Environmental Effects Evaluation of Innovative Renewable Energy Projects

  • Anastasia A. SALNIKOVA Kuban State University, Russian Federation
  • Andrej S. SLAVJANOV Bauman State Technical University, Russian Federation
  • Evgenii Yu. KHRUSTALEV Central Economics and Mathematics Institute Russian Academy of Sciences, Russian Federation
  • Oleg E. KHRUSTALEV Central Economics and Mathematics Institute Russian Academy of Sciences, Russian Federation


Developing sustainable renewable energy projects involves complex decision-making processes. At present time planning and developing of renewable energy projects across the globe imply calculation and consideration of negative environmental effects at all stages of energy project life cycle. The aim of the paper is to develop an environmental effects evaluation methodology based on ecological impact categories through all the stages of lifecycle of renewable energy technologies. We used data envelopment analysis to calculate the efficiency score for each renewable energy technology. EcoInvent database has been chosen as a source of eco-indicators. We suppose the efficiency ratio will remain unchanged when transferring estimates of the life cycle of renewable energy facilities to another territory. This allows us to use data obtained in other regions of the world to extrapolate comparative assessments and make the deliberate choice of the most environmentally preferable technology. The input-oriented DEA modelling has demonstrated geothermal and biogas technologies are the most preferable from an environmental point of view with the highest possible score. The least effective technologies are both modifications of PV with the minimum efficiency score. The results of the presented work indicated that DEA showed great promise to be an effective evaluating tool for future analysis on energy policy issues.


