اثر ارزش افزوده‌ و شدت مصرف انرژی بر انتشار آلودگی‌های زیست‏ محیطی از بخش کشاورزی: کاربرد الگوی خودتوضیحی با وقفه‌های گستردة پنلی (Panel ARDL)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری اقتصاد منابع طبیعی و محیط زیست، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران

2 استادیار اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران.

چکیده

هدف پژوهش حاضر بررسی رابطه میان رشد اقتصادی و تخریب محیط زیست در بخش کشاورزی استان­های ایران بود. بدین منظور، ارزش افزوده بخش کشاورزی به‏ عنوان شاخصی از رشد اقتصادی و میزان انتشار گاز دی‌اکسید کربن در بخش کشاورزی به‏ عنوان معیاری از تخریب محیط زیست در نظر گرفته شد. همچنین، داده ­های 24 استان کشور طی دوره 93-1379 از مرکز آمار ایران گردآوری شد. برای بررسی رابطه علی میان متغیرهای مدل، از روش خودتوضیحی برداری پنلی (PVAR) و برای برآورد مدل، از الگوی خودتوضیحی با وقفه­ های گستردة پنلی (Panel ARDL) استفاده شد. نتایج پژوهش نشان داد که یک رابطه علی یک‏طرفه از ارزش افزوده بخش کشاورزی و شدت مصرف انرژی در این بخش به انتشار گاز دی‌اکسید کربن وجود دارد؛ رابطه رشد بخش کشاورزی و میزان انتشار گاز دی­ اکسید کربن نیز به‏ صورت N شکل و از لحاظ آماری معنی‌دار بود. همچنین، نتایج حاکی از آن بود که افزایش نسبی شدت مصرف انرژی در بخش کشاورزی تأثیری مثبت در انتشار گاز دی‌اکسید کربن دارد. در نتیجه، باید سیاست­های آتی بخش کشاورزی دربرگیرندة برنامه ­های مناسب برای حفاظت از محیط زیست طبیعی کشور باشد.

کلیدواژه‌ها


عنوان مقاله [English]

The impact of value added and energy consumption intensity on environmental pollutions from agricultural sector: application of panel auto-regressive distribution lag (Panel ARDL)

نویسندگان [English]

  • N. Kargar Dehbidi 1
  • M. H. Tarazkar 2
1 Ph. D. Student in Economics of Natural Resources and Environment, Faculty of Agriculture, Shiraz University, Shiraz, Iran
2 Assistant Professor of Agricultural Economics, Faculty of Agriculture, Shiraz University, Shiraz, Iran
چکیده [English]

This study aimed at investigating the relationship between economic growth and environmental degradation in agricultural sector among provinces of Iran. Thus, the value added generated by the agricultural sector was utilized as the index of economic growth and Carbon Dioxide emission from the agricultural sector as the environmental degradation index; then, a panel data of 24 provinces of the country over 1990 to 2014 was obtained from statistical center of Iran. The panel vector autoregressive (PVAR) model was employed to study the causal relationship between variables of the model; also, the panel autoregressive distributed lag (Panel ARDL) was used for estimating the model. Empirical results of the study revealed that there was a unidirectional relationship from agricultural value added and energy consumption intensity to agricultural Carbon Dioxide emission. In addition, the relationship between agricultural Carbon Dioxide emission and the agricultural growth was found N-shaped and statistically significant. Moreover, the results indicated that a relative increase in agricultural energy consumption intensity positively affected the Carbon Dioxide emission. As a result, future policies in the agricultural sector should involve appropriate plans for the conservation of environmental and natural resource of the country.

