Giani Gradinaru Finally we notice that the principal component analysis was not used for weighing, although this statistic method is recommended as a first option by reference works on methodology (OECD and JRC, 2008; Bryant and Yarnold, 1995).
5. A CEI project for Romania In the substantiation of public policies becomes obvious the need for integrated quantitative information that facilitates problem formulation, trend description, differentiation of successful measures from those with poor outcomes, identification of good practices, and investment optimization (Emerson et al., 2010; Esty et al., 2008). Although the trend is visible in various profiles (fields of environmental policy, territorial, decision level) public policy field are in different position on an imaginary curve that represent their evolution. Economy, health, and education are in advanced positions, while environment occupies an inferior position (Esty et al., 2008; Srebotnjak, 2007); developed countries are the ones where composite indexes reflect better and better the policy goals, while in the developing countries there are to be overcame a number of barriers, such as data collection (Srebotnjak, 2007); and higher decision levels (national, European, global) are the ones that mostly require integrated information. In this framework the following remarks could be made on environmental policy in Romania: i. the above mentioned trend is present, since recent assessments made for the preparation of EU adhesion revealed a quite well developed monitoring system, able to provide relevant data; ii. data collection, storage, and processing made important progresses in the last decades, but decoding (understanding) the environmental information provided by them necessitates a high level of expertise. Therefore, we consider that for further progress on this trend it is necessary to construct a CEI that allow a synthetic expression of environmental quality by using the data input from the national monitoring system. The CEI project for Romania comprises a number of steps: i. identification of trajectories that statistically emphasize the effects of social-economic activities; ii. Analysis of structural-qualitative modifications of environmental elements; iii. Study of statistical linkages among selected variables; iv. extraction with principal component analysis; v. construction of CEI; and vi. Interpretation of CEI and its components evolution. All indicators recorded changes in the period that was analyzed, but the significance of these changes in terms of environmental quality reveals that only 41 of the indicators had a favorable trend. Revealing certain significant interdependency relations that cannot be observed by only examining entry data is possible by reducing the complexity of them through identifying a smaller number of factors. This process was performed by applying the principal component analysis (PCA). In order to explore the appropriateness of the method it was built the correlation matrix. PCA could be used then individual indicators are not independent and if they have correlation coefficients that are different from zero (Landau and Evritt, 2004). The correlation matrix showed that these conditions were respected. The application of the PCA extraction method resulted in three principal components. Factor with own values less than 1.00 were ignored. This is occurring because such factors manifest an error variation that cannot be interpreted (Bouroche and Saporta, 1980). Since the first three own values represent approximately 84% of the inertia we will consider the first three principal components. For the first factor, primary energy production – oil and gas, followed by cattle livestock and land use change – arable have the largest weights, and the factor has a strong negative correlation with density of modernized roads and railways, chemical fertilizer consumption and round wood production. This component could be named “climate change” since it incorporates direct and indirect drivers of anthropogenic climate change. For the second factor, the largest weights are obtained for acid emissions and primary energy production – coal, followed by sulfur dioxide emissions and nitrogen oxides emissions. Since this component comprises mainly indicators that express the pressure on air quality could be named “air pollution”. The last factor is also the smallest and it is characterized by positive correlations with the habitat index, followed by forest area, and could be named “ecosystem”, since it expresses the state of the ecosystems.
148