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Impact of economical mechanisms on CO2 emissions from non-ETS district heating in Latvia using system dynamic approach

Impact of economical mechanisms on CO2 emissions from non-ETS district heating in Latvia using... Int J Energy Environ Eng (2018) 9:111–121 https://doi.org/10.1007/s40095-017-0241-9 ORIGINAL RESEARCH Impact of economical mechanisms on CO emissions from non- ETS district heating in Latvia using system dynamic approach 1 1 1 1 • • • • Jelena Ziemele Einars Cilinskis Gatis Zogla Armands Gravelsins 1 1 Andra Blumberga Dagnija Blumberga Received: 15 December 2016 / Accepted: 3 June 2017 / Published online: 14 July 2017 The Author(s) 2017. This article is an open access publication Abstract A system dynamics modeling approach was used Abbreviations to analyze the impact of economical mechanisms on CO T Tariff of thermal energy for the respective 2 i emissions from the Latvian district heating system that is technology i, €/MWh not covered by the European Union (EU) Emission Trading FC Fixed costs for the production, tariff, €/year System (non-ETS). Three policy instruments were included T Distribution tariff, €/MWh tri in the system dynamic model: carbon tax, subsidies for T Production tariff for the respective prodi solar technologies, and funding for energy-efficient build- technology i, €/MWh ing renovations with the aim to decrease energy con- VC Variable costs of the production tariff, €/year sumption. Eight development scenarios were examined, T Sales tariff, €/MWh 3i taking into account different policy mixes for the transition Q Produced amount of heat for the respective prodi of the heat network to the low-temperature regime. The technology i, MWh/year heat tariff was used as the main indicator to determine the i Type of selected technology pace and structure of the technology change. The existing Stock The stock level at time t natural gas technologies and three renewable energy tech- Flow The flow rate influencing the stock during the ðt;tdtÞ nologies (biomass combustion equipment, heat pump, and time period from (t - dt)to t solar collectors with accumulation) were included in the Stock The stock level at the time (t - dt), the initial ðtdtÞ model. Modeling results show substantial CO reduction stock potential; however, the results are highly dependent on the dt The time interval over which the equation applied financial instruments. It is recommended to apply a spans policy mix, including all the proposed policy instruments— E CO emission factor, tCO /MWh CO2 2 2 carbon tax, subsidies for solar technologies, and funding Q Amount of heat produced, MWh per years for energy-efficient renovation. g Efficiency coefficient GHG Greenhouse gas Keywords Fourth generation district heating  Carbon tax  EU The European Union System dynamics modeling  Renewable energy  ETS The emissions trading system Sustainable energy  Non-ETS emissions  Latvia non-ETS Not covered by Emission Trading System GDP Gross domestic product DH District heating 4GDH Fourth generation district heating SD System dynamic & Jelena Ziemele HOB Heat only boiler Jelena.Ziemele@rtu.lv M€ Million euro COP Coefficient of performance Institute of Energy Systems and Environment, Riga R Reinforcing Technical University, Azenes iela 12/1, Riga LV-1048, B Balancing Latvia 123 112 Int J Energy Environ Eng (2018) 9:111–121 1 Sc Scenario 1 6% from 2005 levels until 2030. According to the current 2 Sc Scenario 2 estimates [13], non-ETS emissions will increase by 7%. 3 Sc Scenario 3 System dynamics modeling results show that under the 4 Sc Scenario 4 existing policy regime, GHG emissions may by 2030 5 Sc Scenario 5 increase by 19% above the 2005 level [14]. Modeling with 6 Sc Scenario 6 linear programming optimization MARKAL, Latvia’s 7 Sc Scenario 7 model predicts that taking into account national economic 8 Sc Scenario 8 development forecasts comprising the existing GHG emission reduction policies and measures, emissions in 2030 may increase significantly (up to 26.5%) compared to Introduction 2005 [15]. Investigations [14–17] show that in the agri- cultural sector, it may be difficult or impossible to reduce In 2015, world leaders concluded an agreement to keep the non-ETS emissions below the 2005 level. It may mean that average global temperature increase well below the 2 C those emissions will rather increase, which means that preindustrial levels, with the aim to limit the increase to other sectors will need to achieve more substantial 1.5 C. According to the Paris Agreement, all parties have reductions. committed to making individual voluntary pledges to According to [18], the key focus on non-ETS emissions contribute to the global goal. The Paris Agreement came reduction should be placed on: (1) improving energy effi- into force on November 4, 2016. The EU is one of the ciency (for production, transmission, and end-use); (2) biggest greenhouse gas (GHG) emitters; however, the