Gülgün Kayakutlu

The day-ahead power market has become more complex with the allowance of block purchases from private sales companies. Resource handling has become the prominent problem for both energy suppliers and energy distributers. Complexity of the problem forces the approach by each role player in the market. This research handles the market position of a small hydropower plant owner who has negligible effect on market price construction in a complex competition environment. Based on an optimum schedule of three days, this model proposes policies for the power generator to maximize its profits. An MILP model, which uses the day-ahead market price forecasts from a hybrid SARIMA-ANN price forecasting model, is designed to optimize the day-ahead generation schedule. The case application in Turkish power market shows the increase of profit with a reliable generation schedule.

The authors develop a two-country, two-firm intra-industry trade model. Each firm is operating at its home country and producing homogeneous goods to be consumed in both countries. Governments apply quantity restriction on pollution. Every individual country is affected from the pollution generated during the production process of its own firm. The model shows that efficiency in pollution abatement technology plays a crucial role on welfare maximizing effort of governments. A critical level of pollution abatement technology determines the preponderance of environmental misgivings in welfare maximizing behavior. The more efficient the firms in pollution abatement technology, the less stricter the governments will be in their policies to reduce negative environmental externalities

This paper aims to analyze the impact of energy consumption, economic structure, and manufacturing output on the CO2 emissions of East European countries by applying the Structural Time Series Model (STSM). Several explanatory factors are used to construct the model using annual data of the 1990–2017 period. The factors are: total primary energy supply, GDP per capita and manufacturing value added, and, finally, a stochastic Underlying Emission Trend (UET). The significant effects of all variables on CO2 emissions are detected. Based on the estimated functions, CO2 emissions of Belarus, Ukraine, Romania, Russia, Serbia, and Hungary will decrease, by 2027, to 53.2 Mt, 103.2 Mt, 36.1 Mt, 1528.2 Mt, 36 Mt, and 36.1 Mt, respectively. Distinct from other countries, CO2 emissions of Poland will extend to 312.2 Mt in 2027 due to the very high share of fossil-based supply (i.e., coal and oil) in Poland. The results also indicate that the most forceful factor in CO2 emissions is the total primary energy supply. Furthermore, for Poland, Romania, Hungary, and Belarus, the long-term impact of economic growth on CO2 emissions is negative, while it is positive for Russia, Ukraine, and Serbia. The highest long-term manufacturing value-added elasticity of CO2 emissions is calculated for Serbia and Belarus.