Comparing Water Use Forecasting Model Selection Criteria: The Case of Commercial, Institutional, and Industrial Sector in Southern California

The United States is one of the largest per capita water withdrawers in the world, and certain parts of it, especially the western region, have long experienced water scarcity. Historically, the U.S. relied on large water infrastructure investments and planning to solve its water scarcity problems. These large-scale investments as well as water planning activities rely on water forecast studies conducted by water managing agencies. These forecasts, while key to the sustainable management of water, are usually done using historical growth extrapolation, conventional econometric approaches, or legacy software packages and often do not utilize methods common in the field of statistical learning. The objective of this study is to illustrate the extent to which forecast outcomes for commercial, institutional and industrial water use may be improved with a relatively simple adjustment to forecast model selection. To do so, we estimate over 352 thousand regression models with retailer level panel data from the largest utility in the U.S., featuring a rich set of variables to model commercial, institutional, and industrial water use in Southern California. Out-of-sample forecasting performances of those models that rank within the top 5% based on various in- and out-of-sample goodness-of-fit criteria were compared. We demonstrate that models with the best in-sample fit yeild, on average, larger forecast errors for out-of-sample forecast exercises and are subject to a significant degree of variation in forecasts. We find that out-of-sample forecast error and the variability in the forecast values can be reduced by an order of magnitude with a relatively straightforward change in the model selection criteria even when the forecast modelers do not have access to “big data” or utilize state-of-the-art machine learning techniques

Effect of Vermicompost on Chemical and Biological Properties of an Alkaline Soil with High Lime Content during Celery (Apium graveolens L. var. dulce Mill.) Production

The aim of this study was to investigate impact of vermicompost on chemical and biological properties of an alkaline soil with high lime content in the presence of plant under the open field conditions in semiarid Mediterranean region of Turkey. The study also included farmyard manure and chemical fertilizers for comparison and was conducted in two consecutive growth seasons in the same plots to observe any cumulative effect. Plots were amended with fertilizers in different rates and celery (Apium graveolens L. var. dulce Mill.) was grown as the test plant. In general, vermicompost appeared to be more effective to increase organic matter, N, P, and Ca compared to farmyard manure. Soil alkaline phosphatase and β-glucosidase activities, especially in the second growth season, were significantly elevated by the vermicompost application. Urease activity, however, appeared not to be influenced by the type of organic fertilizer. A slight but statistically significant difference was detected between organic amendments in terms of number of aerobic mesophilic bacteria with vermicompost giving the lower values. Results showed that, in general, vermicompost significantly alters chemical and biological properties of the alkaline soil with high lime content during celery production under field conditions compared to farmyard manure and that it has a high potential to be used as an alternative to conventional organic fertilizers in agricultural production in the Mediterranean region of Turkey

Essays on Environmental and Resource Economics

In this dissertation, I present three essays that empirically study water and energy economics issues in California. The objective of the first chapter is to investigate whether and to what extent farmers' crop choice decision is affected by the irrigation water salinity. Using a highly granular land use data and random coefficients logit method, the effect of irrigation water salinity on crop choice is studied in the context of Sacramento-San Joaquin River Delta--- California's major water source and home to prime agricultural farmlands. The results show that though the effect of salinity was statistically significant during the past decade, highest and most significant coefficients were those of crop class indicators and weather. This finding suggests that it is essential to reach out to the farmer community to ensure that they are fully capable of coping with expected salinity increases in medium to long run. Additionally, there is evidence for heterogeneity in farmers' response to salinity even though the area studied is relatively small. Ignoring the heterogeneity can result in misleading coefficient estimates especially for those researchers who wish to study farmer behavior in larger regions. Finally, revenue losses are simulated under baseline salinity and potential future salinity scenarios due to building a water conveying facility around the Delta, which suggests an expected revenue loss of about 19%.In the second chapter, together with Steven Buck, I question the wisdom of selecting a forecast model based on a within-sample goodness-of-fit criterion in the context of commercial and industrial (C&I) water demand in the Southern California. Initially, a set of about 350 thousand regression models are estimated using retailer level panel data featuring water consumption, price, employment, weather variables, and GDP. Out-of-sample forecasting performances of those models that rank within the top 1 \% based on various in and out-of-sample goodness-of-fit criteria were compared. We found that the models that provide the best in-sample fit are not necessarily the most favorable ones when it comes to forecasting water demand. The results indicate that on average, these models have a significantly higher absolute forecast error and a larger gap between the highest and lowest forecasts that they generate compared to the models that rank high based on out-of-sample fit criteria we defined.Finally, the third chpater investigates the effect of the 2000 California energy crisis on the take up of an engineering audit program funded by the Department of Energy, aiming operational improvements in various domains, including energy efficiency, at small and medium sized firms. Using a detailed data set containing information on both firm characteristics and the specifics of the recommendations made, a linear probability model is estimated using difference-in-difference strategy. In order to keep the treatment and the control groups as comparable as possible to ensure credible identification, the firms that applied to be audited and made the take up decision before the crisis are compared to those that applied right before the crisis and had to decide after the crisis started. The results show that the 2000 California energy crisis was associated with a 16% increase in the take up of the IAC energy efficiency recommendations. The coefficient estimate is statistically significant and robust to different model specifications

