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This paper examines long-term recovery outcomes of businesses impacted by major natural disasters. Data were collected via two large-scale mail surveys—one administered to Santa Cruz County, California businesses 8 years after the Loma Prieta earthquake and the other administered to businesses in South Dade County, Florida, 6 years after Hurricane Andrew. Based on the results of OLS regression models, we argue that long-term recovery experiences of businesses are affected by various factors, including the economic sector in which a business operates, its age and financial condition, and the scope of its primary market; direct and indirect disaster impacts, including physical damage, forced closure, and disruption of operations; and owner perceptions of the broader economic climate. Previous disaster experience, level of disaster preparedness, and use of external sources of aid were not found to significantly affect the long-term economic viability of businesses in the two study communities. [Copyright &y& Elsevier]

The deployment of hydrogen refueling stations is vital to the development of fuel cell vehicles. Past studies on the hydrogen refueling stations didn't consider congestion on paths and assumed all flows are on the shortest paths of OD pairs, which is not consistent with travelers' route choice behavior. In this paper, we study flow refueling location allocation based on the user-equilibrium (UE) status, which is consistent with travelers' route choice behavior. We present a flow-capturing location allocation (FCLA) model on UE conditions. We also presented an equilibrium algorithm avoiding cannibalization to solve the model. Based on the transportation network of Anaheim city in California in the US, we compared the results of applying a non-equilibrium method to the FCLA model and applying the equilibrium method to the FCLA model on UE conditions. The results of the two algorithms and models are different. The equilibrium model and algorithm can capture more flow and has a higher flow-capturing ratio. What's more, the equilibrium model and algorithm can fully capture the expected flow in practice since the real transportation network is basically in UE status. The equilibrium model and algorithm presented in this paper is the first study of this kind to locate stations in cities and is useful in the deployment of hydrogen refueling stations. • Study flow refueling location allocation based on the user-equilibrium (UE) status. • A flow capturing location allocation (FCLA) model on UE conditions. • An equilibrium algorithm avoiding cannibalization to solve the model. • Compare between non-equilibrium method and equilibrium method in flow capturing. [ABSTRACT FROM AUTHOR]

Despite the good results that have been achieved in unimodal segmentation, the inherent limitations of individual data increase the difficulty of achieving breakthroughs in performance. For that reason, multi-modal learning is increasingly being explored within the field of remote sensing. The present multi-modal methods usually map high-dimensional features to low-dimensional spaces as a preprocess before feature extraction to address the nonnegligible domain gap, which inevitably leads to information loss. To address this issue, in this paper we present our novel I mbalance K nowledge- D riven Multi-modal Net work (IKD-Net) to extract features from multi-modal heterogeneous data of aerial images and LiDAR directly. IKD-Net is capable of mining imbalance information across modalities while utilizing a strong modal to drive the feature map refinement of the weaker ones in the global and categorical perspectives by way of two sophisticated plug-and-play modules: the G lobal K nowledge- G uided (GKG) and C lass K nowledge- G uided (CKG) gated modules. The whole network then is optimized using a joint loss function. While we were developing IKD-Net, we also established a new dataset called the N ational Agriculture Imagery Program and 3 D Elevation Program C ombined dataset in California (N3C-California) , which provides a particular benchmark for multi-modal joint segmentation tasks. In our experiments, IKD-Net outperformed the benchmarks and state-of-the-art methods both in the N3C-California and the small-scale ISPRS Vaihingen dataset. IKD-Net has been ranked first on the real-time leaderboard for the GRSS DFC 2018 challenge evaluation until this paper's submission. Our code and N3C-California dataset are available at https://github.com/wymqqq/IKDNet-pytorch. [ABSTRACT FROM AUTHOR]

Transportation agencies rely on scientific information to design and site infrastructure, plan operations, and make policies. However, the ways in which science interfaces with transportation policy are not straightforward: political incentives, usability challenges, implementation failures, and capacity limitations all shape how public organizations use scientific information. To build stronger connections between transportation-relevant science and policy it is necessary to better understand current patterns of scientific information use in practice, particularly as transportation agencies address an expanding purview of policy issues, such as climate change mitigation and intelligent transportation systems. This paper measures science use observed in transportation planning and project documents such as plans, proposals, impact assessments, and other deliverables that agencies are required to produce as part of the policy process. Using state, regional, and county transportation agencies in the state of California as a focal sample, this paper applies automated natural language processing (NLP) tools to analyze documents and identify references to scientific information. Our sample includes 5080 documents from 59 organizations involved in transportation governance in the state of California. We observe that the transportation science-policy interface is a cyclical system in which public agencies act in varying (and simultaneous) capacities as consumers, producers, funders, and brokers of scientific information. For instance, documents produced by state-level entities draw more heavily on academic literature, while regional and county-level agencies rely most heavily on reports produced internally by state agencies and by state-funded research institutes. Understanding where different transportation agencies access scientific information, and how scientific information flows between entities at different levels of government, can help researchers and science-policy boundary organizations increase the uptake of scientific products and design interventions to improve information access and use. • State-level transportation entities reference academic science most frequently. • Regional and county transportation organizations rely more heavily on grey literature. • Agencies draw on a broader range of scientific fields for references to emerging issues. • Transportation organizations act jointly as producers, funders, and users of science. [ABSTRACT FROM AUTHOR]

Inside orchards, turbulent coherent structures dominate the transport of heat, momentum, and moisture between the canopy and the atmosphere. Integrated quadrant analysis is a method to visualize the trajectory of individual turbulent coherent structures using in situ data from three-dimensional anemometry. In this paper, integrated quadrant analysis is used to characterize the turbulent transport of heat and momentum from two orchard experiments: one in the interrow space (the Canopy Horizontal Array Turbulence Study from Dixon, California in May and June 2007) and one in the crown of a tree (the Vertical Array Cherry Experiment from Linden, California in November 2019). By using the integrated quadrant analysis (IQA) method, this paper demonstrates the importance of the cross-wind velocity component in maintaining the turbulent coherent structures. Results from integrated quadrant analysis in three dimensions support the idea that the microfront is collocated with the boundary of a sweep and an ejection in a convective boundary layer. Moreover, in both orchards, there are preferred planar trajectories for individual coherent structures that do not depend on wind regimes. The statistical profile of the turbulence quantities, as well as individual coherent structures, are not appreciably different in the interrow space or within the crown. • IQA provides the 3D coherent structure trajectory in two orchard experiments. • Individual coherent structures show characteristic planar trajectories. • Turbulent coherent structures have peak intensity near the top of the canopy. • The cross-stream velocity is large during the passage of coherent structures. [ABSTRACT FROM AUTHOR]

• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

This paper presents the first systematic study on power control strategies for Modular-Gravity Energy Storage (M-GES), a novel, high-performance, large-scale energy storage technology with significant research and application potential. Addressing the current research gap in M-GES power control technology, we propose two corresponding compensation modes and several power control strategies for Hybrid M-GES and M-GES. The effectiveness of these strategies is validated through simulations on the MATLAB/Simulink platform, including tests using sinusoidal power and feasibility verification based on a natural California load curve. Additionally, we analyze the normal and abnormal operating conditions of M-GES. In conclusion, the characteristics of the proposed control strategies are summarized, providing a guideline for further research. [Display omitted] • Modular-gravity energy storage (M-GES) power control system studied. • Two compensation modes and four control strategies are systematically studied and validated. • The center switching method with hysteresis control (CSM-H) excels in continuous compensation mode. • The center switching method with hysteresis control and dead zone (CSM-HD) excels in overlap compensation mode. • Based on the power control system proposed in this paper, the M-GES plant performs highly in natural power systems. [ABSTRACT FROM AUTHOR]

The evaluation of communities' vulnerability to prolonged power outages offers valuable insights for prioritizing improvements in infrastructure resilience, thereby alleviating societal consequences. This study proposes a data-driven approach aiming at developing a Social Vulnerability Index (SoVI) to prolonged power outages leveraging three county-level datasets in California including (1) demographic features, (2) power outage factors, and (3) backup power factors. Furthermore, the study conducts a sensitivity analysis on three distinct datasets under two scenarios (Scenario 1: the current SoVI in 2022, Scenario 2: the prediction of SoVI in the year 2030). The results of Scenario 1 indicate that the counties with more affected customers, the number of power outages, and less education attainment tend to be more vulnerable to power outages in 2022. Scenario 1 reveals that the number of affected customers and power outages are the primary features influencing around 29% and 18% of counties, while educational attainment, public Electric Vehicles (EVs) chargers, and homes with rooftop photovoltaic (PV) substantially impact approximately 32%, 11%, and 8% of counties, respectively. However, in Scenario 2, crucial factors affecting the anticipated SoVI in 2030 include public EV chargers, houses with rooftop PV, power outages, and adults living alone. In contrast to Scenario 1, the prevalence of adults living alone has emerged as a notable factor impacting SoVI in 2030, while both scenarios underscore the pivotal role of EV chargers in influencing SoVI concerning power outages. The proposed SoVI facilitates informed policy decisions and infrastructure improvements in energy resilience, resource allocation, and disaster preparedness, contributing valuable insights for targeted interventions in these domains. • A data-driven approach was proposed to quantify the social vulnerability index (SoVI) to power outages. • This paper applies Principal Component Analysis (PCA) to identify the most correlated features from county-level datasets. • The paper conducts a sensitivity analysis to predict SoVI in2030 and evaluate how counties' dynamics change over time. • This paper provides insights into how policymakers can reduce the social vulnerability to power outages in California. [ABSTRACT FROM AUTHOR]

