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http://dx.doi.org/10.9719/EEG.2022.55.6.617

Does the Agricultural Ecosystem Cause Environmental Pollution in Azerbaijan?  

Elcin Nesirov (Department of Finance and Economic theory, Azerbaijan State Agricultural university)
Mehman Karimov (Department of Finance and Economic theory, Azerbaijan State Agricultural university)
Elay Zeynalli (Department of Accounting and Audit, Azerbaijan State Agricultural university)
Publication Information
Economic and Environmental Geology / v.55, no.6, 2022 , pp. 617-632 More about this Journal
Abstract
In recent years, environmental pollution and determining the main factors causing this pollution have become an important issue. This study investigates the relationship between the agricultural sector and environmental pollution in Azerbaijan for 1992-2018. The dependent variable in the study is the agricultural greenhouse gas emissions (CO2 equivalent). Eight variables were selected as explanatory variables: four agricultural inputs and four agricultural macro indicators. Unit root tests, ARDL boundary test, FMOLS, DOLS and CCR long-term estimators, Granger causality analysis, and variance decomposition analyses were used to investigate the effect of these variables on agricultural emissions. The results show that chemical fertilizer consumption, livestock number, and pesticide use positively and statistically significantly affect agricultural emissions from agricultural input variables. In contrast, agricultural energy consumption has a negative and significant effect. From agricultural macro indicator variables, it was found that the crop and animal production index had a positive and significant effect on agricultural emissions. According to the Granger causality test results, it was concluded that there are a causality relationship from chemical fertilizer consumption, livestock number, crop and livestock production index variables towards agricultural emissions. Considering all the results obtained, it is seen that the variables that have the most effect on the increase in agricultural emissions in Azerbaijan are the number of livestock, the consumption of chemical fertilizers, and the use of pesticides, respectively. The results from the research will contribute to the information on agricultural greenhouse gas emissions and will play an enlightening role for policymakers and the general public.
Keywords
Azerbaijan; agriculture; environment; greenhouse gases; ARDL bound test; Granger causality;
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1 Agboola, M.O. and Bekun, F.V. (2019) Does agricultural value-added induce environmental degradation? Empirical evidence from an agrarian country. Environmental Science and Pollution Research, v.26, p.27660-27676. https://doi.org/10.1007/s11356-019-05943-z   DOI
2 Ali, E.B. and Anufriev, V.P. (2020) The causal relationship between agricultural production, economic growth, and energy consumption in Ghana. R-Economy, v.6(4), p.231-241. doi: 10.15826/recon.2020.6.4.020   DOI
3 Ali, B., Ullah, A. and Khan, D. (2021) Does the prevailing Indian agricultural ecosystem cause carbon dioxide emission? A consent towards risk reduction. Environmental Science and Pollution Research, v.28(4), p.4691-4703. doi: s11356-020-10848-3   DOI
4 Altan, T., Kanber, R., Ozbek, H. and Sekeroglu, E. (2000) Tarim ve Cevre. Ozgurluk Dunyasi Dergisi, Sayi:102.
5 Appiah, K., Du, J. and Poku, J. (2018) Causal relationship between agricultural production and carbon dioxide emissions in selected emerging economies. Environmental Science and Pollution Research, v.25(25), p.24764-24777. doi: 10.1007/s11356-018-2523-z   DOI
6 Ayyildiz, M. and Erdal, G. (2021) The relationship between carbon dioxide emission and crop and livestock production indexes: a dynamic common correlated effects approach. Environmental Science and Pollution Research, v.28(1), p.597-610. doi: s11356-020-10409-8   DOI
7 Balogh, J.M. (2019) Agriculture-specific determinants of carbon footprint. Studies in Agricultural Economics, v.121(3), p.166-170. doi: 10.7896/j.1918   DOI
8 Balogh, J.M. (2020) The role of agriculture in climate change: A global perspective. International Journal of Energy Economics and Policy, v.10(2), 401. doi: 10.32479/ijeep.8859   DOI
9 Ben Jebli M and Ben Youssef S. (2017) The role of renewable energy and agriculture in reducing CO2 emissions: evidence for North Africa countries. Ecol. Indic., v.74, p.295-301. https://doi:10.1016/j.ecolind.2016.11.032.   DOI
10 Chandio, A.A., Akram, W., Ahmad, F. and Ahmad, M. (2020) Dynamic relationship among agriculture-energy-forestry and carbon dioxide (CO2) emissions: empirical evidence from China. Environmental Science and Pollution Research, v.27(27), p.34078-34089. doi: 10.1007/s11356-020-09560-z   DOI
11 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, v.74(366), p.427-431. doi: 10.1080/01621459.1979.10482531   DOI
12 Dickey, D.A. and Fuller, W.A. (1981) Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Econometric Soc., p.1057-1072. doi: 10.2307/1912517   DOI
13 Drabo, A. (2011) Agricultural primary commodity export and environmental degradation: what consequences for population's health?. Clermont-Ferrand, France: Center for Study and Research on International Development (CERDI).