Comparing Water Use Forecasting Model Selection Criteria: The Case of Commercial, Institutional, and Industrial Sector in Southern California

The United States is one of the largest per capita water withdrawers in the world, and certain parts of it, especially the western region, have long experienced water scarcity. Historically, the U.S. relied on large water infrastructure investments and planning to solve its water scarcity problems. These large-scale investments as well as water planning activities rely on water forecast studies conducted by water managing agencies. These forecasts, while key to the sustainable management of water, are usually done using historical growth extrapolation, conventional econometric approaches, or legacy software packages and often do not utilize methods common in the field of statistical learning. The objective of this study is to illustrate the extent to which forecast outcomes for commercial, institutional and industrial water use may be improved with a relatively simple adjustment to forecast model selection. To do so, we estimate over 352 thousand regression models with retailer level panel data from the largest utility in the U.S., featuring a rich set of variables to model commercial, institutional, and industrial water use in Southern California. Out-of-sample forecasting performances of those models that rank within the top 5% based on various in- and out-of-sample goodness-of-fit criteria were compared. We demonstrate that models with the best in-sample fit yeild, on average, larger forecast errors for out-of-sample forecast exercises and are subject to a significant degree of variation in forecasts. We find that out-of-sample forecast error and the variability in the forecast values can be reduced by an order of magnitude with a relatively straightforward change in the model selection criteria even when the forecast modelers do not have access to “big data” or utilize state-of-the-art machine learning techniques

Determination of the level of healthy life style behaviours and self-efficacy-sufficiency of nurses working in a hospital

Bu çalışma hemşirelerin sağlıklı yaşam biçim davranışları (SYBD) ve öz-etkililik-yeterlilik (ÖEY) düzeylerini belirlemek amacıyla tanımlayıcı olarak yapılmıştır. Araştırmanın evrenini GATA da çalışan tüm hemşireler oluşturmuştur. Evrenin tamamı örnekleme alınmış ve araştırma, anket formunu doldurmayı kabul eden, araştırmacı tarafından ulaşılabilen 538 hemşire üzerinde gerçekleşmiştir. Araştırma kapsamındaki hemşirelerin SYBD ve ÖEY düzeylerini belirlemek amacıyla SYBD Ölçeği II (SYBDÖ II) , ÖEY Ölçeği (ÖEYÖ) ve araştırmacı tarafından geliştirilen Kişisel Bilgi Formu kullanılmıştır. Elde edilen verilerin analizi için SPSS 15.0 paket programı kullanılmıştır. Verilerin frekans ve yüzdesel dağılımları verilmiştir. Verilerin normallik testi sonucunda iki gruplu karşılaştırmalarda Mann Whitney U Testi; üç ve daha fazla gruplarda ise Bonferroni düzeltmeli Kruskall Wallis H Testi kullanılmıştır. Değişkenler arasındaki ilişkiler Pearson Korelasyon analiziyle değerlendirilmiştir. Çalışmadan elde edilen bulgular; hemşirelerin SYBDÖ II toplam puan ortalaması 132.87±17.42, ÖEYÖ toplam puan ortalaması 79.67±13.70 dir. Hemşirelerin, 25 yaş ve altında olanlarında, lisans mezunlarında, 10 yıldan az çalışanlarda, ayaktan tanı ve tedavi biriminde çalışanlarda, sürekli gündüz çalışanlarda, kronik hastalığı olmayanlarda, SYBDÖ II toplam puan ortalaması yüksek bulunmuştur. ÖEY düzeyleri; 36-40 yaş arası bireylerde, bekârlarda, lisans ve lisansüstü mezunlarında, 21 yıldan fazla çalışanlarda, vardiya sistemiyle çalışanlarda, kronik hastalığı olanlarda, sigara kullanmayanlarda ve beden kitle indeksine göre zayıf olanlarda yüksek bulunmuştur. Bu araştırma sonuçlarına göre ÖEYÖ ile SYBDÖ II arasında anlamlı bir korelasyon olmadığı ancak ÖEYÖ alt ölçeklerinin SYBDÖ II ile ilişkili olduğu, özellikle engellerle mücadele alt ölçeğinin SYBDÖ II ve alt ölçeklerinin tamamı üzerinde etkili olduğu görülmüştür.This study has been done to determination of levels of healthy life-style behaviors and self-efficacy-sufficiency of nurses. The sample of research was composed of 538 nurses who work in GATA. Healthy life style behaviors scale II, self-efficacy-sufficiency scale and a personal information form developed by the researcher applied to the sample group. has been used for determination of the Level of Healthy Life Style Behaviors and self-efficacy-sufficiency of nurses in the survey. The data obtained from the analysis of the data obtained were evaluated with the SPSS 15.0 package program. In study, the frequency and percentage distributions of the data given. As a result of data normality test, two-group comparisons, Mann-Whitney U test three or more groups, Kruskal-Wallis H test, the Bonferroni correction was used. Relations between variables were assessed by Pearson's correlation analysis. The findings of the study; nurses healthy life style behaviors scale II average total score is 132.87±17.42, self-efficacy-sufficiency scale average total score is 79.67±13.70. Nurses, the ones under 25 years of age, degree graduates, those working less than 10 years, outpatient diagnostic and treatment unit employees, permanent day workers, those without chronic disease, healthy life style behaviors scale II average total score was higher. Levels of self-efficacy-sufficiency was higher between the ages of 36-40 individuals, singles, bachelor and master graduates, those working more than 21 years, the shift system workers, those with chronic disease, nonsmoking, and body mass index in those with relatively weak. According to the results of this research there was a significant correlation between self-efficacy-sufficiency scale to healthy life style behaviors scale II, but self-efficacy-sufficiency subscales to be associated with healthy life style behaviors scale II, especially in the fight against barriers subscale has been effective. healthy life style behaviors scale II and over the entire sub-scales

