Friday, January 4, 2019

Analysis of Environmental Issues and Economic Performance

Analysis of environmental issues and sparing performance and tribe niggardness executive summary The main(prenominal) goal with the field of study was to analyse the sex actship from 16 contrasting countries on how, if any, carbonic acid gas dismissal per capita is acquire alter by cosmos slow-wittedness and gross domestic growth per capita by using descriptive statistics and regression. The conclusion is that carbonic acid gas sacking per capita is affected by changes in gross domestic product per capita and that state guessness has no evidentiary relation to carbon dioxide procession per capita. Introduction global warming is one of the biggest problems in the bulgeside(a) societies today.The politician keeps discussing how they force out find solutions together to decrease the carbonic acid gas emissions worldwide. In this subject we ordain try to examine if well-established countries request a utmoster carbonic acid gas emissions and we departing examine how community tightfistedness be touch on emission in our partnership today. Aim The aim with this composition is first of all to examine the birth with gross domestic product per capita and carbonic acid gas emission and population meanness and carbonic acid gas emission. Then we ordain examine if graduate(prenominal) gross domestic product per capita leads to game(prenominal) carbon dioxide emission per capita and if countries with low population constriction argon polluting to a greater extent than countries with in tall spirits population density.Hypothesis 1. 1 I believe that a democracy with high gross domestic product argon to a greater extent than likely to gain a higher(prenominal)(prenominal) CO2 emission per capita since a rural with high GDP be more likely to have higher productiveness achieved with higher efficacy routine. We result hence start with measuring the bilinear necktie between these variable quantitys. H0 ? 0?? 1 GDP? 0 ( correlativity) H1 ? 0=? 1 GDP=0 (No correlational statistics) Hypothesis 1. 2 I believe that a rural with high population density atomic number 18 more likely to have a lower CO2 emission per capita since the inha telephone numberants need travel shorter and less often.We get out on that pointfor 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 opening We neediness to find out how a good deal linear association the deuce variables has on CO2 per capita. This can be do 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 satisfy how strong the association these two variables ar against the subject variable CO2 emission per capita. Further on we want to rise the significance of these variables.Data and descriptive statistics The selective information (GDP per capita, CO2 per capita and population density) in this report is a taste of 16 antithetic countries and be downloaded from the transnational Monetary Fund, US department of Energy and OECD. All the entropy argon proportion scale and are continuous. around potential problems with the associated selective information is * Some countries whitethorn have a high productivity achieved by the efficient campaign force and not trough higher energy function. Both ways of high productivity leads to higher GDP per capita, its marvelous to achieve it by efficient cranch force, but it can occur. Some countries (e. g. Australia) may have low population density although they mainly have big populate cities since they have a large come of landmass that is not sui slacken for life. * The contrastive info is not from the same years. CO2 emission per capita is from 2004, population density is from sundry(a) years and GDP per capita is from 2010. To get an supposition of how the entro pyset looks like we need to use up descriptive abridgment. Mean x=xn Median x=n+12th S. D sx=x2-nx2n-1 try variance s2=x2-nx2n-1 Range=xh-xlFor carbon dioxide per capita the have in mind is 9,285 and the medial is 9,49, this will allude that the data is commsolely distributed and we can imbibe in the represent in the auxiliary that t here(predicate) are 8 countries on separately side of the mean. The lopsidedness is 0,71, since the number is absolute it will imply that Co2 emission per capita is slightly skewed to the right. The mean (26226) and median (27407) for GDP per capita come on that this data is normally distributed as well. We can similarly here adopt that in that respect are 8 countries on twain side of the mean. The skewness for GDP per capita is close to zero (0,08) and in that respectfor the scattering is close to symmetric.For population density we have 10 countries underneath the mean. This will imply that the data is not suddenly normally distr ibuted. 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 values in the distribution like South Korea. The skewness for population density is 0,94, this fork out that the distribution is skewed to the right. It is important to remember that the data sample is less than 30 and therefor it makes it arduous to pin down if the data is normally distributed or not.In all the 3 opposite datas we see that the range is high, this is delinquent extreme values on some(prenominal) sides of the mean (countries in totally incompatible stages when it comes to riches, assiduity, population, size and general development). The high outflank within the distribution will therefor lead to and high S. D, its also important to notice that the sample is sexual relation small and will not move over a totally correct picture. Correlation First we w ill start with to attend the Pearson correlation coefficient to measure the linear association between the two variables in guesswork 1. 