کلیدواژه‌ها [English]

  • Agricultural Value Added
  • Energy Consumption Intensity
  • Carbon Dioxide Emission
  • Panel ARDL
  • Iran (Provinces)
  1. Abrigo, M.R. and Love, I. (2015). Estimation of panel vector auto regression in Stata: a package of programs. Manuscript, Feb. 2015. Available at http://paneldataconference2015. ceu. hu/Program/Michael-Abrigo. pdf (Retrieved at 15 Nov. 2017).
  2. Ahmed, A., Uddin, G.S. and Sohag, K. (2016). Biomass energy, technological progress and the environmental Kuznets curve: evidence from selected European countries. Biomass and Bioenergy, 90: 202-208.
  3. Ajmi, A.N., Hammoudeh, S., Nguyen, D.K. and Sato, J.R. (2015). On the relationships between CO2 emissions, energy consumption and income: the importance of time variation. Energy Economics, 49: 629-638.
  4. Alamdarloo, H.N. (2016). Water consumption, agriculture value added and carbon dioxide emission in Iran: environmental Kuznets curve hypothesis. International Journal of Environmental Science and Technology, 13(8): 2079-2090.
  5. Arellano, M. (2003). Panel data econometrics. Oxford University Press.
  6. Baltagi, B. (2008). Econometric analysis of panel data (Vol. 1). John Wiley & Sons.
  7. Baltagi, B.H. and Kao, C. (2001). Nonstationary panels, cointegration in panels and dynamic panels: a survey. In Nonstationary Panels, Panel Cointegration, and Dynamic Panels (pp. 7-51). Emerald Group Publishing Limited.
  8. Barros, V.R., Field, C.B., Dokke, D.J., Mastrandrea, M.D., Mach, K.J., Bilir, T.E. et al. (2014). Climate change 2014: impacts, adaptation, and vulnerability - Part B: regional aspects - Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
  9. Campos, N.F. and Kinoshita, Y. (2008). Foreign direct investment and structural reforms: evidence from Eastern Europe and Latin America (No. 6690). CEPR Discussion Papers.
  10. Chaitip, P., Chokethaworn, K., Chaiboonsri, C. and Khounkhalax, M. (2015). Money supply influencing on economic growth-wide phenomena of AEC open region. Procedia Economics and Finance, 24: 108-115.
  11. Coderoni, S., Esposti, R. (2011). Long-term agricultural GHG emissions and economic growth: the agricultural environmental Kuznets curve across Italian regions. In: Paper Presented at the EAAE 2011 Congress on Change and Uncertainty: Challenges for Agriculture, Food and Natural Resources, August 30 to September 2, 2011, ETH Zurich, Zurich, Switzerland.
  12. Dinda, S. (2004). Environmental Kuznets curve hypothesis: a survey. Ecological Economics. 49(4): 431-455.
  13. Dogan, E., Sebri, M. and Turkekul, B. (2016). Exploring the relationship between agricultural electricity consumption and output: new evidence from Turkish regional data. Energy Policy, 95: 370-377.
  14. Friedl, B. and Getzner, M. (2003). Determinants of CO2 emissions in a small open economy. Ecological Economics, 45(1): 133-148.
  15. Gojarati, D. (2004). Basics of econometrics. Translated by H. Abrishami (Vol. 2). Tehran: Tehran University. (Persian)
  16. Im, K.S., Pesaran, M.H. and Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Econometrics, 115(1): 53-74.
  17. Intergovernmental Panel on Climate Change (IPCC) (2017). Changes in atmospheric constituents and in radiative forcing. Fourth Assessment Report (AR4), Working Group 1 (WG1), Chapter 2, Table 2.14, p. 212. Also, available at http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_wg1_report_the_physical_science_basis.htm (Retrieved at 27 Nov., 2017).
  18. Islam, M., Kazi, M. and Tarique, K.M. (2014). CO/sub 2/emission and agricultural productivity in southeast asian region: a pooled mean group estimation. Science Vision, 20(1): 93-99.
  19. Jovanović, M., Kašćelan, L., Despotović, A. and Kašćelan, V. (2015). The impact of agro-economic factors on GHG emissions: evidence from European developing and advanced economies. Sustainability, 7(12): 16290-16310.
  20. Kang, Y.Q., Zhao, T. and Yang, Y.Y. (2016). Environmental Kuznets curve for CO2 emissions in China: a spatial panel data approach. Ecological Indicators, 63: 231-239.
