برآورد تمایل به پرداخت و عوامل مؤثر بر آن به‌منظور بهبود کیفیت هوا در شهر مشهد

اقتصاد و توسعه کشاورزی

دوره 38، شماره 3 - شماره پیاپی 64، مهر 1403، صفحه 260-243   

نوع مقاله: مقالات پژوهشی

شناسه دیجیتال (DOI): 10.22067/jead.2024.86525.1247

نویسندگان

صابر کلهری¹؛ لیلی ابوالحسنی^{* 1}؛ محمود صبوحی²؛ مریم سرخوش³

¹گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

²دانشگاه فردوسی مشهد

³گروه مهندسی بهداشت محیط، دانشکده بهداشت، دانشگاه علوم پزشکی مشهد، مشهد، ایران

چکیده

از مهم‌ترین مسائل و چالش‌های پیشرو در جهان امروز، آلودگی هوا و به‌خصوص آلاینده PM_2.5 می‌باشد، به‌نحوی‌که این مساله به یکی از معضلات پیچیده و جدی در زندگی انسان‌های سراسر جهان تبدیل‌شده است. قرار گرفتن در معرض سطوح بالای آلودگی هوا موجب پیامدهای منفی سلامتی است. مطالعه حاضر در نظر دارد تا تمایل به پرداخت ساکنین شهر مشهد برای بهبود آلاینده PM_2.5 و عوامل مؤثر بر این تمایل به پرداخت را اندازه‌گیری کند. برای رسیدن به این هدف از روش نمونه‌گیری خوشه‌ای دو مرحله‌ای از 343 نفر و با بکار بردن مدل انتخاب گسسته چندبعدی (MBDC) و الگوی اقتصادسنجی لاجیت ترتیبی استفاده شد. بر اساس نتایج، تمایل به پرداخت مردم مشهد برای بهبود کیفیت آلاینده PM_2.5 به میزان 55488 ده ریال برآورد شد. همچنین نتایج نشان داد که متغیرهایی همچون تحصیلات، سن افراد، داشتن بیماری تنفسی و درآمد بر تمایل به پرداخت افراد اثری مثبت و معنی‌دار و متغیر بعد خانوار اثر منفی و معنی‌داری بر تمایل به پرداخت داشته است. نتایج این پژوهش می‌تواند شهرداری مشهد را نسبت به میزان درآمد احتمالی از عوارض محیط‌زیستی ناشی از آلودگی هوا که شهروندان مایل به پرداخت آن برای بهبود کیفیت هوا هستند، آگاه سازد و پیشنهاد می‌شود شهرداری این عوارض محیط‌زیستی را به‌صورت ماهانه جمع‌آوری و به‌صورت اختصاصی برای پروژه‌های کاهش آلودگی هوا استفاده کند.

