ANALYSIS OF CO2 EMISSION TREND IN SPAIN FROM 1960 TO 2013

ANALYSIS OF CO₂ EMISSION TREND IN SPAIN FROM 1960 TO 2013

INTRODUCTION

The human race is expected to experience critical atmospheric changes in the next few years to come. With the current high rate of pollution all over the globe, States have come to an agreement that to slow down the rapid climate changes, everyone around the world should be responsible for controlling the unceasing pollution. It is the responsibility of every human to reduce the harmful actions on the environment to achieve a desirable natural habitat for our generations to come. Several types of fuels have used in running different production processes around the world. A good example is the cement factory in Spain, where a wide range of fuels, including coal, gaseous, liquid, and solid fuels, had been used in cement production, which releases substantial amounts of CO₂ during the combustion process (Diego Garcia-Gusano, 2015).

The emission of CO₂ from different types of consumed fuel can encourage global warming, thus resulting in long term devastating effects on earth. Recent scientific agreements have lamented that the global temperatures should be below 2⁰C to prevent severe consequences of climate change. The high emissions of CO₂ believed to have been encouraged by human activities. Thus there is an urgent need by firms, governments, and individuals to impress events that lead to minimal emission of CO₂ to the atmosphere. Furthermore, economic strategies have been suggested and implemented to control the emissions of CO₂. These commercial tools are somewhat useful since industries experience equal subsided costs (Eskinder Demisse Gemechu, 2013).

Our study aimed at determining the trend of CO₂ emission from 1960 to 2013 by predicting the amount of CO₂ emissions from different types of fuels. A multiple regression model was used to determine the contribution of each fuel under consideration of the overall volume of CO₂ emitted in Spain. Deciding the quantity of CO₂emission by each fuel will enable industries, governments, and individuals in Spain to be more precautionary on which fuels are relatively friendly to the environment and which ones to minimize consuming them on a regular basis for healthier climate conditions.

The objectives of the research

The general aim of the study was to establish the characteristics of the different types of fuel.

Specific objectives;

To determine the relationship between the total emitted CO₂ with the respective different fuel emissions.
To find the contribution of individual fuel emission on the overall rate of CO₂

Significance of the research

Global warming has become a significant challenge to human society. With increased chemical pollution into the atmosphere, the globe has become hazardous to its habitats. CO₂ is a significant pollutant since it produced in almost every sector in the entire world. Different kinds of fuel have contributed to the production of CO₂. Therefore, it is essential to understand the effect of CO₂ by determining the exact fuel that contributes heavily to the creation of CO₂ to enable people to make environmentally friendly choices on their fuel of preference.

Research assumption

We assumed that the data used met all the regression analysis assumptions, and thus it did not violet the regression assumptions. The variables believed to have a linearity relationship, and therefore there was no modification of the variables for regression analysis.

LITERATURE REVIEW

(Isabela Butnar, 2011) Conducted a study to identify the changes in CO₂emission over some time. He used an empirical formula to model the Spanish economy using environmental information from 2000 to 2005. Their study involved the application of decomposition analysis regarding the input-output subsystem emissions model. Concerning the subsystem method, they analyzed a particular sector without detaching it from the entire production system.

The subscripts and subscripts are the respective groups of m and s, respectively. The results revealed that a general increase in CO₂ emission of all the components. The study found that there were strong variations in the external component, which illustrate high emissions from the service sectors than the non-services sectors (Isabela Butnar, 2011).

(Atonio Manresa, 2004) Estimated the energy intensities and the emission of CO₂ gas in Catalonia. They used the Social Accounting matrix analysis model to determine the effect of CO₂ on the Catalonian economy. The CO₂ emission model showed that in the year 1987, out of the 27 million metric tons of emitted CO₂, about 20 million tons are contributed by production processes such as in factories that use liquid fuel (Atonio Manresa, 2004).

(Aylin Cigdem Kone, 2010) Used a trend analysis approach to model and predicted CO₂ emissions for the period from 1971 to 2007. The results obtained indicated that the regression model could be used to forecast future trends of CO₂radiation. The trend analysis deployed was given in equation form, as shown below:

Such that represents the coefficients and is CO₂ emission for a period of years.

The study found that renewable energy generates energy with minimal emission of air pollutants when compared to fuels. The study findings on trend analysis proved that the model could be used for CO₂ discharge for the future. The amount of CO₂ emissions influenced by other factors, including the types of fuel used (Aylin Cigdem Kone, 2010).

