بررسی اثر هزینه ناشی از آلودگیهای زیستمحیطی بر روی کارایی (مطالعه موردی: مناطق اقتصادی کشور چین)
محورهای موضوعی : اقتصاد محیط زیستفاطمه مهرگان 1 , سهیلا سیدبویر 2
1 - استادیار گروه ریاضی، واحد آبادان، دانشگاه آزاد اسلامی، آبادان، ایران. *(مسوول مکاتبات)
2 - استادیار گروه ریاضی، واحد آبادان، دانشگاه آزاد اسلامی، آبادان، ایران.
کلید واژه: کارایی زیستمحیطی, تحلیل پوششی دادههای دومرحلهای, هزینه ازدسترفته, قوانین زیستمحیطی,
چکیده مقاله :
زمینه و هدف: روشهای تأمین و تولید انرژی، از عوامل تعیینکننده در آلوده کردن محیطزیست میباشند. در روند حرکت جهانی بهسوی توسعه پایدار، توجه به آسیبهای زیستمحیطی امری مهم است. در این راستا، برآورد کارایی زیستمحیطی بسیار مورد توجه قرار گرفته است. هدف در این پژوهش، بررسی اثرات زیستمحیطی روی مقدار کارایی است. صنایع و شرکتها با ایجاد آلودگی زیستمحیطی هزینههایی را به جامعه تحمیل میکنند که در اکثر مواقع این هزینهها در سنجش کارایی در نظر گرفته نمیشوند. در این تحقیق سعی بر این است که با استفاده از تحلیل پوششی دادههای دومرحلهای و با در نظر گرفتن هزینه آلودگیهای زیستمحیطی، مدلی مناسب جهت ارزیابی کارایی ارائه شود. بعلاوه به محاسبه و بحث بر روی هزینه ازدسترفته ناشی از اعمال محدودیتهای زیستمحیطی بر روی خروجیهای نامطلوب نیز پرداخته شده است. روش بررسی: روش بررسی تحلیلی-توصیفی است. از تحلیل پوششی دادههای دومرحلهای برای سنجش کارایی استفاده شده است. ابتدا با توجه به مسئله، محدودیتهای زیستمحیطی ایجاد و به مدل اضافه شدهاند. مدلهای ریاضی ارائهشده با استفاده از نرمافزار GAMS حل شده و مقادیر کارایی به دست آمده است. یافتهها: نتایج حاصل حاکی از این هستند که مقادیر کارایی بهدستآمده برای مدل شامل قوانین زیستمحیطی کمتر از مدل بدون قوانین زیستمحیطی است؛ این امر نشان میدهد که اعمال قوانین زیستمحیطی بر روی خروجیهای نامطلوب، منجر به از دست رفتن تعدادی از خروجیهای مطلوب و درنتیجه بخشی از هزینه میشود. این پژوهش 20 منطقه اقتصادی چین را در نظر گرفته است و مقادیر کارایی و هزینه ازدسترفته را برای آنها محاسبه کرده است. بحث و نتیجهگیری: نتایج نشان میدهند که در مناطق Guizhou و Guangdong، مقدار کارایی در هر دو حالت (با و بدون قوانین زیستمحیطی) برابر است که نشاندهندهی این است که این مناطق روی مرز کارایی قرار دارند. هزینه ازدسترفته در این مناطق صفر است. در صورتیکه در مناطقی نظیر Shaanxi و Liaoning این اختلاف بیشتر است که نشاندهنده این است که توسعه اقتصادی این مناطق برای یک دوره طولانی، وابسته به مصرف مداوم منابع بوده است و این امر باعث کاهش کیفیت محیطزیست در آنها شده است.
Background and Objective: The purpose of this study is to evaluate the environmental impacts on the value of efficiency. Industries and companies making environmental pollution, bring costs to the community, which in the most cases are not taken into account for measuring efficiency. This study has attempted to find a proper model for evaluating efficiency using the two-stage data envelopment analysis, considering the costs of environmental pollution. Cost loss resulted from applying environmental constraints on undesirable outputs, are also discussed and calculated. Method: An analytical-descriptive method is utilized in this study. Two-stage data envelopment analysis has been used here to measure efficiency. At first, environmental constraints have been made up with respect to the problem and then added to the model. The proposed mathematical models are solved using the GAMS software and the values of efficiency are obtained. Findings: Results show that the model with environmental regulations has a lower efficiency value compared to the model without environmental regulation, indicating that applying environmental regulations on undesirable outputs, leads to losing some of desirable outputs and consequently some cost. The study involves 20 economic zones of China whose efficiency and cost loss are calculated. Discussion and Conclusion: The results show that in Guizhou and Guangdong regions, the efficiency is the same in both cases, indicating that these areas are on the edge of efficiency. The cost loss in these areas is zero. However, in areas such as Shaanxi and Liaoning, the difference is greater, indicating that for a long period of time, the economic development of these areas has been dependent on constant consumption of resources, which has caused reduced environmental quality.
