ORIGINAL PAPER
Macroeconomic factors affecting carbon dioxide emissions in Bangladesh: an ARDL approach
1
Economics & Banking, International Islamic University Chittagong, Bangladesh
Submission date: 2023-03-08
Final revision date: 2023-05-05
Acceptance date: 2023-05-23
Publication date: 2023-09-19
Corresponding author
Musa Khan
Economics & Banking, International Islamic University Chittagong, Kumira, Sitakunda, 4000, Chittagong, Bangladesh
Economics & Banking, International Islamic University Chittagong, Kumira, Sitakunda, 4000, Chittagong, Bangladesh
Polityka Energetyczna – Energy Policy Journal 2023;26(3):27-46
KEYWORDS
TOPICS
ABSTRACT
This study investigates how macroeconomic variables in Bangladesh from 1991 to 2021 affected emissions, using data from the World Development Indicators. This study used the autoregressive distributed lag (ARDL) model. The study finds that Bangladesh’s GDP per person, energy use, and trade openness positively and significantly affect both short-term and long-term carbon dioxide emissions. However, statistics show that foreign direct investment does not affect from Bangladesh’s. This study says that policymakers should focus on making energy policies and other economic policies that help the economy grow and have little to no effect on emissions. Additionally, economic growth will not hurt the environment as much if policies are implemented to encourage the growth of both the public and private sectors and make it easier to make money by allocating and distributing resources well. Finally, this study suggests looking for additional variables to improve the model’s fit and using other estimating techniques to obtain more trustworthy findings.
METADATA IN OTHER LANGUAGES:
Polish
Czynniki makroekonomiczne wpływające na emisje dwutlenku węgla w Bangladeszu: podejście ARDL
czynniki makroekonomiczne, rozwój ekonomiczny, emisje, zużycie energii, podejście ARDL
W niniejszym artykule zbadano, wykorzystując dane z World Development Indicators, w jaki sposób w jaki sposób zmienne makroekonomiczne w Bangladeszu w latach 1991–2021 wpłynęły na emisje. W tym badaniu wykorzystano model autoregresyjnego rozproszonego opóźnienia (ARDL). Badanie wykazało, że PKB Bangladeszu na osobę, zużycie energii i otwartość handlu pozytywnie i znacząco wpływają zarówno na krótko-, jak i długoterminowe emisje dwutlenku węgla. Statystyki pokazują jednak, że bezpośrednie inwestycje zagraniczne nie wpływają na sytuację w Bangladeszu. To badanie mówi, że decydenci powinni skupić się na kształtowaniu polityki energetycznej i innych polityk gospodarczych, które pomagają gospodarce rozwijać się i mają znikomy wpływ na emisje. Ponadto wzrost gospodarczy nie będzie tak bardzo szkodził środowisku, jeśli wdrożona zostanie polityka zachęcająca do rozwoju zarówno sektora publicznego, jak i prywatnego, oraz ułatwiająca zarabianie pieniędzy poprzez dobrą alokację i dystrybucję zasobów. Wreszcie, to badanie sugeruje poszukiwanie dodatkowych zmiennych w celu poprawy dopasowania modelu i wykorzystanie innych technik szacowania w celu uzyskania bardziej wiarygodnych wyników.
REFERENCES (59)
1.
Adeleye et al. 2021 – Adeleye, B.N., Osabohien, R., Lawal, A.I. and De Alwis, T. 2021. Energy use and the role of per capita income on carbon emissions in African countries. Plos One 16(11), DOI: 10.1371/journal.pone.0259488.
2.
Ahmad et al. 2019 – Ahmad, M., Ul Haq, Z., Khan, Z., Khattak, S.I., Ur Rahman, Z. and Khan, S. 2019. Does the inflow of remittances cause environmental degradation? Empirical evidence from China. Economic Research-Ekonomska Istraživanja 32(1), pp. 2099–2121, DOI: 10.1080/1331677X.2019.1642783.
3.
Alam, J. 2014. On the relationship between economic growth and CO2 emissions: The Bangladesh experience. IOSR Journal of Economics and Finance (IOSR-JEF) 5(6), pp. 36–41.
4.
Amin et al. 2012 – Amin, S.B., Ferdaus, S.S. and Porna, A.K. 2012. Causal relationship among energy use, CO2 emissions and economic growth in Bangladesh: An empirical study. World Journal of Social Sciences 2(8), pp. 273–290.
5.
Ang, J.B. 2007. CO2 emissions, energy consumption, and output in France. Energy Policy 35(10), pp. 4772–4778, DOI: 10.1016/j.enpol.2007.03.032.
6.