Environmental impact assessment (EIA) is a decision support tool that analyzes the environmental and social impacts of infrastructure projects. This paper focuses on the California Environmental Quality Act (CEQA), a law requiring EIA use in California, to examine where new infrastructure is proposed and whether EIA can shape infrastructure distribution and environmental justice through the review process. We analyze the temporal and spatial distribution of more than 7000 infrastructure projects and their environmental impacts as proposed under CEQA from 2011 to 2020. Using fixed-effects negative binomial regression to model the association between the number of initiated projects and existing socioeconomic and environmental conditions by census tract, and multinomial logistic regression to investigate determinants of a project's level of environmental review, we find an unequal distribution of infrastructure. We find that socio-economically vulnerable communities and those with greater burden of environmental pollution are less likely to be the site of newly proposed infrastructure, but that proposed projects tend to be beneficial, less-polluting infrastructure like parks or schools that could help redress past injustices. Moreover, projects proposed in vulnerable communities are less likely to receive the most stringent reviews or have their impacts mitigated. These findings suggest that CEQA interacts with distributive justice in contradictory ways. They also highlight the need to separately consider environmental amenities versus harms such that EIA processes do not stand as a barrier to constructing beneficial infrastructure in environmental justice communities. • New and upgraded infrastructure helps overcome historic distributional injustices burdening poor, minority communities. • In California, new infrastructure is proposed less often in socioeconomically vulnerable and more polluted neighborhoods. • California's environmental impact assessment (EIA) law is inconsistently addressing distributional justice. • Projects proposed in less vulnerable communities likely receive more stringent EIA reviews and have their impacts mitigated. • EIA is not designed to consider environmental amenities and may limit beneficial infrastructure from redressing past harms. [ABSTRACT FROM AUTHOR]

• Media, science, and legislation poorly represent the concerns of disadvantaged communities. • Developing effective policies requires addressing nuances and issue co-occurrence. • Community-specific knowledge is necessary to advance sustainable, effective solutions. A common approach in scientific research and policy is a commitment to develop projects or legislation trying to improve problems experienced by low-income and rural communities; however, lack of interaction with community members during the process tends to produce unsatisfactory results. We visited disadvantaged communities in the San Joaquin Valley of California and interviewed local stakeholders (community members and leaders, policy advocates, attorneys, and educators). Then we analyzed a corpus related to disadvantaged communities from a pool of California-related publications containing 154,000 scientific papers, 2.6 million newspaper articles, and 11,000 state legislation bills from 2017 to 2020 to estimate the frequency and quality of disadvantaged community representation. Here we present our findings describing the biases and gaps of knowledge by scientific papers, California newspaper articles, and legislation bills with respect to disadvantaged communities in California, and we suggest opportunities for scientists, media communicators, and policymakers to amplify the voices of these stakeholders. In all corpus categories, disadvantaged communities are underrepresented: about one in four Californians live in disadvantaged communities, but only one in 2000 news articles and scientific papers cover them. The concerns and priorities of disadvantaged communities do not match the public perspective of them depicted by the corpus. Developing effective policies requires addressing place-specific nuances and co-occurrence of structural inequities in partnership with local stakeholders. Holistic coverage in newspapers and community-based approaches are necessary platforms to increase awareness and sensibility about disadvantaged communities, helping tailor policy solutions, and building the political leverage needed to implement them. [ABSTRACT FROM AUTHOR]

How to deal with the transformation of place in the face of global capitalism is marked by many active debates. This paper dives into the transformation of the city of Solvang in California, from an agricultural village to a tourist destination. One way to analyse the process is to treat it as commodification, where values produced in places are being turned into exchangeable commodities. What results from such critical studies of capitalism too often result in apathy rather than positive action, it tends to deal less with 'the real world' than thought experiments about possible worlds. Another approach connects to the relational turn and the application of assemblage theory in studies of the dialectic process between economy and society. Critics question the ability of also assemblage theory to point us towards what should be done. We end up seeing assemblages everywhere, but then what? This paper makes the case for an alternative positive critical geography, inspired by Deleuze and Guattari (1972, 1987) and their conceptualization of desire as an active and positive force. Policies should depart from already existing subjectivities that have an interest in their social mileu and have a desire to make it even better. This is the appropriate ground for political engagement, the place to start if we want to contribute to a different future. An analytical scheme for a positive critical geography is presented and "tested out" in a case study of Solvang, where a "real" place continuously emerge. • Commodification of place as a process guided by motive forces caring for the place. • Policies departing from knowledge about the already existing interest in the social mileu and the desire to improve it. • The property of places emerges in a mix of local and non-local parts, but this process does not only happen, it is motivated. • Place as assemblage or commodity does not inform us sufficiently about what should be done. • Desire as departure point for studies about commodification. [ABSTRACT FROM AUTHOR]

Ambitious zero-emission vehicle (ZEV) adoption goals have been proposed to decarbonize the transportation sector, while the current market share pales in comparison. Although the distinct socio-economic characteristics of ZEV early adopters relative to mainstream car buyers are well understood, the two groups' preferences for ZEV attributes are not clear. This knowledge gap hinders the development of effective policies to achieve mass ZEV penetration goals. This paper examines consumers' preferences and willingness to pay for ZEV attributes based on 755 early adopters and 3493 mainstream consumers from the 2019 California vehicle survey data. Results show that early adopters are more sensitive to battery range, acceleration performance, home charging availability, and high occupancy vehicle lane access, while mainstream consumers attach greater importance to cost attributes (e.g., fuel and maintenance costs) and charging time. Moreover, the effects of monetary incentives are found to be significant for both groups, whereas neither early adopters nor mainstream consumers value the availability of public charging stations. The findings of this study inform targeted ZEV policymaking and marketing strategies in different adoption stages. • ZEV preferences are compared between early adopters and mainstream consumers. • Early adopters are more sensitive to battery range and acceleration performance. • Mainstream consumers attach greater importance to cost attributes and charging time. • Home charging availability significantly affects BEV utility for early adopters. • Public charging infrastructure availability plays a limited role for both groups. [ABSTRACT FROM AUTHOR]

Wildfires have caused devastating consequences to property and human and animal lives, which has become a global problem. Consequently, advanced wildfire prediction models are required to treat complex features and climate conditions. As a result, Machine Learning and Deep Learning models are becoming popular. However, creating a balanced true and false labeled dataset in the wild-fire domain is often challenging. Hence, One-class classification models are a promising approach to overcome this concern. In this paper, several One-class classification models are investigated; linear models (Principal Component Analysis and One-Class Support Vector Machines), outlier ensemble models (Lightweight On-line Detector of Anomalies and Locally Selective Combination of Parallel Outlier Ensembles), proximity-based models (Histogram-based Outlier Score and Rotation-based Outlier Detection), probabilistic models (Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions and Copula-Based Outlier Detection), neural network-based models (Deep One-class Classification and Adversarially Learned Anomaly Detection) is used in two case studies for California and Western Australian states. In conclusion, it was found that Deep learning-based One-class classification models outperform other models in terms of performance and feature importance of showcasing the effectiveness of deep neural network models in the wildfire prediction domain. [ABSTRACT FROM AUTHOR]

• This work studies the effects of reconciliation on probabilistic forecasts. • A parametric approach is used to generate probabilistic forecasts. • Reconciliation significantly improves the quality of probabilistic forecasts. • Day-ahead and hour-ahead forecasts are reconciled in a geographical hierarchy. Hierarchical forecasting and reconciliation are new to the field of solar engineering. Previous papers in this series, namely, Yang et al. (2017a, 2017b), and Yagli et al. (2019b), discussed various reconciliation techniques for deterministic solar forecasts obtained across spatio-temporal hierarchies. This paper extends the discussion into probability space, and studies how reconciliation can affect the performance of probabilistic forecasting. More specifically, qualities of the parametric predictive distributions before and after reconciliation are compared. Four minimum-trace-based reconciliation techniques are used to reconcile day-ahead and hour-ahead forecasts generated using two datasets: (1) distributed solar power generation for 318 simulated PV systems in California, and (2) satellite-derived irradiance over Arizona. The empirical result shows that reconciliation not only improves the accuracy of point forecasts, but also leads to high-quality predictive distributions in terms of sharpness, calibration, and skill score. Moreover, such improvement is quite general, and does not seem to depend on data, hierarchy structure, nor the underlying forecasting model. [ABSTRACT FROM AUTHOR]

