عوامل مؤثر بر شدت انتشار آلاینده‌های تولید بخش کشاورزی

نوع مقاله : مقالات پژوهشی به زبان انگلیسی

نویسندگان

• فضل الله غفاریان ¹
• زکریا فرج زاده ²

¹ دانش آموخته کارشناسی ارشد، بخش اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران

² دانشیار بخش اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران

10.22067/jead.2021.68637.1013

چکیده

منشأ اصلی انتشار آلاینده­ها در ایران حامل­های انرژی است، اما در مورد اکسیددی­نیتروژن و متان فرآیند تولید کشاورزی نقش مهمی دارد. در همین راستا، مطالعه حاضر با هدف تحلیل شدت انتشار آلاینده­های منتخب در بخش کشاورزی و ارزیابی عوامل تعیین­کننده آن صورت گرفت. برای این منظور ابتدا با استفاده از روش تحلیل تجزیه، شدت انتشار در بخش کشاورزی به اجزای آن­ تجزیه گردید. سپس با استفاده از تحلیل رگرسیون نقش عوامل تعیین­کننده در شدت انتشار ارزیابی شد. آلاینده­های منتخب در بخش کشاورزی شامل متان، اکسیددی­نیتروزن و دی­اکسیدکربن منتشرشده از فرآیند تولید و دوره مطالعه شامل 95-1352 می­باشد. یافته­ها نشان داد شدت انتشار متان و اکسیددی­نیتروژن در دوره مطالعه سالانه 9/3 و 6/2 درصد در حال کاهش بوده است. سطح تولید در زیربخش­های کشاورزی عامل مهمی در شدت انتشار است. به این ترتیب که انتظار می­رود یک درصد افزایش در سطح تولید زیربخش دام شدت انتشار متان را 9/0 درصد افزایش و شدت انتشار ­اکسیددی­نیتروژن را بیش از 3/3 درصد کاهش دهد. از سوی دیگر همین میزان افزایش در سطح تولید زیربخش زراعت و باغبانی شدت انتشار متان را 9/0 درصد کاهش و شدت انتشار ­اکسیددی­نیتروژن را بیش از 3/3 درصد افزایش خواهد داد. اثر متغیرهای کلان اقتصاد ایران شامل نرخ شهرنشینی و درجه بازبودن اقتصاد بر شدت انتشار در بخش کشاورزی چندان حایز اهمیت ارزیابی نشد. به این ترتیب سیاست­های اتخاذشده برای کاهش شدت انتشار باید متمرکز بر متغیرهای بخش کشاورزی و بصورت مجزا در هر زیربخش دنبال شود.

کلیدواژه‌ها

• اکسیددی‌نیتروژن
• بخش‌‌کشاورزی
• شدت انتشار
• متان

20.1001.1.20084722.1400.35.4.3.9

موضوعات

• اقتصاد منابع طبیعی و محیط زیست

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عنوان مقاله [English]

Factors Affecting Emission Intensity of Pollutants Emitted from Agricultural Production

نویسندگان [English]

• F. Ghafarian ¹
• z. Farajzadeh ²

¹ Graduate Student, Department of Agricultural Economics, College of Agriculture, Shiraz University, Shiraz, Iran

² Associate Professor, Department of Agricultural Economics, College of Agriculture, Shiraz University, Shiraz, Iran

چکیده [English]

Energy products are the main sources of emissions for most of the pollutants in Iran. However, for some pollutants like Methane (CH₄) and Nitrous Oxide (N₂O), the production process, including the agricultural production process, plays a significant role. The aims of this study were to analysis the emissions intensity of the selected pollutants and to introduce the determinants in Iranian agricultural sector. The emission intensity in the agricultural sector was decomposed into its components using decomposition analysis. Then, the regression analysis was applied to investigate the emission intensity determinants. The selected pollutants are Carbon Dioxide (CO₂), CH₄, and N₂O emitted from agricultural production process. The applied data cover 1973-2016. The findings showed that CH₄ emission intensity has been decreasing over the study horizon by 3.9% annually. For N₂O, the corresponding value was 2.6%. Based on the results, output level in agricultural sectors is an important driving factor in the emission intensity. It was found that 1% increase in livestock output level is expected to increase CH₄ emission intensity by 0.9% while it will dampen the N₂O emissions intensity by more than 3.3%. By contrast, the same percentage of increase in the output level of agronomy and horticultural subsector will induce an increase of 3.3% in N₂O emission intensity and will reduce the CH₄ emission intensity more than 0.9%. Macroeconomic variables including urbanization and trade openness failed to affect the agricultural emission intensity significantly. The emission intensity of all pollutants, measured in CO₂ equivalent, has been decreasing over the study period by 3.5% annually. It was also found that, in terms of aggregated emission, output expansion in livestock and forestry sectors may induce higher emission intensity, while agronomy and horticultural output expansion can reduce the emissions intensity. Given that the output level plays a significant role in emission intensity while the macroeconomic variables have nothing to do with emission intensity, the measures taken to reduce the emission intensity in the agricultural sector should be sector-specific. Moreover, the measures should focus on each subsector individually.

