The ongoing transition of former communist countries from planned to market economies has been one of the most important economic phenomena in the last few decades. Among these, China is one of the largest and fastest growing emerging economies in the world since the reforms initiated in the late 1980s. China’s economic growth has been phenomenal. Therefore, understanding China’s energy economy is crucial in the new millennium for politicians, businessmen and energy economists. In particular, China’s energy policy directions will bring about both challenges and opportunities to the world in terms of an increasing share of primary energy consumption and investment in the energy industry. However, after surveying the literature, it is surprising to find that a few major areas of China’s energy economics are missing and the views on China’s energy economics are already out dated. Therefore, given the size and growth of its economy and the effect of its energy consumption on global energy markets, reviewing China’s energy situation and filling the missing literatures are essential for those who are interested in and concerned about China’s economic development in the new millennium. This study was motivated after conducting a survey of the literature on the study of China’s energy economy and reviewing China’s energy situation in the new millennium. The goal of the research is focused on providing readers the most important and the newest information on China’s energy economy. The study consists of three introductory sections and three core sections. The former includes a survey of literature, China’s energy situation in the new millennium, institutional evolution and changing energy prices. The latter includes tests for the emergence of an energy market in China, factor substitution and demand for energy, and technological change and the determinants of energy intensity. The main findings are as follows. China’s energy economy is still underdeveloped. It is crucial to review China’s energy situation in the new millennium. Energy, industrial deregulation and price reforms have been fast in China since the early 1990s. Empirical investigations have found evidence for the emergence of an energy market economy in China. The estimates demonstrate that there appears to be significant substitution possibilities between energy and labor when compared with international findings. Significant effects of substitution mainly come from the adoption of labor-intensive technology. Coal and electricity are significantly substitutable, while the demand for energy is elastic, in general. Finally, decomposing energy intensity shows that the budget constraint (a kind of price effect) reduces energy intensity while technological change increases energy intensity. These findings bring us to the following major implications. Firstly, it is important to understand the potential effect of new energy regulation and pricing mechanism on the future directions of China’s energy economy, which suggests that former predictions of China’s energy demand may have to be significantly discounted, and the potential effect on the global energy markets and emissions may need to be re-evaluated. Secondly, significant substitution between energy and labor is potentially good news as China possesses some of the most abundant labor sources in the world. However, because capital more easily substitutes for energy than labor, more policy incentives are needed for labor to substitute for energy. Thirdly, significant substitution between coal-electricity suggests that the effects of environmental taxes, however, may be smaller than expected due to the fact that most primary energy coming from coal. Also any shift from coal to electricity implies more investment in transmission lines rather than railways. Fourthly, energy constraints on energy supply may only slightly impede economic growth in China because the elasticity of substitution between energy and other factors is quite large compared to internationally. Fifthly, while many factors are responsible for the inelasticity of demand for energy, rising income may be one of the most important given the high levels of energy prices. Increasing energy prices may be unable to constrain energy consumption at present. Thus other energy policies need to be considered to encourage or depress certain types of energy consumption. Finally, reducing exports of energy-intensive commodities, reducing the high-level energy-using sectors, lowering capital investment and constraining imports of second-hand and obsolete equipment, would all help reduce growth in energy intensity. Politically, however, this may be at an unacceptable cost to economic growth. Although this study has conducted a series of investigations into the institutional changes and consumption behavior of China’s energy economy, continuous updating required as more data is continually added in a highly dynamic and changing environment. JEL Classifications: D24, O33, Q41.