sit- reducing the peak load in the electricity distribution sys- uation differs in the 28 member states [1]. The initial tem; (3) increasing the use of biomass and biogas co- commitment of the EU is to reduce GHG emissions at least generation systems as well as biogas in the transport sector; 40% (from 1990 levels) by 2030. The EU energy climate (4) increasing the use of renewable energy in district and policy is considered to be a good example to follow [2, 3]. individual heating; and (5) a wider use of other renewable The EU has a comprehensive GHG reduction strategy sources, including wind energy. [4]. The EU emission trading system (ETS) is the key tool Different modeling approaches may be used for calcu- for reducing greenhouse gas emissions from large-scale lating the impact of climate mitigation measures and for a facilities of the power production and industrial sectors, as broader approach—green economy models including well as the aviation sector. The ETS covers about 45% of econometrics—measuring the relation between two or the EU’s GHG emissions. Non-ETS sectors account for more variables, running statistical analysis of historical some 55% of total EU emissions. These include housing, data and finding the correlation between specific selected non-ETS energy, agriculture, waste, and transport (ex- variables, optimization—generating a statement of the best cluding aviation). EU countries have taken on binding way to accomplish goals, and system dynamics—tools that annual targets to 2020 to cut emissions in these sectors (as provide information on what would happen, where a policy compared to 2005) according to the Effort Sharing Deci- is implemented at a specific point in time and within a sion [5] and draft binding targets for 2030. It is thought that specific context [19]. with early action, the EU can get comparative advantages District heating (DH) is one of the non-ETS emission with other countries that start the implementation process sources. It plays a significant role in countries, where it is later [6]. Modeling results show that the influence of widely used [20]. The fourth generation district heating reaching EU 2030 (as well as 2050) targets may be limited (4GDH) is a new paradigm of DH system moving to zero- regarding GDP and positive employment levels [7]. A new emission level [20]. investigation shows that the influence of anthropogenic In sum, the main aim of this investigation is to evaluate GHG emissions may be significantly greater than previ- how different mechanisms of CO emissions influence non- ously estimated [8]. If these results are confirmed, an ETS DH system development with the goal of moving additional urgent global action might be necessary. towards the zero-emission level and achieving 4GDH Bioenergy will pay a substantial role in reaching EU cli- system. Using the system dynamic (SD) model, policy mate—energy targets [9, 10]. instruments are integrated to determine their correlation to The Latvian non-ETS sector is large compared to other each other with a view to identifying the most efficient way EU countries—79.3% of total emissions (2014) [11]. That of moving towards a 4GDH system. Different scenarios are means—more action should be made at the national level. formed to identify the most efficient combinations of pol- The new draft regulation [12] requires a reduction of up to icy instruments. 123 Int J Energy Environ Eng (2018) 9:111–121 113 Background information on case study The non-ETS DH system in Latvia The DH system is historically well-developed in Latvia; some 67% of the population is connected to the DH system [21]. The structure of DH consumption has not changed 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 significantly in recent years. Space heating consumes Renewable energy share, % Fosil fuel share share, % 65–70%, and hot water preparation 30–35% of the total thermal energy used. 71.1% of the total final consumption Fig. 1 Heat production share at DH system in Latvia [24] of district heating was directed to households; 25.6% for services; 2.2% for industry and construction, and 1.1% for apartment buildings yearly. According to this information, agriculture (2013) [22]. Historically, the vast majority of space heating consumption in 2016 is 152.04 kWh/m per DH operators are non-ETS sector participants (see Table 1) year. This represents a decrease from 157.67 kWh/m in [23]. 