- Email: [email protected]
Prioritization methodology for the determination of national targets
Available online at www.sciencedirect.com
ScienceDirect Available online at www.sciencedirect.com Energy Procedia 00 (2017) 000–000
ScienceDirect
www.elsevier.com/locate/procedia
Energy Procedia 128 (2017) 215–221 www.elsevier.com/locate/procedia
International Scientific Conference “Environmental and Climate Technologies”, CONECT 2017, 10–12 May 2017, Riga, Latvia
Prioritization methodology for the determination of national targets Einars Cilinskis*, Zane Indzere, Dagnija Blumberga Institute of Energy Systems and Environment, Riga Technical University, Azenes iela 12/1, Riga, LV–1048, Latvia
Abstract National level planning documents have different goals, targets and indicators. Introduction of EU level policies require mandatory targets for climate change, energy efficiency, renewable energy and others. Sometimes the goals may be contradictive and decision makers may need to prioritize them. Prioritization methodology based on multi-criteria analysis TOPSIS method is presented in this article with the analysis example on conflicting agricultural and climate goals. This methodology may be combined with other approaches including modeling. © 2017 The Authors. Published by Elsevier Ltd. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the International Scientific Conference “Environmental and Peer review statement - Peer-review under responsibility of the scientific committee of the International Scientific Conference Climate Technologies. “Environmental and Climate Technologies”. Keywords: policy planningl climate goals; non-ETS; energy efficiency goals; multi-criteria analysis; TOPSIS
1. Introduction National development has different goals and indicators. There are global level goals; the most strategic goals are sustainable development goals [1], but there are other goals and targets set by different international agreements. There are goals and targets set at a regional level – for Latvia it is mainly at the European Union (EU) level [2] and there are goals, targets and indicators set at national level, where the main policy planning document for medium term is the National development plan [3]. The goals may be qualitative or quantitative. Quantitative goals may be binding and nonbinding. Environment and climate change sectors have a lot of global and especially EU level binding goals and targets, while there are quite few binding targets regarding economic development and social issues. Theoretically
* Corresponding author. Tel.: +371-29103435. E-mail address: [email protected] 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the International Scientific Conference “Environmental and Climate Technologies.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer review statement - Peer-review under responsibility of the scientific committee of the International Scientific Conference “Environmental and Climate Technologies". 10.1016/j.egypro.2017.09.058
216 2
Einars Cilinskis et al. / Energy Procedia 128 (2017) 215–221 Author name / Energy Procedia 00 (2017) 000–000
national level planning should integrate regional and global level goals, setting appropriate indicators and policy actions. The goals should be practically achievable with the available resources and non-contradictive. In this article, we will analyse if this is the case for Latvia, focusing on EU level climate, energy efficiency and renewable energy binding goals. These goals have been selected because they are binding and implementation of these goals will require significant resources. The main goals and targets of Latvian National development plan and interlinkage between them is presented in Fig. 1.
Fig. 1. Interlinkage of the targets and goals of the Latvian National development plan [3].
The main goal of the National development plan (NDP) -–according to the plan, economic breakthrough (faster economic development than other EU countries reaching convergence) should be achieved through measures monitored by the set of indicators in 3 main directions – 1) growth of national economy, 2) human securitability with the emphasis of reducing inequity and natural population growth 3) growth of regions. Some of NDP targets and indicators are influenced by international treaties and EU obligations, most are not. There is no separate environmental pillar in NDP, however, energy efficiency and renewable energy targets are included as sub targets for economic growth. We should also take into account that at the time the NDP was developed there were no currently developed EU 2030 targets that significantly change the situation – it may be much more difficult to implement these targets, if implementation started after 2020 when current NDP ends. Report [2] analysis the state of implementation of EU binding targets and country specific recommendations. The general conclusion is that Latvia well implements fiscal targets (one of few areas outside environmental and climate sector where are binding EU targets) but the implementation of recommendations in social, health and other sectors is poor or limited.
