This thesis contributes to the energy literature by leveraging insights from empirical and experimental economics. The thesis presents four papers with a common goal of understanding specific themes in the energy sector namely: households energy use patterns, behavioural preferences among entrepreneurs operating energy businesses and applications of behavioural nudges to reduce energy use. The first paper set the tone for the two subsequent chapters. The paper: 'Energy Choices and Tenancy in Rwanda' examines the energy choice patterns of households based on their rental status and dwelling types. The fifth Integrated Household Living Conditions Survey (EICV5) conducted over one year, October 2016 to October 2017, together with a bivariate probit model is used. A heterogeneous analysis focused on gender and income differentiated impacts, as well as geographical differences based on the tenancy status of households, is further examined. The results signal that households energy choices in Rwanda differ by rental and dwelling types. The second and third papers ascertain the role of competition and risk preferences among entrepreneurs working in off-grid renewable energy microenterprises and its effects on business success in the context of including more women as entrepreneurs in the energy sector. Specifically, the second paper: 'Competition and Gender in the Lab vs Field: Experiments with Off-Grid Renewable Energy Entrepreneurs in Rural Rwanda' examines the gender differences in competitiveness and how this affects the business success of entrepreneurs operating renewable energy enterprises. Results from the economic experiments are compared to the day to day activities of the business. Findings show that female entrepreneurs are not less likely to compete and are not outperformed by male entrepreneurs. This stands in contrast to several studies, mostly conducted on university students of developed countries. The third paper: 'Risk attitudes, Gender and Business Performance Among off-grid Renewable Energy Entrepreneurs in Rural Rwanda' in a similar context examines the risk attitudes among entrepreneurs and its effect on the performance from a gender perspective. The study adopts a choice list experimental approach to elicit risk attitudes. The results indicate a strong risk aversion among entrepreneurs. The risk aversion found is higher for women compared to men. Entrepreneurs with high risk-taking abilities also tend to record better performance levels. The paper concludes that policies geared towards hedging against risk aversion in entrepreneurial programs may be vital in reducing gender gaps in business performance. The fourth paper: 'The power of nudging: Using feedback, competition and responsibility assignment to save electricity in a non-residential setting' answers the question 'can behavioural interventions achieve energy savings in non-residential settings where users do not face the financial consequences of their behaviour?' The paper relies on a randomized control trial and two behavioural interventions. Results show that behavioural nudges can be useful in reducing energy consumption in a non-residential environment.

In the first chapter, I examine a variety of the factors that affect the price and demand of natural gas. Prior natural gas price research approaches utilized well-defined time series models. I have taken these historical approaches and explored an alternative approach to estimating the model- defined equilibrium market price based on the market clearing condition. Assuming that the natural gas market is a relatively efficient market, the market equilibrium price induced by the model should track the observed market price. A two-step estimation process includes - reduced formed regression estimations for each market component in the material balance equation, and solves for the market balance equation with identified coefficients and parameters for the market equilibrium price. The model results track the market price quite well, in both one period ahead forecasts and a simulated 36 months forecast case. The second chapter in the series "The Game that Drives the LNG Train" analyzes the strategies and decisions of major oil companies’ on selecting regasification terminal sites for importing liquefied natural gas (LNG) along North American coastlines and delivery of regasified gas into regional domestic markets. Each participating firm’s decision is extensive and complex, involving multi-years of capital and human investments. Furthermore, fierce competition exists among firms procuring LNG cargos and servicing the same set of demand areas, i.e. the North America market. This paper will attempt to condense the whole strategy and decision-making process into a simplified multistage model. The model will focus on exploring the strategic elements of decisions for each participant firm in the competition through a game-theory lens. Extending from previous work on tying, the third chapter seeks a more structured result on the relationship of pre-commitment and exclusion due to tying under a Hotelling framework. A three-stage model is set up, which includes a conditional pre-commitment stage and an entry decision stage preceding the third stage of pricing competition. The paper concludes that: first, exclusion is possible even with zero fixed cost, and it is executed by conditional pre-commitment of tying upon entry. Second, conditional pre-commitment of tying only occurs if entry can be excluded, otherwise, tying is not profitable as independent pricing upon entry. / text