In recent works by Yang et al. (2017a, 2017b), and Yang et al. (2019), geographical, temporal, and sequential deterministic reconciliation of hierarchical photovoltaic (PV) power generation have been considered for a simulated PV dataset in California. In the first two cases, the reconciliations were carried out in spatial and temporal domains separately. To further improve forecasting accuracy, in the third case these two reconciliation approaches were applied sequentially. During the replication of the forecasting experiment, some issues emerged about non-negativity and coherency (in space and/or in time) of the sequentially reconciled forecasts. Furthermore, while the accuracy improvement of the considered approaches over the benchmark persistence forecasts is clearly visible at any data granularity, we argue that an even better performance may be obtained by a thorough exploitation of spatio-temporal hierarchies. To this end, in this paper the spatio-temporal point forecast reconciliation approach is applied to generate non-negative, fully coherent (both in space and time) forecasts. New spatio-temporal reconciliation approaches are adopted, exploiting for the first time some relationships between two-step, iterative and simultaneous spatio-temporal reconciliation procedures. Non-negativity issues of the final reconciled forecasts are discussed and correctly dealt with in a simple and effective way. The spatio-temporal reconciliation procedures are applied to the base forecasts with forecast horizon of 1 day, of PV generated power at different time granularities (1 h to 1 day), of a geographical hierarchy consisting of 324 series along 3 levels. The normalized Root Mean Square Error (nRMSE) and the normalized Mean Bias Error are used to measure forecasting accuracy, and a statistical multiple comparison procedure is performed to rank the approaches. In addition to assuring full coherence and non-negativity of the reconciled forecasts, the results show that for the considered dataset, spatio-temporal forecast reconciliation significantly improves on the sequential procedures proposed by Yang et al. (2019), at any level of the spatial hierarchy and for any temporal granularity. For example, the forecasted hourly PV generated power by the new spatio-temporal forecast reconciliation approaches improve on the NWP 3TIER forecasts in a range from 4.7% to 18.4% in terms of nRMSE. • New approach to produce fully coherent solar forecasts both in space and time. • Complete previous discussions on deterministic solar forecast reconciliation. • Avoid negative forecasts and preserve full spatio-temporal coherence. • Significant improvement of the forecast accuracy through hierarchical forecasting. • Forecast skills over NWP hourly forecasts between 4.7% and 18.4%. [ABSTRACT FROM AUTHOR]

For more than a century, wildlife conservation in the United States has been built on the notion that nonhuman animal populations are resources to be regulated by law and managed efficiently, according to the best available science and in the public trust. This approach, known as the North American Model of Wildlife Management, has come under increasing criticism for excluding diverse viewpoints that have the potential to advance both conservation and environmental justice goals. How might the greater inclusion of Indigenous worldviews and Indigenous Studies concepts, such as radical relationality and kincentricity , improve western wildlife management? In this paper, we review three case studies of tribal wildlife stewardship programs in the land currently known as California—the Maidu Summit Consortium's beaver restoration project, the Karuk Tribe's elk management program, and the Yurok Tribe's condor recovery effort—that illuminate generative connections among ecological restoration, Indigenous cultural practices, community wellbeing, and environmental justice. Radical relationality and kincentricity offer enormous potential for informing stewardship and recovery efforts that produce more just outcomes for both people and wildlife. • Supporting Indigenous worldviews and practices improves outcomes for both people and wildlife. • Indigenous communities create innovative partnerships that hold culture and environmental justice at the center. • The NAM's tenets are flawed and complicit in settler colonialism. • Case studies in beaver, elk, and condor stewardship demonstrate Indigenous leadership in the application of relationality. [ABSTRACT FROM AUTHOR]

This paper explores the impact of parking prices on the decision to drive to work using a California household travel survey dataset and a discrete choice model. The paper tackles estimation challenges posed by insufficient parking information. The first challenge is the estimation of parking prices for those who do not drive, which is addressed by using a sample selection model. The second challenge is to understand the effect of the extent of the prevalence of Employer-Paid parking coupled with incentive programs offered in-lieu of parking. To address this challenge, two extreme scenarios are examined, and a range for the marginal effects of parking prices is estimated; one scenario assumes everyone receives Employer-Paid parking coupled with in-lieu of parking incentives, and the second assumes that no one is offered such incentives. The results suggest that higher parking prices reduce driving, regardless of the followed approach. It is estimated that a 10% increase in parking prices leads to a 1–2 percentage point decline in the probability of driving to work. This range varies with initial parking prices, where the lower end of the range increases at a decreasing rate, and the higher end peaks at $2.5 and decreases with higher prices. Moreover, there seems to be no evidence of sample selection bias. The evidence confirms that parking pricing can indeed be an effective transportation demand management tool. [ABSTRACT FROM AUTHOR]

• Food loss measurements for 20 hand-harvested crops in 123 fields were conducted on California farms in 2016 and 2017. • An average of 11,299 kg/ha of edible produce, or 31.3% of marketed yield, remained in fields after harvest. • When walk-by (unharvested) field losses of 2.4% are included, total losses were 33.7.% of marketed yields. • Food loss rates are highly variable and dependent on crop, prices, consumer and buyer preferences, and labor availability. • Grower surveys and interviews alone, that are not data-based, are not a reliable source for food loss estimates. Prevailing estimates of food loss at the farm level are sparse and often reliant upon grower surveys. A more comprehensive review of food loss at the farm level using field surveys is required to gain an adequate understanding of the depth of this issue. This paper details the results of 123 in-field surveys and 18 in-depth interviews of 20 different, hand-harvested field crops performed largely on midsize to large conventional farms in northern and central California. We also provide estimates of the percentage of fields that go unharvested, commonly known as walk-by fields. The results show that food loss is highly variable and largely dependent upon the crop, variety, market price, labor costs, grower practices, buyer specifications, and environmental conditions. On average, we found 11,299 kg/ha of food loss at the farm level, which equates to 31.3% of the marketed yield. When walk-by losses are included, this figure rises to 33.7%. Our paper also demonstrates that grower estimates are typically very unreliable for estimating on-farm food losses. Actual, measured edible food loss exceeded growers' estimates by a median value of 157%. Strategies to utilize this lost produce could play a significant role in reducing the impact of agriculture on the environment and providing food for the rapidly growing population. [ABSTRACT FROM AUTHOR]

The volumetric instability of steel slag hinders its use in pavement layers and embankments. Accordingly, evaluating the swelling potential of steel slag and designing volumetrically stable steel slag mixtures is required to promote its sustainable utilization. This paper focuses on the comparison of the swelling response of basic-oxygen-furnace steel slag (BOFS), electric-arc-furnace steel slag (EAFS), BOFS-fly ash and BOFS-glass powder mixtures based on the results of three different types of tests: (i) long-term California bearing ratio (CBR) swelling, (ii) heated water bath swelling and (iii) autoclave tests. BOFS mixtures were prepared using glass powder or class F fly ash with 5%, 10%, and 20% replacement ratios. The compaction characteristics, permeability, and California bearing ratio (CBR) of steel slag and steel slag mixtures were also assessed within the framework of a detailed discussion of their swelling response. At the end of swelling tests, the measured maximum 1D strains of EAFS were all at negligible levels. Based on the test results, BOFS and BOFS-glass powder mixtures had ∼5.0–5.5% swelling strains after 20.5 months without signs of stabilization. 20% class F fly ash replacement stabilized the swelling strains of the mixture at about 0.75% after ∼3 months of monitoring in long-term tests. 1D swelling rates measured for all BOFS-class F fly ash mixtures decreased with increasing fly ash content. CBR and swelling response of BOFS-class F fly ash mixtures were more favorable compared to those of BOFS. The heated water bath tests provided higher estimates of the swelling strains compared to those obtained from autoclave tests for the steel slag mixtures that contain 15% or more fines. The results of this study showed that glass powder replacement is not effective in alleviating the swelling of BOFS. The volumetrically stable BOFS-class F fly ash mixtures indicated favorable mechanical properties for their use in pavement layers. [Display omitted] • Glass powder replacement is not effective in alleviating the swelling of BOF slag. • 20% class F fly ash replacement can suppress the 1D strains of BOF slag. • Autoclave may provide relatively low estimates of strain for steel slag with 15% or more fines. • Separate handling of EAF and ladle steel slag in the plants can help their utilization. • BOF steel slag-class F fly ash mixtures show potential to be used in bound pavement layers. [ABSTRACT FROM AUTHOR]