Einars Cilinskis et al. / Energy Procedia 128 (2017) 215–221 Author name / Energy Procedia 00 (2017) 000–000
217 3
2. National targets According to the European Union (EU) plans, Latvia needs to reduce its greenhouse gas (GHG) emissions not covered by the EU emission trading system (non-ETS) by 6 % till 2030, in comparison to 2005. According to the projections [2], there will be no problem to fulfill 2020 target (increase up to 17 % in comparison to 2005 level), however 2030 target will require additional measures, see Fig. 2. 12000
+7% vs 2005
10000
+16% vs 2005
8000 6000 4000 2000 0
2005
2010
2011
2012
2013
2014
2015
2015
2020
2025
2030
Fig. 2. Non-ETS emissions of GHG by different sectors 2005–2014, preliminary results for 2015 and projections till 2030. Information from Latvian Ministry of Environmental and Regional Development (MERD).
From Fig. 2 it can be concluded that non-ETS GHG emissions increase even faster than projected especially in the agriculture sector, however in non-ETS energy sector, emissions decrease and don’t follow the projections. Agricultural non-ETS emissions projections developed by the Ministry of Agriculture and Latvian Agricultural University predict faster increase than projected by the MERD [4]. Differences can be explained by another NDP target – the amount of agricultural land actually used for agriculture (see Fig. 3). MERD projections do not take into account its implementation because of economic reasons, while other projections do.
Fig. 3. NDP targets for the amount of agricultural land used for agriculture (utilised land) and its implementation taken from NDP indicator assessment [5].
Einars Cilinskis et al. / Energy Procedia 128 (2017) 215–221 Author name / Energy Procedia 00 (2017) 000–000
218 4
The EU energy efficiency targets are partially connected with the climate targets, if energy efficiency measures reduce fossil energy consumption in non-ETS sector there is impact on climate target. Latvian energy efficiency targets 2020 are represented in Fig. 4.
Fig. 4. EU targets and implementation measures for energy efficiency 2020 (source: Latvian ministry of Economics).
60% 50%
50% 37,6%
40%
35%
30%
40%
37%
20%
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
0%
2005
10%
2004
Share of renewable energy sources in final consumption,%
According to [2] Latvia has already reached the levels of primary and final energy consumption which are below the indicative national 2020 targets and needs to keep these levels until 2020. However, much additional effort will be needed to achieve mandatory requirements for final energy consumption. Additional mandatory EU energy efficiency requirements will be established to reach EU energy efficiency target 2030. Still there are national goals in energy efficiency sector. Space heating consumption in apartment building 2016 was 152.04 kWh/m2 per year, it decreased from 157.67 kWh/m2 in 2014 [6]. According to the long term energy strategy of Latvia, the goal is to achieve dropping specific heat energy consumption to 100 kWh/m2 per year by 2030 [7]. Latvia has mandatory renewable energy 2020 target 40 % from the energy final consumption. Latvia seems to be on track for implementation of this target see Fig. 5, but additional effort will be needed. However, Latvia is not on trach for renewable energy target in all modes of transport 10 % 2020 – according to the actual figure in 2014 was only 3.2 % [2]. There will be no mandatory EU requirements for 2030, still there are national plans to increase the share up to 50 % [6].
Fig. 5. Share of renewable energy in energy final consumption – targets and implementation (source: Latvian ministry of Economics).
