مقایسه کارایی فنی و زیستمحیطی صنایع انرژیبر استان اصفهان ـ رهیافت تحلیل پوششی تصادفی ناپارامتری داده
محورهای موضوعی :
اقتصاد محیط زیست
منصوره جلایری
1
,
محمدحسن وکیل پور
2
,
صادق خلیلیان
3
,
حامد نجفی
4
1 - دانشجوی دکتری گروه اقتصاد کشاورزی دانشگاه تربیت مدرس.
2 - عضوگروه اقتصاد کشاورزی دانشگاه تربیت مدرس. *(مسوول مکاتبات)
3 - دانشیارگروه اقتصاد کشاورزی دانشگاه تربیت مدرس.
4 - دانشیارگروه اقتصاد کشاورزی دانشگاه تربیت مدرس.
تاریخ دریافت : 1399/05/07
تاریخ پذیرش : 1399/09/23
تاریخ انتشار : 1401/02/01
کلید واژه:
کارایی زیست محیطی,
روش StoNED,
صنایع انرژیبر,
ستانده نامطلوب,
چکیده مقاله :
زمینه و هدف: انتظار میرود سیاستهای توسعه پایدار به نحوی طراحی شوند که فرایندهای تولید و محصولات تولیدی سازگار با محیط زیست بوده و کمترین اثرات جانبی را به دنبال داشته باشند. دغدغه این روزهای کشورهای توسعه یافته دست یافتن به توسعه پایدار است و این مهم با راهکار کارایی زیستمحیطی ارزیابی میشود. کارایی زیستمحیطی شناختهترین و پرکاربردترین شاخص ارزشگذاری در رابطه با توسعه زیستمحیطی است.
روش بررسی: در این مطالعه با استفاده از رویکرد تحلیل پوششی داده و تحلیل پوششی تصادفی ناپارامتری داده ها (StoNED)، کارایی فنی و زیستمحیطی صنایع انرژیبر استان اصفهان را تخمین زده و به مقایسه نتایج میپردازد. دادههای مالی شامل فروش سالانه، نیروی کار، هزینه مواد اولیه اصلی، موجودی سرمایه از صورتهای مالی حسابرسی شده شرکتها برای سال منتهی به 1396 جمع آوری شده و میزان مصرف انرژی و انواع انرژی مصرفی و درصد هریک از آنها از بخش فنی کارخانه با استفاده از پرسشنامه جمعآوری و تکمیل گردیده است.
یافته ها: از فروض پژوهش پایینتر بودن میانگین کارایی زیستمحیطی نسبت به میانگین کارایی فنی بود که نتایج بدست آمده نشان داد میانگین کارایی زیست محیطی به نسبت قابل توجهی پایینتر از میانگین کارایی فنی بنگاههاست. همچنین نتایج نشان داد متوسط کارایی فنی در صنایع انرژیبر منتخب استان اصفهان، با استفاده از روش StoNED، 7/75 % و متوسط کارایی زیستمحیطی، 1/52% است. بدین ترتیب متوسط کارایی زیستمحیطی صنایع مذکور، 6/23% پایینتر از میانگین کارایی فنی آنهاست. در میان سه گروه مورد بررسی، صنایع غیرفلزی مثل کارخانههای تولید سیمان و آجر نسوز متوسط کارایی فنی بالاتری با رقم 35/87% داشته درحالیکه متوسط کارایی زیستمحیطی این گروه با عدد 79/48% پایینتر از دو گروه دیگر است. این موضوع با توجه به اینکه این صنایع از جمله آلایندهترین صنایع کشور در انتشار CO2 هستند کارایی پایینتر زیستمحیطی آنها دور از انتظار نیست و در این مطالعه نیز تایید میشود. متوسط کارایی فنی صنایع فلزی23/81 % و متوسط کارایی زیستمحیطی این گروه 61/55% و متوسط کارایی فنی صنایع شیمیایی و پتروشیمی 9/55% و متوسط کارایی زیستمحیطی این گروه 54/49 % بوده است.
بحث و نتیجه گیری : با توجه به اختلاف میانگین کارایی زیست محیطی محاسبه شده، پیشنهاد میشود برای بالا بردن کارایی زیستمحیطی ، استانداردهای زیست محیطی متناسب با هر صنعت تدوین گردد.
چکیده انگلیسی:
Background and Objective: Sustainable development policies are expected to be designed in such a way that the production process and products are environmentally friendly and have the least negative side effects on the environment. The concern of developed countries these days is to achieve sustainable development, and this is measured by the tools of environmental efficiency. Environmental efficiency is the most well-known and widely used valuation indicator in relation to environmental development.
Material and Methods: this study evaluated the technical and environmental efficiencies of energy industries in Isfahan province and compared the results using the data envelopment analysis and non-parametric random envelopment analysis approach(StoNED). Financial data including annual sales, labor force, cost of raw materials and capital stock were collected from the audited financial statements of companies for the year ending 2017, and the amount of energy consumption and the type of energy consumed and the percentage of each one, collected and Completed using a questionnaire from the technical department of the factory.