• Deriving/estimating plain strain fracture toughness for ASTM A322 material. • Developing flaw tolerance limit plots for ASTM A322 and ASTM A722 material. • Estimating remaining service life of existing post-tension grouted anchors. Brittle failure of grouted post-tensioned anchors used in trunnion girders at dams has given rise to concerns and questions surrounding their continued use and remaining service life. This paper reviews the brittle failure of five grouted post-tensioned anchor rods due to stress corrosion cracking at dams in California. To provide an early warning system to dam owners and expand on available resources for practicing engineers, this study derives plain strain fracture toughness from the failed grouted post-tension anchors using cross sectional photos. Using derived and available fracture toughness values, flaw tolerance limit plots, which can quickly illuminate the severity of a defect for various anchor diameters, were developed for semicircular and straight fronted flaw types using linear elastic fracture mechanics and stress intensity factors for both ASTM A322 and ASTM A722 materials. To help demonstrate the use of flaw tolerance limit plots and estimate the remaining service life of an in-situ anchor, an example problem was developed. [ABSTRACT FROM AUTHOR]

• Case study of wetlands conservation for carbon sequestration in California, USA. • Defines framework for assessing justice of state investment in climate mitigation. • Most vulnerable communities received least funding for wetlands conservation. • Wetlands' climate adaptive co-benefits concentrated in least vulnerable communities. • "Just mitigation" requires producing adaptive co-benefits for most vulnerable. Using the case of wetlands in California, USA, this paper defines (and assesses strategies for advancing) an understudied corollary of maladaptation and "just adaptation": "just mitigation." Wetlands sequester carbon, making their conservation and restoration important for climate mitigation. They also offer co-benefits for climate adaptation, such as greenspace that mitigates the urban heat island and improves local environmental health. However, if such co-benefits are concentrated in the least vulnerable communities, the result would be "unjust mitigation." This analysis uses a distributive justice lens to compare environmental justice indicators between areas of past and potential investment in wetlands conservation and restoration. On average, areas with greater pollution burden and social vulnerability and less greenspace have received less investment in wetlands conservation and restoration and contain fewer wetlands that could receive investment earmarked for wetlands in the future. This begs the question of what degree of such inequality is acceptable in exchange for reducing overall carbon emissions. Advancing "just mitigation" requires prioritizing wetlands near environmentally burdened communities. However, if such wetlands have reduced sequestration potential due to ecological damage, the goals of mitigation, adaptation, and environmental justice may be in tension. [ABSTRACT FROM AUTHOR]

Liquefaction hazard analyses are needed for regional-scale applications, such as risk analysis and upgrades of electrical transmission, water conveyance, and highway systems. In this paper, we expand well-established site-specific liquefaction triggering and manifestation analysis methods for regional scale application by explicitly addressing the uncertainty in groundwater conditions, soil behavior classification, and cyclic resistance. The uncertainty inherent to regional-scale geotechnical properties is quantified by considering the inputs into conventional liquefaction triggering and manifestation analyses as random variables. With this flexible and extensional formulation, the geostatistical properties needed for regional scale analyses may be estimated by either averaging borehole information or spatially interpolating geotechnical properties across broad geologic deposits. We demonstrate the utility of the method by analyzing the liquefaction hazard throughout the south San Francisco Bay area, California, U.S.A. The resulting estimates of the probability of liquefaction manifestation tend to be lower than previously published estimates, largely owing to the geologic structure of the area and the uncertainty in liquefaction surface manifestation once liquefaction is triggered at depth. Uncertainties in groundwater conditions, soil behavior classification, penetration resistance, and liquefaction manifestation are quantified by the regional scale analyses, which provide a complete and systematic evaluation of the liquefaction hazards, localize the uncertainties in subsurface conditions, and facilitate supplemental investigations at areas of high uncertainty to fill data gaps. • Existing liquefaction models are extended for regional-scale application. • Flexible model allows deposit-scale averages or spatially interpolated properties. • Geologic structure and uncertain groundwater depth are applied in the model. • Example application in southern San Francisco Bay area, California, U.S.A. [ABSTRACT FROM AUTHOR]

Many nations and jurisdictions have legalized non-medical adult use of cannabis, or are considering doing so. This paper contributes to knowledge of adult use legalization's associations with cannabis use disorder (CUD) treatment utilization. This study collected data from a dataset of all publicly funded substance use disorder treatment delivered in California from 2010 to 2021 (1,460,066 episodes). A logistic regression model estimates adult use legalization's impacts on CUD treatment utilization using an individual-level pre-post time series model, including individual and county-level characteristics and county and year-fixed effects. Adult use legalization was associated with a significant decrease in the probability of admission to CUD treatment (average marginal effect (AME): −0.005, 95 % CI: −0.009, 0.000). Adult use legalization was also associated with a decrease in the probability of admission to CUD treatment for males (AME: −0.025, 95 % CI: −0.027, −0.023) Medi-Cal beneficiaries (AME: −0.025, 95 % CI: −0.027, −0.023) adults ages 21+ (AME: −0.011, 95 % CI: −0.014, −0.009) and Whites (AME: −0.012, 95 % CI: −0.015, −0.010), and an increase in the probability of admission to CUD treatment for patients referred from the criminal justice system (AME: 0.017, 95 % CI: 0.015, 0.020) and Blacks (AME: 0.004, 95 % CI: 0.000, 0.007) and Hispanics (AME: 0.009, 95 % CI: 0.006, 0.011). Adult use legalization is associated with declining CUD treatment admissions, even though cannabis-related problems are becoming more prevalent. Policies and practices that protect public health, and engage people with CUD in treatment are needed. • Pre-legalization, 19 % of SUD admissions were CUD, compared to 9 % post-legalization. • Adult use legalization was associated with a decrease in CUD treatment admissions. • Differences existed in legalization's associations on different demographic groups. [ABSTRACT FROM AUTHOR]

Ecocultural restoration involves the reciprocal repair of ecosystems and revitalization of cultural practices to enhance their mutual resilience to natural and anthropogenic disturbances and climate change stressors. Resilient ecocultural systems are adapted to retain structure and function in the face of disturbances that remain within historical ranges of severity. To assist in ecocultural restoration and management, understanding how a system has historically responded to different types of disturbances is therefore invaluable in understanding how social-ecological resilience can be maintained in the face of future stressors and disturbances. However, records of disturbances and ecocultural responses can be limited for certain landscapes and human communities. In this methods paper, we demonstrate a mixed-method process for integrating oral history, field-based knowledge, archival information, and historical and contemporary aerial images to gain insight into the changes on the Klamath River in Northern California from the 1940s through 2020. We georegistered historical imagery, quantified changes between land cover classes, and contextualized these classifications with qualitative assessments of changes in larger surrounding areas. By synthesizing these data sources with field measurements, mining and other land survey maps, timber management plans, fire and flood histories, and interviews with members of the Karuk Tribe, we were able to reconstruct the land use and land cover change histories at five sites. We noted that recovery of canopy cover from fire and logging practices was faster than for flood, which was faster than recovery from mining, consistent with the relative severity of likely soil disturbance. By combining different sources of information with complementary strengths, we were able to provide managers with site-specific information on recovery from different types of disturbance. Though this approach was labor-intensive, with emerging tools for supervised classification of high-resolution imagery, mixed-method analytical historical ecology could be applied more broadly, supporting ecocultural restoration on a larger scale. [Display omitted] • Reconstructing landscape histories helps understand ecosystem response to disturbance. • Combining qualitative and quantitative analysis helps reconstruct land-use. • Synthesizing many historical data sources improves ecocultural restoration planning. • Manual classification could improve with machine learning 'human-in-the-loop' tools. [ABSTRACT FROM AUTHOR]

• The California mandate for solar PV systems on residential rooftops will have a negative occupational safety impact. • Results indicate that 16.6 recordable non-fatal injuries are expected to occur between 2020 and 2029 as a result of this policy. • The impact could be reduced by 18% by eliminating work on roofs. Introduction: A 2018 change to the California building code mandates that new residential construction in the state include rooftop solar photovoltaic power systems beginning in 2020. As residential construction (especially work on rooftops) is among the more dangerous occupations in the United States, this paper seeks to quantify the increased risks to workers as a result of this mandate. Method: An analysis of the trends by occupation of nonfatal safety incident rates in the United States combined with a Monte Carlo simulation provide an estimate of the uncertain impact of this new mandate. Results: Recordable safety incidents are anticipated to increase by a total of 16.6 incidents (standard deviation = 1.0 incidents) over the 2020–2029 time period as a result of this policy change. However, lessons from Germany and other industries offer potential avenues to reduce the negative social impact of this mandate. Conclusions: While it is not possible to increase employment in any sector without increasing the expected number of occupational injuries to some degree, these results indicate that risks could be considerably reduced by making solar PV system design decisions that increase worker productivity and reduce roof exposure time. Practical Applications: Changes such as eliminating work on roofs could decrease the expected number of recordable injuries over the 10-year period by 0.30 incidents per year (a reduction of 18%). [ABSTRACT FROM AUTHOR]