Doctor of Philosophy / Department of Economics / Lance Bachmeier / In the first chapter I study the effects of oil price shocks on economic activity at the U.S. state-level, an innovative feature of this dissertation. States which rely more heavily on manufacturing or tourism are more adversely affected by adverse oil price shocks, while states which are major energy producers either benefit or experience insignificant economic changes from historically large oil price increases. Additionally, oil price increases from 1986 to 2011 have not impacted state-level economies to the same degree as increases from 1976 to 1985. This discrepancy can be attributed to a fundamental change in the structure of the U.S. economy, for example, a declining manufacturing sector or an increase in the efficiency with which energy is used in the production process. In the second chapter I explore the effects of alternative measures of energy price shocks on economic activity and examine the relative performance of these alternative measures in forecasting macroeconomic activity. The alternative energy prices I consider are: gasoline, diesel, natural gas, heating oil and electricity. I find that alternative measures of energy price shocks produce different patterns of impulse responses than oil price shocks. The overwhelming evidence indicates that alternative energy price models, excluding a model containing gasoline prices, outperforms the baseline model containing oil prices for many states, particularly at short-to-mid forecast horizons. In the third chapter, which is coauthored with Lance Bachmeier, we determine whether accounting for oil price endogeneity is important when predicting state-level economic activity. We find that accounting for endogeneity matters for in-sample fit for most states. Specifically, in-sample fit would be improved by using a larger model which contains both regular oil price and endogenous oil price movements. However, we conclude that accounting for endogeneity is not important for out-of-sample forecast accuracy, and a simple model containing only the change in the price of oil produces equally accurate forecasts. Accounting for endogeneity is particularly important in an environment in which rising oil prices were caused by a growing global economy, such as in the years 2004-2007.

Application Service Providing (ASP) is a recently emerged software delivery model under which an Application Service Provider hosts, manages and delivers software as a service to customers via the Internet or a private network. The ASP model offers benefits from cost savings, specialized expertise, a faster time to market, and a reduced risk due to a lower capital investment. However, customers who are unsure about the value of ASP services and their demands may be reluctant to commit to ASP contracts. Many are also concerned with security, performance and loss of control. The underlying research identifies determinants influencing adoption intentions in the early technology stages of ASP within electric utilities. The tested model includes characteristics of the innovation, such as relative (dis-) advantage of ASP, as well as characteristics of the environment, the adopting company and importance of social influence. After a broad qualitative study a quantitative web-based survey generated 158 data sets. Multiple linear regression and logistic regression were used to analyze the relationships between multi-item constructs. Results show that the perceived improved service provided by ASP, the perceived calculation accuracy of load and price forecasts, the perceived benefits from the provision of external competence, and the trust in the reliability of the service provider as well as the image gains a company has by using ASP are significant factors influencing the formation of attitude for or against ASP solutions. Furthermore, the dependent variable in this research-the intention to adopt-is determined by the behavioral intention to try, the attitude towards ASP, the perceived cost of ASP, and the company size of the adopting firm. All constructs rank high in measurement quality and the overall model reveals food for an improved conception of ASP as well as managerial practices. Results also indicate the lapse of the diffusion curve of ASP in the electric utilities industry in Austria and Germany. (author's abstract)

<p> With fluctuations in energy price and the uncertainty of energy supply, particularly in the past decade, it has become increasingly important to forecast energy requirements. It is useful to know the response of energy demand to changes in both energy price and supply. In addition, the amount of substitution possibilities would allow forecasting demand for individual energy types. </p> <p> This study focuses on changes in energy intensities in the manufacturing and service sectors from 1962 to 1982. Trends for the sectors as a whole, and individual industries within each sector are analyzed on the basis of significant changes in; total consumption patterns and specific energy types. These trends are helpful in acting as a base for analyzing future energy needs in Canada. </p> / Thesis / Bachelor of Arts (BA)

