land use and land cover (LULC) maps. This study proposed a new unsupervised framework to generate crop type and LULC maps at high spatial resolution (< 5 m) using available PlanetScope data solely without requiring ground truths. We used PlanetScope surface reflectance images and their derived spectral indices during growing seasons to create multi-temporal input features, which were fed into an unsupervised Variational Bayesian Gaussian Mixture Model (VBGMM). The VBGMM, unlike the traditional unsupervised classification methods, (1) first estimated optimal parameters that are most suitable based on the input features and then (2) assigned pixels to the cluster with maximum posteriori probability of a mixture of several Gaussian distributions. The crop type and LULC maps were then generated by labeling the derived clusters using the best possible assignment method, referring to the existing crop type or LULC products. We evaluated the produced PlanetScope-based crop type and LULC maps using true labels, corresponding reference maps, and other unsupervised classification methods. The results demonstrated the robustness and effectiveness of the proposed framework in mapping crop types and LULC at 3–5 m pixels across various ecosystems, climate zones, and human-managed landscapes. The spatial patterns of PlanetScope-based maps were (1) highly comparable with all the reference datasets at 10–30 m spatial resolution and (2) better than the traditional GMM and K-means clustering methods. The VBGMM produced classification maps with high confidence, yielding class probabilities above 0.9 for over 90 % of all study areas. The area percentage for all crop type and LULC classes agreed well with their reference maps, with R2 of 0.95 and RMSE of 1.04 %. The confusion matrices using true labels indicated that PlanetScope-based maps achieved a higher overall accuracy of 84 % than the supervised referenced maps of 81 %. Besides, the entropy comparison showed that our framework-based maps were better at capturing fine-scale features such as developed areas within cities that commonly mix with open space and vegetation, deforestation and cropland conversion in South America, smallholder croplands in Africa and Asia, and generating homogeneous crop fields in North America. This study further highlighted the potential for future research to implement our proposed framework to generate timely and extensive annotated datasets, which can be used for operationally training machine learning models to map crop types and LULC, track deforestation, detect wildfires, and delineate flooded areas at larger scales using medium/coarse Earth observations.

Hyperspectral Band Selection Via Heterogeneous Graph Convolutional Self-Representation Network, Junde Chen, Wenzhao Li, Surendra Maharjan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Hyperspectral image (HSI) band selection (BS) plays a crucial role in HSI dimensionality reduction, aiming to identify a representative subset of bands with minimal redundancy. However, conventional BS approaches primarily operate in the Euclidean domain, often overlooking the structural characteristics of pixels and spectral bands, such as spatial continuity and spectral dependencies. In addition, they handle each HSI as an integrated unit to harness implicit spatial information, disregarding spatial distribution variations across different homogeneous regions. To fully leverage structural information, this study introduces a novel BS method, termed the dual heterogeneous graph convolutional network with enhanced self-representation (ESR-HGCN), for HSI …

Factors Motivating Clothing Choice: Environmental Impact Of T-Shirts, Lauren Hunt

DePaul Discoveries

Second only to oil, the fashion industry is one of the most pollutive industries globally. Due to the rise of fast fashion, this environmental issue is continually growing. The level of production has dramatically increased while quality of apparel has decreased, causing shorter garment lifespans. This research utilizes life cycle assessment to identify the environmental impacts of various textiles used in the production of t-shirts, including cotton, polyester, viscose, and elastane. The research scope focuses from material extraction through garment disposal. The life cycle assessment research is paired with a community survey to determine consumer behavioral patterns surrounding clothing consumption …

Remote Sensing-Based Assessment Of Evapotranspiration Patterns In A Unesco World Heritage Site Under Increasing Water Competition, Maria C. Moyano, Monica Garcia, Luis Juana, Laura Recuero, Lucia Tornos, Joshua B. Fisher, Néstor Fernández, Alicia Palacias-Orueta