Einars Cilinskis et al. / Energy Procedia 128 (2017) 215–221 Author name / Energy Procedia 00 (2017) 000–000
219 5
Energy efficiency, climate and renewable energy are not the only mandatory targets – there are EU targets or requirements on circular economy and waste targets, biodiversity, water and air quality targets implementation of which is interlink each other as well as targets mentioned in this article and also other economic and social targets. Sometimes the targets are contradictive– for example, climate targets and agricultural land actually used for agriculture target. The authors of [9] have proposed the modelling approach to link different priorities. Green economy modelling has been used in other countries as well [10, 11]. This article proposes methodology for the prioritisation of the targets using Multi-Criteria analysis (MCA). 3. Methodology The MCA method may be used in different situations, considering available time, applied methods, the amount of data and necessary analytical skills. The main feature of the MCA method is that it is based on the judgment of the decision-making team. The MCA method is based on the performance matrix in which each row describes an option and each column describes the performance of the option against the criteria. The options have different extents in which they achieve objectives. Costs and benefits usually conflict, sometimes short term benefits conflict with the long–term benefits [12]. Review of Multi-Criteria Decision-making Methods (MCDM) in energy and environmental modelling has been analysed in [13]. The review shows increased popularity of MCDM methods. Most often several MCDM methods have been used in combination with other decision-making methods. Authors of [14] have analysed 393 articles policed 2000–2014 using MCDM methods. Energy, environment and sustainability are among the areas where the MCDM methods are most often used, it is concluded that hybrid and modular methods are becoming more important. New MCDM methods are constantly developed. The authors of the review of multi criteria decision-making towards sustainable renewable energy development [15] conclude that MCDM models, involving multiple scenarios, are best suited for analysis of modern energy needs at disintegrated levels. Another review of MCDM, including the aspect of Group decision methods (GDM) [16] shows a strong relationship between the basic features of the problem situation and the method used. The TOPSIS method in comparison to other MCDM methods is easy to implement and understand; it includes the rational human choice and can be implemented without special computer programs. Authors of [17] have reviewed the use of TOPSIS applications and concluded that the classic TOPSIS can be effectively used to combine different performance criteria with a composite index to compare and rank alternatives. The extension of TOPSIS method – fuzzy TOPSIS is a solution to handle the uncertainty of data. MCDM methods can be combined with MARKAL optimisation models; the authors of [18] have used different MARKAL scenario MCDM analysis complementing the goal of cost minimization with additional social and economic criteria. MCDM methods can also be combined with system dynamics approach [19–21]. Different state targets or criteria can be divided into different areas, in our approach we used 4 different categories (see Fig. 6). Criteria ‐ aspects
Economical
Technological
inovations
productivity
investments
economic development (GDP)
Socio‐ economic
employment
Environmental
inequity
emissions
Fig. 6. General aspects of state development classified for the analysis of specific targets.
ecoefectivity
Einars Cilinskis et al. / Energy Procedia 128 (2017) 215–221 Author name / Energy Procedia 00 (2017) 000–000
220 6
TOPSIS method was used for analysis. We analysed 4 specific targets 1) climate (non-ETS target 2030); 2) Energy efficiency (including several national and EU targets for 2020 and 2030); 3) Renewable energy sources (EU targets set for Latvia 2020 and national target 2030); 4) amount of utilized agricultural land (national target 2020, according to actual implementation trajectory Fig. 3. will unlikely be implemented in time). Target 4) has been chosen, because it significantly contradicts with climate targets. 4. Results Results of the TOPSIS analysis are represented in Table 1 and Fig. 7. Table 1. Relative weights of different aspects and expert evaluation of aspects to each target in the scale from 1–5 (maximizing all scores). Aspects
Weight
Technological
Target Climate
Energy efficiency
0.3
3
3
Renewable energy sources 5
Economical
0.4
3
4
5
4
Socioeconomic
0.1
2
5
4
4
Environmental
0.2
5
4
3
3
Agriculture
Agriculture 4
0,451
Renewable energy sources
0,705
Energy efficiency
0,414
Climate
0,284 0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
Fig. 7. Results of multi criteria analysis using TOPSIS method.
The results show that to achieve other state targets we should focus on the development and promotion of renewable energy sources, while focusing specifically on climate change issues may be the least preferable option. 5. Conclusions Implementation of 2020 and especially 2030 EU climate, energy efficiency and other mandatory targets will require additional resources and measures. However, a lot of national level priorities are sometimes seen more important by the decision makers. To achieve optimal strategy, different measures can be combined, including modelling and multicriteria analysis. There may be contradicting goals and in this case these goals may need to be assessed. TOPSIS methodology is simple and good for preliminary screening. The same method may be used for preliminary assessment of different climate change mitigation measures that will be presented in our future work. Acknowledgements The work has been supported by the National Research Program “Energy efficient and low-carbon solutions for a secure, sustainable and climate variability reducing energy supply (LATENERGI)”.