2014. There is a limited success in renovating older Only 20 Latvian operators are taking part in the apartment buildings because of legal problems and a 2013–2020 stage of the ETS. This means that the operators shortage of resources [25]. of the non-ETS DH systems will have reduced their Since the amount of energy produced in non-ETS DH emissions, and ideally, they should conform to the princi- systems is directly related to energy consumption in ples of the fourth generation of the DH system. The total buildings, it was assumed in this study that the rate of thermal energy produced in 2013 was 6.65 TWh, 1.79 TWh change of energy produced will be the same as the rate of of this was produced by heat only boiler (HOB) technology change of energy consumption in apartment buildings. [24]. The rate of change of energy consumption for heating Two fuels dominate in the Latvian DH system: natural apartment buildings was calculated based on the existing gas and biomass. The share of thermal energy produced energy consumption in apartment buildings and the from biomass has begun to increase only in last few years expected investments in building renovation. (see Fig. 1)[24]. It was assumed that apartment buildings will be reno- Systems operate within the second and third generation vated only if co-funding for renovation is received, con- temperature regimes, respectively, 110/70 or 90/60 (sup- sidering that there were no new renovation projects after ply/return). Heat production 1.79 TWh per year by pro- the middle of 2015 when EU co-funding became portion 30/70% (biomass/natural gas) was used as the unavailable. initial data for modeling. The available co-funding for apartment building reno- vation for the new EU funds programming period is 156.3 Energy consumption in buildings million EUR. On average, co-funding makes up 43% of total building renovation costs. This means that the total The main consumers for the DH non-ETS sector are investment for apartment building renovation can be apartment buildings. Pursuant to the Latvian regulation on assumed to be 363.5 million EUR. This investment must be building energy performance, the Ministry of Economics spent by 2022, which means a yearly investment of 60.6 updates certification space heating energy consumption in million EUR. The average cost of building renovation Table 1 Number of heat plants Heat capacity Heat plants, numbers Installed heat capacity, MW and installed capacity at DH system in Latvia [23] 2012 2013 2014 2012 2013 2014 Total 308 316 311 2135.3 1850.3 1834.2 B0.2 MW 57 66 69 7.7 8.9 9.2 0.2 \ P B 0.5 MW 42 45 45 13.9 14.8 15.0 0.5 \ P B 1 MW 41 41 39 31.6 31.8 29.8 1 \ P B 5 MW 104 109 104 265.0 280.3 268.9 5 \ P B 20 MW 44 38 37 409.3 364.3 375.1 20\ P B50 MW 11 10 10 333.5 299.4 276.1 \50 MW 9 7 7 1074.3 850.8 860.1 Heat production share, % 114 Int J Energy Environ Eng (2018) 9:111–121 (according to the results of previous EU funding period) is taken into account when developing a new model, to 2 2 120 EUR/m . This means that 505 thousand m of apart- acquire correct and reliable results [32]: ment buildings can be renovated each year. In total, there • stocks, flows and feedback loops; are 61 million m of households in Latvia [25]. It was • precisely defined boundaries of the system; assumed that after building renovation, the average space • causal relationships, not correlations. heating consumption will be 70 kWh/m , which is the level of heat consumption in those apartment buildings in Latvia In the previous research of the authors [30], a conceptual that have already been renovated. It was assumed that after framework of the non-ETS DH model (Fig. 2) was devel- oped using the five main steps of SD—problem formula- 2022 when EU co-funding for apartment building renova- tion ends, energy consumption will decrease at the same tion, creating a dynamic hypothesis, model formulation and rate as during the period 2017–2022, assuming that other simulation, model testing, and scenario simulation. economical mechanisms promoting renovation will be Within this research, various scenarios for CO reduc- introduced. tion using different methods were examined: CO tax, The goal of the long-term energy strategy of Latvia is to subsidies for renewable energy technologies and grants for reduce specific heat consumption to 100 kWh/m per year conservation measures in buildings. These measures were by 2030 [26]. In this scenario, it is assumed that sufficient applied separately as well as in different combinations. In amount of economical mechanisms for reaching this goal this research, economic indicators in the context of the reduced CO emissions, which describes the sustainability will have been introduced. of the scenarios, were also examined. CO tax policy and other economical instruments The reduction of CO emissions (E, tCO per year), 2 2 2 in Latvia achieved by switching from fossil fuel technologies to renewable energy technologies in heat production, was Latvia has applied a carbon tax for non-ETS emissions in calculated using the following formula: the energy sector; the tax is relatively small, 2.85 EUR/t in E ¼ E : ð1Þ CO 2014, 3.5 EUR/t 2015–2016, and 4.50 EUR/t beginning in 2 2017. One of the arguments is that tax should not differ The 4GDH model was developed with the program significantly from the carbon price in the EU ETS system. ‘‘Powersim Studio 8’’. The model used in this research is Still, the Latvian non-ETS tax rate is one of the lowest in an extended version of the authors’ previous research [30]. comparison with other countries, including many EU The previous model was built to predict how the future DH countries [27]. There is an assumption that the price of system will look. Four different technologies were chosen carbon in the form of tax or ETS system should be at least for this purpose and they form the main stocks of the 20 EUR/t [28, 29]. The authors of [29] even discuss model—natural gas boilers and biomass boilers which are options about simultaneously applying a carbon tax and an already used in DH system as well as solar collectors with ETS system. According to [27], a realistic carbon tax of seasonal storage and heat pumps, which are taken as around 60 USD/t (55 EUR/t) may be set. plausible future technologies [30]. The initial share of The proceeds from the ETS auctions, according to the installed capacity of natural gas combustion technology in Latvian legislation, should be used for GHG mitigation SD model is 70% and the share of biomass combustion measures and for adapting to climate change. technology—30%. A share of the different resource tech- nologies depends on the capacity changes. Incoming investment flows and outgoing depreciation flows deter- Methodology mine the installed capacity stocks [33]. In general, the stock level at a particular time (t) is determined by dif- Overview of developed system dynamics model ferential equation [32]: CO tax policy and other economic instruments were Stock ¼ Flow  dt þ Stock : ð2Þ t ðt;tdtÞ ðtdtÞ integrated into the non-ETS system dynamics model made in previous research [30]. Then, the dynamics of the DH Technologies compete with each other. Economically system development towards the fourth generation system most profitable technologies are installed in the following were investigated. The aim of the system dynamics model years. is to analyze how complex systems change over time. To To determine which of these technologies will develop do so, the elements within the system as well as their in the future and how the DH system will look, heat tariffs mutual relationships have to be identified [31]. The SD of all four technologies are compared. Heat tariffs are taken theory is based on the three main concepts that should be 123 Int J Energy Environ Eng (2018) 9:111–121 115 Fig. 2 Conceptual framework CO2 tax policy and of applied methodology other economical Statistical Problem Results are instruments and technical formulation acceptable data Scenario Creating a Model Model Validation with formulation dynamic formulation and and testing historical data hypothesis simulation simulation Change CO2 policy or other economical Modify Analysis of instruments the model Results are not results acceptable Previous study part Yes No Has the target Results been reached? This study part as the main driving force when making decisions about the total produced amount of heat, but by the amount of which of the four technologies to install after the old ones heat transmitted to the consumers. have reached the end of their technical lifetime. Tariffs are Variable costs concern the cost of fuel and the cost of compared at the time when decisions about the change of the production process required electricity as well as technologies should be made. The heat tariff calculation natural resources tax, income tax, and others. Fixed costs methodology based on the heat calculation methodology include maintenance and operating costs, which account developed by the Public Utility Commission of Latvia [34]. both labor and administration salaries, as well as repair Methodology determines the procedures by which suppli- and other additional expenses. One of the most important ers are calculated following the tariff-regulated thermal fixed costs is investments and the related loan repay- energy supply services: the production, distribution, and ments. The main input data for modeling are collected in sale. The single-part tariff is applied by research and cal- Table 2. culated so, as to cover both the fixed cost as well as the In addition, the efficiency of all provided technologies variable cost: will be gradually increased until 2030. This will increase the efficiency of biomass boilers from 0.8 to 0.95 [38]. T ¼ T þ T þ T : ð3Þ i prodi tri 3i Owing to the efficiency increase of the condenser Each part of the tariff is constituted by the values of economizer, the COP of the heat pump may be increased fixed costs (FC) and variable costs (VC): from 3.5 to 4.5, the efficiency of solar collectors—from 0.45 to 0.55, and the efficiency of gas boilers—from T ¼ðVC þ FC Þ=Q : ð4Þ prod R R prod 0.92 to 0.97 [38]. The initial price of natural gas was The distribution of the fixed and variable costs is 32 EUR/MWh, which is based on the average price in maintained also for the transmission and sales tariff; the year 2016 and the initial price of biomass was however, different from Eq. (4), costs are divided not by 12 EUR/MWh. Table 2 Main input data for modeling Parameters Technologies