14 Elliott, G., Rothenberg, T.J. and Stock, J. (1996) Efficient tests for an autoregressive unit root. Econometrica, v.64(4), p.813-836. doi: 10.2307/2171846   DOI
15 Engle, R.F. and Granger, C.W.J. (1987) Cointegration and error correction: Representation, estimation and testing. Econometrica, v.55, p.251-276. doi: 10.2307/1913236   DOI
16 Fuller, W.A. (1976) Introduction to Statistical Time Series. New York: Wiley
17 Granger, C.W. (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica, v.37, p.424-438. doi: 10.2307/1912791   DOI
18 Gurbuz, I.B., Nesirov, E. and Ozkan, G. (2021) Does agricultural value-added induce environmental degradation? Evidence from Azerbaijan. Environmental Science and Pollution Research, v.28(18), p.23099-23112. doi: 10.1007/s11356-020-12228-3   DOI
19 Granger, C.W. and Newbold, P. (1974) Spurious regressions in econometrics. J. Econom., v.2(2), p.111-120. doi: 10.1016/0304-4076(74)90034-7   DOI
20 Gujarati, D.N. and Porter, D.C. (2009) Basic Econometrics, New York: McGraw-Hill
21 Hongdou, L., Shiping, L. and Hao, L. (2018) Existing agricultural ecosystem in China leads to environmental pollution: an econometric approach. Environmental Science and Pollution Research, v.25(24), p.24488-24499. doi: 10.1007/s11356-018-2461-9   DOI
22 Ismael, M., Srouji, F. and Boutabba, M.A. (2018) Agricultural technologies and carbon emissions: evidence from Jordanian economy. Environmental Science and Pollution Research, v.25(11), p.10867-10877. doi: s11356-018-1327-5   DOI
23 Johansen, S. (1988) Statistical analysis of cointegration vectors. JEDC, v.12(2-3), p.231-254. doi: 10.1016/0165-1889(88)90041-3   DOI
24 Johansen, S. and Juselius, K. (1990) Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford B Econ. Stat., v.52(2), p.169-210.   DOI
25 Koshta, N., Bashir, H.A. and Samad, T.A. (2020) Foreign trade, financial development, agriculture, energy consumption and CO2 emission: testing EKC among emerging economies. Indian Growth and Development Review, v.14(1), p.50-80. doi: 10.1108/IGDR-10-2019-0117   DOI
26 Kucukaksoy, I., Cifci, I. and Ozbek, R.I. (2015) Export-led Growth Hypothesis: Turkey Application. Cankiri Karatekin University Journal of The Faculty of Economics and Administrative Sciences, v.5(2), p.691-720.
27 Narayan, S. and Narayan, P.K. (2004) Determinants of demand for Fiji's exports: An empirical investigation. Dev. Econ., v.42(1), p.95-112. https://doi.org/10.1111/j.1746-1049.2004.tb01017.x   DOI
28 Leitao, N.C. and Balogh, J.M. (2020) The impact of energy consumption and agricultural production on carbon dioxide emissions in Portugal. AGRIS on-line Papers in Economics and Informatics, v.12(1), p.49-59. doi: 10.7160/aol.2020.120105   DOI
29 Liebig, M.A., Franzluebbers, A.J. and Follett, R.F. (2012) Agriculture and climate change: Mitigation opportunities and adaptation imperatives. Acedemic Press, San Diego, CA, 3-11.
30 Liu, X., Zhang, S. and Bae, J. (2017a) The impact of renewable energy and agriculture on carbon dioxide emissions: Investigating the environmental Kuznets curve in four selected ASEAN countries. J. Cleaner Prod., v.164, p.1239-1247. https://doi.org/10.1016/j.jclepro.2017.07.086   DOI
31 Narayan, P.K. (2005) The saving and investment nexus for China: evidence from cointegration tests. Appl. Econ., v.37(17), p.1979-1990. https://doi.org/10.1080/00036840500278103   DOI
32 Nazlioglu, S. (2010) Makro iktisat politikalarinin tarim sektoru uzerindeki etkileri: Gelismis ve gelismekte olan ulkeler icin bir karsilastirma. Doktora Tezi. Kayseri: Erciyes universitesi.