کلیدواژه‌ها [English]

• Agricultural sector
• Emissions intensity
• Methane
• Nitrous oxide

مراجع

1. Alam, S., and A. Fatima. 2007. Sustainable development in Pakistan: the context of energy consumption demands and environmental degradation. Journal of Asian Economics, 18: 825–837.
2. Ang, B.W. 2015. LMDI decomposition approach: A guide for implementation. Energy Policy 86: 233–238.
3. Baltagi, B.H. 2008. Econometric Analysis of Panel Data. Chichester, John Wiley & Sons Ltd.
4. Barghi Oskooi, M.M. 2008. Effects of trade liberalization on emissions (Carbon Dioxide) in the Kuznets environmental curve. Journal of Economic Research, 82: 1-21. (In Persian)
5. Barrios, S., L. Bertinelli, and E. Strobl. 2006. Climatic change and rural–urban migration: The case of sub-Saharan Africa. Journal of Urban Economics, 60: 357-351.
6. Behboodi, D., F. Fallahi, and I. Barghi Golazani. Economic and social factors affecting the per capita carbon dioxide emissions in Iran (1383-1346). Journal of Economic Research 90: 1-17. (In Persian)
7. Central Bank of Iran. 2017. Available at: http://tsd.cbi.ir/Display/Content.aspx.
8. Cramer, J.C. 2002. Population growth and local air pollution: Methods, models, and results. Population and Development Review, 28(1): 22-52.
9. Dong, F., B. Yu, T. Hadachin, Y. Dai, Y. Wang, S. Zhang, and R. Long. 2018. Drivers of carbon emission intensity change in China. Resources, Conservation and Recycling, 129: 187-201.
10. European Commission. 2010. Food, Farming, Fisheries. Retrieved from https://ec.europa.eu/food/safety/chemical_safety/contaminants/catalogue/aflatoxins_en
11. Fan, Y., L.C. Lui, and G. Wu. 2006. Analyzing impact factors of CO₂ emission using STIRPAT model. Environmental Impact Assessment Review 4: 377– 395.
12. FAO (Food and Agriculture Organization). 2017. Retrieved from http://www.fao.org/faostat/en/#home
13. Farajzadeh, Z. 2012. Environmental and Welfare Impacts of Trade and Energy Policy Reforms in Iran. Ph.D thesis. Shiraz University, Shiraz, Iran. (In Persian)
14. Farajzadeh, Z. 2018. Emissions tax in Iran: Incorporating pollution disutility in a welfare analysis. Journal of Cleaner Production 186: 618-631.
15. Farajzadeh, Z., and M. Bakhshoodeh. 2015. Economic and environmental analyses of Iranian energy subsidy reform using Computable General Equilibrium (CGE) Model. Energy for Sustainable Development, 27: 147-154.
16. Fischer, G., W. Winiwarter, T. Ermolieva, G. Y. Cao, and H. Qui. 2010. Integrated modeling framework for assessment and mitigation of nitrogen pollution from agriculture: Concept and case study for China. Agriculture, Ecosystem and Environment, 136 (1–2): 116–124.
17. Han, X., T. Cao, and T. Sun. 2019. Analysis on the variation rule and influencing factors of energy consumption carbon emission intensity in China's urbanization construction. Journal of Cleaner Production, 238: 117-958.
18. Hübler, M. 2009. Energy saving technology diffusion via FDI and trade: A CGE model of China. In: Kiel Working Papers 1479. Kiel Institute for the World Economy.
19. Iran’s Energy Balance. 2016. Deputy of Electricity and Energy Affairs, Ministry of Energy. http://pep.moe.org.ir.
20. Jones, D. 1991. How urbanization affects energy-use in developing countries. Energy Policy, 19(7): 621-630.
21. Kasman, A., and Y.S. Duman. 2015. CO₂ emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: A panel data analysis. Economic Modeling, 44: 97–103.
22. Li, W., Q.X. Ou, and Y.L. Chen. 2014. Decomposition of China's CO₂ emissions from agriculture utilizing an improved Kaya identity. Environmental Science Pollution Research, 21(22): 13000–13006.
23. Lin, S., D. Zhao, and D. Marinova. 2009. Analysis of the environmental Kuznets curve for CO₂: Evidence from pooled Mean Group. Economic Letters, 82(1): 121-126.
24. Ma, S.Z., and H. Feng. 2013. Will the decline of efficiency in China's agriculture come to an end? An analysis based on opening and convergence. China Economic Review, 27: 179–190.
25. Malakootikhah, Z., and Z. Farajzadeh. 2020. Climate change impact on agriculture value added. Agricultural Economics and Development, 111: 1-30. (In Persian)
26. Manahan, S. 2010. Environmental Chemistry. (9th Ed.). Boca Raton, FL, USA, CRC Press.
27. Marrero, G. 2010. Greenhouse gases emissions, growth and energy mix in Europe. Energy Economics, 32: 1356-1363.
28. McKinnish, T. 2005. Lagged dependent variables and specification bias. Economics Letters, 88(1): 55-59.
29. Monchuk, D. C., Z. Chen, and Y. Bonaparte. 2010. Explaining production inefficiency in China's agriculture using data envelopment analysis and semi-parametric bootstrapping. China Economic Review, 21(2): 346–354.