Findings: One of the assumptions of this study was that the average environmental efficiency was lower than the average technical efficiency and expected a significant difference between environmental and technical efficiency in each group of industries. The results showed that the average environmental efficiency was significantly lower than the average technical efficiency of firms. The results also showed that the average technical efficiency in selected energy industries in Isfahan province, using DEA method, was calculated 67.4% and using StoNED method was 75.7% and the average environmental efficiency in StoNED method was 52.1%. Thus, the average environmental efficiency of energy industries in Isfahan province was 23.6% lower than their average technical efficiency. Among the three groups studied, non-metallic industries such as cement and refractory brick factories had the highest average technical efficiency with 87.35%, while the average environmental efficiency of this group with 48.79% was lower than the other two groups. The average technical efficiency of metal industries was 81.23% and the average environmental efficiency of this group was 55.61% and the average technical efficiency of chemical and petrochemical industries was 55.9% and the average environmental efficiency of this group was 49.54%.
Discussion and Conclusion: Due to the difference in the average environmental efficiency calculated for selected industries, it is suggested that environmental standards appropriate to each industry be developed to have a greater impact on environmental efficiency.
منابع و مأخذ:
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Emami Meybodi, A., KHoshkalam-kh, , mahdavi, R., (2013)" . Efficiency and productivity from an economic point of view" , Allameh Tabatabai University Publication. (In Persian)
Amadhe,H, Rezaei,A (2011). " Environmental efficiency measurement using the efficiency model The universal is the desired and undesired result of the universal inseparable In the electric energy production department of regional electricity companies ", Quarterly Journal of Energy Economics Studies,1,125-154. (In Persian)
Seifi ,A, Salimifar M, Fanudi ,H., 2012. Environmental Performance Measurement: A Case Study of Thermal Power Plants in South Khorasan, Razavi and North Khorasan Provinces. Iranian Energy Economics Quarterly.7,17-41. (In Persian)
Shahiki Tash, M., Khajeh Hasani, M., Jafari, S., 2015. Assessment of the Environmental Performance in Energy Intensive Industries of Iran by Using Directional Distance Function Approach. Quarterly Journal of Applied Theories of Economics, 1, 99-120. (In Persian)
Molaei, Hesari, N. Javanbakht, A, 2017 "Estimation of environmental efficiency of input-oriented agricultural products (case study environmental efficiency of rice production) " , Agricultural Economics Journal, 2, 157-172
Mao j, Du Y, Xu L, Zeng Y,(2011), " Quantification of energy related industrial eco-effciency of China". Frontiers of Environmental & Science & Engineering in China, vol 5, Issue 4, 585-596.
Picazo-Tadeo, A. J., Castillo-Giménez, J., & Beltrán-Esteve, M. (2014). An intertemporal approach to measuring environmental performance with directional distance functions: greenhouse gas emissions in the European Union. Ecological Economics, 100, 173-18.
Vlontzos, G, Niavis, S, Manos, B (2014)." A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries",Renewable and Sustainable Energy Reviews 40, 91–96.
Guide for calculating and reporting greenhouse gas emissions , . (2017) Oil Ministry
Choi, Y and Lee H. S., 2016. Are Emissions Trading Policies Sustainable? A Study of the Petrochemical Industry in Korea. Global E-Governance Program, Inha University. mdpi.com/journal/sustainability
Motafakker Azad, M., Pourebadollahan Covich, M., Fallahi, F., Ranj Pour, R., Sojoodi, S.,, 2014. Measuring the Technical Efficiency of Iranian Thermal Power Plants and Analysis of its Determinants: Application of Stochastic Nonparametric Data Envelopment Method. Journal of Economic Research (Tahghighat- E- Eghtesadi), 1, 93-113. (In Persian)
Kuosmanen, T., & Kortelainen, M., 2012. Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints. Journal of Productivity Analysis, 1, 11–28.
Kuosmanen, T., & Johnson, A., 2008. Data Envelopment Analysis as Nonparametric Least Squares Regression. SSRN Electronic Journal, 1–30. doi:10.2139/ssrn.1158252
Kuosmanen, T. and Johnson,A.L., 2010. Data envelopment analysis as nonparametric least squares regression, Operations Research.1, 149-160.
Kuosmanen T., 2016. Stochastic Nonparametric Envelopment of Panel Data: Frontier Estimation with Fixed and Random Effects Approaches. EWEPA X.
Baradaran V, yaghoubi N., 2016 . Valuate the Efficiency of Iranian Electric Power Distribution Companies by Stochastic Nonparametric Envelopment of Data (StoNED) Approach. Iranian Electric Industry Journal of Quality and Productivity. 2,15-26 . (In Persian)
Mekaroonreung, M., & Johnson,A. L., 2012. Estimating the shadow prices of SO2 and NOx for U.S. coal power plants: A convex nonparametric least squares approach. Energy Economics. 3,723–732.
Shephard, R. W. (1970). Theory of Cost and Production Functions. Princeton: Princeton University Press. Retrieved from http://www.jstor.org/stable/2230285
Rečka, L., & Ščasný, M., 2012. Emission Shadow Price Estimation Based on Distance Function: a Case of the Czech Energy Industry. In INTERNATIONAL DAYS OF STATISTICS AND ECONOMICS. 1,543–554.
Jondrow, J. C., Lovell, A. K., Materoy, I. S., & Schmidt, P., 1982 . On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19, 233 – 238.
Guide for calculating and reporting greenhouse gas emissions ,(2017) . Oil Ministry. (In Persian)
Iran's energy balance sheet ., 2010. Ministry of Energy . Power and Energy Affairs (In Persian with English abstract).
Iran's energy balance sheet ., 2016. Ministry of Energy . Power and Energy Affairs (In Persian with English abstract).
Intergovernmental Panel on Climate Change (IPCC), 2006 IPCC Guidelines for National Greenhouse Gas Inventories