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

In water-scarce regions, natural ecosystems and agriculture increasingly compete for limited water resources, intensifying stress during periods of drought. To assess these competing demands, we applied a modified PT-JPL model that incorporates the thermal inertial approach as a substitute for relative humidity (RH) in estimating soil evaporation—a method that significantly outperforms the original PT-JPL formulation in Mediterranean semi-arid irrigated areas. This remote sensing framework enabled us to quantify spatial and temporal variations in water use across both natural and agricultural systems within the UNESCO World Heritage site of Doñana. Our analysis revealed an increasing evapotranspiration (ET) trend in intensified agricultural …

A Novel Approach To Increase Accuracy In Remotely Sensed Evapotranspiration Through Basin Water Balance And Flux Tower Constraints, Kul Khand, Gabriel B. Senay, Mackenzie Friedrichs, Koong Yi, Joshua B. Fisher, Lixin Wang, Kosana Suvočarev, Arman Ahmadi, Housen Chu, Stephen Good, Kanishka Mallick, Justine Missik, Jacob A. Nelson, David E. Reed, Tianxin Wang, Xiangming Xiao

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Remote sensing-derived evapotranspiration (RSET) products capture the spatiotemporal variations of evapotranspiration (ET) from field to basin scales with unprecedented details. However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address …

Assessment Of Spatial Autocorrelation And Scalability In Fine-Scale Wildfire Random Forest Prediction Models, Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Wildfire prediction models that can be applied across diverse regions at fine scales (<  100 m) are critical for wildfire management. Remote sensing offers a path forward by providing heterogeneous and dynamic measurements of fuel load, type, and flammability. Machine learning methods such as random forests provide an empirical framework that are high-accuracy, computationally efficient, interpretable and able to model complex ecological relationships. Here we use high resolution (70 m, every 3–5 days) remote sensing observations of evapotranspiration and evaporative stress index, which represent plant water stress, from Ecosystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS), as well as topography and weather data, to predict burn severity and occurrence for 8 large wildfires that burned 3715 km² from 2021 and 2022 in New Mexico, USA. These fires ranged from low to high burn intensity, and covered a diverse range of ecoregions (deserts, grasslands, forests), plant species, and topographies. We used a single model to predict the burn severity of all wildfires one week before occurrence. The prediction accuracy was greatest when using all predictors (ECOSTRESS, weather, topography) (R² = 0.77). We assessed the role of spatial autocorrelation in driving model performance by: (1) increasing the sample spacing of our dataset, (2) …

Object-Based Image Analysis And Artificial Intelligence Identification Of Anthropogenic Disturbance On Lesser Prairie Chicken Habitat In Cheyenne County, Colorado, Tara Hoelzer

Geography and the Environment: Graduate Student Capstones

Renewable energy projects often require extensive landcover for their operations. When one of these projects encroaches into territory of threatened species, such as Lesser Prairie Chickens, an analysis of habitat suitability and human disturbance is required to proceed. Traditionally, this involved manually reviewing aerial imagery within a 6-mile radius, digitizing features, and interpreting them using a human technician—an approach that was time-consuming and prone to human error. By using pretrained AI models within Model Builder™, the identification of roads and structures was automated, making the process faster and more consistent than manual visual analysis. As AI and technology continue to …

Integrating A Stakeholder-Centric Approach Into Dynamic Adaptation Policy Pathways For Equitable Coastal Flood Risk Management In East Boston, Shailee M. Desai

Graduate Doctoral Dissertations

This research aims to illustrate a methodology incorporating diverse stakeholder values in adaptation planning to promote equitable outcomes using a hypothetical case study of the Lower Border Street area in East Boston, using the Dynamic Adaptive Policy Pathways (DAPP) framework for managing coastal flood risk. It offers three significant contributions to the field of adaptation planning: (i) It demonstrates how to integrate a stakeholder-specific assessment within the DAPP framework and illustrates individual impacts to a variety of stakeholders, including high- and low-income residents, commercial actors, government actors, and coastal landowners hoping that the planning process remains transparent and motivates local …