30. Moyen Uddin, M.M. 2020. What are the dynamic links between agriculture and manufacturing growth and environmental degradation? Evidence from different panel income countries. Environmental and Sustainability Indicators 7: 100041.
31. Nayak, D., E. Saetnan, K. Cheng, W. Wang, and F. Koslowski. 2015. Management opportunities to mitigate greenhouse gas emissions from Chinese agriculture. Agriculture Ecosystem and Environment 209: 108–124.
32. Poumanyvong, P., and S. Kaneko. 2010. Does urbanization lead to less energy use and lower CO₂ emissions? A cross-country analysis. Ecological Economics, 70 (2): 434-444.
33. Rao, B.B. 2010. Estimates of the steady state growth rates for selected Asian countries with an extended Solow model. Economic Modelling, 27: 46–53.
34. Reed, W. R., and M. Zhu. 2017. On estimating long-run effects in models with lagged dependent variables. Economic Modelling, 64: 302-311.
35. Rodríguez, M., and Y. Pena-Boquete. 2017. Carbon intensity changes in the Asian dragons: Lessons for climate policy design. Energy Economics, 66: 17-26.
36. Sadorsky, P. 2013. Do urbanization and industrialization affect energy intensity in developing countries?. Energy Economics, 37: 52-59.
37. Shahbaz, M., S. J. H. Shahzad, N. Ahmad, and S. Alam. 2016. Financial development and environmental quality: The way forward. Energy Policy 98: 353-364.
38. Shi, A. 2003. The impact of pressure on global carbon dioxide emission, 1975-1996, Evidence from pooled cross country data. Ecological Economics, 44: 29 – 42.
39. Taylor, L., A. Rezaei, R. Kumar, N. H. Barbosa-Filho, and L. Carvalho. 2014. Wage increase, transfers, and the socially determined income distribution in the USA. Working papers Series 11, Institute for New Economic Thinking.
40. UNDP (United Nations Development Programme). 2010. Department of Environment. Iran Second National Communication to United Nations Framework Convention on Climate Change (UNFCCC). National Climate Change Office, Department of Environment, Tehran.
41. United Nations. 2010. United Nations Development Program, Department of Environment. Iran second National Communication to United Nations Framework Convention on Climate Change (UNFCCC). National Climate Change Office, Department of Environment, Tehran, Iran.
42. Wan, N. F., X.Y. Ji, J.X. Jiang, H. X. Qiao, and K.H. Huang. 2013. A methodological approach to assess the combined reduction of chemical pesticides and chemical fertilizers for low-carbon agriculture. Ecological Indicator, 24: 344–352
43. Wang, C., J. Chen, and J. Zhou. 2005. Decomposition of energy-related CO₂ emission in China. Energy Economics 30: 73–83.
44. Wei, Y.M., L.C. Liu, Y. Fan, and G. Wu. 2008. China Energy Report: CO₂ Emissions Research. Science Press, Beijing, China.
45. World Bank 2016. Data. Retrieved from https://data.worldbank.org/indicator/EN.ATM.CO2E.PC?end=2014&start=1960
46. Wu, L., S. Kaneko, and S. Matsuoka. 2005. Driving forces behind the stagnancy of China's energy-related CO₂ emissions from 1996 to 1999: The relative importance of structural change, intensity change and scale change. Energy Policy 33: 319–335.
47. Xu, B., and B. Lin. 2017. Factors affecting CO₂ emissions in China’s agriculture sector: Evidence from geographically weighted regression model. Energy Policy, 104: 404-414.
48. York, R., E.A. Rosa, and T. Dietz, 2003. Footprints on the earth: The environmental consequences of modernity. American Sociological Review, 68(2): 279-300.
49. York, R., E.A. Rosa, and T. Dietz. 2003. STIRPAT, IPAT and ImPACT: Analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics, 46(3): 351-365.
50. Zhang, C., B. Su, K. Zhou, and S. Yang. 2019. Decomposition analysis of China's CO₂ emissions (2000–2016) and scenario analysis of its carbon intensity targets in 2020 and 2030. Science of Total Environment 668: 432-442.
51. Zhang, P., and Hao. 2020. Rethinking China's environmental target responsibility system: Province-level convergence analysis of pollutant emission intensities in China. Journal of Cleaner Production, 242: 118472.
52. Zhang, Y., A.L. Collins, P.J. Johnes, and J.I. Jones. 2017. Projected impacts of increased uptake of source control mitigation measures on agricultural diffuse pollution emissions to water and air. Land Use Policy, 62: 185–201.
53. Zibaei, M., and M.H. Tarazkar. 2004. Investigating short-term and long-term relationships between value added and energy consumption in the agricultural sector. Journal of Agricultural Bank, 6: 157-171. (In Persian)