Subject The article considers the issues of production base development for solar energy in Russia and process chain creation, which can meet the growing demand of the domestic market and, in the long run, approach international photovoltaics markets. Objectives The purpose of the study is to assess negative impact of various photovoltaic (PV) technologies on environment to select and support the most ecologically friendly production, standardize and identify the best available technologies. Methods We assess the negative impact of rival PV technologies on environment, using the Life Cycle Analysis technique. Based on the CML 2001 methodology, we select the categories and quantifiable indicators of the impact. Comparative evaluation of integrated eco-efficiency of rival PV technologies was carried out using the Data Envelopment Analysis. Results We found that cadmium telluride and copper indium selenide photovoltaic panels on film solar cells are the most ecologically efficient throughout the life cycle. Economic indicators that determine the preference of one or another technology at the same level of environmental friendliness can be derived from standard procedures for techno-economic analysis. For the rest of the PV technologies, we calculated target values of fifteen indicators of negative environmental impact, which, if achieved, provide for their eco-efficiency. Conclusions The findings can be applied to design and amend State programs for solar energy development in Russia.
Keywords: Life Cycle Analysis, LCA, ecological effect, solar energy, Data Envelopment Analysis
References:
Budavari Z., Szalay Z. Indicators and weighting systems, including normalisation of environmental profiles. URL: Link
Hong J., Chen W., Qi C. et al. Life cycle assessment of multicrystalline silicon photovoltaic cell production in China. Solar Energy, 2016, vol. 133, pp. 283–293. doi: 10.1016/j.solener.2016.04.013
Akinyele D.O., Rayudu R.K., Nair N.K.C. Life cycle impact assessment of photovoltaic power generation from crystalline silicon–based solar modules in Nigeria. Renewable Energy, 2017, vol. 101, pp. 537–549. doi: 10.1016/j.renene.2016.09.017
Monteiro H., Freire F. Life-cycle assessment of a house with alternative exterior walls: Comparison of three impact assessment methods. Energy and Buildings, 2012, vol. 47, pp. 572–583. doi: 10.1016/j.enbuild.2011.12.032
Badyda K., Krawczyk P., Pikoń K. Relative environmental footprint of waste-based fuel burned in a power boiler in the context of end-of-waste criteria assigned to the fuel. Energy, 2016, vol. 100, pp. 425–430. doi: 10.1016/j.energy.2016.02.024
Tulokhonova A.V., Ulanova O.V. Otsenka zhiznennogo tsikla integrirovannykh sistem upravleniya otkhodami [Life Cycle Analysis of integrated systems of waste management]. Irkutsk, ISTU Publ., 2014, 191 p.
Piskunov A.A., Ivanyuk I.I., Danilina E.P., Lychev A.V., Krivonozhko V.E. [A system to rate regions using the DEA methodology]. Vestnik AKSOR = AKSOR Bulletin, 2008, no. 4, pp. 24–30. (In Russ.)
Krivonozhno V.E., Safin M.M., Utkin O.B., Lychev A.V. [The EffiVision software solution to analyze the operation of complex systems]. Informatsionnye tekhnologii i vychislitel'nye sistemy = Information Technology and Computer Systems, 2005, no. 3, pp. 85–95. (In Russ.)
Mel'nikov R.M. [Developing a methodology for assessing the efficiency of scientific and innovative programs based on foreign experience]. Innovatsii = Innovations, 2016, no. 10, pp. 65–73. (In Russ.)
Zemtsov S.P., Baburin V.L. [How to evaluate the efficiency of regional innovation systems in Russia?]. Innovatsii = Innovations, 2017, no. 2, pp. 60–66. (In Russ.)
Seiford L.M., Zhu J. Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 2002, vol. 142, iss. 1, pp. 16–20.
Chung Y.H., Färe R., Grosskopf S. Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 1997, vol. 51, iss. 3, pp. 229–240.
Färe R., Grosskopf S., Pasurka C.A.Jr. Accounting for air pollution emissions in measures of State manufacturing productivity growth. Journal of Regional Science, 2001, no. 41, iss. 3, pp. 381–409.
Färe R., Grosskopf S., Hernandez-Sancho F. Environmental performance: An index number approach. Resource and Energy Economics, 2004, vol. 26, iss. 4, pp. 343–352.
Veselova K.A. [The best available technologies: Implementing a complex approach]. Ekologiya proizvodstva =The Ecology of Production, 2010, no. 12, pp. 88–90. (In Russ.)
Cooper W.W., Seiford L.M., Tone T. Introduction to Data Envelopment Analysis and Its Uses: With DEA-Solver Software and References. New York, Springer, 2006.
Cook W.D., Seiford L.M. Data Envelopment Analysis (DEA) – Thirty years on. European Journal of Operational Research, 2009, vol. 192, iss. 1, pp. 1–17. doi: 10.1016/j.ejor.2008.01.032
Wang Ke, Wei Yiming, Zhang Xian. A comparative analysis of China’s regional energy and emission performance: Which is a better way to deal with undesirable outputs? Energy Policy, 2012, vol. 46, pp. 574–584. doi: 10.1016/j.enpol.2012.04.038
Ratner S.V., Ratner P.D. [Design of the structure of regional energy system based on data envelopment model]. Russian Journal of Management, 2015, vol. 3, no. 2, pp. 159–166. (In Russ.)
Khrustalev E.Yu., Ratner P.D. [Selecting an optimal strategy for conversion of the regional energy system to low-carbon technologies]. Innovatsii = Innovations, 2015, no. 9, pp. 86–92. (In Russ.)