کلیدواژه‌ها

تمایل به پرداخت؛ روش انتخاب گسسته چندبعدی (MBDC)؛ PM2.5؛ مشهد

مراجع

1. Abedian, S., Mirsanjari, M.M., & Salmanmahiny, A. (2021). Estimation of citizens’ willingness to pay District 5 of Tehran to improve air quality using a conditional valuation method. The Journal of Economic Policy, 12(24), 1–40. (In Persian with English abstract). https://doi.org/10.22034/epj.2021.13244.2048
2. Adeli, O.A., Arabi, H., & Rahimi Kahkashi, S. (2021). Assessment attitudes and factors affecting the willingness of Mohajeran citizens to pay to improvement of air quality. Journal of Natural Environment, 73(4), 715–728. (In Persian with English abstract). https://doi.org/10.22059/jne.2021.309450.2068
3. Arceo, E., Hanna, R., & Oliva, P. (2016). Does the effect of pollution on infant mortality differ between developing and developed countries? Evidence from Mexico City. The Economic Journal, 126(591), 257–280. https://doi.org/10.1111/ecoj.12273
4. Baik, S., Davis, A.L., & Morgan, M.G. (2018). Assessing the cost of large‐scale power outages to residential customers. Risk Analysis, 38(2), 283–296. https://doi.org/10.1111/risa.12842
5. Becker, G.S. (2009). Human Capital: A Theoretical and Empirical Analysis, with Special.
6. Bharadwaj, P., Gibson, M., Zivin, J.G., & Neilson, C. (2017). Gray matters: Fetal pollution exposure and human capital formation. Journal of the Association of Environmental and Resource Economists, 4(2), 505–542. https://doi.org/10.1086/691591
7. Cameron, T.A., & Huppert, D.D. (1989). OLS versus ML estimation of non-market resource values with payment card interval data. Journal of Environmental Economics and Management, 17(3), 230–246. https://doi.org/10.1016/0095-0696(89)90018-1
8. Cooper, B., Burton, M., & Crase, L. (2011). Urban water restrictions: Attitudes and avoidance. Water Resources Research, 47(12), 12527. https://doi.org/10.1029/2010WR010226
9. Cooper, B., Burton, M., & Crase, L. (2019). Willingness to pay to avoid water restrictions in Australia under a changing climate. Environmental and Resource Economics, 72(3), 823–847. https://link.springer.com/article/10.1007/s10640-018-0228-x
10. Eftec (Economics for the Environment Consultancy). (2006). In association with Environmental Futures Limited Valuing Our Natural Environment Final Report NR0103 For Department for Environment, Food and Rural Affairs. https://iwlearn.net/resolveuid/31143e1709af2d73752e773c6af552d9
11. Eskandari, H., Noroozi, H., Naybandi Atashi, M. Rasool, Kalhori, S., & Rafiee, H. (2019). Estimating the Willingness to pay for air quality improvement with emphasis on agriculture and natural resources in Ahvaz County. Iranian Journal of Agricultural Economics and Development Research, 50(3), 451–465. (In Persian with English abstract). https://doi.org/10.22059/ijaedr.2018.252648.668567
12. Greene, W.H. (2003). Econometric analysis. Pearson Education India, Delhi. References - Scientific Research Publishing.
13. Hanemann, M.J.L.-A. (1991). Statistical efficiency of double‐bounded dichotomous choice contingent valuation. Wiley Online LibraryM Hanemann, J Loomis, B KanninenAmerican Journal of Agricultural Economics, 1991•Wiley Online Library, 73(4), 1255–1263. https://doi.org/10.2307/1242453
14. Hanley, N., Shogren, J.F., & White, B. (1997). Environmental economics in theory and practice. Environmental Economics in Theory and Practice. https://doi.org/10.1007/978-1-349-24851-3
15. Hanna, R., Oliva, P., Thank, W., Hill, J., Kimble, K., Bustos, S., Abadie, A., Card, D., Davis, L., Dellavigna, S., Glaeser, E., Greenstone, M., Khwaja, A., Kuhn, P., Lundberg, S., Mullainathan, S., Neidell, M., Olken, B., Pande, R., & School, H. K. (2011). The effect of pollution on labor supply: Evidence from a natural experiment in Mexico City. https://doi.org/10.1016/j.jpubeco.2014.10.004