Arouri et al. 2012 – Arouri, M.E.H., Youssef, A.B., M’henni, H. and Rault, C. 2012. Energy consumption, economic growth, and CO2 emissions in the Middle East and North African countries. Energy Policy 45, pp. 342–349, DOI: 10.1016/j.enpol.2012.02.042.
7.
Aslam et al. 2021 – Aslam, B., Hu, J., Majeed, M.T., Andlib, Z. and Ullah, S. 2021. Asymmetric macroeconomic determinants of CO2 emission in China and policy approaches. Environmental Science and Pollution Research 28(31), 41923–41936, DOI: 10.1007/s11356-021-13743-7.
8.
Asumadu-Sarkodie, S. and Owusu, P.A. 2016. Carbon dioxide emissions, GDP, energy use, and population growth: A multivariate and causality analysis for Ghana, 1971–2013. Environmental Science and Pollution Research 23(13), pp. 13508–13520, DOI: 10.1007/s11356-016-6511-x.
9.
Baek et al. 2009 – Baek, J., Cho, Y. and Koo, W.W. 2009. The environmental consequences of globalization: A country-specific time-series analysis. Ecological Economics 68(8–9), pp. 2255–2264, DOI: 10.1016/j.ecolecon.2009.02.021.
10.
Banerjee, P.K. and Rahman, M. 2012. Some determinants of carbon dioxide emissions in Bangladesh. International Journal of Green Economics 6(2), pp. 205–215, DOI: 10.1504/IJGE.2012.050345.
11.
Ben Jebli, M. 2016. On the causal links between health indicator, output, combustible renewables and waste consumption, rail transport, and CO2 emissions: The case of Tunisia. Environmental Science and Pollution Research 23(16), pp. 16699–16715, DOI: 10.1007/s11356-016-6850-7.
12.
Ben Jebli, M. and Ben Youssef, S. 2015. Economic growth, combustible renewables and waste consumption, and CO2 emissions in North Africa. Environmental Science and Pollution Research 22(20), pp. 16022–16030, DOI: 10.1007/s11356-015-4792-0.
13.
Bernstein et al. 2008 – Bernstein, L., Bosch, P., Canziani, O., Chen, Z., Christ, R. and Riahi, K. 2008. IPCC, 2007: Climate change 2007: synthesis report. IPCC.
14.
Brown et al. 1975 – Brown, R.L., Durbin, J. and Evans, J.M. 1975. Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society: Series B (Methodological) 37(2), pp. 149–163.
15.
Cole, M.A. 2004. Trade, the pollution haven hypothesis, and the environmental Kuznets curve: Examining the linkages. Ecological Economics 48(1), pp. 71–81.
16.
Cole et al. 2011 – Cole, M.A., Elliott, R.J. and Zhang, J. 2011. Growth, foreign direct investment, and the environment: Evidence from Chinese cities. Journal of Regional Science 51(1), pp. 121–138, DOI: 10.1111/j.1467-9787.2010.00674.x.
17.
Cruz et al. 2022 – Cruz, N.A., Dalumpines, D.V. and Canto, D. 2022. Macroeconomic Factors Affecting Carbon Dioxide Emissions in the Philippines: A Time Series Analysis. Journal of Economics, Finance and Accounting Studies 4(1), pp. 642–659, DOI: 10.32996/jefas.2022.4.1.46.
18.
Dai, A. 2013. Increasing drought under global warming in observations and models. Nature Climate Change 3(1), pp. 52–58, DOI: 10.1038/nclimate1633.
19.
Dickey, D.A. and Fuller, W.A. 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74(366a), pp. 427–431, DOI: 10.1080/01621459.1979.10482531.
20.
Dogan, E. and Seker, F. 2016a. Determinants of CO2 emissions in the European Union: The role of renewable and non-renewable energy. Renewable Energy 94, pp. 429–439, DOI: 10.1016/j.renene.2016.03.078.
21.
Dogan, E. and Seker, F. 2016b. The influence of real output, renewable and non-renewable energy, trade, and financial development on carbon emissions in the top renewable energy countries. Renewable and Sustainable Energy Reviews 60, pp. 1074–1085, DOI: 10.1016/j.rser.2016.02.006.
22.
Hamilton, C. and Turton, H. 2002. Determinants of emissions growth in OECD countries. Energy Policy 30(1), pp. 63–71, DOI: 10.1016/S0301-4215(01)00060-X.
23.
Haug, A.A. 2002. Temporal aggregation and the power of cointegration tests: A Monte Carlo study. Available at SSRN 334965.
24.
Hossain, M.S. 2011. Panel estimation for CO2 emissions, energy consumption, economic growth, trade openness, and urbanization of newly industrialized countries. Energy Policy 39(11), pp. 6991–6999, DOI: doi.org/10.1016/j.enpol.2011.07.042.
25.
