Download CO2 and climate change in sub Saharan Africa

The 1st International Congress on Regional Economic Development,
Information Technology, and Sustainable Business
Economic predictors for managing the energy consumption system in
the sub-Saharan Africa region.
Modeling of possible long-term sustainable solutions respectful the
environment
Aboyitungiye Jean Baptiste
Faculty of Economic &Business,Sebelas Maret University
Jl Ir. Sutami 36 A Surakarta Jawa Tengah Indonesia
Tel: +6282243174970 E-mail: [email protected]
Evi Gravitiani
Faculty of Economic &Business,Sebelas Maret University
Jl Ir. Sutami 36 A Surakarta Jawa Tengah Indonesia
Tel : +6282225034455 E-mail: [email protected]
Energy and environment pollution
o Energy consumption is a global issue that affects the
extent of climate change and environmental pollution.
o Energy consumption contributes to pollution,
environmental deterioration, and global greenhouse
emissions.
o Increases in energy consumption are driven by
population growth and economic development that tend
to increase energy use per capita.
Polluting energy accelerate dangerous
climate change around the world,
Causes considerable harm to people,
communities, workers and the
environment.
 CO2 emissions are due to human activities related to the
production and consumption of energy.
 SSA's minimal contribution to CO2 emissions is linked to
the weakness of its industrialization.
World Population & Energy demand growth
National Energy Technology Laboratory ,2019
Historical trends suggest that increased annual energy use per capita, which
promotes a decrease in population growth rate, is a good surrogate for the standard
of living factors. If these trends continue, the stabilization of the world’s population
will require the increased use of all sources of energy, particularly as cheap oil and
gas are depleted.
Energy access in SSA
In terms of access to energy, Sub-Saharan Africa is the world's
"poorest in electricity’’ region:

More than 620 million people (two-thirds of the population) do
not have access to electricity and are thus denied the positive
development effects of access to energy (WEO, 2016)

Electricity access rate is 35 % overall and only 19% in rural areas
(WEIM 2019-Africa).

Nearly 730 million people in Africa still have to use inefficient and
harmful cooking fuels such as biomass (WEO, 2016b) .

