Human Population Impacts on Global Wildfire Emissions

Question: Explain about the Human Population Impacts on Global Wildfire Emissions. Answer: According to the question, the functional form used for the institution of the graph for all countries is best suited with the help of linear, exponential form in which two variables are concerned, one is dependent, and other is independent. Here, the dependent variable is Global Carbon Fossil Fuel Emissions (FF) taken in million metric tons carbon and independent variables taken are fossil fuel carbon emissions in different countries such as China, USA, India, Russia, Japan and Germany. However, to fit the best trend line, the dependent variable is converted to log form. The equation followed for the graph is Log Y = + 1X1 + 2X2 + 3X3 + 4X4 + 5X5 + 6X6 + Ɇº (Log - Linear Form: On Semilog axis) (Gujarati 2014). The results are carried out using Excel. The graph fitted is depicted in Figure 1. Figure 1 Fossil Fuel (FF)-based carbon emissions plot The problem illustrated to state that the there is slightest changes in Mauna Loa (land) values, but not much can be experienced in the seasonal variations of Antarctica as referring to values. However, the land has more carbon emissions than water body due to increased levels of greenhouse gasses (Muradov 2014, pp. 43-77). Figure 2 Seasonal Variations Of Mauna Loa According to the question, the results show an increasing rate of carbon emissions on Mauna Loa and Antarctica. The growth rates individually calculated show a fluctuating trend. Nevertheless, the trends were first calculated by averaging the yearly ppmv value and later the growth rate was computed. However, the fluctuation is shown on an increasing trend. The graph illustrating the trend is depicted in Figure 3. Figure 3 Atmospheric Growth Rate The carbon sinks for every year are computed by subtracting the sequestered land and ocean from the annual total of fossil fuel and land use of carbon emissions. The steps that are used for calculation are: The annual total (fossil fuel +land use) is changed to PPMV value from metric million tons. The total of Mauna Loa and Antarctica value is even totaled The total value (Mauna Loa and Antarctica) subtracted from the total annual value of carbon emissions. Later, the growth rate is calculated in percentage. Figure 4 depicts the graph of the growth rate, which depicts the weakening of carbon emissions that is creating a climate change. Figure 4 Weakening of Carbon Sink On the other part of the question, the results depicted on the hypothesis of the decadal means, the different means for both annual data and total of Mauna Loa and Antarctica is calculated. The results depicted can be shown in Table 1 Total (Antarctica and ML) Total Annual PPMV (Fossil + Land) Mean 695.5860934 2889.866323 Variance 1971.789643 556970.9355 Observations 52 52 Hypothesized Mean Difference 0 df 51 t Stat -21.16460209 P(T=t) one-tail 3.23803E-27 t Critical one-tail 1.675284951 P(T=t) two-tail 6.47606E-27 t Critical two-tail 2.007583728 Table 1 Decadal Means The t value calculated shows the t statics to be insignificant as Antarctica, and Mauna Loa are a part of Total Annual Carbon Emissions data and does not influence in a sound pattern. However, though the p-value also states to be insignificant which states that there are huge differences in the variances of the two. If stated on decadal means, then the hypothesis is minimal, proves not to be aligned and does not support the average of the data. The various carbon emission scenarios are plotted in two different graphs in Figure 5 and Figure 6. Figure 5 Projected Emissions Figure 6 Corresponding CO2 Concentrations Discussions The global emissions since 50 years have been increasing due to the changes that are leading to increasing in greenhouse gasses and alterations of human activities both by the accumulation of fossil fuels for energy, transportation, industrial processes and land use changes. However, the global emissions are influencing the ability of natural sinks that are adding to the atmosphere with an increase in the industrial revolution. However, the CO2 emissions from fossil fuels are due to long and short term factors like changes in energy prices, technologies and seasonal temperatures. Largely, the transport emissions have been increased from 5% 1960 to 21% in 2011 (Muradov 2014, pp. 43-77). The mean magnitude of the carbon sinks depicts that the weakening growth rate of 50 years is 0.027%, which states that there is large variability if analyzed from year to year. The large change in the emissions is declining, and CO2 concentrations continue to increase because of expanding human population that continues to grow in urban areas and that is the reason for the change in climate pattern. However, the emissions will rise due to the risk of demographic change and CO2 fertilization leading to higher fuel loads continuity (Knorr, Jiang and Arneth 2016). Data Interpretation The current yearly increase is not steep for current periods due to rise in Mauna Loa of land use and fossil fuel emissions. However, the countries the countries that have lowered their carbon emissions are Germany and Russia followed by USA and Japan with the slightest change. Hence, these countries were part of Kyoto Protocol (Schnepf 2013). According to the CO2 interannual and decadal time, the emission is declining but the results from decadal times show less emissions from water than land use. However, according to the actual data, it showed a significant result. However, when analyzed on the robust growth, the fluctuations are long-term and continue to affect the growth per annual total of CO2 (land use and fossil fuel). The emissions scenario that has been more realistic one is the CO2 concentrations. According to the Figure 5 and 6, the stabilization process can be based on CO2 concentrations, which provides the ground for the basis for future projections. However, according to the projections, the emissions are projected to be higher in the future scenario. Nevertheless, the concentration level has increased since the 400ppmv concentrations of carbon on the different level of projections. References Gujarati, D., 2014.Econometrics by example. Palgrave Macmillan. Knorr, W., Jiang, L. and Arneth, A., 2016. Climate, CO 2 and human population impacts on global wildfire emissions.Biogeosciences,13(1), pp.267-282. Muradov, N., 2014. What Is So Unique About CO2?. InLiberating Energy from Carbon: Introduction to Decarbonization(pp. 43-77). Springer New York. Raupach, M.R., Gloor, M., Sarmiento, J.L., Canadell, J.G., Frlicher, T.L., Gasser, T., Houghton, R.A., Le Qur, C. and Trudinger, C.M., 2014. The declining uptake rate of atmospheric CO 2 by land and ocean sinks.Biogeosciences,11(13), pp.3453-3475. Schnepf, A., 2013. Soil carbon models used for Kyoto Protocol reporting.BELOWGROUND CARBON TURNOVER IN EUROPEAN FORESTS, p.12.