16. Hassanvand, M.S., Naddafi, K., Faridi, S., Arhami, M., Nabizadeh, R., Sowlat, M.H., Pourpak, Z., Rastkari, N., Momeniha, F., Kashani, H., Gholampour, A., Nazmara, S., Alimohammadi, M., Goudarzi, G., & Yunesian, M. (2014). Indoor/outdoor relationships of PM10, PM2.5, and PM1 mass concentrations and their water-soluble ions in a retirement home and a school dormitory. Atmospheric Environment, 82, 375–382. https://doi.org/10.1016/J.ATMOSENV.2013.10.048
17. Health Effects Institute. (2020). Annual Report 2020 | Health Effects Institute. https://www.healtheffects.org/annual-report-2020
18. Hu, L., & Liao, W. (2023). Is there a stronger willingness to pay for air quality improvement with high education: new evidence from a survey in China. Environmental Science and Pollution Research, 30(11), 28990–29014. https://doi.org/10.1007/S11356-022-24108-Z
19. Jackson, J.M. (1960). Structural characteristics of norms. Teachers College Record, 61(10), 136-163. https://doi.org/10.1177/016146816006101007
20. Jiang, N., Ao, C., Xu, L., Wei, Y., & Long, Y. (2023). Will information interventions affect public preferences and willingness to pay for air quality improvement? An empirical study based on deliberative choice experiment. Science of The Total Environment, 868, 161436. https://doi.org/10.1016/J.SCITOTENV.2023.161436
21. Lee, J.F.J., Springborn, M., Handy, S.L., Quinn, J.F., & Shilling, F.M. (2010). Approach for economic valuation of environmental conditions and impacts. Prepared for Caltrans’. Final Valuation Report UCD, 123.
22. Levy, P.S., & Lemeshow, S. (2008). Sampling of populations : methods and applications. 576. https://books.google.com/books/about/Sampling_of_Populations.html?id=XU9ZmLe5k1IC
23. Li, C.Z., & Mattsson, L. (1995). Discrete choice under preference uncertainty: An improved structural model for contingent valuation. Journal of Environmental Economics and Management, 28(2), 256–269. https://doi.org/10.1006/JEEM.1995.1017
24. Liao, T.F. (1994). Interpreting probability models: Logit, probit, and other generalized linear models. Quantitative Application in the Social Science/Sage Publication.
25. Liu, X., He, J., Xiong, K., Liu, S., & He, B.J. (2023). Identification of factors affecting public willingness to pay for heat mitigation and adaptation: Evidence from Guangzhou, China. Urban Climate, 48, 101405. https://doi.org/10.1016/J.UCLIM.2022.101405
26. Maddala, G.S. (1983). Limited-dependent and qualitative variables in econometrics (Issue 3). Cambridge university press.
27. Malik, S., Arshad, M.Z., Amjad, Z., & Bokhari, A. (2022). An empirical estimation of determining factors influencing public willingness to pay for better air quality. Journal of Cleaner Production, 372, 133574. https://doi.org/10.1016/J.JCLEPRO.2022.133574
28. Mao, B., Ao, C., Wang, J., Sun, B., & Xu, L. (2020). Does regret matter in public choices for air quality improvement policies? A comparison of regret-based and utility-based discrete choice modelling. Journal of Cleaner Production, 254, 120052. https://doi.org/10.1016/J.JCLEPRO.2020.120052
29. Mashhad Environmental Pollutants Monitoring Center. (2021). A comprehensive report of the monitoring network of urban environmental pollutants in Mashhad. https://epmc.mashhad.ir/s/mfahvXC
30. Mitchell, R.C., & Carson, R.T. (2013). Using surveys to value public goods: the contingent valuation method. Rff press.
31. Montazer-Hojat, A.H., Anvari, E., & Bashi, M. (2018). Economic valuation of tourist attractions of Shushtar’s ancient waterfalls. Tourism Management Studies, 13(42), 21–39. (In Persian with English abstract). https://doi.org/10.22054/tms.2018.9013
32. Portney, P.R. (1994). The contingent valuation debate: Why economists should care. Journal of Economic Perspectives, 8(4), 3–17. https://doi.org/10.1257/JEP.8.4.3
33. Rowe, R.D., Schulze, W.D., & Breffle, W.S. (1996). A test for payment card biases. Journal of Environmental Economics and Management, 31(2), 178–185. https://doi.org/10.1006/JEEM.1996.0039
34. Sajadyan, S.M., Neshat, A., & Fatahi Ardakani, A. (2020). Economic study of possiblity of improvement of air quality in Arak metropolis. Agricultural Economics Research, 12(46), 19–34. (In Persian with English abstract). https://jae.marvdasht.iau.ir/article_4131.html
