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California has experienced one of the most intense and prolonged droughts in its history in the last decade, prompting the state government to undertake a series of policy responses. This paper compares the impact of the 2015 urban water-use reduction mandate with respect to pre-policy voluntary conservation efforts and post-policy water consumption trends by examining utility-level water demand dynamics of the Metropolitan Water District in Southern California. The findings overall indicate that despite voluntary measures resulting in a gradual decline in urban water-use, the introduction of the regulatory stick achieved swift reduction targets that remained even after removal of the mandatory policy. [ABSTRACT FROM AUTHOR]

Background: The Early Markers for Autism (EMA) study is a population-based case–control study designed to learn more about early biologic processes involved in ASD. Methods: Participants were drawn from Southern California births from 2000 to 2003 with archived prenatal and neonatal screening specimens. Across two phases, children with ASD (n = 629) and intellectual disability without ASD (ID, n = 230) were ascertained from the California Department of Developmental Services (DDS), with diagnoses confirmed according to DSM-IV-TR criteria based on expert clinical review of abstracted records. General population controls (GP, n = 599) were randomly sampled from birth certificate files and matched to ASD cases by sex, birth month and year after excluding individuals with DDS records. EMA has published over 20 papers examining immune markers, endogenous hormones, environmental chemicals, and genetic factors in association with ASD and ID. This review summarizes the results across these studies, as well as the EMA study design and future directions. Results: EMA enabled several key contributions to the literature, including the examination of biomarker levels in biospecimens prospectively collected during critical windows of neurodevelopment. Key findings from EMA include demonstration of elevated cytokine and chemokine levels in maternal mid-pregnancy serum samples in association with ASD, as well as aberrations in other immune marker levels; suggestions of increased odds of ASD with prenatal exposure to certain endocrine disrupting chemicals, though not in mixture analyses; and demonstration of maternal and fetal genetic influence on prenatal chemical, and maternal and neonatal immune marker and vitamin D levels. We also observed an overall lack of association with ASD and measured maternal and neonatal vitamin D, mercury, and brain-derived neurotrophic factor (BDNF) levels. Limitations: Covariate and outcome data were limited to information in Vital Statistics and DDS records. As a study based in Southern California, generalizability for certain environmental exposures may be reduced. Conclusions: Results across EMA studies support the importance of the prenatal and neonatal periods in ASD etiology, and provide evidence for the role of the maternal immune response during pregnancy. Future directions for EMA, and the field of ASD in general, include interrogation of mechanistic pathways and examination of combined effects of exposures. [ABSTRACT FROM AUTHOR]

Background: The Early Markers for Autism (EMA) study is a population-based case–control study designed to learn more about early biologic processes involved in ASD. Methods: Participants were drawn from Southern California births from 2000 to 2003 with archived prenatal and neonatal screening specimens. Across two phases, children with ASD (n = 629) and intellectual disability without ASD (ID, n = 230) were ascertained from the California Department of Developmental Services (DDS), with diagnoses confirmed according to DSM-IV-TR criteria based on expert clinical review of abstracted records. General population controls (GP, n = 599) were randomly sampled from birth certificate files and matched to ASD cases by sex, birth month and year after excluding individuals with DDS records. EMA has published over 20 papers examining immune markers, endogenous hormones, environmental chemicals, and genetic factors in association with ASD and ID. This review summarizes the results across these studies, as well as the EMA study design and future directions. Results: EMA enabled several key contributions to the literature, including the examination of biomarker levels in biospecimens prospectively collected during critical windows of neurodevelopment. Key findings from EMA include demonstration of elevated cytokine and chemokine levels in maternal mid-pregnancy serum samples in association with ASD, as well as aberrations in other immune marker levels; suggestions of increased odds of ASD with prenatal exposure to certain endocrine disrupting chemicals, though not in mixture analyses; and demonstration of maternal and fetal genetic influence on prenatal chemical, and maternal and neonatal immune marker and vitamin D levels. We also observed an overall lack of association with ASD and measured maternal and neonatal vitamin D, mercury, and brain-derived neurotrophic factor (BDNF) levels. Limitations: Covariate and outcome data were limited to information in Vital Statistics and DDS records. As a study based in Southern California, generalizability for certain environmental exposures may be reduced. Conclusions: Results across EMA studies support the importance of the prenatal and neonatal periods in ASD etiology, and provide evidence for the role of the maternal immune response during pregnancy. Future directions for EMA, and the field of ASD in general, include interrogation of mechanistic pathways and examination of combined effects of exposures. [ABSTRACT FROM AUTHOR]