[1] Acheampong, A. O. 2018. Economic growth, CO2 emissions and energy consumption: What causes what and where? Energy Economics, 74:677-692. DOI: https://doi.org/10.1016/j.eneco.2018.07.022
[2] Lewin, A.Y., and Lovell C.A.K.1995. Productivity Analysis: Parametric and Non-Parametric Applications. European Journal of Operational Research, 80(3): 451-705.
[3] Djordjević, B., Krmaca, E., and Mlinarić, T.J. 2018. Non-radial DEA model: A new approach to evaluation of safety at railway level crossings. Safety Science, 103: 234-246. DOI: https://doi.org/10.1016/j.ssci.2017.12.001
[4] Campos-Guzmán V., et al. 2019. “Life Cycle Analysis with Multi-Criteria Decision Making: A review of approaches for the sustainability evaluation of renewable energy technologies” Renewable and Sustainable Energy Reviews, 104: 343-366. DOI: https://doi.org/10.1016/j.rser.2019.01.031
[5] Charnes, W., Cooper, W., and Rhodes, E. 1978. Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6): 429-444.
[6] Dubey, S., Jadhav, N.Y. and Zakirova, B. 2013. Socio-Economic and Environmental Impacts of Silicon Based Photovoltaic (PV) Technologies. Energy Procedia, 33: 322-334. DOI: 10.1016/j.egypro.2013.05.073
[7] Lotfi F.H., et al. 2010. Relationship between MOLP and DEA based on output-orientated CCR dual model. Expert Systems with Applications, 37: 4331–4336. DOI: 10.1016/j.eswa.2009.11.066
[8] Halkos, G.E., and Polemis, M.L. 2018. The impact of economic growth on environmental efficiency of the electricity sector: A hybrid window DEA methodology for the USA. Journal of Environmental Management. 211(1): 334-346. DOI: https://doi.org/10.1016/j.jenvman.2018.01.067
[9] Lee, H., and Choi, Y.2018. Greenhouse gas performance of Korean local governments based on non-radial DDF. Technological Forecasting and Social Change. 135: 13-21 DOI: https://doi.org/10.1016/j.techfore.2018.07.011
[10] Iosifov V.V., et al. 2017. The Problem of Harmonizing the Environmental Priorities of Electricity Generating Companies and Regional Socio-economic Systems: DEA-based Approach. International Journal of Energy Economics and Policy, 7 (5): 159-165.
[11] Jebali, E., Essid, Hé., and Khraief, N. 2017. The analysis of energy efficiency of the Mediterranean countries: A two-stage double bootstrap DEA approach. Energy, 134: 991-1000. DOI: https://doi:10.1016/j.energy.2017.06.063
[12] Hu, J.L., Wang, S.H., and Yeh, F.Yu. 2006. Total-factor water efficiency of regions in China. Resources Policy, 31(4): 217-230.
[13] Jin, J., Zhou, D., and Zhou, P. 2014. Measuring environmental performance with stochastic environmental DEA: The case of APEC. Economic Modelling, 38: 80-86. DOI: https://doi.org/10.1016/j.econmod.2013.12.017
[14] Huanga, J., Dua, D., and Hao, Yu. 2017. The driving forces of the change in China's energy intensity: An empirical research using DEA-Malmquist and spatial panel estimations. Economic Modelling, 65: 41-50. DOI: http://dx.doi.org/10.1016/j.econmod.2017.04.027
[15] Kuhn, L., Balezentis, T., Hou, L., and Wang, D. 2018. Technical and environmental efficiency of livestock farms in China: A slacks-based DEA approach. China Economic Review Available online 23 August In Press, Corrected Proof. DOI: https://doi.org/10.1016/j.chieco.2018.0 8.009
[16] Tavana, M., et al. 2018. Efficiency decomposition and measurement in two-stage fuzzy DEA models using a bargaining game approach. Computers & Industrial Engineering, 118: 394-408. DOI: https://doi.org/10.1016/j.cie.2018.03.010
[17] Toloo, M., and Nalchigar, S. 2009. A new integrated DEA model for finding most BCC-efficient DMU. Applied Mathematical Modelling, 33(1): 597-604. DOI: 10.1016/j.apm.2008.02.001
[18] Meng, W., et al. 2008. Two-level DEA approaches in research evaluation. Omega, 36(6): 950-957
[19] Nian, V., Liu, Y., and Zhong, S. 2019. “Life cycle cost-benefit analysis of offshore wind energy under the climatic conditions in Southeast Asia – Setting the bottom-line for deployment”. Applied Energy, 233–234: 1003-1014. DOI: https://doi.org/10.1016/j.apenergy.2018.10.042
[20] Nizhegorodtsev, R.M., and Ratner, S.V. 2016. Trends in the development of industrially assimilated renewable energy: the problem of resource restrictions. Thermal Engineering, 63(3): 197-207. DOI: 10.1134/S0040601516030083
[21] Ratner, S.V., and Iosifov, V.V. 2017. Strategizing for solar energy development in Russia subject for the environmental impact. Economic Analysis: Theory and Practice, 8: 1522-1540 [in Russian] DOI: https://doi.org/10.24891/ea.16.8.1522
[22] Ratner, S.V., Iosifov, V.V., and Ratner, M.D. 2018. Optimization of the regional energy system with high potential of use of bio-waste and bioresources as energy sources with respect to ecological and economic parameters: The Krasnodar Krai case. Regional Economics: Theory and Practice, 16 (12): 2383–2398 [In Russian]. DOI: https://doi.org/10.24891/re.16.12.2383
[23] Ratner, S.V., and Klochkov, V.V. 2017. Scenario Forecast for Wind Turbine Manufacturing in Russia. International Journal of Energy Economics and Policy, 7 (2): 144-151.
[24] Ratner, S.V., and Nizhegorodtsev, R.M. 2017. Analysis of renewable energy projects’ implementation in Russia. Thermal Engineering, 64(6): 429-436. DOI:10.1134/S0040601517060052
[25] Ratner, S.V., and Ratner, P.D. 2017. Developing a Strategy of Environmental Management for Electric Generating Companies Using DEA-Methodology. Advances in Systems Science and Applications, 17(4): 78-92. DOI: https://doi.org/10.25728/assa.2017.17.4.521
[26] Ratner, S.V., and Ratner. P.D. 2016. Regional Energy Efficiency Programs in Russia: The Factors of Success. Region, 3(1): 68-85.
[27] Ratner, S., and Zaretskaya, M. 2018. Forecasting the Ecology Effects of Electric Cars Deployment in Krasnodar Region: Learning Curves Approach. Journal of Environmental Management and Tourism, 9(1): 82-94. DOI: https://doi.org/10.14505//jemt.v9.1(25).11
[28] Färe, R., and Grosskopf, S. 2004. Modeling undesirable factors in efficiency evaluation: Comment. European Journal of Operational Research, 157(1): 242-245.
[29] Färe, R., Grosskopf, S., Hayes, K.J., and Margariti, D. 2008. Estimating demand with distance functions: Parameterization in the primal and dual. Journal of Econometrics, 147(2): 266-274.
[30] Badiezadeh, T., Saen, R.F., and Samavati, T. 2018. Assessing sustainability of supply chains by double frontier network DEA: A big data approach Computers & Operations Research, 98: 284-290 DOI: https://doi.org/10.1016/j.cor.2017.06.003
[31] Yeh, T.I., Chen, T.I., and Lai, P.Y. 2010. A comparative study of energy utilization efficiency between Taiwan and China. Energy Policy, 38(5): 2386-2394. DOI: https://doi.org/10.1016/j.enpol.2009.12.030
[32] Cai, Y., Sam, C.Y, and Chang, T. 2018. Nexus between clean energy consumption, economic growth and CO2 emissions. Journal of Cleaner Production, 182: 1001-1011. DOI: https://doi.org/10.1016/j.jclepro.2018.02.035
How to Cite
SALNIKOVA, Anastasia A. et al. Environmental Effects Evaluation of Innovative Renewable Energy Projects. Journal of Environmental Management and Tourism, [S.l.], v. 10, n. 1, p. 100-108, may 2019. ISSN 2068-7729. Available at: <https://journals.aserspublishing.eu/jemt/article/view/3194>. Date accessed: 27 may 2022. doi: https://doi.org/10.14505//jemt.v10.1(33).10.