For renewable hydrogen to be a significant part of the future decarbonized energy and transportation sectors, a rapid and massive build-out of hydrogen production facilities will be needed. This paper describes a geospatial modeling approach to identifying the optimal locations for renewable hydrogen fuel production throughout the state of California, based on least-cost generation and transport. This is accomplished by (1) estimating and projecting California renewable hydrogen demand scenarios through the year 2050, (2) identifying feedstock locations, (3) excluding areas not suitable for development, and (4) selecting optimal site locations using commercial geospatial modeling software. The findings indicate that there is a need for hundreds of new renewable hydrogen production facilities in the decades preceding the year 2050. In selecting sites for development, feedstock availability by technology type is the driving factor. • Extensive build-out of new renewable hydrogen facilities is needed for hydrogen economy. • Zoning and terrain limit the potential renewable hydrogen production facility sites. • Proximity to feedstock dominates cost-optimal site selection. [ABSTRACT FROM AUTHOR]

• Impact of COVID-19 on ECE programs differed greatly by program type, funding source. • Family child care homes fared worse in most measures of economic well-being with direct implications for individual providers during COVID-19. • Lower attendance, staffing concerns were pandemic challenges for center-based care. • Voucher-receiving centers more likely to face negative impacts during COVID-19. • Head Start/state-contract centers more able to support staff well-being in pandemic. The COVID-19 crisis has overwhelmed and weakened the United States early care and education (ECE) sector, jeopardizing a system that was already precariously situated atop a weak foundation. While multiple national- and state-level studies have highlighted the overwhelming impacts of the pandemic on the ECE sector, little has been reported about how much variation in impacts exists, and in what forms, within the ECE sector. Based on a statewide survey of 953 licensed care providers in California conducted in June 2020, this paper examines the impact of COVID-19 experienced by ECE providers, focusing on the variations between centers and family child care homes (FCCs) and among center-based programs. Results indicate that the challenges programs face differ greatly depending on program type and funding source. Compared to center-based programs, FCCs fared worse in most measures of economic hardship that directly impact individual providers with medium to large effect sizes. Centers were more likely than FCCs to struggle with reduced attendance and changes in program operations by medium to large effect sizes and report staffing challenges by small to medium effect sizes. Among the center-based programs, subsidized programs holding contracts with Head Start or the California Department of Education (such as state preschool programs) were more stable and better able to financially support their staff during the pandemic, with effect sizes ranging from medium to large. Centers receiving government subsidies in the form of vouchers were more likely to be negatively impacted by the pandemic compared to unsubsidized centers and Head Start and state-contracted centers. Implications for future research and policy are discussed in the context of addressing the complex delivery system of ECE services and supporting outcomes that are effective and equitable for children, families, and the ECE workforce. [ABSTRACT FROM AUTHOR]

• AV disengagement on public roads dominated by causes due to a planning issue. • AV disengagement induced by lacking certain numbers of radar and LiDAR sensors. • Drivers' take-over time impacted by disengagement cause and roadway characteristics. • To improve AV disengagements five or more radar sensors are needed. • Optimal number of LiDAR sensors to be installed is 3 or 4. Automated vehicles (AV) testing on the public roads is ongoing in several states in the US as well as in Europe and Asia. As long as the automated vehicle technology has not achieved full automation (Level 5), human drivers are still expected to take over the steering wheel and throttles when there is an automated vehicle disengagement. However, contributing factors and the mechanism about automated vehicle-initiated disengagement has not been quantitatively and comprehensively explored and investigated due to the lack of field test data. Besides, understanding human drivers' perception and promptness of reaction to the AV disengagement is essential to ensure safety transition between automated and manual driving. By harnessing California's Autonomous Vehicle Disengagement Report Database, which includes the AV disengagement data from field tests in 2016–2017, this paper quantitatively investigated the AV disengagement using multiple statistical modeling approaches that involve statistical modeling and classification tree. Specifically, the paper identifies the contributing factors impacting human drivers' promptness to AV disengagements, and quantitatively investigates the underlying causes to AV disengagements. Results indicate that current AV disengagement on public roads is dominated by causes due to a planning issue. The cause of an AV disengagement is significantly induced by lacking certain numbers of radar and LiDAR sensors installed on the automated vehicles. These thresholds of these sensors needed are revealed. Cause of disengagement and roadway characteristics significantly impact drivers' take-over time when facing an AV disengagement. AV perception or control issue-based disengagement can significantly extend drivers' perception-reaction time to take over the driving. The quantitative knowledge obtained ultimately facilitates revealing the mechanisms of the automated vehicle disengagements to ensure safe AV operations on public roads. [ABSTRACT FROM AUTHOR]

Abstract Prior research on plug-in electric vehicle (PEV) adoption has revealed that early adopters tend to be wealthy consumers, this may mean that the benefits of PEVs are not being equitably distributed. Extensive research has shown that low-income and minority commutes are disproportionately impacted by environmental and transportation injustice. PEVs can contribute to importing air quality and could provide lower cost and more reliable transportation to low-income and minority communities if they are deployed there. This paper takes an early quantitative look at PEV adoption in disadvantaged communities (DACs), which are census tracts in California that suffer from a combination of economic barriers and environmental burden. We use six datasets to examine PEV market share, socioeconomic characteristics of PEV owners, and PEV charging infrastructure. Analysis confirms that adoption of both new and used PEVs in DACs occurs at very low rates - 5.7% and 8.7% of all PEV sales, respectively - that are disproportionate with the number of households that reside in these areas. Owners of new or used PEVs in DACs have slightly lower incomes than PEV owners in non-disadvantaged communities. However, as a group they have higher incomes, are higher educated, and fewer are home-renters than the DAC average, indicating that they are not representative of their surrounding community. Encouragingly, charging infrastructure is present in DAC census tracts, suggesting that further PEV adoption could be supported. Additionally, there are higher proportions of used PEVs in DACs than new PEVs, which may indicate potential for adoption of these lower-priced vehicles, however rates of adoption are still low. Despite the considerable benefits that PEVs could offer in DACs, there are still substantial barriers to PEV. Key barriers for policy-makers to continually address are the prohibitive price of the technology, lack of knowledge about or ease of accessing PEV incentives, and lack of access to public or private charging infrastructure located near multi-unit housing. Highlights • This paper investigates EV adoption in disadvantaged communities (DACs) in California. • Adoption of new or used electric vehicles is lower in DACs than in non-DACs. • Some DAC residents do purchase new and used electric vehicles. • These buyers are not typical DAC resident though, having high household incomes. • More work is needed to increase electric vehicle adoption in DACs. [ABSTRACT FROM AUTHOR]

Achieving further reductions in building electricity usage requires a detailed characterization of electricity consumption in homes. Understanding drivers of consumption can inform strategies for promoting conservation and efficiency. While there exist numerous approaches for modeling building energy demand, the use of regularization methods in statistical models can address challenges inherent to building energy modeling while also enabling more accurate predictions and better identification of variables that influence consumption. This paper applies five regularization techniques to regression models of original survey and electricity consumption data for more than one thousand households in California. It finds that of these, elastic net and two extensions of the lasso—group lasso and adaptive lasso—outperform other approaches in terms of prediction accuracy and model interpretability. These findings contribute to methodological approaches for modeling energy consumption in buildings as well as to our understanding of key drivers of consumption. The paper shows that while structural factors predominate in explaining annual electricity consumption patterns, habitual actions taken to save energy in the home are important for reducing consumption while pro-environmental attitudes and energy literacy are not. Implications for improving building energy modeling and for informing demand reduction strategies are discussed in the context of the low-carbon transition. • Reviews literature on modeling energy consumption and determinants of residential electricity consumption. • Reviews regularization methods in regression analysis to improve prediction and model interpretability. • Assembles dataset of 58 predictive factors and annual electricity usage for one thousand households California. • Applies five regularization techniques to these data to compare model prediction and variable selection. • Highlights influence of occupant socio-demographics, physical dwelling characteristics, and occupant behaviors. [ABSTRACT FROM AUTHOR]