33 Park, J.Y. (1992) Canonical cointegrating regressions. Econometrica: Journal of the Econometric Society, v.60(1), p.119-143. doi: 10.2307/2951679   DOI
34 Pedroni, P. (2000) Fully-Modified OLS for heterogeneous cointegrated panels. Advances in Econometrics, v.15, p.93-130. doi: 10.1016/S0731-9053(00)15004-2   DOI
35 Pedroni, P. (2001) Purchasing Power Parity Tests in Cointegrated Panels. Review of Economics and Statistics, v.83, p.727-731. doi: 10.1162/003465301753237803   DOI
36 Phillips, P. and Perron, P. (1988) Testing for a unit root in time series regression. Biometrika, v.75(2), p.335-346. doi: 10.1093/biomet/75.2.335   DOI
37 Pesaran, M.H. and Smith, R.P. (1998) Structural analysis of cointegrating VARs. J. Econ. Survey, v.12, p.471-505. https://doi.org/10.1111/1467-6419.00065   DOI
38 Pesaran, M.H. and Shin, Y. (1999) An autoregressive distributed lag modeling approach to cointegration analysis, In: Strom, S., Holly, A., Diamond, P. (Eds.), Centennial Volume of Rangar Frisch, Cambridge University Press, Cambridge.
39 Pesaran, M.H., Shin, Y. and Smith, R.J. (2001) Bounds testing approaches to the analysis of level relationships. J. Appl. Econom., v.16, p.289-326. doi: 10.1002/jae.616   DOI
40 Phillips, P.C. and Hansen, B.E. (1990) Statistical inference in instrumental variables regression with I (1) processes. The Review of Economic Studies, v.57(1), p.99-125. doi: 10.2307/2297545   DOI
41 Ramachandra, T.V., Aithal, B.H. and Sreejith, K. (2015) GHG footprint of major cities in India. Renew. Sust. Energ. Rev., v.44, p.473-495. https://doi.org/10.1016/j.rser.2014.12.036   DOI
42 Rehman, A., Ozturk, I. and Zhang, D. (2019) The causal connection between CO2 emissions and agricultural productivity in Pakistan: empirical evidence from an autoregressive distributed lag bounds testing approach. Applied Sciences, v.9(8), 1692. doi: 10.3390/app9081692   DOI
43 Ronaghi, M., Saghaian, S., Reed, M. and Mohammadi, H. (2018) The impact of the agricultural sector in developing countries that produce natural gas on greenhouse gas emissions. International Journal of Food and Agricultural Economics (IJFAEC), 6(1128-2019-555), p.53-69. doi: 10.22004/ag.econ.283874   DOI
44 Ullah, A., Khan, D., Khan, I. and Zheng, S. (2018) Does agricultural ecosystem cause environmental pollution in Pakistan? Promise and menace. Environmental Science and Pollution Research, v.25, p.13938-13955. https://doi.org/10.1007/s11356-018-1530-4   DOI
45 Sarkodie, S.A. and Owusu, P.A. (2017) The relationship between carbon dioxide, crop and food production index in Ghana: By estimating the long-run elasticities and variance decomposition. Environmental Engineering Research, v.22(2), p.193-202. doi: 10.4491/eer.2016.135   DOI
46 Signor, D. and Cerri, C.E.P. (2013) Nitrous oxide emissions in agricultural soils: a review. Pesquisa Agropecuaria Tropical, v.43(3), p.322-338. doi: 10.1590/S1983-40632013000300014   DOI
47 Tang, T.C. (2003) Japanese aggregate import demand function: Reassessment from the bounds testing approach. Jpn. World Econ., v.15, p.419-436. https://doi.org/10.1016/S0922-1425(02)00051-8   DOI
48 Zivot, E. and Andrews, D.W.K. (1992) Further evidence on the great crash, the oil-price. J. Bus. Econ. Stat., v.10(3), p.251-270. doi: 10.2307/1391541   DOI
49 Adom, P.K., Amakye, K., Barnor, C. and Quartey, G. (2015) The long-run impact of idiosyncratic and common shocks on industry output in Ghana. OPEC Energy Review, v.39(March), p.17-52. doi: 10.1111/opec.12039   DOI