The mining industry in South Africa has long been a crucial contributor to the Gross Domestic Product (GDP) starting in the 18th century. In 2010, the direct contribution towards the GDP from the mining industry was 10% and 19.8% indirect. During the last decade global financial uncertainty resulted in commodity prices hitting record numbers when Gold soared to a high at $1900/ounce in September 2011, and thereafter the dismal decline to a low of $1200/ounce in July 2013. Executives in these markets have reacted strongly to reduce operational costs and focussing on better production efficiencies. One such a cost for mining within South Africa is the Operational Expenditure (OPEX) associated with electrical energy that has steadily grown on the back of higher than inflation rate escalations. Companies from the Energy Intensive User Group (EIUG) witnessed energy unit prices (c/kWh) and their percentage of OPEX grow to 20% from 7% in 2008. The requirement therefore is for more accurate energy budget forecasting models to predict what energy unit price escalations (c/kWh) occur along with the required units (kWh) at mines or new projects and their impact on OPEX. Research on statistical models for energy forecasting within the mining industry indicated that the historical low unit price and its notable insignificant impact on OPEX never required accurate forecasting to be done and thus a lack of available information occurred. AngloGold Ashanti (AGA) however approached Deloittes in 2011 to conclude a study for such a statistical model to forecast energy loads on one of its operations. The model selected for the project was the Monte Carlo analysis and the rationale made sense as research indicated that it had common uses in energy forecasting at process utility level within other industries. For the purpose of evaluation a second regression model was selected as it is well-known within the statistical fraternity and should be able to provide high level comparison to the Monte Carlo model. Finally these were compared to an internal model used within AGA. Investigations into the variables that influence the energy requirement of a typical deep level mine indicated that via a process of statistical elimination tonnes broken and year are the best variables applicable in a mine energy model for conventional mining methods. Mines plan on a tonnage profile over the Life of Mine (LOM) so the variables were known for the given evaluation and were therefore used in both the Monte Carlo Analysis that worked on tonnes and Regression Analysis that worked on years. The models were executed to 2040 and then compared to the mine energy departments’ model in future evaluations along with current actuals as measured on a monthly basis. The best comparison against current actuals came from the mine energy departments’ model with the lowest error percentage at 6% with the Regression model at 11% and the Monte Carlo at 20% for the past 21 months. This, when calculated along with the unit price path studies from the EIUG for different unit cost scenarios gave the Net Present Value (NPV) reduction that each model has due to energy. A financial analysis with the Capital Asset Pricing Model (CAPM) and the Security Market Line (SML) indicated that the required rate of return that investors of AGA shares have is 11.92%. Using this value the NPV analysis showed that the mine energy model has the best or lowest NPV impact and that the regression model was totally out of line with expectations. Investors that provide funding for large capital projects require a higher return as the associated risk with their money increases. The models discussed in this research all work on an extrapolation principle and if investors are satisfied with 6% error for the historical 2 years and not to mention the outlook deviations, then there is significance and a contribution from the work done. This statement is made as no clear evidence of any similar or applicable statistical model could be found in research that pertains to deep level mining. Mining has been taking place since the 18th century, shallow ore resources are depleted and most mining companies would therefore look towards deeper deposits. The research indicates that to some extent there exist the opportunity and some rationale in predicting energy requirements for deep level mining applications. Especially when considering the legislative and operational cost implications for the mining houses within the South African economy and with the requirements from government to ensure sustainable work and job creation from industry in alignment with the National Growth Path (NGP). For this, these models should provide an energy outlook guideline but not exact values, and must be considered along with the impact on financial figures. / MBA, North-West University, Potchefstroom Campus, 2014