Highlights • Appropriately effective DUI deterrence is described in a systems framework. • Granger-causality analysis is used to examine dynamic and feedback effects. • An exploratory diagnostic framework is demonstrating for California, 1990–2010. Abstract This paper builds a method to detect the apparent restrictiveness or permissiveness of communities towards drunk-driving. A framework of three mutually interacting community domains is used to motivate a minimum set of DUI patterns to be expected from an appropriately deterrent environment. Based on the (simplified) system dynamics model, an empirical estimation strategy and scoring methodology is outlined. This "macroscopic" approach is demonstrated using results from time-series panel analyses of California's 58 counties for the years 1990 to 2010 (Van Vleck et al., 2017). The process successfully classified three-quarters of California counties, encompassing almost 90% of the state population. The paper demonstrates a potential tool to classify communities' systemic behavior toward drinking-and-driving and other enforcement-sensitive subjects. [ABSTRACT FROM AUTHOR]

In this paper, we estimate the Genuine Progress Indicator (GPI), which is a measure of sustainable economic welfare, for California for a five-year period, 2010–2014. This relatively short time period, which covers the recovery from a deep recession, allows us to examine how integration of environmental degradation, nonmarket activities, and inequality affects the GPI of California. The California GPI is only 52% of Gross State Product (GSP) – comparable to other GPI to GDP proportions – because the large negative environmental components offset the large positive social components and because many government expenditures, such as those related to defence and law enforcement, are excluded. Between 2010 and 2014, California GSP grew 9.2% and GPI grew 9.8%. We evaluate our estimation of the California GPI (CA-GPI) in two specific ways. First, we compare California’s GPI to an alternative indicator of social welfare, the Human Development Index (HDI) for California. Our comparison points out that the GPI is a more holistic measure of sustainable economic well-being, although the HDI is useful for evaluating educational attainment, life expectancy, or earnings across regions or demographic groups in the state. Second, we compare our estimation of CA-GPI to the California results from a recent GPI estimation for all fifty states for 2011, in order to evaluate how different methodological decisions and data selection affect the results. Our overall estimate is 13 percent higher, with the primary differences reflecting discrepancies in methodological assumptions or data sources in the calculation of a few key variables, including the value of time used for calculating nonmarket activities. These two estimates of CA-GPI allow us to analyze the sensitivity of two widely used approaches for calculating the GPI, and the sensitivity of using California-specific public data sources compared to national public data sources (scaled to California). The variation in the two California GPI estimations demonstrates the importance of standardizing the method and the data sources, with the goal of creating a viable alternative to the GDP for measuring economic performance. Comparison of the two GPI estimations shows how the use of region-specific data increases the accuracy of estimates, which is important for evaluating regional outcomes and trends over time. However, using data and method that prioritizes standardization is essential for cross-regional comparability, even though the trade-off is diminished regional accuracy. The paper concludes with a discussion of the uses of the GPI to evaluate policies, and suggests fruitful steps forward. [ABSTRACT FROM AUTHOR]

An alarming, consistent increase in U.S. pedestrian fatalities since 2009 culminated in a 28-year high of 6,283 pedestrians killed in 2018. Yet these numbers obscure a second alarming trend: 75% of pedestrian fatalities occur in darkness, and nearly 90% of the increase in pedestrian fatalities from 2009 to 2018 occurred in darkness. This paper examines data on pedestrian fatalities at the national level and pedestrian fatalities and serious injuries in California from 2012 to 2017 to better understand correlates of severe pedestrian injuries in darkness. Binary and multinomial logit models reveal that variables related to roadway design and operations (e.g., speed limits, number of lanes, roadway type, and presence of traffic control) – but not speeding – are significantly associated with the likelihood of a pedestrian fatality or serious injury occurring in darkness as compared to daylight. Critically, these factors – which were consistent for fatalities regardless of lighting presence and roadway type, with few exceptions – are all worse in darkness because they are negatively affected by a lack of visibility. Alcohol usage by drivers or pedestrians and sociodemographic characteristics were also positively associated with severe injuries in darkness. Our findings urge an explicit consideration of pedestrian safety in darkness in all future design and retrofit decisions, and particularly on higher-speed, multi-lane roadways. Immediate solutions include roadway designs and policies that slow drivers, particularly at night, and that increase illumination and driver attention, such as through additional roadway lighting, high-visibility countermeasures and protected crossings, and adaptive lighting and detection technology for vehicles. • Pedestrian fatalities on multilane roads significantly more likely in darkness. • Fatalities at speeds over 30 mph are significantly more likely in darkness. • Black and Native American pedestrian deaths significantly more likely in darkness. • Efforts to slow drivers and provide safe crossings in darkness are needed. • Roadway design efforts should explicitly consider pedestrian safety in darkness. [ABSTRACT FROM AUTHOR]

• Analysis of 275-day long time series of county level COVID-19 data in California state. • Multiple change point estimation in the framework of mixture modeling and model-based clustering. • Change point given by gap between logit transformed segments in negative binomial nonhomogeneous Levy process. • Three geographically meaningful clusters, each with several change points indicating the spread and decline of infection. In this paper, a model-based clustering algorithm relying on a finite mixture of negative binomial Lévy processes is proposed. The algorithm models heterogeneous stochastic count process data and automatically estimates multiple change points upon fitting the mixture model. Such change point estimation identifies time points when deviation from the standard process has occurred and serves as an important diagnostic tool for analyzing temporal data. The proposed model is applied to the COVID-positive ICU cases in the state of California with very interesting results. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between smartphone use by drivers and traffic accidents in California between 2001 and 2013. In order to estimate smartphone use, we first show that widespread adoption of modern smartphones began in 2009 after the release of the iPhone 3G and T-Mobile G1. This information is combined with annual 3G coverage maps that are constructed from cellular tower information in a machine learning framework. In a difference-in-differences framework, we estimate the combined effect of smartphone adoption and 3G coverage along quarter-mile road segments. Controlling for census tract population density, road and year fixed effects, Poisson regression results show that there is a statistically significant increase in the traffic accident rate along a road segment when smartphone use becomes possible. Our preferred specification suggests smartphones caused accident rates to increase by 2.9 percent, resulting in 3500 additional accidents per year in California. Event study results rule out the possibility that our smartphone treatment is capturing a trend in the accident rate. The results are robust to a variety of specifications and consistent with individual-level studies showing that cell phone use leads to lower driving quality. The findings also provide guidance for policies aimed at reducing cell phone related accidents and distracted driving. [ABSTRACT FROM AUTHOR]

Ever since California experienced its energy crisis two decades ago, dynamic pricing of electricity has been the topic of discussion in numerous conferences and papers. Scores of pilots involving some 400 treatments of time-varying rates have been done to assess customer response to dynamic pricing around the globe, beginning with California's Statewide Pricing Pilot that ran from 2003 to 04. In the past few years, we are witnessing large scale deployment of static time-varying rates in states such as California and Michigan. Colorado is about to embark on that journey. However, other than OG&E's deployment of dynamic pricing, we have not seen much deployment of dynamic pricing in North America. California's power outages in August 2002 have rekindled interest in the topic. But almost all of the discussion about dynamic pricing has focused on summer peaking utilities. In this paper, we discuss the experience of Hydro Quebec, a winter peaking utility in Canada. Hydro Quebec has tested both critical-peak pricing and peak-time rebates. The results are very encouraging and quite consistent with results from summer peaking utilities. The article is based on a question and answer format with Frederic Pelletier, who advises Hydro Quebec on tariff strategy. He had posted some results from their dynamic pricing deployment on LinkedIn in response to some results I had posted from three-pilots with TOU rates that had been carried out in Maryland. I put a few questions to Frederic and this article evolved out of that conversation. In the article that follows, the questions are mine and the answers are exclusively his. [ABSTRACT FROM AUTHOR]

• Factors affecting injury severity in rollover crashes were explored. • Random parameters logit model with unobserved heterogeneity in means and variances. • Findings reveal temporal instability of model specifications across individual years. • Aggregate-to-component shift quantified using out-of-sample prediction. • Implications for mitigating the associated driver injury severity. Single-vehicle rollover crashes have been acknowledged as a predominant highway crash type resulting in serious casualties. To investigate the heterogeneous impact of factors determining different injury severity levels in single-vehicle rollover crashes, the random parameters logit model with unobserved heterogeneity in means and variances was employed in this paper. A five-year dataset on single-vehicle rollover crashes, gathered in California from January 1, 2013, to December 31, 2017, was utilized. Driver injury severities that were determined to be outcome variables include no injury, minor injury, and severe injury. Characteristics pertaining to the crash, driver, temporal, vehicle, roadway, and environment were acknowledged as potential determinants. The results showed that the gender indicator specified to minor injury was consistently identified as a significant random parameter in four years' models and the joint five-year model, excluding the 2016 crash model where the night indicator associated with no injury was observed to produce the random effect. Additionally, two series of likelihood ratio tests were conducted to assess the year-to-year and aggregate-to-component temporal stability of model estimation results. Marginal effects of explanatory variables were also calculated and compared to analyze the temporal stability and interpret the results. The findings revealed an overall temporal instability of model specifications across individual years, while there is no significant aggregate-to-component variation. Injury severities were observed to be stably affected by several variables, including improper turn indicator, under the influence of alcohol indicator, old driver indicator, seatbelt indicator, insurance indicator, and airbag indicator. Furthermore, the year-to-year and aggregate-to-component shift was quantified and characterized by calculating the differences in probabilities between within-sample observations and out-of-sample predictions. The overall results imply that continuing to expand and refine the model to incorporate more comprehensive datasets can result in more robust and stable injury severity prediction, thus benefiting in mitigating the associated driver injury severity. [ABSTRACT FROM AUTHOR]

