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CO2 Emissions and Development Indicators: a Causality Analysis for Bangladesh

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Abstract

This paper aims to check the dynamic linkages between CO2 emissions and development indicators (per capita Gross Domestic Product (GDP), population density, and urbanisation) of Bangladesh. Using annual data in IPAT model for the period of 1972–2015, we have applied Johansen co-integration and Granger causality tests to examine the long-run co-integration and short-run dynamics of the concerned variables. The obtained results reveal that there is bidirectional causality between CO2 emissions and urbanisation, and CO2 emissions and GDP per capita for the study period in Bangladesh. This implies that GDP growth and urbanisation process are the major sources of CO2 emissions in the country. Therefore, policy makers should take extreme care in designing and implementing the environmental protection policies to curb CO2 emissions with a desired level of economic growth and urbanization.

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Authors and Affiliations

  1. Bangladesh Bank, Dhaka, Bangladesh

    Mohammed Abul Kashem

  2. School of Commerce, University of Southern Queensland, West Street, Toowoomba, 4350, Australia

    Mohammad Mafizur Rahman

Authors
  1. Mohammed Abul Kashem
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  2. Mohammad Mafizur Rahman
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Correspondence to Mohammad Mafizur Rahman.

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Appendices

APPENDIX 1

Table 7 Summary of literature on the nexus between CO2 emissions, income, urbanisation and population density
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APPENDIX 2

1.1 Unit Root or Stationarity Tests

Based on a regression equation with a constant and trend, the augmented Dickey-Fuller (ADF) test is formed as follows:

$$ \varDelta {\mathrm{X}}_{\mathrm{t}}={\mathrm{a}}_1+{\mathrm{a}}_2\mathrm{t}+{\mathrm{bX}}_{\mathrm{t}-1}+\sum \limits_{i=1}^k{\uprho}_{\mathrm{i}}\varDelta {\mathrm{X}}_{\mathrm{t}-1}+{\upvarepsilon}_{\mathrm{t}\kern0.75em } $$
(9)

where, ∆Xt = Xt - Xt-1 and X is the variable under study, k denotes the lag number of the dependent variable, chosen by Schwarz criterion, and εt represents the error term. The null hypothesis of a unit root is that b in the above equation is zero. Fuller (1976) provided cumulative distribution of the ADF statistics, indicating that if the calculated value of the coefficient is less than the critical value of the Fuller Table, then X is stationary. If the null hypothesis is rejected, the series is stationary, and hence to confirm stationarity, no differencing in the series is necessary. The Phillips and Perron (1988) test also justified this result. Their unit root tests differ from ADF test mostly in how they deal with autocorrelation and heteroscadasticity in errors. Generally, the ADF test uses a parametric autoregression to estimate the ARMA structure of the errors. The PP test based on the regression function is as follows:

(10)

where t is I(0) and may be heteroscadastic. The PP test has power to correct the autocorrelation and heteroscadasticity in the errors t of the test regression modifying the test statistics directly. Thus, PP unit root and the ADF tests are for the null hypothesis that a time series Xt is I(1). In contrast, stationarity test is for the null hypothesis that Yt is I(0).

APPENDIX 3

Table 8 Correlogram Q-Statistics test
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Table 9 Correlogram square residuals test
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Table 10 Heteroskedasticity test: Breusch-Pagan-Godfrey
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Table 11 Breusch-Godfrey serial correlation LM test
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Fig. 2
figure 2

CUSUM test

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Fig. 3
figure 3

CUSUM of square test

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Kashem, M.A., Rahman, M.M. CO2 Emissions and Development Indicators: a Causality Analysis for Bangladesh. Environ. Process. 6, 433–455 (2019). https://doi.org/10.1007/s40710-019-00365-y

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