Time series analysis of air pollution trends in Kenya using environmental Kuznets curve

Introduction

The environmental Kuznets curve is a graph that aims to explain the hypothesized relationship between economic growth and the level of environmental quality. It proposes that in the early stages of economic growth, environmental degradation increases due to increased levels of pollution, however beyond a certain level of income per capita, high income levels lead to the improvement of the quality of the environment. This implies that the curve is an inverted U in shape and is graphed by modeling environmental quality as a function of income, Grossman and Krueger (1991). The environment is one of the major pillars of sustainable development, without it most of the activities that we undertake daily, if not all, would be impossible. This is because the environment fulfils development needs by providing us with human, physical and natural capital as well as enhancing intra-and intergenerational equity (Bass 2006).

Statement of the Problem

The CO2 emissions in Kenya have been rising over the years. The increase may be attributed to the increased electricity supply accelerated growth of up to 9.7 percent in 2015. The energy mix in Kenya has diversified over the years from relying on hydroelectric power, to the development of geothermal which currently contributes to large amount of power to the grid. There is a shift from use of solid fuel at homesteads to adoption of gases and electric sources which has significantly influenced CO2 emissions. Interventions through tree planting as a salvage to the increasing pollution trends steers the core of this study. The study is thus anchored on the need to reduce the environmental effects of economic growth by identifying the existing relationship between environmental degradation and per capita income through forest change in Kenya.

Objectives of the Study 1.2.1 General Objective

To analyze Air pollution trends in Kenya using Environmental Kuznets curve.

Specific Objectives

Research Methodology 2.1 Data Source

The study used annual data from 1990 -2018. The variables were sourced from the World development indicators (World Bank, 2018)

Method of Analysis

A study by Ghosh and Dutta (2003) decomposed the relationship between economic growth and the environment by incorporating the variables that capture the underlying causes of the relationship.

As the economy grows, more inputs are needed in production, hence more natural resources are used up. This is known as the scale effect. More output also implies more waste and emissions as by-products which contribute to environmental degradation. It is expected to be a monotonically increasing function of income.

To do so, the study employed a time series data covering the years from 1990 to 2018 collected from World Bank Development Indicators as proposed by the following functional form for the case of Kenya.

CO2 in Eq. ( 1) represents the carbon dioxide emissions per capita in metric tons, while EC is energy consumption of Kenya and GDP is real GDP per capita calculated by the2005 US$ prices, which is taken as an indicator for living standards. In this study, the natural logarithmic form of the CO2 and GDP and EC variables is used. To indicate trade openness which is calculated as the sum of percentage of exports and percentage of imports to GDP as suggested by Ak ın (2014) and Hossain (2011); UR stands for urban population which is measured as the share of urban population in total population and the subscript t represents the time period.

The scale effect function was expressed as (Panayotou, 1997)

Where ED t -Level of environmental degradation β -Parameters to be estimated x t -GDP per capita εerror term to show a stationery series To find the turning point of income,

To capture the composition effect, the study used the industry, agriculture and services sectors as proxies for structural change. The change from agriculturalindustrial economies is expected to add to environmental degradation; therefore, coefficients of the industry and agriculture variables are expected to be positive while that of the services variable may be negative seeing to the fact that the services sector may not be pollution intensive. The composition effect may be expressed as:

Where ED tenvironmental degradation measure Indshare of the industry sector in GDP Agrshare of the agricultural sector in GDP Sershare of the services sector in GDP αparameter to be estimated εerror term Higher incomes result in increased demand for better environmental quality and it provides resources for investment in environmentally friendly technology. Therefore, people express their desire for environmental conservation based not only on their income levels but also on their rights and freedoms (Eriksson and Persson 2002).

According to Marshall and Jugger (2000), policy formulation in any governing system can be influenced by changes in people's preferences. Policy itself has been used in some studies as a measure of how democratic the structure of a government is. The model that relates the level of environmental degradation to a flexible function of per capita income, that covers all three effects is specified as:

Where ED t -Proxy measure of environmental degradation; CO2 emissions X t -Per capita GDP Y -A vector of other variables that often affect the environment ε -Error term

Data Analysis, Presentation and Interpretation 3.1 Introduction

This chapter presents a brief overview of the data used, results and analysis of this study as set out in the research objectives and methodology, using version 3 of the R-statistical software.

The data employed in the study was converted to factor to enable them to be read in R software. The study analyzed the Air pollution trends in Kenya using Environmental Kuznets Curve using forest change as proxy.

Analysis of Air Pollution Trends in Kenya Through Forest Change Between 1990-2018

Regressing CO2 emissions vs forest cover There is a high r squared between the variables forest cover and CO2 emission (0.87387). This shows a good fit since the model explains 87.387% of the data. -0.28914 From the model summary of regressing Forest cover and CO2 emissions, there is a significant decrease in forest cover with increase in the CO2 emission quantity. This is also visually observed in figure 1 below. Figure 2 above shows the forecast of the CO2 emissions for an additional 5 years within the confines of the current trend. A significant rise is predicted. From table 7 above, with increase of a unit in forest cover, there is a decrease of income per capita by 2.224 units, decrease in wages by -1.265 units and decrease in co2 emissions by -0.502 units. This practically shows the sense that reduction in forest cover has led to the increase of industrial human activities therefore reduction of forest cover increases the wage, co2 emissions and the income per capita. However, this is depleting of a natural resource. From table 8 above, R squared is 0.887 meaning that the model explains 88.7% of the data rendering it a good fit. Table 9 shows that there's a high correlation between the 2 explanatory variables and CO2 emissions (0.693). This shows that the model fits well.

Analysis of the relationship between GDP, income per capita, human capital, wages and air pollution trends through forest change between 1990-2018

Analysis of the Environmental Kuznets Curve for Kenya through Forest Change between 1990-2018

Coefficients

With one unit of increase in CO2 emissions, there is a 0.82 increase in Agricultural GDP while with one increase in CO2 emissions there is a 1.138 increase in industrial GDP share. This shows that in some way shape or form industrial factors are contributors to CO2 emissions by large margins

Summary, Conclusions and Recommendations

The study investigated the relationship between CO2 emissions, forest cover and GDP in Kenya between 1990 2018. The result suggests that there exists a long-run relationship among the variables. The positive sign of GDP and negative sign of GDP2 confirms that EKC hypothesis is supported in the country. Increased forest cover decreases CO2 emissions in both short and long run. The significant existence of EKC shows that the country's effort to reduce CO2 emissions and pursue sustainable development pathways. This indicates the reasonable achievement of controlling environmental degradation in Kenya. The recent launching of the National Climate Change Policy and the National Environmental Policy in 2014 will solidify the country's effort towards sustainable development by reducing emissions. However, findings based on aggregate data may not be able to show the emission patterns of individual regions in the country. It must be emphasized that the implementation of these policies are a necessary but not a sufficient condition. The need for effective enforcement of environmental laws and regulation is paramount not only at the national but also the regional and district levels to prevent the "pollution haven hypothesis". Furthermore, research and development activities on environmental degradation which are important to attain sustainable development must be strongly pursued in Kenya.

The study limited the scope of environmental degradation to only the emission of CO2 and forest cover and did not consider other gasses such as nitrogen oxide (NO2), sodium oxide (SO2) and Biochemical Oxygen Demand-BOD (a measure of pollution of water bodies) as a result of data limitations. The data for all the variables of interest in the study retrieved from the World Development Indicators (WDI) database was up to 2018 due to non-availability of data for some variables beyond this period. Further researchers should use regionally disaggregated emissions data to attain a comprehensive impact which will provide new insights to policy makers in controlling degradation at the regional levels.

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