Natural gas combustion Biomass combustion Heat pump (large Solar collectors with seasonal (HOB) (boiler) scale) accumulation Fuel price, €/MWh 32 12 – – The pace of annual fuel price 3[35]5 – – increase, % Technology costs, M€/MW 0.1 [36] 0.25 [37] 0.6 [36] 240 [36] The pace of annual technology cost 0.33 [36] 1.25 [36]3[36]5[36] reduction, % a 2 Solar collectors price in €/m 123 116 Int J Energy Environ Eng (2018) 9:111–121 Creation of dynamic hypotheses different processes in society including: approved stan- dards and regulations, legislative requirements about The causal loop diagram consists from one reinforcing energy efficiency, and available funding to perform con- (R) and one balancing (B) loop. The reinforcing loop servation measures. describes a situation in which renewable technologies To weaken the balancing loop, a CO tax for fossil fuels replace fossil, or in this case, natural gas technologies, and was implemented in the model. A CO tax is already by so doing, attempt to decrease network temperature and applied in several EU countries (see ‘‘CO2 tax policy and increase the efficiency of the whole system. This con- other economical instruments in Latvia’’). tributes to even further development of renewable tech- nologies. The balancing loop tries to prevent renewable Scenarios showing the impact of economical technologies from being implemented thus slowing down mechanisms on CO emissions from non-ETS the transition. The heat tariff is the element that determines district heating in Latvia whether the reinforcing or the balancing loop will be the stronger; it actually determines which way the DH system Various policy instruments are used in different scenarios will develop—towards fossil technologies or towards to analyze the behavior of non-ETS DH systems during the 4GDH system. transition to the 4GDH (Table 3). The main dynamic hypothesis is that non-ETS DH The first two scenarios developed in this article are systems will be the first ones from this sector to shift to the relatedonlytoenergy-efficiencymeasuresinbuildings, fourth generation system, which is characterized by its high taking into account the change in energy consumption. renewable energy share, low-temperature networks, and Scenario 1 is based on the available funding for apartment low specific heat consumption at end users. building renovation; while the high energy-efficiency To strengthen the reinforcing loop, even more, subsidies scenario 2 is based on reaching heat consumption of for solar collectors with seasonal storage at the amount of 100 kWh/m per year in 2030 (for government goal, see 25% were integrated into the model. Subsidies should help ‘‘Energy consumption in buildings’’). The consumption of to reduce the necessary investment for solar collectors and produced heat for non-ETS DH systems gradually thus promote solar’s faster introduction into the system by decreases from 1.75 to 1.64 TWh per year in scenario 1 replacing existing technical solutions. The availability of and from1.75to1.15 TWh peryearin scenario2(see solar subsidies depends on renewable energy policy and Fig. 4). available funding. Additional complications are caused by In scenarios 3–8, a CO tax for fossil fuel is introduced. the fact that use of solar technologies in Latvia is in its The CO tax is increased gradually from 4.5 EUR/tCO 2 2 early stages, which leads to additional costs (inconvenience (2016) to 20.0 EUR/tCO (2030) in scenarios 3, 5, and 7. A cost) (see Fig. 3). more rigorous policy is applied in scenarios 4, 6, and 8, The reduction of heat consumption at the end users where the CO tax is gradually increased from 4.5 EUR/ reduces the total energy required. It is associated with tCO (2016) to 55.0 EUR/tCO (2030). 2 2 The share of fossil fuel New technology Heat tariff of inconvenience cost technology renewable Access to Heat tariff of energy finance fossil fuel + Energy sources + conservation The share of Heat decrease measures Tax for fossil renewable energy consumption in buildings fuel sources R Standards and + B + normative Investment in fossil Energy efficiency Energy fuel technologies requirements in legislation conservation Heat losses in Investment in measures in network buildings renewable energy + Subsidies for solar + technologies collectors with Capacity of fossil acumulation fuel energy Capacity of Heat network sources renewable Subsidies for solar + + - + collectors with temperature energy sources + acumulation Access to Renewable finance source policy Renewable source New technology Energy + + policy Tax for fossil dependence inconvenience cost fuel Budget policy Emission limits + + Fig. 3 Causal loops diagram for non-ETS DH system 123 Int J Energy Environ Eng (2018) 9:111–121 117 Table 3 Description of scenarios Scenarios Energy-efficiency measures in buildings CO tax gradually increase for fossil Subsidies for investment for solar collectors Heat production gradually decrease until fuels until 2030, EUR/tCO