Forest fires cause every year damages to biodiversity, atmosphere, and economy activities. Forest fire simulation have improved significantly, but input data describing fire scenarios are subject to high levels of uncertainty. In this work the two-stage prediction scheme is used to adjust unknown parameters. This scheme relies on an input data calibration phase, which is carried over following a genetic algorithm strategy. The calibrated inputs are then pipelined into the actual prediction phase. This two-stage prediction scheme is leveraged by the cloud computing paradigm, which enables high level of parallelism on demand, elasticity, scalability and low-cost. In this paper, all the models designed to properly allocate cloud resources to the two-stage scheme in a performance-efficient and cost-effective way are described. This Cloud-based Urgent Computing (CuCo) architecture has been tested using, as study case, an extreme wildland fire that took place in California in 2018 (Camp Fire). • Data-driven calibration to deal with uncertainty in forest fire spread prediction. • Cloud-based urgent computing implementation of a two-stage prediction model. • Use of utility function to deal with the cost-performance trade-off. • Validation against a deadly and destructive wildfire with promising results. [ABSTRACT FROM AUTHOR]

Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event, thus investigating the resilience of an area over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel framework utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires. Taking the Mendocino Complex and Camp wildfires - the largest and most deadly wildfires in California to date, respectively - as case studies, we capture the robustness and vulnerability of communities based on human mobility data from 2018 to 2019. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, providing a new lens to study disasters and their long-term impacts on society. • Quantifying community resilience is an open research challenge • This paper develops a novel framework to quantify resilience after a disaster using network analysis • Using human mobility data associated with two wildfires, we measure robustness and vulnerability of communities • Results show community resilience closely tied to socio-economic and built environmental traits of the affected areas • Our approach paves a way to study disasters and their long-term impacts on society [ABSTRACT FROM AUTHOR]

The quality of traffic services depends on the accuracy and completeness of the collected traffic data. However,the existing traffic data imputation methods usually only rely on the predefined road network structure to capture the spatio-temporal features and only consider the imputation effect from a single perspective, which are very limited for imputation of different missing patterns of road traffic data. In this paper, we propose a novel deep learning framework called Hierarchical Spatio-temporal Graph Convolutional Neural Networks(HSTGCN) to impute traffic data,through the macro layer and the road layer. The model constructs macro graph of the road network based on the data temporal correlation clustering, which can mine the temporal dependencies of road traffic data from a hierarchical perspective. Besides, a temporal attention mechanism and adaptive adjacency matrix are introduced in the road layer to better extract the spatio-temporal information of the road traffic data. Finally, we use graph convolution neural networks to learn the spatio-temporal feature representations of the road layer and macro layer, which are then fused to achieve data imputation. To illustrate the efficient performance of the model, experiments are conducted on traffic data collected from California and Seattle. The proposed model performs better than the comparison model for traffic data imputation. • A full dynamic graph is built based on the urban road structure. • Extracting spatio-temporal features from multiple dimensions using hierarchical thinking. • Gate-GCN perform effective feature selection. [ABSTRACT FROM AUTHOR]

Solar farms have PV arrays in arid and semi-arid regions where ensuring the system's reliability is paramount and face uncertain events like dust storms. The deposition of random dust patterns over panel arrays is called uneven soiling, which diminishes the power generation of such farms. This paper finds the most suitable hybrid algorithm model, the wavelet transform-based support vector regression variants (WT-SVR) algorithm, and the empirical model decomposition-based support vector regression variants (EMD-SVR) to predict the extent of soiling levels and uncertain events on PV arrays. The soiling dataset is taken from NREL's Soiling Station Number 3 in Imperial County, Calipatria, California, from December 30, 2014, to December 31, 2015. This research tested four SVR variants on soiling data, viz., ε SVR, LSSVR, TSVR, and ε TSVR, then compared with the benchmark random forest. The hyperparameters for each model are meticulously tuned to enhance the robustness of the trained algorithms. Results reveal that the WT-TSVR model outperforms the WT-SVR model in terms of wavelet transform decomposition by a margin of 91.6%. Similarly, the EMD-TSVR model showcases an 85.7% enhancement in performance over the EMD-SVR model based on empirical mode decomposition. All SVR variants outperform the benchmark model (RF). Furthermore, EMD models exhibit enhanced efficiency in forecasting random events compared to WT, which is attributed to their reduced computational time. This model applies to multi-cleaning agent robots, aligning with recommendations from the state-of-the-art literature. [ABSTRACT FROM AUTHOR]

• We analyzed trends and breakpoints for three streamflow signatures of drying patterns in 540 non-perennial streams over CONUS • Nonlinear changes in streamflow are more common than linear changes for all three stream intermittency signatures. • The past behavior of non-perennial streams might be a poor predictor of future drying patterns Stream drying patterns – including duration, timing, and dry-down rates – affect aquatic ecosystems and nutrient exports in non-perennial streams. Because hydrologic processes are often nonlinear, changes in drying may also be nonlinear, but analyses of historical changes in stream drying to date have not characterized the frequency or functional forms of nonlinear change. Understanding the extent of nonlinear change in non-perennial streams is essential for advancing our fundamental knowledge of hydrological processes, aquatic ecosystems, and watershed functioning under a warming climate. This paper uses a polynomial-based trend detection technique (PolyTrend) to analyze the linear and nonlinear trend behaviors of three intermittency signatures (annual no-flow days specifying longer or shorter drying duration, day of first no-flow occurrence specifying timing of stream drying, and days from peak to no-flow specifying dry-down rates) at 540 non-perennial gage stations over 38 years (1980–2017) across the continental United States (CONUS). Additionally, we carried out a breakpoint analysis to characterize the discontinuities in the time series of each intermittency signature. Analysis of annual no-flow days shows that about 37 % of the total streamflow stations are drying for longer each year, whereas about 22 % are wetter for longer than in the past. The day of first no-flow occurrence analysis shows that 10 % of the streams are drying earlier, and 19 % are drying later. On the other hand, analysis of days from peak to no-flow shows that 14 % of streams are drying faster, and 17 % are drying more slowly. For all these metrics, among the significant trends, at least half of the relationships were nonlinear. For annual no-flow days , the breakpoint analysis shows more discontinuities in the second half of the analysis period (1999 to 2017) than in the first half, with more discontinuities in the Southern Great Plains than in other regions. The other two signatures demonstrate less frequent discontinuities in the second half of the analysis period, suggesting decreased nonlinear dynamics in recent years. Nonlinear no-flow duration trends are common in Mediterranean California, and the dry-down rate has increased in recent decades. Our findings indicate that nonlinear change in stream drying is widespread and must be accounted for in watershed planning and management. [ABSTRACT FROM AUTHOR]

Machine learning methods are increasingly being used in the field of air quality research to investigate the relationship between air pollutant levels, emissions, and meteorological changes over time. This research is used for both scientific investigation, and policy assessment and development. However, there is a lack of studies that have compared the performance of different machine learning methods. To address this gap, this paper employed various machine learning techniques, including decision tree, random forest (RF), support vector machine (SVM), support vector regression (SVR), k-nearest neighbor, neural network, and Gaussian process regression, to predict daily average PM 2.5 levels and the number of days with PM 2.5 exceedance in the South Coast Air Basin of California from 2000 to 2019. The models were trained using meteorological factors, estimated emissions, and large-scale climate indices as inputs. The SVR model demonstrated the highest predictive accuracy for PM 2.5 levels and the SVM model gave the most accurate results for predicting the number of days with PM 2.5 exceedances. Conversely, the decision tree model performed the least accurately. The results also showed that emissions have a greater impact on PM 2.5 levels over time compared to meteorological factors, though meteorology is responsible for daily variability. The most important meteorological factors were identified as surface relative humidity and relative humidity at 850 mbars, which are related to partitioning, cloud cover and wet deposition. We conducted sensitivity tests on the model's response to emissions and meteorological factors. The predicted PM 2.5 from RF and SVR showed large correlations with emissions at the early period (2000–2010). However, the changes were minimal in more recent years (2011–2019), implying that there are biases in machine learning models, in which the models consistently predict the minimum PM 2.5 levels at a baseline. [Display omitted] • SVR is more accurate on daily PM 2.5 , particularly PM 2.5 exceedances predictions. • Surface RH was the most important meteorological factor for PM 2.5 prediction. • The impact of emissions on PM 2.5 was significant before 2010 but reduced thereafter. • ML models predict past better than future; all the ML models are limited at extremes. [ABSTRACT FROM AUTHOR]