• Propose a method for embedding demand flexibility into building benchmarking. • Utilizes emerging building characteristics and time-series energy data streams. • Apply method to case study of 306 buildings in Southern California, USA. • Demonstrates ability to cluster buildings by attributes of demand flexibility. • Can inform decarbonization performance-specific policy targeting for buildings. The intermittency of carbon-free renewables and the demand changes associated with the widespread push for electrifying the transportation and building sectors provides an opportunity for buildings to go beyond energy efficiency and push towards providing demand flexibility to the electricity grid. The duality of energy efficiency and demand flexibility is necessary for success in a sustainable and reliable energy transition. Current building energy benchmarking models are limited in their ability to integrate concepts of demand flexibility and/or utilize granular smart meter data. Thus, current benchmarking methods are focused annual energy usage and fail to incorporate how the time of use of energy consumption impacts emissions in a quickly changing energy grid. Without a more comprehensive view of energy usage and associated real-time emissions, current benchmarking methods are unlikely to realize the full decarbonization potential of buildings. New emerging data streams provide an opportunity to develop a new generation of benchmarking energy models that embed dimensions of energy efficiency, grid interactivity, and demand flexibility into their analysis. In this paper, we propose a four-step method for embedding grid interactivity and demand flexibility into building benchmarking models that utilizes emerging building and time-series electricity data streams. We first engineer features to produce a mix-type dataset that encompasses many attributes of grid-interactive and efficient buildings, and then we apply K-medoids using Gower's Distance to produce peer-group clusters. We apply the method to a case study of 306 primary and secondary schools in southern California, USA. The results show that the method effectively clusters buildings by attributes of demand flexibility and energy efficiency. The clustering results reveal patterns in inefficient building operations and demand inflexibility at the building peer group level. The interpretation of clusters can serve as an integrated energy efficiency and demand flexibility benchmarking model and inform performance-specific policy targeting for buildings that go beyond traditional efficiency measures. [ABSTRACT FROM AUTHOR]

Focusing on the energy history of Southern Pacific Company in Southern California between 1865 and 1926, this paper emphasizes the resilience of private property as a pillar of oil-based capitalism in the United States. As the western segment of the first transcontinental railroad, and as a consequence of land grants provided by the federal government, the Southern Pacific emerged at the turn of the twentieth century as the largest private owner of proven oil territory in California. Based on this ownership, the Southern Pacific became a major producer, consumer and transporter of oil-based energy in Southern California. Even when the federal government threatened this ownership as part of a strategy to conserve fossil fuels in the first two decades of the twentieth century, the rights of private property remained firm and continued to reinforce the dominance of the Southern Pacific as a major regional developer of oil-based energy. This article contributes to existing literature on the political economy of fossil fuels by emphasizing how the durability of private property has culminated in a strong inertia that sustains the dominance of oil-based capitalism. [ABSTRACT FROM AUTHOR]

• Sampling and dispersion modeling method proposed to determine methane emissions. • Method determines uncertainty in emission estimates based on dispersion models. • Estimated methane emissions from manure lagoons at two dairies in California. • Two dispersion models yield emission estimates that can differ by factor of two. • Emission estimates from model can vary between 0.8 to 1.4 times the best fit value. Manure lagoons in dairies make significant contributions to emissions of methane, a major greenhouse gas; however, there is a high level of uncertainty in these emissions. In this paper, we apply dispersion models in combination with a unique sampling strategy, which involves stationary measurements at multiple points around the lagoons to estimate methane emissions from manure lagoons located in two dairies, one in Southern California and the other in Central California. We then estimate the uncertainty associated with the results from this approach by interpreting our measurements with two dispersion models, a numerical Eulerian model (EN) and a backward Lagrangian stochastic (bLS) model. The range of emissions inferred from these two models is a measure of uncertainty related to differences in the formulation of these models. We also estimate 95% confidence intervals for the emission estimates from each of the models by bootstrapping the residuals between model estimates and measurements. Both models explain more than 85% of the variance of the methane concentrations measured at the two dairies. For the Southern California dairy (1066 milking cows), the 95% confidence interval of the emission rate inferred by the EN model is 282 kg/d to 482 kg/d. The corresponding interval for the bLS model is 174 kg/d to 246 kg/d. The best fit value from the EN model is about 1.9 times that from the bLS model. For the Central California dairy (3200 milking cows), the best emission rates from the two models differ by about 10%. The emission rate inferred by the EN model ranges from 3198 kg/d to 5312 kg/d, and that from the bLS ranges from 2943 kg/d to 4977 kg/d. Our results are consistent with methane emissions derived from information on dairy cow population and manure management practices at these two dairies. These results suggest this measurement technique is easily deployed and effective at quantifying uncertainties associated with methane emissions from manure lagoons. [ABSTRACT FROM AUTHOR]