Analysis on growth of electricity generation from renewable sources with panel data approaches

In recent years, interest in the energy and electricity production from renewable sources has rapidly increased as a result of concerns about energy supply security and global climate change related issues. Renewable energy is locally produced and this production has a positive impact on resource allocation by reducing energy import dependency. By doing so renewables help improving security of energy supply. Also, renewables are the best alternative for environmental protection. This study focuses on determination of drivers which increase electricity generation from renewable resources during period of 1990-2010. It is expected that developed and developing countries has different motivations to support renewable energy. With respect to dominant role of developed countries in progress of resources except hydro, it is predicted that gross domestic product will be more powerful in analysis which performed for electricity generation from "new" renewable sources. For hydraulics resources developing economies excel, so impact of gross domestic product is expected less in analysis of total renewable electricity generation. In the second part of the study, the change of basic energy-related indicators were examined separately for regions named OECD and non OECD during the period of 1971-2010. The growth of total primary energy supply and total electricity consumption and their main drivers were discussed and renewables were highlighted as an important component of the growth, deployment of renewable sources was explained. The relationship between economic development and energy/electricity consumption was elaborated at the third part and it is determined that OECD and non OECD countries has different trends for referred relationships. In developed countries, total primary supply of energy is more stable than economic growth but there is more powerful relationship between economic growth and energy consumption in developing countries, which means that less energy is required to create income in developed countries when compared to developing countries. It is confirmed that the relationship between economic growth and electricity consumption is more robust. Increasing gross domestic product causes more electricity demand by population especially in developing countries. After obtaining robust relationship between economic growth and electricity consumption, it is aimed to find quasi effect of economic growth on renewable electricity production in the fourth section. Function of the economic development on the electricity generation from renewable sources has tried to determine but it is not possible to find a distinct relation like a one between economic development and total electricity consumption. Therefore, the idea of electricity generation from renewable sources has affected by some other drivers has emerged. xx Possible drivers which stimulate electricity generation from renewable sources were defined and it was aimed to test effect of these variables are same or not for total renewable generation and "new" renewable generation. These drivers are chosen as total electricity generation (TEEÜ), gross domestic product (GSYH), population (NFS) energy import dependency (YET) and support systems (PE). It is concluded that regions which prefer hydro as a resource and other resources without hydro is different from each other. So two different models were built for study, one for total renewable electricity (TYEÜ) and the other for renewable electricity without hydroelectricity or in other words new renewable electricity (HHEÜ). Model structures are as follows: M1: TYEÜ = f (TEEÜ, GSYH, NFS, YET, PE) M2: HHEÜ = f (TEEÜ, GSYH, NFS, YET, PE) M3: TYEÜ/NFS = f (TEEÜ/NFS, GSYH/NFS, YET, PE) M4: HHEÜ/NFS = f (TEEÜ/NFS, GSYH/NFS, YET, PE) In studies involving econometric models, it is necessary to collect data of all variables from reliable sources and prefer the most appropriate method for available data. Otherwise the reliability and validity of the analysis will be affected negatively. In econometric analysis there are three types of data which are named time series data, cross sectional data and panel data. Definition of time series data is quantities that represent or trace the values taken by a variable over a period such as a month, quarter, or year. Time series data occurs wherever the same measurements are recorded on a regular basis. Cross sectional data is a type data collected by observing many subjects (such as individuals, firms, countries, or regions) at the same point of time, or without regard to differences in time. Analysis of cross-sectional data usually consists of comparing the differences among the subjects. Panel data consists as combination of these two data types described above. Panel data refers to multi-dimensional data frequently involving measurements over time. Panel data contain observations of multiple phenomena obtained over multiple time periods for the same firms or individuals or countries. n corresponds to number of units and each unit comprises T numbers of observations. To perform analysis of this study, it is preferred to use panel data methods for the sake of reflection of both time and unit effects simultaneously. In the OECD/IEA database, there are 136 countries that have complete data of relevant variables for the period of 1990-2010. But some of them do not have renewable electricity generation at all. That is why a subset was investigated which had high power of representation among these 136 countries. After this investigation, 40 countries had been determined for the analysis. Panel data set has been prepared by selecting 40 countries as a cross sectional data (n) and setting the years between 1990 and 2010 as time series data (T). Study is completed after performing three panel data methods separately to find motivations of countries on the renewable electricity production. The first method claims that only independent variables cause the variation of dependent variables. The second one claims both units and independent variables cause the variation of dependent variables. The third one claims that variation among dependent variable arises randomly. After xxi a series of test the second method determined as the most convenient approach. This approach is called "Fixed Effects Model". The results obtained from fixed effect model can be interpreted as follows: Total electricity generation: This regressor has a positive and significant impact both on total renewable electricity generation and renewable without hydroelectricity generation as expected. Which means that a portion of increased electricity generation is obtained from renewable sources. Related coefficient is higher in M2 because hydroelectric resources has saturated and increasing demand is met by "new" renewable sources in developed countries. Gross domestic product: This regressor has a positive and significant impact on M1 and M2. While gross domestic product rises, interest of renewable electricity increases. Even hydroelectric generation growth rate is constant in developed countries in recent years, it is still the highest share on total renewable electricity production in the world and developing countries are shining out by increasing hydroelectric production. So in M2 model, related coefficient is higher because developed economies are more interested in "new" renewables. But total renewable electricity production is still contributed by developing countries. Population: This regressor has a negative impact on M1 and M2. It is not significant for M1. This is an unexpected results. Energy import dependency: This regressor has a positive and significant impact on M1 and M2. So countries which are energy exporters are more interested in renewable sources. It is possible that increasing renewable energy production makes them energy exporter and decrease their energy import dependency. Support systems: This regressor has a positive and significant impact on M1 and M2 as expected. Electricity generation from renewable sources will increase with the help of incentive systems. Related coefficient is higher in model M2. Because of the fact that hydro sources are saturated but "new" sources must be rapidly integrated into the system. That's why it is not unusual that new resources are supported more than hydro resources. In M3 and M4 models variables used in per capita forms. But there is not remarkable difference between M1 and M3 or M2 and M4. The obtained results were nearly same. In conclusion, results which are obtained from this study give us a chance to make multidimensional comparisons among countries and defined regions. These results can be used in decision making process about energy economics related fields as a premise source. The results will also help to create a road map to achieve the targets of renewable energy. This analysis can be a part of a study which focuses on energy supply-demand scenarios. Support systems to increase renewable electricity generation will be evaluated in terms of efficiency and cost as future work.