Einars Cilinskis et al. / Energy Procedia 128 (2017) 215–221 Author name / Energy Procedia 00 (2017) 000–000
221 7
References [1] United Nations. Sustainable development goals. 17 goals to transform our world; 2015. Available: http://www.un.org/sustainabledevelopment/sustainable-development-goals/ [2] European Commission. Country Report Latvia 2017. SWD (2017) 79 final. Available: https://ec.europa.eu/info/sites/info/files/2017european-semester-country-report-latvia-en.pdf [3] Cross-Sectoral Coordination Centre (CCSC). National Development Plan of Latvia for 2014–2020; 2012. Available: http://www.pkc.gov.lv/images/NAP2020%20dokumenti/NDP2020_English_Final__.pdf [4] Information from the Latvian ministry of Agriculture. Available: https://www.zm.gov.lv/en/statiskas-lapas/ministry-of-agriculture-and-theeuropean-union-eu-?id=4196#jump [5] Information from the Latvian Cross Sectoral Coordination Centre. Available: https://www.pkc.gov.lv/en [6] Miezis M, Zvaigznitis K, Stancioff N, Soeftestad L. Climate change and buildings energy efficiency – the key role of residents. Environmental and Climate Technologies 2016;17:30–43. [7] Informative Report Long-Term Energy Strategy of Latvia 2030 – Competitive Energy for the Society. Available: http://www.vvc.gov.lv/advantagecms/LV/tulkojumi/dokumenti.html;jsessionid=C43447540FA60E91B74B52DA7357E910?folder=%2Fdo cs%2FLRTA%2FCiti%2F¤tPage=4 [8] Saeima of the Republic of Latvia. Sustainable Development Strategy of Latvia until 2030; 2010. Available: http://www.pkc.gov.lv/images/LV2030/LIAS_2030_en.pdf [9] Cilinskis E, Vesere R, Blumberga A, Blumberga D. Sustainable National Policy Planning with conflicting goals. Energy Procedia 2016;113:259–264. [10] Bassi AM. Moving Towards Integrated Policy Formulation and Evaluation: The Green Economy Model. Environmental and Climate Technologies 2015;16:5–19 [11] Mercure JF, Pollitt H, Bassi AM, Vinuales JE, Edwards NR. Modelling complex systems of heterogeneous agents to better design sustainability transitions policy. Global environmental change 2016;37:102–115. [12] Department for Communities and Local Government: London. Multi-criteria analysis: a manual; 2009. Available: http://eprints.lse.ac.uk/12761/1/Multi-criteria_Analysis.pdf [13] Wang Q, Poh KL. A survey of integrated decision analysis in energy and environmental modeling. Energy 2014;77:691–702. [14] Mardani A, Jusoh A, Nor KMD, Khalifah Z, Zakwan N, Valipour A. Multiple criteria decision-making techniques and their applications – A review of the literature from 2000 to 2014. Economic Research–Ekonomska Istrazivanja 2015;28(1):516–571. [15] Kumar A, Sah B, Singh AR, Deng Y, He X, Kumar P, Bansal RC. A review of multi criteria decision-making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews 2017;69(1):596–609. [16] Diaz-Balteiro L, Gonzalez-Pachon J, Romero C. Measuring systems sustainability with multi-criteria methods: A critical review. European Journal of Operational Research 2017;258(2):607–616. [17] Zavadskas EK, Mardani A, Turskis Z, Jusoh A, Nor KM. Development of TOPSIS method to solve complicated decision-making problems - an overview on developments from 2000 to 2015; International Journal of Information Technology and Decision making 2016;15(3):645–682. [18] Shmelev S, van den Bergh JCJM. Optimal diversity of renewable energy alternatives under multiple criteria: An application to the UK. Renewable and Sustainable Energy Reviews 2016;60:679–691. [19] Ziemele J, Pakere I, Talcis N, Blumberga D. Multi-criteria analysis of district heating systems in Baltic States. Paper presented at the Energy Procedia 2014;61:2172–2175. [20] Ziemele J, Pakere I, Blumberga D. The future competitiveness of the non-emissions trading scheme district heating systems in the Baltic States. Applied Energy 2016; 162:1579–1585. [21] Ziemele J, Gravelsins A, Blumberga A, Vigants G, Blumberga D. System dynamics model analysis of pathway to 4th generation district heating in Latvia. Energy 2016;110:85–94.