This dissertation examines the employment impacts of investments in renewable energy and energy efficiency in the U.S. A broad expansion of the use of renewable energy in place of carbon-based energy, in addition to investments in energy efficiency, comprise a prominent strategy to slow or reverse the effects of anthropogenic climate change. This study first explores the literature on the employment impacts of these investments. This literature to date consists mainly of input-output (I-O) studies or case studies of renewable energy and energy efficiency (REEE). Researchers are constrained, however, by their ability to use the I-O model to study REEE, since currently industrial codes do not recognize this industry as such. I develop and present two methods to use the I-O framework to overcome this constraint: the synthetic and integrated approaches. In the former, I proxy the REEE industry by creating a vector of final demand based on the industrial spending patterns of REEE firms as found in the secondary literature. In the integrated approach, I collect primary data through a nationwide survey of REEE firms and integrate these data into the existing I-O tables to explicitly identify the REEE industry and estimate the employment impacts resulting from both upstream and downstream linkages with other industries. The size of the REEE employment multiplier is sensitive to the choice of method, and is higher using the synthetic approach than using the integrated approach. I find that using both methods, the employment level per $1 million demand is approximately three times greater for the REEE industry than for fossil fuel (FF) industries. This implies that a shift to clean energy will result in positive net employment impacts. The positive effects stem mainly from the higher labor intensity of REEE in relation to FF, as well as from higher domestic content and lower average wages. The findings suggest that as we transition away from a carbon-based energy system to more sustainable and low-carbon energy sources, approximately three jobs will be created in clean energy sectors for each job lost in the fossil fuel sector.

"Natural Gas Consumption and Economic Growth in European Union" by Sergey Balitskiy Abstract: The objective of this Master thesis is an evaluation of relationship between natural gas consumption and economic growth in Europe. The sample includes panel time series data over the period from 1997 to 2011 for 26 EU member states (countries of the Euro zone). Based on neoclassical growth model, a multivariate model including gross fixed capital formation and total labor forces of a country as additional explanatory variables was created. Using recent econometric techniques: panel cointegration tests and error correction modeling, it was found that there existed long-run relationship between economic growth, natural gas consumption, labor and capital. In addition, it was investigated that in the short-run there existed bidirectional causality between natural gas consumption and economic growth. It appears that the causality between economic growth and the natural gas consumption is positive. On the other hand, the reverse causality (a relationship between natural gas consumption and economic growth) appears to be negative.

<p>The changes to the environment caused by anthropogenic climate change pose major challenges for energy production in the next century. Carbon Capture and Storage (CCS) is a group of technologies that would permit the continued use of carbon-intense fuels such as coal for energy production while avoiding further impact on the global climate system. The mechanism most often proposed for storage is injection of CO2 below the surface of the Earth in geological media, with the most promising option for CO2 reservoirs being deep saline aquifers (DSA's). Unlike oil and gas reservoirs, deep saline aquifers are poorly characterized and the variability in their properties is large enough to have a high impact on the overall physical and economic viability of CCS. Storage in saline aquifers is likely to be a very high-capacity resource, but its economic viability is almost unknown. We consider the impact of geological variability on the total viability of the CO2 storage system from several perspectives. First, we examine the theoretical range of costs of storage by coupling a physical and economic model of CO2 storage with a range of possible geological settings. With the relevant properties of rock extending over several orders of magnitude, it is not surprising that we find costs and storage potential ranging over several orders of magnitude. Second, we use georeferenced data to evaluate the spatial distribution of cost and capacity. When paired together to build a marginal abatement cost curve (MACC), this cost and capacity data indicates that low cost and high capacity are collocated; storage in these promising areas is likely to be quite viable but may not be available to all CO2 sources. However, when we continue to explore the impact of geological variability on realistic, commercial-scale site sizes by invoking capacity and pressure management constraints, we find that the distribution costs and footprints of these sites may be prohibitively high. The combination of issues with onshore storage in geological media leads us to begin to evaluate offshore storage potential. By considering the temperature and pressure regimes at the seafloor, we locate and quantify marine strata that has "self-sealing" properties, a storage option that we find is plentiful off the coasts of the United States. We conclude that further research into transport optimization that takes into account the true variation in geological media is necessary to determine the distribution of costs for carbon capture and storage to permit the full evaluation of CCS as a mitigation option.</p> / Dissertation

In three essays, the relationship between environmental regulation and firm behavior is explored. First, firms are found to influence the price in a regulatory market for emissions permits by exercising market power within their own price market. As the degree of market power in three high emitting industries increased, the price of NOx permits in Southern California’s RECLAIM permit increased as well. Second, the process of firm learning is explored. Power plants facing more stringent environmental regulation are found to learn from environmental regulation and experience efficiency gains. These gains are found to spillover to other power plants facing less stringent regulation through knowledge networks. Third, the decision of electricity firms to participate in reserve markets is modeled and simulated in order to measure the effect of increased wind power in the market on this decision. As the amount of wind power in the market increased, the expected capacity available in the market decreased, inviting the possibility of electricity shortages. However, the reduced variability in market outcomes when wind penetration increases suggests that policy makers may be better able to manipulate existing market mechanisms to induce investment in new capacity.

