Environment-related sustainable development goals are often less prioritized in developing countries. But as an agricultural country, methane emission is an important issue in Bangladesh. This paper became Champion in 2nd Bangladesh Economics Summit. With ARDL bounds testing approach, we found that Environmental Kuznet Curve does not hold for Methane in Bangladesh both in short run and long run.

1. “We’re in a giant car heading towards a brick wall and
everyone is arguing over where they’re going to sit”
-David Suzuki

2. : ARDL BOUNDS TESTING APPROACH
DETERMINANTS OF METHANE EMISSION IN
BANGLADESH USING ENVIRONMENTAL KUZNETS
CURVE ANALYSIS
Farhin Islam, Anuva Afsana
BSS 4th Year
Department of Economics
University of Dhaka

3. METHANE & CLIMATE CHANGE
 Methane (CH4) is the 2nd most potent GHG (after CO2)
in its impact on climate change.
 It traps 32 times more heat per mass unit than CO2
over a 100-year time horizon.
 It will increase 4ᵒC global mean surface temperature
at a chosen point of time relative to the temperature
change by the emission of equal amount of CO2.

4. SOURCES OF METHANE
 Natural
 Anthropogenic
Estimated global anthropogenic methane emissions by source
(%), 2010

5. BANGLADESH CONTEXT
World, 43.3%
China, 21.9%
India, 7.9%
United States,
6.2%
Brazil, 6.0%
Indonesia,
2.8%
Pakistan, 2.0%
Australia,
1.6%
Iran, 1.5%
Mexico,
1.5% Vietnam, 1.4%
Canada,
1.3%
Thailand,
1.3%
Bangladesh
1.3%
Top 13 Countries in the World
 13th highest methane
emitter in world
 3rd highest in South Asia

6. 82000
87000
92000
97000
102000
107000
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Methane emissions (kt of CO2 equivalent)
BANGLADESH CONTEXT

7. Data set World Rank Value % of
total
Year
Agricultural Methane
Emission (kt of CO2
equivalent)
11 67,373 68.9 2008
Methane Emission in
Energy Sector (kt of CO2
equivalent)
40 11,284 11.5 2008
BANGLADESH CONTEXT

8.  Developing country
 Lack of resources and technology
 Prioritizing among conflicting goals
 Agriculture sector
 contributes 13.07% of total GDP
 Major source of methane
BANGLADESH CONTEXT

9. RESEARCH OBJECTIVES
 Determinants of methane emission in Bangladesh in
short run and long run.
 If there is an EKC for methane in Bangladesh in short run
and long run.
 How agricultural land, fossil fuel energy consumption,
livestock production, waste and renewable energy
consumption affect methane emission in Bangladesh.

10. THEORETICAL BACKGROUND OF EKC HYPOTHESIS
𝑙𝑛(𝐸/𝑃)𝑖𝑡 = 𝛼𝑖 + 𝛾𝑡 + 𝛽1 𝑙𝑛(𝐺𝐷𝑃/𝑃)𝑖𝑡 + 𝛽2(ln
𝐺𝐷𝑃
𝑃
)2
𝑖𝑡
+ 𝜀𝑖𝑡
• Kuznets (1954)
• Grossman & Krueger (1995)
• Shafik & Bandyopadhyay
(1992)
• Panayotou (1993)
• Stern (2004)
• Dasgupta, et al. (2002)

11. EKC HYPOTHESIS IN DIFFERENT CONTEXT: PREVIOUS STUDIES
Cruz, et al.
(2018)
Sinha &
Bhatt (2017)
Balin, et al.
(2018)
Lu (2017) Wang, et al.
(2017)
Islam, et al.
(2013)
Kubatko
(2008)
Rayhan &
Islam (2017)
Sulaiman, et
al. (2013)
Benavides,
et al. (2017)
Kubicova
(2014)
Sarkodie &
Strezov
(2019)
Yurttaguler &
Kutlu (2017)
Choi, et al.
(2010)
Aruga (2019)
Armeanu, et
al. (2018)
Keho (2015) Naminse &
Jincai (2018)
Dogan &
Turkekul
(2015)
Kalchev
(2016)

12. DATA
 Source: World Development Indicators of World Bank
 Observations: 42 years (1971-2012)

13. LM= Natural log of Methane emission (kt of CO2 equivalent) per capita from
human activities, as the proxy of environmental degradation
LGDPPC= Natural log of GDP (current US$) per capita
LGDPPCSQ = Square of Natural log of GDP (current US$) per capita
LAGRI= Natural log of Agricultural land (sq. km) per capita
LFFE= Natural log of Fossil fuel energy consumption (% of total)
LLPI= Natural log of Livestock production index (2004-2006 = 100)
LCRW= Natural log of % of energy consumption from combustible
renewables and waste
ԑ is a white noise process.
METHODOLOGY: MODEL

14. METHODOLOGY: ARDL APPROACH
• Stationarity test: Augmented Dickey-Fuller (ADF) Test
• Step 1: ARDL Bounds testing for Cointegration
• Step 2: Short run Error Correction Model Estimation