ScienceDirect Available online at www.sciencedirect.com Energy Procedia 00 (2017) 000–000
ScienceDirect
www.elsevier.com/locate/procedia
Energy Procedia 128 (2017) 215–221 www.elsevier.com/locate/procedia
International Scientific Conference “Environmental and Climate Technologies”, CONECT 2017, 10–12 May 2017, Riga, Latvia
Prioritization methodology for the determination of national targets Einars Cilinskis*, Zane Indzere, Dagnija Blumberga Institute of Energy Systems and Environment, Riga Technical University, Azenes iela 12/1, Riga, LV–1048, Latvia
Abstract National level planning documents have different goals, targets and indicators. Introduction of EU level policies require mandatory targets for climate change, energy efficiency, renewable energy and others. Sometimes the goals may be contradictive and decision makers may need to prioritize them. Prioritization methodology based on multi-criteria analysis TOPSIS method is presented in this article with the analysis example on conflicting agricultural and climate goals. This methodology may be combined with other approaches including modeling. © 2017 The Authors. Published by Elsevier Ltd. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the International Scientific Conference “Environmental and Peer review statement - Peer-review under responsibility of the scientific committee of the International Scientific Conference Climate Technologies. “Environmental and Climate Technologies”. Keywords: policy planningl climate goals; non-ETS; energy efficiency goals; multi-criteria analysis; TOPSIS
1. Introduction National development has different goals and indicators. There are global level goals; the most strategic goals are sustainable development goals [1], but there are other goals and targets set by different international agreements. There are goals and targets set at a regional level – for Latvia it is mainly at the European Union (EU) level [2] and there are goals, targets and indicators set at national level, where the main policy planning document for medium term is the National development plan [3]. The goals may be qualitative or quantitative. Quantitative goals may be binding and nonbinding. Environment and climate change sectors have a lot of global and especially EU level binding goals and targets, while there are quite few binding targets regarding economic development and social issues. Theoretically
* Corresponding author. Tel.: +371-29103435. E-mail address: [email protected] 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the International Scientific Conference “Environmental and Climate Technologies.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer review statement - Peer-review under responsibility of the scientific committee of the International Scientific Conference “Environmental and Climate Technologies". 10.1016/j.egypro.2017.09.058
216 2
Einars Cilinskis et al. / Energy Procedia 128 (2017) 215–221 Author name / Energy Procedia 00 (2017) 000–000
national level planning should integrate regional and global level goals, setting appropriate indicators and policy actions. The goals should be practically achievable with the available resources and non-contradictive. In this article, we will analyse if this is the case for Latvia, focusing on EU level climate, energy efficiency and renewable energy binding goals. These goals have been selected because they are binding and implementation of these goals will require significant resources. The main goals and targets of Latvian National development plan and interlinkage between them is presented in Fig. 1.
Fig. 1. Interlinkage of the targets and goals of the Latvian National development plan [3].
The main goal of the National development plan (NDP) -–according to the plan, economic breakthrough (faster economic development than other EU countries reaching convergence) should be achieved through measures monitored by the set of indicators in 3 main directions – 1) growth of national economy, 2) human securitability with the emphasis of reducing inequity and natural population growth 3) growth of regions. Some of NDP targets and indicators are influenced by international treaties and EU obligations, most are not. There is no separate environmental pillar in NDP, however, energy efficiency and renewable energy targets are included as sub targets for economic growth. We should also take into account that at the time the NDP was developed there were no currently developed EU 2030 targets that significantly change the situation – it may be much more difficult to implement these targets, if implementation started after 2020 when current NDP ends. Report [2] analysis the state of implementation of EU binding targets and country specific recommendations. The general conclusion is that Latvia well implements fiscal targets (one of few areas outside environmental and climate sector where are binding EU targets) but the implementation of recommendations in social, health and other sectors is poor or limited.