Islam et al. 2021 – Islam, M., Khan, M.K., Tareque, M., Jehan, N. and Dagar, V. 2021. Impact of globalization, foreign direct investment, and energy consumption on CO2 emissions in Bangladesh: Does institutional quality matter? Environmental Science and Pollution Research 28(35), pp. 48851–48871, DOI: 10.1007/s11356-021-13441-4.
26.
Jebli et al. 2016 – Jebli, M.B., Youssef, S.B. and Ozturk, I. 2016. Testing environmental Kuznets curve hypothesis: The role of renewable and non-renewable energy consumption and trade in OECD countries. Ecological Indicators 60, pp. 824–831, DOI: 10.1016/j.ecolind.2015.08.031.
27.
Jordan, S. and Philips, A.Q. 2018. Cointegration testing and dynamic simulations of autoregressive distributed lag models. The Stata Journal 18(4), pp. 902–923.
28.
Khan et al. 2019a – Khan, K.A., Çera, G. and Nétek, V. 2019a. Perception of the Selected Business Environment Aspects by Service Firms. Journal of Tourism and Services 10(19), pp. 111–127, DOI: 10.29036/jots.v10i19.115.
29.
Khan et al. 2019b – Khan, M.K., Teng, J.-Z., Khan, M.I. and Khan, M.O. 2019b. Impact of globalization, economic factors and energy consumption on CO2 emissions in Pakistan. Science of the Total Environment 688, pp. 424–436, DOI: 10.1016/j.scitotenv.2019.06.065.
30.
Kheder, S.B. and Zugravu, N. 2012. Environmental regulation and French firms’ location abroad: An economic geography model in an international comparative study. Ecological Economics 77, pp. 48–61, DOI: 10.1016/j.ecolecon.2011.10.005.
31.
Kim et al. 2019 – Kim, D.-H., Suen, Y.-B. and Lin, S.-C. 2019. Carbon dioxide emissions and trade: Evidence from disaggregate trade data. Energy Economics 78, pp. 13–28, DOI: 10.1016/j.eneco.2018.08.019.
32.
Kılıçarslan, Z. and Dumrul, Y. 2017. Foreign direct investments and CO2 emissions relationship: The case of Turkey. Business and Economics Research Journal 8(4), pp. 647–660, DOI: 10.20409/berj.2017.73.
34.
Lee, S.-H. and Jung, Y. 2018. Causal dynamics between renewable energy consumption and economic growth in South Korea: Empirical analysis and policy implications. Energy & Environment 29(7), pp. 1298–1315, DOI: 10.1177/0958305X18776546.
35.
Lau et al. 2009 – Lau, L.C., Tan, K.T., Lee, K.T. and Mohamed, A.R. 2009. A comparative study on the energy policies in Japan and Malaysia in fulfilling their nations’ obligations towards the Kyoto Protocol. Energy Policy 37(11), pp. 4771–4778, DOI: 10.1016/j.enpol.2009.06.034.
36.
Masuduzzaman, M. 2012. Electricity consumption and economic growth in Bangladesh: Cointegration and causality analysis. Global Journal of Management and Business Research 12(11), pp. 46–56.
37.
McMichael et al. 2006 – McMichael, A.J., Woodruff, R.E. and Hales, S. 2006. Climate change and human health: Present and future risks. The Lancet 367(9513), pp. 859–869, DOI: 10.1016/S0140-6736(06)68079-3.
38.
Mikayilov et al. 2018 – Mikayilov, J.I., Galeotti, M. and Hasanov, F.J. 2018. The impact of economic growth on CO2 emissions in Azerbaijan. Journal of Cleaner Production 197(1), pp. 1558–1572, DOI: 10.1016/j.jclepro.2018.06.269.
39.
Moutinho et al. 2018 – Moutinho, V., Madaleno, M., Inglesi-Lotz, R. and Dogan, E. 2018. Factors affecting CO2 emissions in top countries on renewable energies: A LMDI decomposition application. Renewable and Sustainable Energy Reviews 90, pp. 605–622, DOI: 10.1016/j.rser.2018.02.009.
40.
Muhammad, B. 2019. Energy consumption, CO2 emissions, and economic growth in developed, emerging, and Middle East and North Africa countries. Energy 179, pp. 232–245, DOI: 10.1016/j.energy.2019.03.126.
41.
Pesaran et al. 2001 – Pesaran, M.H., Shin, Y. and Smith, R.J. 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16(3), pp. 289–326.
42.
Pesaran et al. 1999 – Pesaran, M.H., Shin, Y. and Smith, R.P. 1999. Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association 94(446), pp. 621–634.
43.
Pope III, C.A. and Dockery, D.W. 2006. Health effects of fine particulate air pollution: Lines that connect. Journal of the Air & Waste Management Association 56(6), pp. 709–742.
44.