Intermittency in Wind Energy and Emissions from the Electricity Sector: Evidence from 13 Years of Data

Renewable subsidies and mandates currently play a central role in the environmental and energy policy in the United States, one of the world’s top greenhouse gas emitters. Therefore, accurately estimating the environmental benefits from wind energy is key to evaluating the existing policies and setting future directions and has been studied within a growing body of the literature. However, most of the existing studies do not take the intermittency into account, and the small number of studies that do only study a relatively short time period limiting the extent to which they can be informative within different ranges of wind generation capacity. In this paper, we present the first estimates of the environmental benefits of wind energy generation using a dataset that spans well over a decade. Specifically, we use 13 years of hourly and sub-hourly data to estimate the causal effect of wind generation and its intermittency on CO2, NOx, and SO2 emissions from the electricity sector in Texas. Additionally, we compared the full sample results to those from sub-samples where the dataset is divided into subgroups based on wind output level. We found that while wind generation clearly has a statistically significant negative marginal effect on all pollutants we studied, the marginal effect of intermittency varies across different wind output levels in a highly irregular way. Our findings suggest that conducting pooled analyses has the potential to mask the irregularity in the variation of the effect of intermittency in wind generation across different wind output levels

Intermittency in Wind Energy and Emissions from the Electricity Sector: Evidence from 13 Years of Data

Renewable subsidies and mandates currently play a central role in the environmental and energy policy in the United States, one of the world’s top greenhouse gas emitters. Therefore, accurately estimating the environmental benefits from wind energy is key to evaluating the existing policies and setting future directions and has been studied within a growing body of the literature. However, most of the existing studies do not take the intermittency into account, and the small number of studies that do only study a relatively short time period limiting the extent to which they can be informative within different ranges of wind generation capacity. In this paper, we present the first estimates of the environmental benefits of wind energy generation using a dataset that spans well over a decade. Specifically, we use 13 years of hourly and sub-hourly data to estimate the causal effect of wind generation and its intermittency on CO2, NOx, and SO2 emissions from the electricity sector in Texas. Additionally, we compared the full sample results to those from sub-samples where the dataset is divided into subgroups based on wind output level. We found that while wind generation clearly has a statistically significant negative marginal effect on all pollutants we studied, the marginal effect of intermittency varies across different wind output levels in a highly irregular way. Our findings suggest that conducting pooled analyses has the potential to mask the irregularity in the variation of the effect of intermittency in wind generation across different wind output levels