Einars Cilinskis et al. / Energy Procedia 128 (2017) 215–221 Author name / Energy Procedia 00 (2017) 000–000
217 3
2. National targets According to the European Union (EU) plans, Latvia needs to reduce its greenhouse gas (GHG) emissions not covered by the EU emission trading system (non-ETS) by 6 % till 2030, in comparison to 2005. According to the projections [2], there will be no problem to fulfill 2020 target (increase up to 17 % in comparison to 2005 level), however 2030 target will require additional measures, see Fig. 2. 12000
+7% vs 2005
10000
+16% vs 2005
8000 6000 4000 2000 0
2005
2010
2011
2012
2013
2014
2015
2015
2020
2025
2030
Fig. 2. Non-ETS emissions of GHG by different sectors 2005–2014, preliminary results for 2015 and projections till 2030. Information from Latvian Ministry of Environmental and Regional Development (MERD).
From Fig. 2 it can be concluded that non-ETS GHG emissions increase even faster than projected especially in the agriculture sector, however in non-ETS energy sector, emissions decrease and don’t follow the projections. Agricultural non-ETS emissions projections developed by the Ministry of Agriculture and Latvian Agricultural University predict faster increase than projected by the MERD [4]. Differences can be explained by another NDP target – the amount of agricultural land actually used for agriculture (see Fig. 3). MERD projections do not take into account its implementation because of economic reasons, while other projections do.
Fig. 3. NDP targets for the amount of agricultural land used for agriculture (utilised land) and its implementation taken from NDP indicator assessment [5].
Einars Cilinskis et al. / Energy Procedia 128 (2017) 215–221 Author name / Energy Procedia 00 (2017) 000–000
218 4
The EU energy efficiency targets are partially connected with the climate targets, if energy efficiency measures reduce fossil energy consumption in non-ETS sector there is impact on climate target. Latvian energy efficiency targets 2020 are represented in Fig. 4.
Fig. 4. EU targets and implementation measures for energy efficiency 2020 (source: Latvian ministry of Economics).
60% 50%
50% 37,6%
40%
35%
30%
40%
37%
20%
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
0%
2005
10%
2004
Share of renewable energy sources in final consumption,%
According to [2] Latvia has already reached the levels of primary and final energy consumption which are below the indicative national 2020 targets and needs to keep these levels until 2020. However, much additional effort will be needed to achieve mandatory requirements for final energy consumption. Additional mandatory EU energy efficiency requirements will be established to reach EU energy efficiency target 2030. Still there are national goals in energy efficiency sector. Space heating consumption in apartment building 2016 was 152.04 kWh/m2 per year, it decreased from 157.67 kWh/m2 in 2014 [6]. According to the long term energy strategy of Latvia, the goal is to achieve dropping specific heat energy consumption to 100 kWh/m2 per year by 2030 [7]. Latvia has mandatory renewable energy 2020 target 40 % from the energy final consumption. Latvia seems to be on track for implementation of this target see Fig. 5, but additional effort will be needed. However, Latvia is not on trach for renewable energy target in all modes of transport 10 % 2020 – according to the actual figure in 2014 was only 3.2 % [2]. There will be no mandatory EU requirements for 2030, still there are national plans to increase the share up to 50 % [6].
Fig. 5. Share of renewable energy in energy final consumption – targets and implementation (source: Latvian ministry of Economics).