1 and 1. 2.After that we will trial run the significant of the correlation coefficient. The rationalness 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 turn off 1 and 2 in the appendix. The table and the graph 1. 1 show that there is a strong affinity 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. little value of t t(n-2,? 2)=t(14,0. 25)=2,145 (with 9 5% effrontery interval) The t value in the table shows that there is a significant relationship between Co2 emission per capita and GDP since 2,145<4,0186. Therefor we will keep the H0 in our hypothesis 1. 1. The t value for Co2 emission per capita and population density shows that there is no significant relationship -2,145<-1,2281<2,145.We will therefore need to reject H0 in favour of H1 in hypothesis 1. 2. multivariate regression We now want to use multivariate regression to test the main hypothesis. In most cases there are unlikely there are only one explanatory factor affecting a mutualist variable. We will therefor use multivariate regression to test if the two different explanatory variables (pop. density and GDP per capita) are affecting the dependent variable CO2 emission per capita. From the table we get the regression line CO2per capita = 4,49432+ 0,0002207 GDP-0,0095956 pop. density+ ?The coefficient of multiple determination (R uncoiled) is 0,59879 normally th is would mean that 59,87% of the changes can be explained. However since we are using a sample, have only a few observation and more than one explanatory factor, adjusted R square will give us a more correct and unprogressive picture. When you add more variables to regression analysis Adjusted R square will only increase if that new variable increases the predictive power of the equation. The adjusted R square shows us that 53,706% of the changes in CO2 emission per capita can be explained by GDP per capita and population density.Significance F tells us that there are only 0,26% pass that the output was obtain by hit-or-miss chance. If we look at residuals in the graph over (the difference between the real(a) value of the dependent value and the predicted dependent value) compared to the predicted value, we can see that there are no certain pattern and that there are cantered around zero. See the appendix for the residual output. By using the F-test we can test if the overall m odel is significant, we will use 95% confidence interval. The searing f-value is 3,806. Since F value (9,70089) is larger than the detailed f-value the model is useful.Since we now know that the overall model is useful we will test the main hypothesis to see if both variables contribute to the model. critical value of t t(n-2,? 2)=t(13,0. 25)=2,160 (with 95% confidence interval) The t-value for GDP per capita is 4,03122, since 4,03122<2,160 we will keep H0 ? 1 GDP? 0. This shows us that GDP per capita is contributing to the model and are affecting CO2 emission per capita. The t-value for population density is -1,43036, since -2,160<-1,43036<2,160 we will reject H0 in favour of H1 ? 2 pop. ensity=0, which means that population density is not contributing to the model. banter in wider social, economic and political linguistic context The results in this report shows that countries with higher GDP per capita are polluting more CO2 per capita. The reason for this is that countr ies with high GDP per capita are achieving this through higher energy use. This means that countries with high wealth have more industry and are consuming more goods and services. Examples of higher pulmonary tuberculosis can be cars, travels, rut and lightning. So the result of higher consumption is higher CO2 emission.A problem is that the wealth in the world is not divide equally between countries or redden within the different countries this implies that CO2 emission is not equally distributed. The Kyoto accord is an internationalist pact whereby countries agree to overturn their nub of green house gases (CO2 is the most important). The treaty opens for countries to barter for credits if its cheaper to reduce the CO2 emission in some other country. This can create a honorable problem since the pie-eyed countries can buy themselves out of the world-polluting problem. ConclusionThe report is using a sample of 16 observation/countries to show that GDP per capita is corr elating with CO2 emission per capita and that a higher GDP per capita leads to higher CO2 emission per capita. This proves that countries with high GDP are more likely to achieve higher productivity through higher energy use. The report also shows that population density has no significant relationship with CO2 emission per capita. We can from all the different observations see that there is a truly large spread between the wealthy and not wealthy countries.The main jot from this report is to investigation further on how you can increase energy capability in a competitive economic world. References * GDP per capita Gross domestic product per capita in US dollars, 2010 Source International Monetary Fund. http//www. imf. org/external/pubs/ft/weo/2012/02/weodata/ (assessed borderland 2012) * Population density number of inhabitants per square kilometre, Source OECD Various years. United Nations. * cytosine Dioxide Emissions in 2004 carbon dioxide emissions per capita (tons/capi ta) 2004, Source US Department of Energy Appendix

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