The primary source of energy in the region is solid biomass-like
fuelwood and charcoal which accounts for more than 75 percent of the
total energy consumed in the region
(Hafner, Tagliapietra, and de Strasser 2018)
Electricity generation(Africa,TWh),
The Shift Project DATA PORTAL ,2019
The charcoal remains the main source of energy for electricity generation
Environmental issues in SSA
Three factors strongly increase the threat of environmental degradation in SSA:
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deforestation
soil erosion,
desertification,
wetland degradation,
insect infestation.
Land contamination
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poverty,
demographics,
heavy burden of foreign debt,
the absence of democracy
(Bradshaw and Di Minin 2019).
(Sharma and World Bank 1994),
Efforts to deal with these problems, however, have been handicapped by a real failure to
understand their nature and possible remedies.
Given the continuous expansion of the demand for energy, rapid population growth,
and low growth in energy production in SSA, one of the top priorities for sustainable
development and environmental protection is to reduce energy usage by resort to
the clean energy in the energetic mixt and improve energy consumption efficiency.
Objectif of the study
Based on annual data covering the period from 1990 to 2014 for the SubSaharan Africa region.
This study empirically plays around CO2 emissions scenarios. Attention has
been taken on the features of energy consumption and environment variations.
We propose to study co-integration based on Kaya's identity because it makes it
possible to take into account several variables in the same equation namely: CO2
emissions, the index of energetic dirt, the energy intensity, the income, and the
active population.
The data set of the variables is taken from World Development Indicator
which is published by World Bank(updated 2018) and Essential Climate
Variables (ECV), data access matrix which is published by Global Climate
Observing Systems (GCOS).
Previous Studies
 Sari, Ewing, and Soytas (2008) investigated the impact of energy consumption and
CO2 emissions in the US using the Granger causality. They found that the income is not
Granger cause of CO2 in the long-run but energy use causes income
 Alam (2013) used a modified version of the Granger causality test to investigate the
causal relationship between CO2 emissions, renewable, and nuclear energy
consumption and real GDP for the US. The results of the study show there is
unidirectional causality from nuclear energy to consumption to CO2 emissions, but no
causality from renewable energy to CO2 emissions.
 Van Ruijven, De Cian, and Sue Wing (2019) have taken a study on a relationship between
energy consumption and climatic change variables, the results of the study indicate that
there exists a long-run equilibrium relationship between energy consumption and
climatic variables which shows climatic variations due to changes in energy
consumption in different regions of the world.
 Akhmat et al. investigate the relationship between greenhouse gas (GHG) emissions,
energy mix and carbon emissions in the panel of 35 developed countries. The results
conclude that electricity production from oil, gas, and coal sources increases the GHG
emissions and air pollution in the region, however, the intensity is far less than through
fossil fuel (Akhmat et al. 2014).
Many scientists believe that current developments
will lead to increased extreme events (high
magnitude storms and cyclones, catastrophic floods
or multi-year drought), as well as an increase in
annual average temperatures over large areas of the
globe.
The primary cause of global warming would be the
emission of increasing amounts of carbon dioxide in
the atmosphere associated with the large-scale use of
fossil fuels (coal, oil, natural gas).
The Kaya identity model
𝐶𝑂
2
𝐶𝑂2 = 𝐸𝑛𝑒𝑟𝑔𝑦
∗
𝐸𝑛𝑒𝑟𝑔𝑦
𝐺𝐷𝑃
𝐺𝐷𝑃
∗ 𝑃𝑂𝑃. ∗ 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
we associate it with an additive stochastic form defined by:
𝐿𝑛𝐶𝑂2
= 𝛽0 + 𝛽1 𝐿𝑛𝐸𝐷𝐼 + 𝛽2 𝐿𝑛𝐸𝐼 + 𝛽3 𝐿𝑛𝑔𝑑𝑝𝑐 + 𝛽4 𝐿𝑛𝐴𝑃 + 𝜀
This equation states that population; economic growth and
technology (in other words, intensities energy and CO2) are the
determinants of greenhouse gas emissions. Otherwise, any upward or
downward variation in the amount of CO2 emissions must correspond to an
equivalent change in at least one of the decomposition factors.
Results presentation
1. Unit root test result
The standard Augmented Dickey-Fuller (ADF) unit root test was exercised to check the order
of the integration of these variables.
2. Augmented Dickey-Fuller test result
The sequential Augmented Dicky Fuller test
(ADF),shows that the difference series are
stationary and are integrated of order 1.
3. Johansen Cointegration test
The presence of the co-integration vectors shows that there exists a long-run relationship
among the variables. This study further examined the existence of a long-run relationship
between the variables. The JOHANSEN cointegration test sheds light on the number of
cointegration relationship and its functional form according to different criteria:
4. VECM
Through VECM, cointegration provides a systematic framework for jointly analyzing shortrun and long-run properties.
Hence, the existence of cointegration may improve the long-term forecasting of economic
time series.
With the long run equilibrium relationship;
𝒍𝒏𝒄𝒐𝟐 = 𝟎. 𝟗𝟏𝟑𝟎𝟒𝟓𝒍𝒏𝒆𝒅𝒊 − 𝟏. 𝟎𝟒𝟗𝟏𝟓𝟗𝒍𝒏𝒆𝒊 + 𝟎. 𝟎𝟕𝟎𝟒𝟏𝟖𝒍𝒏𝒈𝒅𝒑𝒄 + 𝟎. 𝟑𝟖𝟓𝟕𝟗𝟒𝒍𝒏𝒂𝒑 + 𝜺𝒕
Energy dirty index, gross domestic product and active population(labor force) have a positive
impact on carbonic gas emission while energy intensity has a negative impact on emissions .
This goes beyond our theories stipule that people use biomass fuel instead of clean efficient
energy in a lot rural milieu of sub-Sahara African regions.
5. Short run Causality test results
Lags
LnCO2
Ln edi
Lnei
Lngdpc
Lnap
2.108070
1.326810
1.316322
2.71076
(0.3485)
(0.5151)
(0.5178)
(0.2579)
130.8839
1.462513
1.021166
0.486423
(0.0000)
(0.4813)
(0.6001)
(0.7841)
LnCO2
Lnedi
Lnei
Lngdpc
Lnap
5.475366
7.320860
0.804321
1.218718
(0.0647)
(0.0257)
(0.6689)
(0.5437)
4.788916
2.789693
5.474525
0.153450
(0.0912)
(0.2479)
(0.0647)
(0.9261)
32.05340
2.776180
3.962660
2.869827
(0.0000)
(0.2496)
(0.1379)
(0.2381)
 Unidirectional causality between population ,energy dirt index to carbonic dioxide but
not vice-versa
 The causality between energy intensity and energy dirt index is appeared in the
analysis.
5. Variance decomposition
 Outputs of the study
 A significant CO2 emissions is due to the supply and consumption
of energy,
 the combustion is predominant in fossil fuels.
 CO2 emissions increase more proportionally in the short term than
in the long term following an increase in the energy dirt index.
We obtain on average an innovation of carbon dioxide pollution which, for its part
contributes a value of 38.36% of its variance of the forecast error,
The energy dirty index (EDI) largely contributes a 36.49% of its own variance of the error.
The gross domestic product (GDPC) contributes on average 20.2% of its own variance of
the error,
Energy intensity 4.18% of its own variance of the error and 0.7% for the active population.
 carbon dioxide (CO2) emissions contribute a good part in
determining the variance of forecast error.
 The discovery of a Granger unidirectional causality between CO2
emissions and its variables(EDI and Pop) suggests that per unit of
energy, its consumption is the stimulating input for improving
emissions of variable gases from the environment.
 The awareness of global environmental challenges makes it
essential that there is some understanding of the causal effects of
energy consumption (Energy dirt Index) on carbonic gas emission.
Eliminating or reducing greenhouse gas emissions is a major study that could be carried out anywhere in the world. It is
therefore important to have the tools to better understand and predict their evolution.
The international energy agency has studied several scenarios:



the first takes into account the energy reduction objectives declared by the States but not yet implemented.
the second says "sustainable" requires "a rapid change in all energy systems around the world", and
the third says "constant policy", simply prolongs current trends.
However, only the second scenario (unfortunately the least likely) shows a decline in emissions from 2020.
Our research estimated the short-run and decompose the variance of CO2; the error correction model(ECM)
pull out the corroborated outputs with the analysis of the impulse response functions which have shown that
in the long term the energy intensity has a decline impact on CO2 but population, per capita income and
energy dirt index give respectively an increase impact on carbon dioxide emissions.
Great attention can, therefore, be paid to the release of gases harmful to the greenhouse by the use of
polluting fuels. Hence the substitution by natural gas: "Increase the contribution of natural gas in final energy
consumption in the various sectors of residential, tertiary and industrial activity".
Environmental economic elements focused on these factors and combined with other reforms should
undoubtedly contribute to the reduction of pollutants due to carbon dioxide for future horizons.