Enhancing Water Supply Resilience & Sustainability: Potential Solutions To Address Water Scarcity In Bogotá, Colombia, Sophia Alvarez Wong

Master's Projects and Capstones

Municipal water utilities are in urgent need of adapting to the intensifying impacts of climate change and population growth—key drivers of urban water scarcity worldwide. In rapidly expanding cities like Bogota, Colombia, these pressures have contributed to a severe water crisis driven by prolonged droughts, ecosystem degradation, increasing water demands, and overdependence on limited water sources. This study explores potential adaptation and management strategies that utilities can implement to sustainably strengthen supply system resilience and enhance water security, in the context of low- to middle-income regions. A series of comparative and case study analyses were conducted to assess the feasibility …

Remotely Sensed High-Resolution Soil Moisture And Evapotranspiration: Bridging The Gap Between Science And Society, Jingyi Huang, Vinit Sehgal, Laura V. Alvarez, Luca Brocca, Shuohao Cai, Rui Cheng, Xinghua Cheng, Jinyang Du, Bassil El Masri, K. Arthur Endsley, Yilin Fang, Jie Hu, Mahesh Jampani, Md Golam Kibria, Gerbrand Koren, Lingcheng Li, Laibao Liu, Jiafu Mao, Hernan A. Moreno, Angela Rigden, Mingjie Shi, Xiaoying Shi, Yaoping Wang, Xi Zhang, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

This paper reviews the current state of high-resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio-geo-physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). …

Prevalence Of Well Water Contaminants In Georgia From 2010-2022, Angelique B. Willis, Suhasini Ramisetty-Mikler, Christine E. Stauber, Uttam Kumar Saha

Journal of the Georgia Public Health Association

Background: Safe, reliable, and clean drinking water sources are a basic necessity. More than 1.7 million individuals rely on private wells for drinking water in Georgia and are confronted with water quality challenges stemming from chemical contamination as private wells are not under mandated regulations as public water supplies. In Georgia, previous studies suggested that the variation of soil and rock in a physiographic province (region) plays an essential role in the quality of private well water. There is a need to understand the distribution of these chemical contaminants above the federal Maximum Contaminant Level (MCL) and how different geologies …

From The Ground Up: Community-Based Participatory Research Reclaiming The Science Of Lead, Juan Manuel Rubio, Bavisha Kaylan, Anthony Diaz, Patricia Flores, Maya Cheav, David C. Bañuelas, Ashley Green, Annika Hjelmstad, Ariane Jong-Levinger, Tim Schütz, Maya Carrasquillo, Alana M. W. Lebron, Jun Wu

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

For decades, the dominant approach to lead poisoning has been to focus on homes affected by lead paint and to treat children who are already suffering from lead poisoning. This individualizing approach developed in the context of the defunding and deregulation of government agencies in the 1980s. In recent years, however, community–academic partnerships have reframed lead as an environmental issue produced by the development of the lead industry in the twentieth century and connected to overlapping histories of exploitation, discrimination, and inaction. These community-based projects have contributed to shifting research agendas (by emphasizing historical analysis and the study of the …

Development And Application Of Self-Supervised Machine Learning For Smoke Plume And Active Fire Identification From The Fire Influence On Regional To Global Environments And Air Quality Datasets, Nicholas Lahaye, Anastasija Easley, Kyongsik Yun, Hugo Lee, Erik Linstead, Michael J. Garay, Olga V. Kalashnikova

Engineering Faculty Articles and Research

Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. With as much as a 10% …

Transfer Learning In Junction With A Light Use Efficiency Model For Estimating Grassland Gross Primary Production, Ruiyang Yu, Yunjun Yao, Qingxin Tang, Xueyi Zhang, Changliang Shao, Joshua B. Fisher, Jiquan Chen, Xiaotong Zhang, Yufu Li, Jia Xu, Lu Liu, Zijing Xie, Jing Ning, Jiahui Fan, Luna Zhang