The mining industry in South Africa has long been a crucial contributor to the Gross Domestic Product (GDP) starting in the 18th century. In 2010, the direct contribution towards the GDP from the mining industry was 10% and 19.8% indirect. During the last decade global financial uncertainty resulted in commodity prices hitting record numbers when Gold soared to a high at $1900/ounce in September 2011, and thereafter the dismal decline to a low of $1200/ounce in July 2013. Executives in these markets have reacted strongly to reduce operational costs and focussing on better production efficiencies. One such a cost for mining within South Africa is the Operational Expenditure (OPEX) associated with electrical energy that has steadily grown on the back of higher than inflation rate escalations. Companies from the Energy Intensive User Group (EIUG) witnessed energy unit prices (c/kWh) and their percentage of OPEX grow to 20% from 7% in 2008. The requirement therefore is for more accurate energy budget forecasting models to predict what energy unit price escalations (c/kWh) occur along with the required units (kWh) at mines or new projects and their impact on OPEX. Research on statistical models for energy forecasting within the mining industry indicated that the historical low unit price and its notable insignificant impact on OPEX never required accurate forecasting to be done and thus a lack of available information occurred. AngloGold Ashanti (AGA) however approached Deloittes in 2011 to conclude a study for such a statistical model to forecast energy loads on one of its operations. The model selected for the project was the Monte Carlo analysis and the rationale made sense as research indicated that it had common uses in energy forecasting at process utility level within other industries. For the purpose of evaluation a second regression model was selected as it is well-known within the statistical fraternity and should be able to provide high level comparison to the Monte Carlo model. Finally these were compared to an internal model used within AGA. Investigations into the variables that influence the energy requirement of a typical deep level mine indicated that via a process of statistical elimination tonnes broken and year are the best variables applicable in a mine energy model for conventional mining methods. Mines plan on a tonnage profile over the Life of Mine (LOM) so the variables were known for the given evaluation and were therefore used in both the Monte Carlo Analysis that worked on tonnes and Regression Analysis that worked on years. The models were executed to 2040 and then compared to the mine energy departments’ model in future evaluations along with current actuals as measured on a monthly basis. The best comparison against current actuals came from the mine energy departments’ model with the lowest error percentage at 6% with the Regression model at 11% and the Monte Carlo at 20% for the past 21 months. This, when calculated along with the unit price path studies from the EIUG for different unit cost scenarios gave the Net Present Value (NPV) reduction that each model has due to energy. A financial analysis with the Capital Asset Pricing Model (CAPM) and the Security Market Line (SML) indicated that the required rate of return that investors of AGA shares have is 11.92%. Using this value the NPV analysis showed that the mine energy model has the best or lowest NPV impact and that the regression model was totally out of line with expectations. Investors that provide funding for large capital projects require a higher return as the associated risk with their money increases. The models discussed in this research all work on an extrapolation principle and if investors are satisfied with 6% error for the historical 2 years and not to mention the outlook deviations, then there is significance and a contribution from the work done. This statement is made as no clear evidence of any similar or applicable statistical model could be found in research that pertains to deep level mining. Mining has been taking place since the 18th century, shallow ore resources are depleted and most mining companies would therefore look towards deeper deposits. The research indicates that to some extent there exist the opportunity and some rationale in predicting energy requirements for deep level mining applications. Especially when considering the legislative and operational cost implications for the mining houses within the South African economy and with the requirements from government to ensure sustainable work and job creation from industry in alignment with the National Growth Path (NGP). For this, these models should provide an energy outlook guideline but not exact values, and must be considered along with the impact on financial figures. / MBA, North-West University, Potchefstroom Campus, 2014

This project aims to make a comparison between the Swedish and Indianelectricity market, the design of new improvements will achieve a betteroperation for both markets as well as the price forecasting for markets. Thisresults will give a clear idea about the electricity prices, different energy uses andpeak hours and also the carbon dioxide emissions.Also the main organizations of the market and their roles has been characterized,discussing about the functions of the Market Operator and the System Operator.And also the different markets, the trading products and the price formation havebeen explained and giving an idea about the market structure with enough details.Moreover, Time Series Analysis explained in a detail manner and some of themost used methods in Time Series Analysis are also explained in a very goodmanner. Mainly the results section includes the description of the market situationin Swedish and Indian electricity markets comparison, which includes Powerinstalled capacity, electricity generation, main renewable technologies andpolicies to increase the renewable energy share in total electricity generated.After this analysis, the strengths and weakness of both markets are presented andthe main problems of Swedish electricity system like dependency for nuclearpower, uncertainty for solar electricity generation and the Indian electricitysystem problems like high losses in power system, power quality issues, and veryless focus on energy mix with renewable systems.Finally, due to the quick development of the energy sector in the last few yearsto reach a new design for the electricity market, different kinds ofrecommendations for the future have been considered.

abstract: The United States building sector was the most significant carbon emission contributor (over 40%). The United States government is trying to decrease carbon emissions by enacting policies, but emissions increased by approximately 7 percent in the U.S. between 1990 and 2013. To reduce emissions, investigating the factors affecting carbon emissions should be a priority. Therefore, in this dissertation, this research examine the relationship between carbon emissions and the factors affecting them from macro and micro perspectives. From a macroscopic perspective, the relationship between carbon dioxide, energy resource consumption, energy prices, GDP (gross domestic product), waste generation, and recycling waste generation in the building and waste sectors has been verified. From a microscopic perspective, the impact of non-permanent electric appliances and stationary and non-stationary occupancy has been investigated. To verify the relationships, various kinds of statistical and data mining techniques were applied, such as the Granger causality test, linear and logarithmic correlation, and regression method. The results show that natural gas and electricity prices are higher than others, as coal impacts their consumption, and electricity and coal consumption were found to cause significant carbon emissions. Also, waste generation and recycling significantly increase and decrease emissions from the waste sector, respectively. Moreover, non-permanent appliances such as desktop computers and monitors consume a lot of electricity, and significant energy saving potential has been shown. Lastly, a linear relationship exists between buildings’ electricity use and total occupancy, but no significant relationship exists between occupancy and thermal loads, such as cooling and heating loads. These findings will potentially provide policymakers with a better understanding of and insights into carbon emission manipulation in the building sector. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2018