with accumulation technologies 2030, TWh per year From 1.75 (2016) From 1.75 (2016) From 4.5(2016) From 4.5 (2016) 25% to 1.64 (2030) to 1.15 (2030) to 20.0 (2030) to 55.0 (2030) Scenario 110 0 0 0 (1 Sc) Scenario 201 0 0 0 (2 Sc) Scenario 310 1 0 0 (3 Sc) Scenario 410 0 1 0 (4 Sc) Scenario 501 1 0 0 (5 Sc) Scenario 601 0 1 0 (6 Sc) Scenario 701 1 0 1 (7 Sc) Scenario 801 0 1 1 (8 Sc) 1 policy is applied, 0 policy is not applied 110 40 2016 2018 2020 2022 2024 2026 2028 2030 1 Sc. 2 Sc. 3 Sc. 4 Sc. 5 Sc. 6 Sc. 7 Sc. 8 Sc. Projected specific heat consumption with existing funding Projected specific heat consumption with additional funding Share of CO2 emissions in 2030 compared to 2016 level Share of CO2 emissions in 2020 compared to 2016 level Projected specific heat consumption with government goal Fig. 5 Comparison of CO emission reductions from 2016 (100%) to Fig. 4 Projected specific heat consumption for scenarios 1 and 2 2020 and 2030, respectively Subsidies for investment in solar collectors with accu- The level of CO emissions in Fig. 5 is shown in relation mulation technologies are applied in scenarios 7 and 8. with the current state of DH systems (2016). In the short term, the emission reduction fluctuates from 11.6 to 35.0% in every scenario. A CO emission reduction to 11.6% (1 Sc) is expected in the worst-case scenario, which is if the Results country continues with its existing residential sector energy-efficiency policy, and if funding, which is the main Comparison of short- and long-term perspectives driving force of this process, is not increased. However, if of non-ETS CO emission mitigation in DH system the funding is increased and other economic incentives development implemented including, for example, the promotion of Energy service companies (ESCO), to achieve the gov- A comparison of CO emission decrease for all eight sce- ernment’s energy-efficiency goal for 2030 (2 Sc), then it is narios described in this article for both the short-term possible to reach a 20% reduction in the short term. The (2020) and long-term perspectives (2030) is shown in difference between 1 Sc and 2 Sc is only 8.4% in the short Fig. 5. Specific heat consumption for heating, kWh/m per year Specific share of CO2 emissions, % 118 Int J Energy Environ Eng (2018) 9:111–121 1 Sc.2 Sc. 3 Sc. 4 Sc.5 Sc.6 Sc.7 Sc.8 Sc. Share of natural gas production Share of biomass production Fig. 6 Technologies share in 2030. The share of heat pump production is negligible and not included in the figure term, but in the long term, it doubles and reaches 15.6%. required total amount of subsidies is 185.4 million EUR The financial support necessary for implementing the res- until 2030 in 7 Sc and 195.3 million EUR in 8 Sc. idential sector housing renovation processes is 60.6 million EUR per year in the case of 1 Sc. By 2030, the total costs Heat production technologies shares: a comparison will be 848 million EUR. From the standpoint of energy of short- and long-term perspectives for non-ETS efficiency, the more sustainable 2 Sc goal will cost 363.6 DH system million EUR per year, and by 2030, the total costs are estimated to be up to 4643 million EUR (5.48 times more Figure 6 shows the distribution of technical solutions in the than 1 Sc). short term (2020). The share of natural gas-fired tech- The implementation of a CO tax and its gradual nologies will decrease from 10% (1 Sc) to 24.6% (8 Sc) increase (from 4.5 to 20 EUR/tCO ) reduces emissions (70% share at the start of modeling). The share of biomass- an additional 5% (1 Sc compared to 3 Sc) in the short fired equipment increases from 9.9% (1 Sc) to 23.9% (8 Sc) term and 9.9% in the long term. In addition, it will (30% share at the start of modeling). increase Government revenues by 28.3 million EUR (3 Unfortunately, the use of solar collectors with accumu- Sc) to 2030 which 50% more than in 1 Sc. Where a lation technologies does not exceed 1% in all scenarios in more rigorous CO policy is applied, the emissions will the short term (2020). The share of solar collectors and heat be reduced by 7% (1 Sc compared to 4 Sc) in the short pump productions is negligible and not included in Fig. 6. term and 14% in the long term when the CO tax is Solar collectors, as well as heat pump usage, are not pro- increased from 4.5 to 55 EUR/tCO . 