  21. Katircioğlu, S.T. and Taşpinar, N. (2017). Testing the moderating role of financial development in an environmental Kuznets Curve: Empirical evidence from Turkey. Renewable and Sustainable Energy Reviews, 68: 572-586.
  22. Levin, A., Lin, C.F. and Chu, C.S.J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Econometrics, 108(1): 1-24.
  23. Li, W., Ou, Q. and Chen, Y. (2014). Decomposition of China’s CO2 emissions from agriculture utilizing an improved Kaya identity. Environmental Science and Pollution Research, 21(22): 13000-13006.
  24. Lopez-Menendez, A.J., Perez, R. and Moreno, B. (2014). Environmental costs and renewable energy: re-visiting the environmental Kuznets Curve. Environmental Management, 145: 368-373.
  25. Love, I. and Zicchino, L. (2006). Financial development and dynamic investment behavior: evidence from panel VAR. The Quarterly Review of Economics and Finance, 46(2): 190-210.
  26. Nasrnia, F. and Esmaeili, A. (2009). The causal relationship between energy and employment, investment and value added in the agricultural sector. Seventh Iranian Agricultural Economics Conference. Campus of Agriculture and Natural Resources of Tehran University. (Persian).
  27. Nayak, D., Saetnan, E., Cheng, K., Wang, W., Koslowski, F. et al. (2015). Management opportunities to mitigate greenhouse gas emissions from Chinese agriculture. Agriculture, Ecosystems and Environment, 209: 108-124.
  28. Özokcu, S. and Özdemir, Ö. (2017). Economic growth, energy, and environmental Kuznets Curve. Renewable and Sustainable Energy Reviews, 72: 639-647.
  29. Pant, K.P. (2009). Effects of agriculture on climate change: a cross country study of factors affecting carbon emissions. J. Agric. Environ., 10: 84-102.
  30. Pesaran, M.H. and Shin, Y. (1998). An auto regressive distributed lag modelling approach to cointegration analysis. Econometric Society Monographs, 31: 371-413.
  31. Pesaran, M.H. and Smith, R. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Econometrics, 68(1): 79-113.
  32. 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): 621-634.
  33. Rehman, H. and Bashir, F. (2015). Energy consumption and agriculture sector in middle income developing countries: a panel data analysis. Pakistan Journal of Social Sciences (PJSS), 35(1): 479-496.
  34. Samargandi, N. (2017). Sector value addition, technology and CO2 emissions in Saudi Arabia. Renewable and Sustainable Energy Reviews, 78: 868-877.
  35. Sebri, M. and Abid, M. (2012). Energy use for economic growth: a trivariate analysis from Tunisian agriculture sector. Energy Policy, 48: 711-716.
  36. Seneviratne, S.I., Donat, M.G., Pitman, A.J., Knutti, R. and Wilby, R.L. (2016). Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587): 477-483.
  37. Shahbaz, M., Lean, H.H. and Shabbir, M.S. (2012). Environmental Kuznets Curve hypothesis in Pakistan: cointegration and Granger causality. Renewable and Sustainable Energy Reviews, 16(5): 2947-2953.
  38. Shahbaz, M., Solarin, S.A., Sbia, R. and Bibi, S. (2015). Does energy intensity contribute to CO2 emissions? A trivariate analysis in selected African countries. Ecological Indicators, 50: 215-224.
  39. Smith, P., Bustamante, M., Ahammad, H. et al. (2014). Agriculture, forestry and other land use (AFOLU). In: Edenhofer O., Pichs-Madruga, R, Sokona, Y. et al. (eds) Climate change 2014: mitigation of climate change.
  40. Smith, P., Martino, D., Cai, Z. et al. (2008). Greenhouse gas mitigation in agriculture. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 363(1492): 789-813.
  41. Statistical Center of Iran (2015). Statistical yearbook 2015. Available at https://www.amar.org.ir/news/ID/2540/1394 (Retrieved at 11 Nov., 2017). (Persian)
  42. Wesseh, P.K. and Lin, B. (2017). Climate change and agriculture under CO2 fertilization effects and farm level adaptation: where do the models meet?. Applied Energy, 195: 556-571.
  43. Zafeiriou, E. and Azam, M. (2017). CO2 emissions and economic performance in EU agriculture: some evidence from Mediterranean countries. Ecological Indicators, 81: 104-114.