Protocol, K. 1997. United Nations framework convention on climate change. Kyoto Protocol, Kyoto 19(8), pp. 1–21.
45.
Rambeli et al. 2018 – Rambeli, N., Ramli, N.A.J., Hashim, E., Mahdinezhad, M., Hashim, A., Belee, and Bakri, S.M. 2018. The impact of selected macroeconomic variables on Carbon dioxide (CO2) emission in Malaysia. International Journal of Engineering & Technology 7(4.15), pp. 204–208, DOI: 10.14419/ijet.v7i4.15.21447.
46.
Saboori et al. 2012 – Saboori, B., Sulaiman, J. and Mohd, S. 2012. Economic growth and CO2 emissions in Malaysia: A cointegration analysis of the environmental Kuznets curve. Energy Policy 51, pp. 184–191, DOI: 10.1016/j.enpol.2012.08.065.
47.
Sakamoto et al. 1986 – Sakamoto, Y., Ishiguro, M. and Kitagawa, G. 1986. Akaike information criterion statistics. Dordrecht, The Netherlands: D. Reidel, 81(10.5555), 26853.
48.
Sarkar et al. 2018 – Sarkar, M.S.K., Sadeka, S. and Sikdar, M.M.H. 2018. Energy consumption and CO2 emission in Bangladesh: Trends and policy implications. Asia Pacific Journal of Energy and Environment 5(1), pp. 41–48, DOI: 10.18034/apjee.v5i1.249.
49.
Sarkodie, S.A. and Strezov, V. 2018. The empirical study of the environmental Kuznets curve and environmental sustainability curve hypothesis for Australia, China, Ghana, and the USA. Journal of Cleaner Production 201, 98–110, DOI: 10.1016/j.jclepro.2018.08.039.
50.
Shaari et al. 2020 – Shaari, M.S., Masnan, F., Harun, N.H. and Zainal, N.F. 2020. Macroeconomic Factors affecting CO2 Emissions in Malaysia: ARDL Approach. IOP Conference Series: Materials Science and Engineering 864(1), DOI: 10.1088/1757-899X/864/1/012050.
51.
Shahbaz et al. 2013 – Shahbaz, M., Hye, Q.M.A., Tiwari, A.K. and Leitão, N.C. 2013. Economic growth, energy consumption, financial development, international trade, and CO2 emissions in Indonesia. Renewable and Sustainable Energy Reviews 25, pp. 109–121, DOI: 10.1016/j.rser.2013.04.009.
52.
Shahbaz et al. 2015 – Shahbaz, M., Mallick, H., Mahalik, M.K. and Loganathan, N. 2015. Does globalization impede environmental quality in India? Ecological Indicators 52, pp. 379–393, DOI: 10.1016/j.ecolind.2014.12.025.
53.
Sherafatian-Jahromi, R. and Othman, M.-S.H. 2020. Macroeconomic Determinants of CO2 Emissions in Australia: Evidence From ARDL Bound Testing Approach. Asia-Pacific Social Science Review 20(4).
54.
Tachie et al. 2020 – Tachie, A.K., Xingle, L., Dauda, L., Mensah, C.N., Appiah-Twum, F. and Adjei Mensah, I. 2020. The influence of trade openness on environmental pollution in EU-18 countries. Environmental Science and Pollution Research 27(28), pp. 35535–35555, DOI: 10.1007/s11356-020-09718-9.
55.
Tiwari et al. 2013 – Tiwari, A.K., Shahbaz, M. and Hye, Q.M.A. 2013. The environmental Kuznets curve and the role of coal consumption in India: Cointegration and causality analysis in an open economy. Renewable and Sustainable Energy Reviews 18, pp. 519–527, DOI: 10.1016/j.rser.2012.10.031.
56.
Ur Rahman et al. 2019 – Ur Rahman, Z., Chongbo, W. and Ahmad, M. 2019. An (a) symmetric analysis of the pollution haven hypothesis in the context of Pakistan: A nonlinear approach. Carbon Management, 10(3), 227–239, DOI: 10.1080/17583004.2019.1577179.
57.
Wang et al. 2011 – Wang, S.S., Zhou, D.Q., Zhou, P. and Wang, Q.W. 2011. CO2 emissions, energy consumption, and economic growth in China: A panel data analysis. Energy Policy 39(9), pp. 4870–4875, DOI: 10.1016/j.enpol.2011.06.032.
58.
Woodcock, N. 2002. Climate Change 2001: Synthesis Report/Climate Change 2001: The Scientific Basis. Geological Magazine 139(4), 490.
59.
World Development Indicators (WDI) | DataBank 2022. [Online] https://databank.worldbank.org... [Accessed: 2023-04-23].
| eISSN: | 2720-569X |
| ISSN: | 1429-6675 |
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