35. Sharzehi, G., & Jalili Kamjoo, S. P. (2013). Choice modeling:a new approach to valuation of environmental commodity; case study: Ganjnameh, Hamadan. Mdrsjrns, 13(3), 1–18. (In Persian). http://ecor.modares.ac.ir/article-18-3344-en.html
36. Shelby, B. (1981). Encounter norms in backcountry settings: Studies of three rivers. Journal of Leisure Research, 13(2), 129–138. https://doi.org/10.1080/00222216.1981.11969475
37. Suk, (2018). An Estimate of internal carbon pricing of Korean companies under the emission trading scheme. Korean Environmental Economics Association. https://www.iges.or.jp/en/pub/estimate-internal-carbon-pricing-korean-0/en
38. Tan-Soo, J.S., Finkelstein, E., Pattanayak, S., Qin, P., Zhang, X., & Jeuland, M. (2022). Air quality valuation using online surveys in three Asian megacities. Resources, Environment and Sustainability, 10, 100090. https://doi.org/10.1016/J.RESENV.2022.100090
39. Tan-Soo, J.S., & Pattanayak, S.K. (2019). Seeking natural capital projects: Forest fires, haze, and early-life exposure in Indonesia. Proceedings of the National Academy of Sciences of the United States of America, 116(12), 5239–5245. https://doi.org/10.1073/pnas.1802876116
40. Tian, X., Yu, X., Holst, R., Tian, X., & Yu, X. (2011). Applying the payment card approach to estimate the WTP for green food in China. https://www.econstor.eu/handle/10419/50786
41. Venkatachalam, L. (2004). The contingent valuation method: a review. Environmental Impact Assessment Review, 24(1), 89–124. https://doi.org/10.1016/S0195-9255%2803%2900138-0
42. Vossler, C.A., Poe, G.L., Welsh, P., & Ethier, R.G. (2004). Bid design effects in multiple bounded discrete choice contingent valuation. Environmental and Resource Economics, 29(4), 401–418. https://doi.org/10.1007/s10640-004-9457-2
43. Wang, H. (1997). Treatment of “Don’t-Know” responses in contingent valuation surveys: A random valuation model. Journal of Environmental Economics and Management, 32(2), 219–232. https://doi.org/10.1006/JEEM.1996.0965
44. Wang, H., He, J., & Huang, D. (2018). Valuation biases caused by public distrust: Identification and calibration with contingent valuation studies of two air quality improvement programs in China. Cahier de recherche/Working Paper, 18, 05. https://ideas.repec.org/p/shr/wpaper/18-05.html
45. Wang, W., & Ye, X. (2020). The potential supply and demand of farmers’ land contract rights-based on 697 households in four provinces of China. Land, 9(3), 80. https://doi.org/10.3390/LAND9030080
46. Welsh, M.P., & Bishop, R.C. (1993). Multiple bounded discrete choice models. Benefits & costs transfer in natural resource planning, Western Regional Research Publication, W-133, Sixth Interim Report, Department of Agricultural and Applied Economics, University of Georgia, 331-352.
47. Welsh, M.P., & Poe, G.L. (1998). Elicitation effects in contingent valuation: comparisons to a multiple bounded discrete choice approach. Journal of Environmental Economics and Management, 36(2), 170–185. https://doi.org/10.1006/JEEM.1998.1043
48. WHO. (2018). 9 out of 10 people worldwide breathe polluted air, but more countries are taking action. https://www.who.int/news/item/02-05-2018-9-out-of-10-people-worldwide-breathe-polluted-air-but-more-countries-are-taking-action
49. Zhao, Y., Wang, C., Sun, Y., & Liu, X. (2018). Factors influencing companies’ willingness to pay for carbon emissions: Emission trading schemes in China. Energy Economics, 75, 357–367. https://doi.org/10.1016/J.ENECO.2018.09.001
50. Zhou, R., Fukuda, H., Li, Y., & Wang, Y. (2023). Comparison of Willingness to pay for quality air and renewable energy considering urban living experience. Energies, 16(2), 992. https://doi.org/10.3390/EN16020992
51. Zvobgo, L. (2021). Consumer ability and willingness to pay more for continuous municipal water supply in Chitungwiza. Sustainable Water Resources Management, 7(2), 1–15. https://doi.org/10.1007/S40899-021-00498-9