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- Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, Vol.30, pp.1078-1092.
- Chambers, R.G., Fare, R., Grosskopf, S., 1996. Productivity growth in APEC countries. Pacific Economic Review 1 Vol. 3, pp.181–190.
- Cook, W.D., Liang, L., Zhu, J., 2010. Measuring performance of two-stage network structures by DEA: A review and future perspective. Omega, Vol. 38, pp. 423-430.
- Färe, R., Grosskopf, S., 2005. Nonparametric Productivity Analysis with Undesirable Outputs: Comment. American Journal of Agricultural Economics, Vol. 85, pp.1070-1074.
- Hailu, A.,2003. Nonparametric Productivity Analysis with Undesirable Outputs: Reply. American Journal of Agricultural Economics, Vol. 85, pp.1075-1077.
- Kuosmanen, T., 2005. Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs. American Journal of Agricultural Economics, Vol.87, pp.1077-1082.
- Renshaw, E.F.,1981. Energy efficiency and the slump in labor productivity in the USA. Energy Economics, Vol.1, pp.36–42.
- Seiford, L.M. Zhu, J. Profitability and Marketability of the Top 55 U.S. Commercial Banks, Management Science, 45 (1999) 1270-1288.
- Sexton, T., Lewis, H., 2003. Two-Stage DEA: An Application to Major League Baseball. Journal of Productivity Analysis, Vol.19, pp. 227-249.
- Wang, C., Gopal, R., Zionts, S., 1997. Use of Data Envelopment Analysis in assessing Information Technology impact on firm performance. Ann Oper Res, Vol. 73, pp.191-213.
- Yang, L., Wang, Ke.L., 2013. Regional differences of environmental efficiency of China’s energy utilization and environmental regulation cost based on provincial panel data and DEA method. Mathematical and Computer Modelling, Vol.58, pp.1074–1083.
- Hailu, A., Veeman, T.S., 2001. Non-parametric Productivity Analysis with Undesirable Outputs: An Application to the Canadian Pulp and Paper Industry. American Journal of Agricultural Economics, Vol. 83, pp.605-616.
- Jenne, C.A., Cattell, R.K.,1983. Structural change and energy efficiency in industry. Energy Economics, Vol.2, pp. 114–123.
- Wilson, B., Trieu, L.H., Bowen, B., 1994. Energy efficiency trends in Australia. Energy Policy, Vol.4, pp.287–295.
- Wang, ke., Huang, W., Jie, W., Ying, L., 2014. Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, Vol 44, pp. 5-20.
- Färe, Z., 2006. A two-stage DEA model to evaluate the overall performance of Canadian life and health insurance companies, Mathematical and Computer Modelling, 43, Issues 7–8, pp. 910-919.
- Murty M., Kumar, S., and Paul. M., 2006. Environmental Regulation, Productive Efficiency and Cost of Pollution Abatement: A Case Study of the Sugar Industry in India, Environmental Management 1: 1-9.
- Färe, R., Grosskopf, S., Pasurka, C., 2007. Environmental production functions and environmental directional distance functions, Energy, 32, pp.1055-1066.
- Nasrallahi, Zahra, Sadeghi Arani, Zahra; Ghafarigolak, Marzieh. Measuring the efficiency of Iran's manufacturing industries with the data envelopment analysis approach and emphasizing unfavorable outcomes (environmental pollutants), economic policies, 2012, Vol. 1. pp. 87-110. (In Persian)
- Imami Meybodi, Ali; Jaredi, Farzaneh. Measurement of biomass of Iranian oil refineries using data envelopment analysis method. Economic Research, 2014, Vol. 4. P. 89-96. (In Persian)
- Seyfi, Ahmad; Salimifar, Mostafa, Fennoudi, Hania. Environmental efficiency using random boundary analysis. Iranian Energy Economics Researches,1392. (In Persian)
- Molaei, Morteza; Sani, Fatemeh. Estimation of environmental efficiency of agricultural sector using DEA method in two situations despite favorable and unfavorable outcomes. Knowledge of Agriculture and Sustainable Production, 1394. 2, pp. 91-102. (In Persian)
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- Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research, Vol. 2, pp.429-44.
- Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, Vol.30, pp.1078-1092.
- Chambers, R.G., Fare, R., Grosskopf, S., 1996. Productivity growth in APEC countries. Pacific Economic Review 1 Vol. 3, pp.181–190.
- Cook, W.D., Liang, L., Zhu, J., 2010. Measuring performance of two-stage network structures by DEA: A review and future perspective. Omega, Vol. 38, pp. 423-430.
- Färe, R., Grosskopf, S., 2005. Nonparametric Productivity Analysis with Undesirable Outputs: Comment. American Journal of Agricultural Economics, Vol. 85, pp.1070-1074.
- Hailu, A.,2003. Nonparametric Productivity Analysis with Undesirable Outputs: Reply. American Journal of Agricultural Economics, Vol. 85, pp.1075-1077.
- Kuosmanen, T., 2005. Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs. American Journal of Agricultural Economics, Vol.87, pp.1077-1082.
- Renshaw, E.F.,1981. Energy efficiency and the slump in labor productivity in the USA. Energy Economics, Vol.1, pp.36–42.
- Seiford, L.M. Zhu, J. Profitability and Marketability of the Top 55 U.S. Commercial Banks, Management Science, 45 (1999) 1270-1288.
- Sexton, T., Lewis, H., 2003. Two-Stage DEA: An Application to Major League Baseball. Journal of Productivity Analysis, Vol.19, pp. 227-249.
- Wang, C., Gopal, R., Zionts, S., 1997. Use of Data Envelopment Analysis in assessing Information Technology impact on firm performance. Ann Oper Res, Vol. 73, pp.191-213.
- Yang, L., Wang, Ke.L., 2013. Regional differences of environmental efficiency of China’s energy utilization and environmental regulation cost based on provincial panel data and DEA method. Mathematical and Computer Modelling, Vol.58, pp.1074–1083.
- Hailu, A., Veeman, T.S., 2001. Non-parametric Productivity Analysis with Undesirable Outputs: An Application to the Canadian Pulp and Paper Industry. American Journal of Agricultural Economics, Vol. 83, pp.605-616.
- Jenne, C.A., Cattell, R.K.,1983. Structural change and energy efficiency in industry. Energy Economics, Vol.2, pp. 114–123.
- Wilson, B., Trieu, L.H., Bowen, B., 1994. Energy efficiency trends in Australia. Energy Policy, Vol.4, pp.287–295.
- Wang, ke., Huang, W., Jie, W., Ying, L., 2014. Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, Vol 44, pp. 5-20.
- Färe, Z., 2006. A two-stage DEA model to evaluate the overall performance of Canadian life and health insurance companies, Mathematical and Computer Modelling, 43, Issues 7–8, pp. 910-919.
- Murty M., Kumar, S., and Paul. M., 2006. Environmental Regulation, Productive Efficiency and Cost of Pollution Abatement: A Case Study of the Sugar Industry in India, Environmental Management 1: 1-9.
- Färe, R., Grosskopf, S., Pasurka, C., 2007. Environmental production functions and environmental directional distance functions, Energy, 32, pp.1055-1066.
- Nasrallahi, Zahra, Sadeghi Arani, Zahra; Ghafarigolak, Marzieh. Measuring the efficiency of Iran's manufacturing industries with the data envelopment analysis approach and emphasizing unfavorable outcomes (environmental pollutants), economic policies, 2012, Vol. 1. pp. 87-110. (In Persian)
- Imami Meybodi, Ali; Jaredi, Farzaneh. Measurement of biomass of Iranian oil refineries using data envelopment analysis method. Economic Research, 2014, Vol. 4. P. 89-96. (In Persian)
- Seyfi, Ahmad; Salimifar, Mostafa, Fennoudi, Hania. Environmental efficiency using random boundary analysis. Iranian Energy Economics Researches,1392. (In Persian)
- Molaei, Morteza; Sani, Fatemeh. Estimation of environmental efficiency of agricultural sector using DEA method in two situations despite favorable and unfavorable outcomes. Knowledge of Agriculture and Sustainable Production, 1394. 2, pp. 91-102. (In Persian)