Analysis of Environmental Issues and Economic Performance

Analysis of environmental issues and economic performance and population assiduity Executive summary The main goal with the report was to analyse the relationship from 16 different countries on how, if any, CO2 emission per capita is energiseting affected by population tautness and gross domestic product per capita by development descriptive statistics and regression. The conclusion is that CO2 emission per capita is affected by changes in GDP per capita and that population tightness has no square relation to CO2 emission per capita. Introduction Global warming is one of the expectantgest problems in the international societies today.The politician keeps discussing how they can find solutions together to decrease the CO2 emissions worldwide. In this report we give try to bear witness if well-established countries engender a spunkyer CO2 emissions and we allow take apart how population absorption be affecting emission in our society today. Aim The aim with this report i s first to examine the relationship with GDP per capita and CO2 emission and population density and CO2 emission. Then we will examine if spunky GDP per capita leads to higher CO2 emission per capita and if countries with low population density be polluting to a greater extent than countries with high population density.Hypothesis 1. 1 I believe that a country with high GDP are more likely to have a higher CO2 emission per capita since a country with high GDP are more likely to have higher productivity achieved through higher zipper use. We will then start with measuring the linear association amid these variables. H0 ? 0 1 GDP? 0 (Correlation) H1 ? 0=? 1 GDP=0 (No correlation) Hypothesis 1. 2 I believe that a country with high population density are more likely to have a write down CO2 emission per capita since the inhabitants need travel shorter and less often.We will at that placefor measure the linear association for CO2 emission per capita and population density. H0 ? 0 2 pop. density? 0 (Correlation) H1 ? 0=? 2 pop. density=0 (No correlation) Main hypothesis We want to find out how much linear association the devil variables has on CO2 per capita. This can be done with this model CO2per capita = ? 0+ ? 1 GDP+? 2 pop. density+ ? H0 ? 1 GDP? 0 H1 ? 1 GDP=0 H0 ? 2 pop. density? 0 H1 ? 2 pop. density=0 We can then see how strong the association these two variables are against the dependent variable CO2 emission per capita. Further on we want to test the significance of these variables.Data and descriptive statistics The data (GDP per capita, CO2 per capita and population density) in this report is a sample of 16 different countries and are downloaded from the International Monetary Fund, US department of Energy and OECD. All the data are ratio subdue and are continuous. Some potential problems with the associated data is * Some countries may have a high productivity achieved by the efficient labour force and not trough higher energy use. Both shi p canal of high productivity leads to higher GDP per capita, its unlikely to achieve it by efficient labour force, and it can occur. Some countries (e. g. Australia) may have low population density although they mainly have big populated cities since they have a large amount of landmass that is not suitable for life. * The different data is not from the same years. CO2 emission per capita is from 2004, population density is from various years and GDP per capita is from 2010. To get an idea of how the dataset looks like we need to use descriptive analysis. Mean x=xn Median x=n+12th S. D sx=x2-nx2n-1 Sample variance s2=x2-nx2n-1 Range=xh-xlFor carbon dioxide per capita the mean is 9,285 and the median is 9,49, this will propose that the data is unremarkably distributed and we can see in the graph in the appendix that there are 8 countries on each lieu of the mean. The skewness is 0,71, since the number is positive it will imply that Co2 emission per capita is slightly skewed to t he right. The mean (26226) and median (27407) for GDP per capita show that this data is normally distributed as well. We can in any case here see that there are 8 countries on both side of the mean. The skewness for GDP per capita is close to zero (0,08) and therefor the distribution is close to symmetric.For population density we have 10 countries underneath the mean. This will imply that the data is not perfectly normally distributed. We can also see that mean (151) and the median (118) differs a bit too much too be normally distributed. Since the mean is higher than the media it suggest that the mean is affected by the high thorough values in the distribution like South Korea. The skewness for population density is 0,94, this show that the distribution is skewed to the right. It is big to remember that the data sample is less than 30 and therefor it makes it difficult to determine if the data is normally distributed or not.In all the 3 different datas we see that the range is high, this is due extreme values on both sides of the mean (countries in totally different stages when it comes to wealth, industry, population, size and general development). The high spread within the distribution will therefor lead to and high S. D, its also crucial to notice that the sample is relative small and will not give a totally fructify picture. Correlation First we will start with to calculate the Pearson correlation coefficient to measure the linear association between the two variables in hypothesis 1. 1 and 1. 2.After that we will test the significant of the correlation coefficient. The reason we will use the Pearson correlation coefficient instead of Spearman correlation coefficient is that the data are continuous and in ratio scale. sx=x2-nx2n-1 sy=y2-ny2n-1 sxy=i=1n(xi-x)(yi-y)n-1 rxy= sxysxsy t=r1-r2n-2tn-2 For the calculation see table 1 and 2 in the appendix. The table and the graph 1. 1 show that there is a strong relationship between Co2 emission per capita and GDP (0,7319). In graph 1,2 and the table we see that Co2 and population density have a weak shun correlation (-0,3118).Further on we will need to use a t-test in order to determine the significant of the correlation coefficient and to find out if we are going to keep or reject our hypothesis 1. 1 and 1. 2. critical value of t t(n-2,? 2)=t(14,0. 25)=2,145 (with 95% confidence interval) The t value in the table shows that there is a significant relationship between Co2 emission per capita and GDP since 2,145