Data Acquisition
This study is conducted in a global scale. The emission data and economic-related data of 263 countries and regions are analyzed in the study. The datasets utilized for analysis are obtained from the World Bank.
Four dataset are implemented during the statistical analysis in this study, detailed info are as described below:
CO2 Emissions (kt): Carbon dioxide emissions are caused by the burning of fossil fuels, which include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. (World Bank, 2016)
Nitrous oxide (N2O) emissions (thousand metric tons of CO2 equivalent): Nitrous oxide emissions are emissions from agricultural biomass burning, industrial activities, and livestock management. (World Bank, 2012)
GDP per capita (current US$) : GDP per capita is gross domestic product divided by midyear population. Data are in current U.S. dollars. (World Bank, 2019)
Population: Total population counts all residents of a country regardless of legal status or citizenship. The values shown are midyear estimates. (World Bank, 2018)
Analytical Approaches
The software of R is used for the data management, data transformation and most importantly, statistical analysis. Population is an important factors in the study and all comparisons analyzed on per-capita basis. All the emission dataset are converted into emission per capita in order to standardize the dataset and make the comparison between countries more rational, which furtherly eliminates possible bias that may caused by the great disparity in the populations of different countries.
The outliers in CO2 emission per capita and N2O emission per capita are analyzed and identified, and the outliers identified in this part of the study is not the statistically significant outliers, but the countries which contain highest emission per capita that visually observed using scatterplot.
The trend of greenhouse gas emission for each country during selected ten years is also analyzed during statistical analysis. In order to determine the relationship and compare the trend of emissions, the linear regression between per-capita CO2/N2O emission and year are tested for each country between 2001 - 2012. The identification of outliers is also involved, the outliers identified in this part is the countries which (visually observed) contain the greatest rate of change (of both growth and reduction).
This study is conducted in a global scale. The emission data and economic-related data of 263 countries and regions are analyzed in the study. The datasets utilized for analysis are obtained from the World Bank.
Four dataset are implemented during the statistical analysis in this study, detailed info are as described below:
CO2 Emissions (kt): Carbon dioxide emissions are caused by the burning of fossil fuels, which include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. (World Bank, 2016)
Nitrous oxide (N2O) emissions (thousand metric tons of CO2 equivalent): Nitrous oxide emissions are emissions from agricultural biomass burning, industrial activities, and livestock management. (World Bank, 2012)
GDP per capita (current US$) : GDP per capita is gross domestic product divided by midyear population. Data are in current U.S. dollars. (World Bank, 2019)
Population: Total population counts all residents of a country regardless of legal status or citizenship. The values shown are midyear estimates. (World Bank, 2018)
Analytical Approaches
The software of R is used for the data management, data transformation and most importantly, statistical analysis. Population is an important factors in the study and all comparisons analyzed on per-capita basis. All the emission dataset are converted into emission per capita in order to standardize the dataset and make the comparison between countries more rational, which furtherly eliminates possible bias that may caused by the great disparity in the populations of different countries.
The outliers in CO2 emission per capita and N2O emission per capita are analyzed and identified, and the outliers identified in this part of the study is not the statistically significant outliers, but the countries which contain highest emission per capita that visually observed using scatterplot.
The trend of greenhouse gas emission for each country during selected ten years is also analyzed during statistical analysis. In order to determine the relationship and compare the trend of emissions, the linear regression between per-capita CO2/N2O emission and year are tested for each country between 2001 - 2012. The identification of outliers is also involved, the outliers identified in this part is the countries which (visually observed) contain the greatest rate of change (of both growth and reduction).