48.9 million EUR cesses that can evolve without the application of additional will be collected in the country’s budget by 2030 (4 Sc). financial aid [30]. We can conclude that a more stringent CO tax policy A better situation for solar technologies can be achieved gives an additional 4.1% emission reduction (3 Sc by 2030 (Fig. 7). However, even taking into account compared to 4 Sc in the long term). A similar result (a technology developments without the subsidies, we cannot 3.8% emission reduction) is achieved if we apply this increase the use of solar collectors by more than 10% (1, 2, CO tax policy to the advanced energy-efficiency sce- 3, 4, 5, and 6 Sc). The share of solar collector production narios for apartment buildings (5 Sc compared to 6 Sc in increase in scenarios 7 and 8 in the long term (2030) is the long term). Using this CO tax policy augmented based on the reductions dynamics of solar technology cost. with subsidies for solar technologies (7 Sc compared to The heat tariff of the solar technology remains competitive 8 Sc), the result is similar—4% additional emission from the 2023 year. On the other hand, in all other sce- reduction. Subsidies for solar technology installations narios (1, 2, 3, 4, 5, and 6 Sc), it happens only in result in the largest additional emission reductions. A 2027–2028, which does not allow installed solar tech- subsidy policy together with a moderate CO tax nologies to compete with biomass combustion technology. (4.5–20 EUR/tCO ) results in a decrease in emissions by However, thanks to the additional subsidies, which speeds 11.4% in the short term and by 18.9% in the long term. up the tariff reduction, solar technology installation (2 Sc compared to 7 Sc). Where subsidies are added to increases significantly in scenarios 7 and 8 (Fig. 6). The the more stringent CO taxpolicy(from 4.5to55EUR/ rate of solar collectors with accumulation technologies tCO ), emissions are reduced by 15% in the short term usage can increase to about 50% if subsidies of 25% above and by 22.9% in long term (2 Sc compared to 8 Sc). The the amounts invested are included. A substantial reduction Technologies share in 2020, % Int J Energy Environ Eng (2018) 9:111–121 119 Fig. 7 Technologies share in 2020. The share of solar collectors and heat pump productions is negligible and not included in the figure 1 Sc. 2 Sc. 3 Sc. 4 Sc. 5 Sc. 6 Sc. 7 Sc. 8 Sc. Share of Natural gas production Share of biomass production Share of solar collectors production in the use of natural gas technologies can be achieved. A 1.75 to 1.64 TWh per year in scenario 1 and from 1.75 reduction of 31.1% (1 Sc) in the use of natural gas tech- to 1.15 TWh per year in scenario 2. Implementation of both scenarios requires large capital investments. The nologies is projected. Usage could fall to 14.5% (8 Sc) from 70% at the start of modeling. In scenarios 1–6, the investment for scenario 2, which is more sustainable, is estimated up to 4643 million euro (5.48 times more main alternative for natural gas-fired technologies is bio- mass-fired technologies. Only by adding the subsidies for than 1 Sc). Energy-efficiency measures implementa- tion by consumers’ side allows reducing CO emis- solar technology, we can increase its usage to 43.8–49.4% at the same time reducing the usage of natural gas tech- sions by 62% (2 Sc). nologies from 18.8 to 14.5%. This will also have to effect 2. Moreover, increasing of CO tax is added to energy- of not allowing biomass-fired technologies to become the efficiency measures by renovating in buildings. dominant technology. Biomass-fired technology will have Increased CO tax can be implemented in two ways: an important role in Latvia’s future 4GDH system, but we gradually from 4.5 EUR/tCO (2016) to 20.0 EUR/ should take into account that we can produce different tCO (2030) and from 4.5 EUR/tCO (2016) to 2 2 55.0 EUR/tCO (2030). This paper provides four sce- products with higher added value from biomass rather than burning it in DH systems [39]. Still, biomass has an narios, which combine these two mechanisms with to energy-efficiency measures by renovating in buildings. important role in stabilizing daily and seasonal fluctuations that happen with solar energy sources. It will also make DH A more rigorous CO policy allows collecting systems more flexible. This means that scenarios 7 and 8 48.9 million euro in the country’s budget by 2030 (4 are optimal to encourage technology diversification. Sc). Unfortunately, it does not cover the necessary investment for solar technology which is up to 136 million euro. The emissions will be reduced by Conclusions 55.5% (1 Sc compared to 4 Sc) in the long term (2030) when the CO tax is increased from 4.5 to 55 EUR/ 1. According to the Paris Agreement, all EU countries tCO . 3. In addition, 25% subsidies for investment for solar have committed to making individual voluntary pled- ges to reduce GHG emissions. The article has inves- collectors with accumulation technology were included in scenario 7 and scenario 8. The CO emission tigated different means for reducing CO emissions, 2 2 from further development of non-ETS DH systems, to reduces at 76% for scenario 7 and at 80% for scenario developing 4GDH systems that move towards zero 8. In that case, scenarios 7 and 8 are the most emission levels: increasing CO tax, the energy-effi- sustainable, because they achieve the highest emission ciency measures by heat consumers’ side, and solar reduction from natural gas and the highest share of technology subsidies. This paper provides eight dif- solar collectors while keeping the share of biomass ferent scenarios that