15. EMPIRICAL FINDINGS: STATIONARITY
ADF Test for Stationarity (with Intercept and Trend)
At level At 1st Difference
DecisionVariable
ADF test
statistics P-value Variable
ADF test
statistics P-value
LM 1.490239 1.0000 ΔLM -5.454992 0.0003*** I(1)
LGDPPC -6.145555
0.0000**
* ΔLGDPPC -7.1378450 0.0000*** I(0)
LGDPPCSQ -3.493969 0.0534* ΔLGDPPCSQ -7.137845 0.0000*** I(0)/I(1)
LAGRI -1.192119 0.8990 ΔLAGRI -5.18463 0.0007*** I(1)
LLPI -2.476358 0.3375 ΔLLPI -6.535574 0.0000*** I(1)
LFFE -0.42721 0.9823 ΔLFFE -3.218099 0.0995* I(1)
LCRW -2.034195 0.5658 ΔLCRW -5.422091 0.0004*** I(1)

16. EMPIRICAL FINDINGS: COINTEGRATION
Estimated Equation
LM=f(LGDPPC, LGDPPCSQ, LAGRI, LLPI,
LFFE, LCRW, DUM)
Optimal Lag Structure ARDL (2, 0, 0, 0, 1, 1, 0)
F-stat 7.183579***
Critical values (T=40)
Significant Level Lower Bounds I(0) Upper Bounds I(1)
10% 2.831 4.04
5% 3.327 4.7
1% 4.527 6.263
Diagnostic Tests F-statistics
J-B Normality test 0.7003
Breusch-Godfrey LM 2.577
ARCH LM test 0.8285
Ramsey RESET test 1.1456
CUSUM Stable
CUSUM of square Stable
Source: Authors’ calculation
*** Significant at 1% level
1458 models

17. EMPIRICAL FINDINGS: DIAGNOSTIC TESTS (ARDL)
Figure a: Normality Test (ARDL Cointegration Model)
Figure b: Chart of CUSUM and CUSUMSQ test to check Stability (ARDL Cointegration Model)

18. EMPIRICAL FINDINGS: LAG LENGTH SELECTION
Lag Order Selection Criteria
Lag LogL LR FPE AIC SC HQ
0 105.28 NA 0.000431 -4.914007 -4.618454 -4.807145
1 113.779 13.59760* 0.000297 -5.288932 -4.951156* -5.166803
2 115.272 2.314459 0.000291* -5.313592* -4.933594 -5.176197*
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz criterion
HQ: Hannan-Quinn information criterion

19. EMPIRICAL FINDINGS: LONG RUN RELATIONSHIP
Dependent Variable LM
Variable Coefficient Std. Error t-Statistic Prob.
LGDPPC -0.70007 0.496915 -1.40884 0.1703
LGDPPCSQ 0.078875 0.046804 1.685222 0.1035
LAGRI 0.393786 0.199947 1.969456 0.0592
LLPI 0.268639 0.096172 2.793327 0.0095
LFFE -0.36939 0.184793 -1.99891 0.0558
LCRW 0.114738 0.288937 0.397105 0.6944

20. EMPIRICAL FINDINGS: SHORT RUN (ECM)
Variable Coefficient Std. Error t-Statistic Prob.
C -0.025478 0.012356 -2.06211 0.0502
Δ(LM(-1)) -0.063302 0.205829 -0.30755 0.7611
Δ(LGDPPC) -0.38034 0.297941 -1.27656 0.214
Δ(LGDPPC(-1)) 0.098353 0.296307 0.33193 0.7428
Δ(LGDPPCSQ) 0.041385 0.028328 1.4609 0.157
Δ(LGDPPCSQ(-1)) -0.009261 0.028889 -0.32057 0.7513
Δ(LAGRI) 0.030328 0.187257 0.16196 0.8727
Δ(LAGRI(-1)) 0.21159 0.152329 1.38904 0.1776
Δ(LLPI) 0.196889 0.033457 5.88475 0
Δ(LLPI(-1)) -0.019008 0.0542 -0.35071 0.7289
Δ(LFFE) 0.114768 0.128288 0.89461 0.3799
Δ(LFFE(-1)) -0.137708 0.128933 -1.06806 0.2961
Δ(LCRW) 0.060873 0.176172 0.34553 0.7327
Δ(LCRW(-1)) -0.040201 0.180484 -0.22274 0.8256
DUM 0.018233 0.007395 2.46557 0.0212
ECT(-1) -0.875822 0.243793 -3.59248 0.0015
R-squared 0.836969
Adjusted R-squared 0.735075
Akaike info criterion 0.012023
Schwarz criterion 0.003469
Durbin-Watson stat 130.2958
F-statistic 8.214093
Prob(F-statistic) 0.000004

21. EMPIRICAL FINDINGS: DIAGNOSTIC TESTS (ECM)
Tests F-statistics
J-B Normality test 0.34803
Breusch-Godfrey LM 0.60881
ARCH LM test 1.58587
Ramsey RESET test 0.14069
CUSUM Stable
CUSUM of square Stable
Figure c: Normality Test (ECM)
Figure d: Chart of CUSUM and CUSUMSQ
test to check Stability (ECM)

22. CONCLUSION
 Methane emission will not come down automatically with
the economic growth
 Minimizing methane leak while extracting and
transporting fossil fuel with appropriate use of
technology
 Optimum combination of consumption of renewable and
non-renewable sources of energy to save environment
 Imposing tax on methane emission like carbon taxing

23. THANK YOU!