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

It is significant to simulate grassland gross primary production (GPP) to understand the terrestrial carbon budget over Inner Mongolia (IMG), China. Nevertheless, there is not sufficient in situ GPP data over this region. In this study, we proposed a novel model-based transfer learning (MTL) approach with generative adversarial networks-long short-term memory (GAN-LSTM) and light use efficiency (LUE) models to derive grassland GPP over IMG, China. We first used 25 grassland eddy covariance sites over the conterminous United States to establish the GAN-LSTM model and then fine-tuned it with six sites over IMG to estimate water constraints that were embedded into …

Modeling Wild Rice Bed Health: A Dual Metric Approach For Bed Strength And Invasive Impact, Seth Sisneros-Martinez

Journal of Earth and Life Science

Wild rice (mannoomin; Zizania palustris) is an aquatic grain that grows in slow-moving rivers and shallow bays throughout northern Minnesota. In Headquarters Bay on Leech Lake, wild rice has served as a cultural staple for Indigenous communities for centuries. However, its population has declined in recent years due to multiple environmental issues. One potential contributor is Eurasian watermilfoil (Myriophyllum spicatum; EWM), an invasive species that forms dense mats capable of outcompeting native aquatic vegetation, including wild rice. EWM was first recorded on the southern shoreline of Leech Lake in 2005 and has since become widespread. This study …

The Effect Of Zebra Mussels On Minnesota Fish Communities, Benjamin D. Strei

Journal of Earth and Life Science

Zebra mussels Dreissena polymorpha are an aquatic invasive species that has been spreading quickly throughout Minnesota water bodies since 1989. Zebra mussels are known to change certain morphological conditions in lakes such as turbidity, phytoplankton abundance, and aquatic invertebrate populations. However, studies showing impacts of the infestation of zebra mussels on the health of a fish community have been hard to prove due to a lack of repeated surveys and a suite of other confounding variables such as natural and seasonal variability, plant community, and lake morphometry. Therefore, the objective of this study is to determine whether zebra mussels have …

The Economic And Environmental Impact Of Shinkansen And High-Speed Rail Infrastructure: A Comparative Analysis Of Economic Growth And Carbon Emissions Reduction, Dillan R. Victory

SPARK Symposium Presentations

The development of Japan's high-speed rail system, the Shinkansen, has played a pivotal role in the country's post-war economic resurgence. Introduced in 1964 with the Tokaidō Shinkansen, this transformative infrastructure investment significantly reduced travel times, bolstered economic activity around station hubs, and facilitated regional development by enabling urban decentralization. This paper explores the long-term economic benefits of high-speed rail, including its impact on land value, business expansion, and carbon emissions. The case study of the Linear Chuo Shinkansen, Japan's latest maglev project, underscores both the economic promise and the political resistance to expansion, particularly in regions such as Shizuoka.

Using …

Assessing Understory Along The Elk River (Hikshari') In Headwaters Forest Reserve, Humboldt County, California, Julie R. Meyers, Aliyah M. Ingram, Caleb N. Larkin, Joshua L. Carpenter

Environmental Science & Management Senior Capstones

Northern California’s extensive logging history and lack of forest management has altered old-growth coast redwood (Sequoia sempervirens) forests, leaving dense, homogenous second and third-growth forest stands. This has led to the use of modern restoration techniques such as variable density thinning (VDT) which aims to rehabilitate old-growth forest conditions by improving stand heterogeneity and light availability for understory species. Our study was done in Headwaters Forest Reserve (HFR) along the Elk River (Hikshari’) Trail in Humboldt County, California. Initially, the project was supervised by the Bureau of Land Management (BLM) Arcata Field Office. However, the 2025 U.S. government …

