Analysis of Environmental Issues and Economic Performance

Analysis of environmental issues and economic performance and tribe tautness Executive summary The main close with the report was to analyse the relationship from 16 antithetical countries on how, if any, carbon dioxide sack per capita is getting touch on by existence slow-wittedness and gross domestic product per capita by using descriptive statistics and regression. The conclusion is that carbonic acid gas liberation per capita is affected by changes in gross domestic product per capita and that population niggardliness has no hearty relation to CO2 electric discharge per capita. Introduction Global warming is unmatched of the biggest problems in the international societies today.The politician economises discussing how they can find solutions together to light the CO2 emissions worldwide. In this report we allow for try to examine if well-established countries find a senior broad(prenominal)er(prenominal) CO2 emissions and we forget examine how population im mersion are affecting emission in our society today. Aim The channelize with this report is first to examine the relationship with gross domestic product per capita and CO2 emission and population density and CO2 emission. Then we will examine if high gross domestic product per capita leads to high CO2 emission per capita and if countries with low population density are polluting more than countries with high population density.Hypothesis 1. 1 I cogitate that a plain with high GDP are more probable to have a higher CO2 emission per capita since a country with high GDP are more likely to have higher productivity achieved through higher energy use. We will then lettuce with measuring the linear tie between these variables. H0 ? 0 1 GDP? 0 (Correlation) H1 ? 0=? 1 GDP=0 (No correlativity) Hypothesis 1. 2 I believe that a country with high population density are more likely to have a lower CO2 emission per capita since the inhabitants need travel shorter and less often.We will i n that respectfor stripe 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 theory We want to find out how much linear association the two variables has on CO2 per capita. This can be done with this imitate 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 exit how upstanding 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 entropy (GDP per capita, CO2 per capita and population density) in this report is a savour of 16 different countries and are downloaded from the International Monetary Fund, US department of free energy and OECD. All the information are ratio scale and are ceaseless. rough potential problems with the associated data is * Some countries may have a high productivity achieved by the efficient labour force and not gutter higher energy use. Both ways 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 liberal amount of landmass that is not suimesa for life. * The different data is not from the same days. 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 carbonic acid gas per capita the opine is 9,285 and the median is 9,49, this will suggest that the data is normally distributed and we can see in the interpret in the appendix that there are 8 countries on for each one side 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 the right. The mean (26226) and median (27407) for GDP per capita confront that this data is normally distributed as well. We can also 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 extreme set apart in the distribution like southeastern Korea. The skewness for population density is 0,94, this envision that the distribution is skewed to the right. It is primal to remember that the data sample is less than 30 and therefor it makes it severe 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 wholly different stages when it comes to wealth, industry, population, coat and general development). The high spread within the distribution will therefor lead to and high S. D, its also important to notice that the sample is relative small and will not give a totally correct 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 th e 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 negative 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