(JaeHyun Park, 2013) conducted analysis of the correlation of Carbon dioxide emission and energy utilization in South Korea. They analyzed secondary data for the period from 1991 to 2011 using regression analysis among the different elements. The regression analysis showed that the economy moves together with the rate of CO₂ emission. Fuels were that emit a lot of carbon dioxide found to correlate highly with economic growth. Coal found to be consumed mostly in industries where petroleum fuels mostly used in the transport sector (JaeHyun Park, 2013).

DATA SOURCE, DESCRIPTION, AND METHODS

This chapter describes the multiple regression analysis we applied in modeling the fuel types on determining their effect on CO₂ production, our data source, and description as well as the software we used to analyze the data.

Data source and description

Secondary data obtained from a database of different sets of data where we chose CO₂ emission in Spain for 1960-2013 as our study sample, which consisted of seven variables. CO₂ discharge in (kt) considered as the dependent variable and the six considered independent variables were;

Gaseous fuel emissions
Liquid fuel emission
Other sector emissions
Residential building emissions
Solid fuel emission
Emissions from transport

Some variables, including radiation from transport, residential discharge, and other sector emissions, their observations presented in percentages, but for uniformity purposes, we converted them into whole figures. All the observations of the variables were in metric kiloton (kt).

Data analysis tool

We used Excel for analysis

Theory of the regression analysis model

The multiple linear regression model is said to be a probabilistic model that consists of more than one independent variable. Mathematically it is given as

Where β₀, β₁, β₂………. β_kare the coefficients of regression predictors’ and is an error term.

Considering only one predictor and keeping the rest constant, then the regression coefficient gives any amount of change in y corresponds to a unit change in the predictor.

RESULTS AND ANALYSIS

This chapter presents the findings obtained from the analysis of our data. We began by determining the specific measurements of our variables and then the regression analysis.

Descriptive analysis

Here we based our analysis on objective one, where we aimed to characterize our variables and understand their measurements. The table below gives the descriptive statistics of our variables.

From the above table, the lowest CO₂ emission from 1960 to 2013 was 48928.78 metric kiloton, and the highest registered discharge between that period was 358236.564 metric kilotons. The average CO₂ produced from 1960 to 2013 was 206485.6463(kt). Gaseous fuel emission registered a mean of 20624.56615(kt) with the highest CO₂ produced by gaseous fuel was 79955.268(kt) and a minimum of 0(kt). Liquid fuel had a high CO₂ emission of about 182616.6(kt), and the lowest emission of about 14246.295(kt), the mean CO₂ production by liquid fuel for the duration from 1960 to 2013 was 117650.6019(kt). CO₂ emission by other sectors recorded a high volume of 19480.801 and the least volume of 1396.661 metric kilotons, with a mean emission of 6432.466852(kt). Emissions related to residential buildings recorded high CO₂ emission of about 30713.267 and lower emission of 5568.364 metric kilotons. The average discharge of CO₂from residential buildings was 17541.90483. Solid fuel emission had a maximum volume emission of 82393.823(kt) and lowest emission volume of 31033.821(kt). The mean solid fuel emission was 55296.05115(kt). Emissions due to transport systems recorded a high CO₂ production of 123078.197 and a minimum product of 15326.197 metric kilotons. The average emission with regards to transport systems was about 62666.18456 metric kilotons. The total CO₂ emission from 1960 to 2013 in Spain was 11150224.9 metric kilotons.

Correlation matrix

It is a table that shows correlation coefficients between given variables. A correlation coefficient tells how strongly two variables are related. Below is a correlation matrix of our study data.

The results from the above table based on objective two, where we determined the relationship between the different fuel emissions with the global CO₂ emission. The above results showed that there were high positive correlations between CO₂ emissions with varying emissions of fuel. For instance, the association of CO₂ with gaseous fuel, liquid fuel, residential emissions, solid fuel, and transport emissions was 0.79, 0.96, 0.97, 0.72, and 0.96, respectively. Thus an increase in any of the fuel emissions increases the global CO₂ emission.

Scatterplots

It is a graphical representation of the correlation between two variables. We generated scatterplots of the dependent variable CO₂ emission against each independent variable to obtain the coexisting relationship between the variables.

Scatterplot of CO₂ emission against gaseous fuel emission

Scatter plot of CO₂ emission against liquid fuel emissions

Scatter plot of CO2 emission against emission from other sectors

Scatter plot of CO₂ emission against residential building emissions

Scatter plot of CO2 emission against solid fuel emissions

Scatter plot between CO2 emission and emissions from transport

From the above scatterplots, there was a positive relationship between the general CO₂ produced against the CO₂ emission from different fuel consumption and other sectors except for solid fuel emission, which showed that there was no relevant relationship between the variables.