Consumption Pressure In Estuaries Peaks At Intermediate Salinities, Catherine E. De Rivera, Amy A. Larson, Benjamin G. Rubinoff, Luna R. Soto, Seth L. Wright, Edwin D. Grosholz, Gregory M. Ruiz, Andrew L. Chang

Environmental Science and Management Faculty Publications and Presentations

The nature and strength of biotic interactions change along stress gradients, but the importance of these interactions across estuarine gradients is under studied. Here, we examined how consumption varies across estuarine salinity gradients by deploying standardized baits (‘squidpops’) to measure consumption pressure along the gradients of five estuaries in Oregon, USA. The relationship between consumption and stress was nonlinear: consumption pressure peaked slightly at mid salinity and decreased at low salinity, especially as temperature increased, in the five estuaries studied. This finding does not support either of two existing models for consumption across gradients, including the Consumer Stress Model and …

An Economic And Production Analysis Of Seaweed Farms In California And The Pacific Northwest, Brian Donovan

Cal Poly Humboldt theses and projects

Since the early 2010s, the seaweed aquaculture industry has steadily expanded in the United States, with states like Alaska, Maine, Hawaii, and Connecticut leading the way in the development of this emerging sector. However, information gaps remain concerning certain aspects of the domestic seaweed aquaculture industry. The purpose of this study was to examine the status of the seaweed aquaculture industry along the West Coast of the United States; specifically in the states of California, Oregon, and Washington. Seaweed farmers in these states were invited to voluntarily complete surveys asking important production, economic, and commercial questions regarding their operations. Data …

Complexity, Polycentricity, And Climate Change: An Embedded, Multi-Level Case Study Of A Climate Meta-Network, Timothy Staub

Antioch University Dissertations & Theses

The earth is in crisis, and without urgent, systemic, and collective global action to address the emerging threat of climate change, we will continue to see catastrophic events impacting humanity, threatening our very survival in many regions of the world. Temperatures will rise, ice shelves will melt, seas will rise, crops will fail, water scarcity from drought and new weather patterns will increase and spread, wildfires will accelerate, and food and water insecurity, violence, and the largest human migration in history will ensue. These climate crises will displace over 1.2 billion people by 2050 at a rate currently exceeding 21.5 …

Impact Of Urban Development On Uv Exposure: A Clustering And Machine Learning Assessment, Taufik Roni Sahroni Mr., Verdi Yasin, Lulut Alfaris, Reza Ariefka, Ruben Cornelius Siagian, Mohammad Alfin Karim, Nana Rahdiana, Ade Suhara

Journal of Environmental Science and Sustainable Development

The relocation of Indonesia's capital city is anticipated to promote inclusive economic growth while embracing cultural diversity. However, this transition may affect ultraviolet (UV) radiation exposure patterns. The study investigated variations in UV exposure in the IKN region, focusing on urban development factors such as land use and population density that affect public health, sun protection, and skin cancer prevention. The research hypothesized that UV radiation is significantly correlated with these factors. UV Index data from 2010-2023, a hierarchical clustering method, identifies complex data patterns without determining the number of clusters. XGBoost, a machine learning model, was used for handling …

Role Of Greenfield Investment, Institutional Quality, And Economic Activities On Environmental Performance In Belt & Road Initiative Countries, Jamal Hussain, Sajid Hassan

CBER Conference

Researchers have produced varying conclusions on the matter of coexistence of economic activities with the protection or improvement of environmental performance. This study investigates the association between greenfield investments, economic activity, institutional quality, and environmental performance in Belt & Road Initiative (BRI) countries from 2003 to 2022. Yale University's Environmental Performance Index (EPI) assesses environmental performance. Advanced second-generation econometrics estimation approaches address concerns like cross-sectional dependence, potential endogeneity, and heterogeneity. Further analysis is employed across four income-level sub-panels: lowincome (LI), lower-middle-income (LMI), upper-middle-income (UMI), and high-income (HI) countries, covering the years 2003 to 2021 to validate reliability and consistency. This …