This report aims to make a comparison between the Swedish and Spanish electricity market, the design of new improvements that could achieve a better operation for both markets as well as the price forecasting for both spot markets. These enhancements are oriented to decrease electricity prices, energy use and the system CO2 emissions. Also, the main organizations of the market and their roles has been characterized, clarifying the functions of the Market Operator and the System Operator. In addition, the different markets, the trading products and the price formation have been explained and the picture of the market structure has been achieved with enough depth. Moreover, some of the most used methods in Time Series Analysis has been enumerated to understand which techniques are needed for forecast the electricity prices and the methodology used (Box-Jenkins Method) has been explained in detail. Later, all these methods have been implemented in an own code developed in Python 3.6 (TSAFTools .py) with the help of different statistics libraries mentioned during the method chapter. On the other hand, the description of the market situation has been carried out for both countries. Power installed capacity, electricity generation, average prices, main renewable technologies and policies to increase the renewable energy share has been analysed and corresponding described. Then, to estimate the market’s future spot electricity prices, ARIMA models have been selected to analyse the evolution of the day-ahead price using the TSAFTools.py. The final models show a proper performance in the two markets, especially in the Nordpool, achieving an RMSE: 37.68 and MAPE: 7.75 for the year in 2017 in Nordpool and a RMSE: 270.08 and MAPE: 20.24 in OMIE for 2017. Nordpool spot prices from 2015 to 2016 has been analysed too but obtaining a result not as good as the year 2017 with an RMSE: 49.01 and MAPE: 21.42. After this analysis, the strengths and weaknesses of both markets are presented and the main problems of the Spanish electricity system (power overcapacity, fuel dependency, non-cost-efficient renewable energies policies, lack of interconnexion capacity etc.) and the Swedish electricity system (dependency for nuclear power, uncertainty for solar electricity Generation) are presented. Finally, due to the quick development of the energy sector in the last years and the concern of the European Committee to reach a new design for the electricity market, different kinds of recommendations for the future have been considered.

自1970年代以來有許多研究指出，能源價格衝擊對於一個國家的總體經濟表現有顯著的影響。但對於能源價格究竟是以何種形式，以及透過什麼管道對總體經濟產生影響，卻沒有一致的看法。同時，經濟決策者對於能源價格變動的反應，經常因為有不確定性的存在而有延後反映的現象。本文利用台灣1981年到2009年的能源價格，建構數種對稱與不對稱之能源價格變動設定，以Granger因果關係檢定探討能源價格變動與台灣其他相關的總體經濟變數資料間的關係；並透過自我迴歸分配落後模型 (Autoregressive Distributed Lag Model, ARDL) 模型估計能源價格與台灣產出的長期關係。我們的實證結果顯示：能源價格，相較於台灣的總體經濟體系，具有外生性。能源價格成長率對產出與失業率沒有顯著的影響；但能源價格的波動程度對台灣產出成長率卻有顯著的負面影響。能源價格波動率與台灣實質產出具有長期均衡關係，而且能源價格波動將對台灣實質產出有負面影響。 / Since the 1970s, numerous studies have demonstrated that energy price impact can have a significant influence on a country’s macroeconomy. However, there is no consensus regarding in what form, or by which channel can energy price changes affect the macroeconomy. In addition, economic decision makers often respond to energy price changes with a time lag due to the existence of uncertainty. This paper constructs several indicators of symmetric and asymmetric energy price changes based on the energy prices in Taiwan for the period from 1981 to 2009. We employ the Granger’s causality test to examine the relationship between energy price changes and related macroeconomic variables; and utilize the autoregressive distributed lag model (ARDL) to estimate the long-run relationship between energy price volatility and Taiwan’s real GDP. Our empirical results show that energy price exhibits exogeneity relative to important macroeconomic variables; the energy price growth rate does not have significant impact on output and unemployment rate, while the energy price volatility has negative impact on Taiwan’s macroeconomy. There is long-run relationship between the energy price volatility and Taiwan’s real GDP. Furthermore, the energy price volatility do have negative impact on Taiwan’s real GDP.