The multiple linear regression analysis

Our regression analysis based on objective three; that to determine the significance of the different fuels on CO₂ emission and finding their contribution towards CO₂ contribution for future prediction and control measures. A model of all the variables was performed where CO₂ emission in metric kilotons was used as the response variable. Theoretically, the model applied presented as;

Where;

GFE= CO₂ emissions from gaseous fuel

LFE= CO₂ emission from liquid fuel

OSE= CO₂ emission from other sectors

RBE= CO₂ emission from residential buildings

SFE= CO₂ emission from solid fuel

TE= CO₂ emission from transport systems

The complete regression output after the analysis shown below

From the above outputs, we can write the prediction regression line as

CO₂emission ~ 571.75+ 1.0499 gas fuel emission + 1.0921iquid fuel emission+ 0.1876 other emission

-0.7183 residential emission + 1.0844 solid fuel emission + 0.1149 transport emission

The ANOVA table of the regression analysis, based on determining the relevance of the independent variables in explaining the dependent variable. We considered the hypothesis that;

H_o: the regression coefficients are all zero

H_a: at least a few of the regression coefficients are not zero

From the ANOVA output, the computed test statistic is smaller than the significance level at 95% confidence interval; hence we rejected the null hypothesis to favor the alternative hypothesis. We concluded that regression coefficients are non-zero, and thus, the model is useful in explaining CO₂ emission in Spain.

Gas fuel emission, Liquid fuel emission, residential emission, and solid fuel emission found to be significant variables in explaining the rate of CO₂ emission in Spain since their probability values were small than the significance value at 95% confidence interval. Transport emission and other sector emissions were insignificant in explaining the outcome variable.

From the regression coefficients, all the independent variables had a positive relationship with the dependent variable except for residential building emission. A unit increase of gaseous fuel emission increases CO₂ emission by 1.049869549, while a unit increase in liquid fuel emission increases CO₂ emission by 1.092140284. Emission from other sectors, when increased by one unit, increases CO₂ emission 0.187563085. Solid fuel emission and transport emission increases CO₂ emission by 1.084364795 and 0.114949493, respectively. When we increase residential residence emission by one unite, CO₂ emission is reduced by 0.718348401.

The regression produced an R² of 0.99, which implies that the model takes care of 99% of the variations in CO₂ emission. The regression explains 99% of the observations of our data set.

CONCLUSION

The research committed to model the different types of fuel that contribute to CO₂ emission towards predicting the trend of CO₂ pollution over some time. Liquid fuel was determined to be the highest producer of CO₂ gas as compared to other types of fuel. However, it was determined that the trend of CO₂ emission due to the significant variables gas fuel emission, liquid fuel emission, and solid fuel emission expected to increase since these variables have an increasing effect on the general CO₂ emission. Residential building emission breaks the expanding trend of CO₂ emission since it relates negatively to CO₂ emission. Even though emission from other sources and transport emission is not significant, we cannot rule out their impact on CO₂ emission prediction trends since they contribute to higher CO₂ production.

RECOMMENDATION

Protecting climate change is a responsibility for every individual around the globe. The results from the analysis recommend that Spanish citizens should impress the use of residential building fuel as they reduce the CO₂ emission into the atmosphere. We recommended that people should minimize the use of liquid fuel as they have the highest production of CO₂ gas.

References

Atonio Manresa, F. s. (2004). Energy intensities and CO2 emission in Catalonia: A SAM analysis. Environment, Workplace, and Employment, 91-106.

Aylin Cigdem Kone, T. B. (2010). Focusting of CO2 emission from fuel combustion using trend analysis. Renewable and Sustainable Energy Reviews, 2906-2915.

Diego Garcia-Gusano, H. C. (2015). Long-term behavior of CO2 emissions from cement production in Spain: scenario analysis using energy optimization model. Journal of nuclear production, 1-11.

Eskinder Demisse Gemechu, I. B. (2013). Economic and environmental effects of CO2 taxation: an input-output analysis. Journal of environmental planning and management, 37-41.

Isabela Butnar, M. L. (2011). Structural decomposition analysis and input-output subsystems: changes in CO2 emission of Spanish service sectors (2000-2005). Ecological Economics, 2012-2019.

JaeHyun Park, T. H. (2013). Analysis of South Korea’s economic growth, carbon dioxide emission, and energy consumption using a Markov switching model. Renewable and Sustainable Energy Review, 543-551.