Einars Cilinskis et al. / Energy Procedia 128 (2017) 215–221 Author name / Energy Procedia 00 (2017) 000–000
219 5
Energy efficiency, climate and renewable energy are not the only mandatory targets – there are EU targets or requirements on circular economy and waste targets, biodiversity, water and air quality targets implementation of which is interlink each other as well as targets mentioned in this article and also other economic and social targets. Sometimes the targets are contradictive– for example, climate targets and agricultural land actually used for agriculture target. The authors of [9] have proposed the modelling approach to link different priorities. Green economy modelling has been used in other countries as well [10, 11]. This article proposes methodology for the prioritisation of the targets using Multi-Criteria analysis (MCA). 3. Methodology The MCA method may be used in different situations, considering available time, applied methods, the amount of data and necessary analytical skills. The main feature of the MCA method is that it is based on the judgment of the decision-making team. The MCA method is based on the performance matrix in which each row describes an option and each column describes the performance of the option against the criteria. The options have different extents in which they achieve objectives. Costs and benefits usually conflict, sometimes short term benefits conflict with the long–term benefits [12]. Review of Multi-Criteria Decision-making Methods (MCDM) in energy and environmental modelling has been analysed in [13]. The review shows increased popularity of MCDM methods. Most often several MCDM methods have been used in combination with other decision-making methods. Authors of [14] have analysed 393 articles policed 2000–2014 using MCDM methods. Energy, environment and sustainability are among the areas where the MCDM methods are most often used, it is concluded that hybrid and modular methods are becoming more important. New MCDM methods are constantly developed. The authors of the review of multi criteria decision-making towards sustainable renewable energy development [15] conclude that MCDM models, involving multiple scenarios, are best suited for analysis of modern energy needs at disintegrated levels. Another review of MCDM, including the aspect of Group decision methods (GDM) [16] shows a strong relationship between the basic features of the problem situation and the method used. The TOPSIS method in comparison to other MCDM methods is easy to implement and understand; it includes the rational human choice and can be implemented without special computer programs. Authors of [17] have reviewed the use of TOPSIS applications and concluded that the classic TOPSIS can be effectively used to combine different performance criteria with a composite index to compare and rank alternatives. The extension of TOPSIS method – fuzzy TOPSIS is a solution to handle the uncertainty of data. MCDM methods can be combined with MARKAL optimisation models; the authors of [18] have used different MARKAL scenario MCDM analysis complementing the goal of cost minimization with additional social and economic criteria. MCDM methods can also be combined with system dynamics approach [19–21]. Different state targets or criteria can be divided into different areas, in our approach we used 4 different categories (see Fig. 6). Criteria ‐ aspects
Economical
Technological
inovations
productivity
investments
economic development (GDP)
Socio‐ economic
employment
Environmental
inequity
emissions
Fig. 6. General aspects of state development classified for the analysis of specific targets.
ecoefectivity
Einars Cilinskis et al. / Energy Procedia 128 (2017) 215–221 Author name / Energy Procedia 00 (2017) 000–000
220 6
TOPSIS method was used for analysis. We analysed 4 specific targets 1) climate (non-ETS target 2030); 2) Energy efficiency (including several national and EU targets for 2020 and 2030); 3) Renewable energy sources (EU targets set for Latvia 2020 and national target 2030); 4) amount of utilized agricultural land (national target 2020, according to actual implementation trajectory Fig. 3. will unlikely be implemented in time). Target 4) has been chosen, because it significantly contradicts with climate targets. 4. Results Results of the TOPSIS analysis are represented in Table 1 and Fig. 7. Table 1. Relative weights of different aspects and expert evaluation of aspects to each target in the scale from 1–5 (maximizing all scores). Aspects
Weight
Technological
Target Climate
Energy efficiency
0.3
3
3
Renewable energy sources 5
Economical
0.4
3
4
5
4
Socioeconomic
0.1
2
5
4
4
Environmental
0.2
5
4
3
3
Agriculture
Agriculture 4
0,451
Renewable energy sources
0,705
Energy efficiency
0,414
Climate
0,284 0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
Fig. 7. Results of multi criteria analysis using TOPSIS method.
The results show that to achieve other state targets we should focus on the development and promotion of renewable energy sources, while focusing specifically on climate change issues may be the least preferable option. 5. Conclusions Implementation of 2020 and especially 2030 EU climate, energy efficiency and other mandatory targets will require additional resources and measures. However, a lot of national level priorities are sometimes seen more important by the decision makers. To achieve optimal strategy, different measures can be combined, including modelling and multicriteria analysis. There may be contradicting goals and in this case these goals may need to be assessed. TOPSIS methodology is simple and good for preliminary screening. The same method may be used for preliminary assessment of different climate change mitigation measures that will be presented in our future work. Acknowledgements The work has been supported by the National Research Program “Energy efficient and low-carbon solutions for a secure, sustainable and climate variability reducing energy supply (LATENERGI)”.
Einars Cilinskis et al. / Energy Procedia 128 (2017) 215–221 Author name / Energy Procedia 00 (2017) 000–000
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