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This dissertation consists of three separate research papers. Each paper uses a different econometric technique to analyze a problem relating to the social aspects of climate change. The first paper investigates a potential adaptive strategy to counteract warming stream waters through stream intervention projects. Using novel non-parametric matching estimation techniques it is shown that these intervention projects have positive effects on homeowners that are near to the stream but downstream of the project site. The second paper uses Bayesian econometric techniques to analyze survey data regarding the welfare losses experienced as a result of power outages across Europe. This paper shows how the severity and spatial distribution of these welfare losses will change as the climate warms, which enables the current electricity grid expansion taking place in Europe to account for these effects of climate change. The third paper uses Classical econometric techniques to estimate the effect of temperature on visitor recreation choices around Lake Tahoe. It is then shown that under climate scenarios the demand for beach and water access at Lake Tahoe will greatly increase, which suggests that lake managers begin to plan regulations and build infrastructure to account for this demand increase. / Ph. D.

This dissertation is centered on applying `nonlinearity' across various fields, a decision informed by the understanding that linear models frequently fail to capture the full extent of real-world complexities. This approach is driven by the distinct insights that nonlinearity offers, insights crucial for a more profound and precise understanding of diverse phenomena. In this regard, I have explored a variety of empirical methodologies and theoretical frameworks, each chosen for its effectiveness in unraveling and accurately depicting the complexity inherent in different subjects. The first paper, "Warming Temperatures and Potential Adaptation through Breeding: Evidence from U.S. Soft Winter Wheat," examines the impact of climate change on wheat production. Given wheat's role as a major staple for much of the global population, its susceptibility to rising temperatures presents significant challenges to food security. Despite its importance, comprehensive production data for wheat considering extensive U.S. regions is relatively scarce. To address this, I compiled a unique dataset on wheat production comprising 35,000 observations over 50 years from USDA-ARS hard-copy publications. Findings obtained through the mixed-effects model reveal significant variations in the influence of nonlinearly specified temperature on wheat production. This analysis identifies a decline not only in wheat yield across various U.S. farming sites but also in wheat quality, an aspect often neglected in similar studies. The issue is further compounded by simulations I conducted, which predict worrying decreases in both yield and quality due to rising temperatures. Despite these challenges, my analysis of varietal improvements indicates modest yet significant progress in countering the effects of warming, offering viable strategies for agricultural adaptation. In the second chapter, "Semiparametric Analysis of Out-farm Migration in China," I explore the nonlinear relationship between sectoral migration and the income gap within China. This work builds on my co-authored publication, "Intersectoral Labor Migration and Agriculture in the United States and Japan," published in Agricultural Economics. While the earlier study employed discrete thresholds and kink approaches to explore migration patterns in developed countries such as Japan and the United States, it did not reveal significant nonlinear relationships. This led me to investigate whether the results were influenced by the economic development status of the countries in question. Focusing on China, a developing country with distinct labor dynamics, I employ semiparametric methodologies to assess migration patterns, diverging from the linear assumptions common in existing literature. By using nationally representative data, it suggests a potential nonlinear relationship between farmers' sectoral migration and the income gap, providing new insights into labor migration in developing contexts. The last chapter, "Enhanced Salience of Nonlinear Pricing and Energy Conservation," explores the energy consumption of residents of Hanoi in Vietnam, using a large-scale randomized control trial. I study whether enhancing salience of information with respect to the nonlinear pricing can help energy conservation. The novelty of the project lies in its experimental design and the utilization of digital tools such as smart meters and mobile apps, adopting technologies with the potential to alter consumer behavior. Currently, we are in the post-intervention data collection phase. Supported by the International Growth Centre (IGC), the project aims to bridge the research gap in energy consumption behavior in developing countries, thereby contributing to policymaking in energy management and development in these regions. Through these diverse yet interconnected chapters, I attempt to use the varied applications of nonlinearity in studying economic and environmental issues. The main objective is to contribute to both academic knowledge and practical policymaking in these fields, addressing complexities that are often oversimplified. This approach aims to provide a more comprehensive understanding of the intricate dynamics in these areas. / Doctor of Philosophy / This dissertation focuses on the application of `nonlinearity' across various fields, acknowledging that linear models often fail to fully capture the complexities of real-world scenarios. This approach yields essential insights for a more precise understanding of diverse phenomena, incorporating a range of empirical methods and theoretical models, each selected for their efficacy in accurately depicting complexities in various subjects. The first part of my dissertation examines the agricultural sector, specifically the impact of weather on crop yields. Instead of traditional methods that use basic temperature data like minimum, maximum, or average values, I use a nonlinear approach that aligns with the specific growth stages of crops. In the area of labor economics, the dissertation explores migration patterns in China. It questions the traditional linear relationship between migration and income disparities, suggesting a more complex model that better represents the dynamics of a developing economy. The final chapter addresses the field of energy policy, examining consumer responses to nonlinear electricity pricing models in practice. This section explores the challenges faced by individuals in understanding such policies and assesses the impact of providing real-time information about nonlinear pricing on promoting energy conservation.

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