How much should electric utilities pay to maintain a reliable electricity system? This paper describes an open-ended stated-preference experiment that generates estimates for how advanced notification impacts household willingness-to-pay (WTP) to avoid outages. We find positive and statistically significant WTP to avoid power outages of $10/kWh, consistent with the expectation that outages are costly to the residential sector. We find notification reduces the WTP, but the effects are not statistically significant. There is limited evidence that these results vary by income and wealth levels. Back-up power ownership is positively correlated with respondents' WTP to avoid outages. • Stated preference survey estimates WTP to avoid power outages. • Notifications reduce WTP but result is not statistically significant. • Back-up power ownership is positively correlated with WTP. • Results inform deployment of California PSPS and EPSS programs. [ABSTRACT FROM AUTHOR]

Fuzzy Neural Networks (FNN) have the ability of decision-making based on constructing semi-ellipsoidal clusters in the input space as the antecedent parts of their fuzzy rules. To determine the output value for each input instance, FNNs consider its membership degree to different sub-regions of the input space. However, forming such meaningful sub-regions is not possible in all applications due to the nonlinear interactions among input variables and their low information gain. Indeed, the samples could be distributed on a manifold in the input space. Therefore, to cover the input space, we need lots of rules, each representing a small region of input space. This issue decreases the generalization ability of the model along with its explainability. Consequently, to efficiently form fuzzy rules, first, it is necessary to unfold the manifold by mapping the samples to an appropriate embedding space. Next, the fuzzy rules in the form of semi-ellipsoidal regions should be constructed in this extracted feature space. Deep Fuzzy Neural Networks address this problem by representation learning through stacking multiple cascade mapping layers. In this paper, we propose a novel approach for nonlinear function approximation and time-series prediction problems, based on using the kernel trick to implicitly learn the mapping function to the new feature space. Moreover, to initialize the fuzzy rules, a KNN-based method using the kernel trick is proposed. A hierarchical Levenberg–Marquardt approach is applied to learn the model's parameters. The performance and structure of the proposed method are studied and compared with some other relevant methods in synthetic and real-world benchmarks. Based on these experiments, the proposed method has the best performance with the most parsimonious architecture. According to these experiments, the test RMSE of the proposed method is 0.002 for Mc-Glass chaotic time-series prediction, 0.015 for a Nonlinear dynamic system identification, 0.0345 for Box–Jenkins nonlinear system identification, 0.0609 for Fuel consumption prediction of automobiles, 10.24 for Sydney stock price tracking, and 0.595 for California housing. • Interpreting a deep fuzzy neural network as a kernel machine • Proposing a novel architecture able to implicitly extract features • Forming non-separable fuzzy rules in for the new implicitly extracted feature space • Proposing a kernelized version of K-Nearest Neighbor method for rules initialization • Adapting the hierarchical Levenberg–Marquardt method for parameters fine-tuning [ABSTRACT FROM AUTHOR]

• Experimental performance of 45 m2 non-tracking solar thermal array described. • Performance evaluated at 80 °C, 135 °C, and 170 °C in dirty and clean conditions. • Annual thermal generation estimated 1000 kWh/m2 at 100 °C and 700 kWh/m2 at 160 °C. • $300/m2 installed, $6.5/m2 annual O&M, levelized cost of heat 2–4 cents per kWh. • Applications in desalination, evaporation, steam generation, process heating. In this paper, the experimental performance of a 45 m2 solar field of non-tracking external compound parabolic (XCPC) collectors installed at the University of California, Merced is described. The solar field was operated during July-August 2020 in both clean and dirty conditions and at varying operating temperatures (70, 135, 170 °C) while operating an air heater, thermal evaporator, and double effect absorption chiller. Performance data was used to develop an instantaneous solar field performance model which was then incorporated into an annual performance model using TMY3 data to estimate yearly production from the solar field. The model predicts an annual generation of ∼1100 kWh/m2-year at 80 °C, ∼1000 kWh/m2-year at 100 °C, ∼900 kWh/m2-year at 120 °C, ∼800 kWh/m2-year at 140 °C, and ∼700 kWh/m2-year at 160 °C in California. The XCPC technology is currently expected to have an installed cost of $300/m2 and an annual operations and maintenance cost of $6.5/m2-year. Over a 25 year lifetime it provides a levelized cost of heat at 2–4 cents per kWh th delivered. This is below the cost of commercial natural gas in California and at temperatures ≤ 120 °C below the cost of industrial natural gas, which highlights the potential of the XCPC technology for decarbonizing thermal applications such as water and space heating, drying, sterilization, desalination, evaporation, low pressure steam, double effect absorption chilling, process heating, and more. The lifetime cost of emissions reductions is ∼$169 per metric ton of avoided CO 2 when replacing natural gas, ∼$137/MT CO 2 when replacing propane, and ∼$83/MT CO 2 when replacing electric heating. [ABSTRACT FROM AUTHOR]

Understanding how maritime hunter-gatherer diets changed through time in response to increasing social complexity can help us understand important transitions in early human history. This paper presents new baseline stable isotope values for southern California with an emphasis on marine plant and animal species. We use our baseline database to reevaluate human stable isotope values from the region using Bayesian mixing models to interpret dietary patterns across time and geographic space. Our analysis compares categories of foods consumed between island, coastal, and interior populations across the Middle and Late Holocene (circa 8000 to 168 cal BP) occupational history of precolonial southern California. Our results show a clear increase in the importance of high trophic marine foods, such as finfish, relative to low trophic level food, such as shellfish through time, paralleling increases in population size, economic intensification, and village aggregation in the Channel Region. This case study displays the capacity of Bayesian modeling to infer patterns of dietary change in the past when applied to human isotope values and adds to previous studies on the relationship between population growth, technological innovation, and the intensification of resource extraction in the region. [ABSTRACT FROM AUTHOR]

With a recently concluded pilot program in the State of California, road user charge (RUC) was explored as an alternative means of transportation funding. The goal was to gather public opinion, address privacy and data security concerns. The pilot program was meant for the future implementation of RUC in the State. However, the impact of RUC on travel demand changes impacting California's industry sectors was not explored or probed. It is expected that by 2020 or later, RUC could have the potential to be implemented in the State causing some initial mode shifts or reduced number of trips made by private vehicles to minimize the cost of travel. This could have a potential impact on employment-based trips to industry sectors of California. Therefore, this paper explores the effects of RUC on the 'attractiveness' of four key industry sectors of the State using potential accessibility changes. The changes are evaluated from the RUC implementation for the year 2019 using the year 2013 as the base year. The four key industry sectors used in the evaluation are (i) Manufacturing, (ii) Retail Trade, (iii) Health Care and Social Assistance, and (iv) Accommodation and Food Services. In this evaluation, four vehicle fuel-types are considered, namely gasoline, compressed natural gas (CNG), diesel, and electric for California's rural and urban counties. On average, the percentage change in potential accessibility was found to be largest for counties for the Health Care and Social Assistance industry sector across all fuel types for both rural and urban counties in California. This indicates that the commuters from this industry sector would experience the most considerable benefit with RUC implementation. The lowest average percentage potential accessibility change was observed for the Accommodation and Food Services. This sector's labor force would be the least affected by the RUC implementation for the four industry sectors analyzed. • Accessibility impacts due to road user charge (RUC) in California are evaluated. • Impacts are studied for four key industry sectors of California. • Vehicle fuel types are also taken into consideration. • Analyses are carried out for RUC implementation in California. • Health Care and Social Assistance industry commuters will have the largest accessibility benefit. [ABSTRACT FROM AUTHOR]

Stormwater is a complex political and geographical problem. It is at once bound to land-use decisions, tied to geographical features such as lakes and rivers, and capable of flowing across different political boundaries and jurisdictions. In this paper, I empirically focus on how disparate understandings of stormwater are forged through different institutional arrangements and the ways multiple actors interact across scales of governance in Los Angeles. The results indicate four discourses influence decisions on urban stormwater management and are articulated through different forms of knowledge and power in environmental governance. The discourses diverge over contrasting perspectives on infrastructural interventions, the role of economic approaches, and the need for new institutions and rules. I suggest that disagreement may not deter integration and collaboration across different scales of governance, but without addressing conflict over key discursive claims about how stormwater governance should proceed, broadly accepted outcomes may remain elusive. With current trends in environmental governance moving towards hybrid forms that bring together groups that transcend traditional organizational structures, this paper reveals how more sustainable outcomes are being devised through current configurations of knowledge and power. [ABSTRACT FROM AUTHOR]