combine these three mechanisms. combustion technologies at a sustainable 30–40% level. It can be concluded that for achieving emission The first two scenarios examine the reduction in CO emissions due to energy-efficiency measures by reno- reductions in the non-ETS sector as well as energy- efficiency goals and taking into account biodiversity vating in buildings. The consumption of produced heat for non-ETS DH systems gradually decreases from goals, Government should as much as possible apply a Technologies share in 2030, % 120 Int J Energy Environ Eng (2018) 9:111–121 12. COM (2016) 482 final 2016/0231 (COD) https://ec.europa.eu/ policy mix that includes taxation, solar technology transparency/regdoc/rep/1/2016/EN/1-2016-482-EN-F1-1.PDF. subsidies, and promotion of energy efficiency in Accessed 1 Nov 2016 buildings. 13. SWD (2016) 82 final COMMISSION STAFF WORKING DOCUMENT Country Report Latvia 2016 http://ec.europa.eu/ europe2020/pdf/csr2016/cr2016_latvia_en.pdf. 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Impact of economical mechanisms on CO2 emissions from non-ETS district heating in Latvia using system dynamic approach

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Springer Journals
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Copyright © 2017 by The Author(s)
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Engineering; Renewable and Green Energy
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2008-9163
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10.1007/s40095-017-0241-9
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Abstract

Int J Energy Environ Eng (2018) 9:111–121 https://doi.org/10.1007/s40095-017-0241-9 ORIGINAL RESEARCH Impact of economical mechanisms on CO emissions from non- ETS district heating in Latvia using system dynamic approach 1 1 1 1 • • • • Jelena Ziemele Einars Cilinskis Gatis Zogla Armands Gravelsins 1 1 Andra Blumberga Dagnija Blumberga Received: 15 December 2016 / Accepted: 3 June 2017 / Published online: 14 July 2017 The Author(s) 2017. This article is an open access publication Abstract A system dynamics modeling approach was used Abbreviations to analyze the impact of economical mechanisms on CO T Tariff of thermal energy for the respective 2 i emissions from the Latvian district heating system that is technology i, €/MWh not covered by the European Union (EU) Emission Trading FC Fixed costs for the production, tariff, €/year System (non-ETS). Three policy instruments were included T Distribution tariff, €/MWh tri in the system dynamic model: carbon tax, subsidies for T Production tariff for the respective prodi solar technologies, and funding for energy-efficient build- technology i, €/MWh ing renovations with the aim to decrease energy con- VC Variable costs of the production tariff, €/year sumption. Eight development scenarios were examined, T Sales tariff, €/MWh 3i taking into account different policy mixes for the transition Q Produced amount of heat for the respective prodi of the heat network to the low-temperature regime. The technology i, MWh/year heat tariff was used as the main indicator to determine the i Type of selected technology pace and structure of the technology change. The existing Stock The stock level at time t natural gas technologies and three renewable energy tech- Flow The flow rate influencing the stock during the ðt;tdtÞ nologies (biomass combustion equipment, heat pump, and time period from (t - dt)to t solar collectors with accumulation) were included in the Stock The stock level at the time (t - dt), the initial ðtdtÞ model. Modeling results show substantial CO reduction stock potential; however, the results are highly dependent on the dt The time interval over which the equation applied financial instruments. It is recommended to apply a spans policy mix, including all the proposed policy instruments— E CO emission factor, tCO /MWh CO2 2 2 carbon tax, subsidies for solar technologies, and funding Q Amount of heat produced, MWh per years for energy-efficient renovation. g Efficiency coefficient GHG Greenhouse gas Keywords Fourth generation district heating  Carbon tax  EU The European Union System dynamics modeling  Renewable energy  ETS The emissions trading system Sustainable energy  Non-ETS emissions  Latvia non-ETS Not covered by Emission Trading System GDP Gross domestic product DH District heating 4GDH Fourth generation district heating SD System dynamic & Jelena Ziemele HOB Heat only boiler Jelena.Ziemele@rtu.lv M€ Million euro COP Coefficient of performance Institute of Energy Systems and Environment, Riga R Reinforcing Technical University, Azenes iela 12/1, Riga LV-1048, B Balancing Latvia 123 112 Int J Energy Environ Eng (2018) 9:111–121 1 Sc Scenario 1 6% from 2005 levels until 2030. According to the current 2 Sc Scenario 2 estimates [13], non-ETS emissions will increase by 7%. 3 Sc Scenario 3 System dynamics modeling results show that under the 4 Sc Scenario 4 existing policy regime, GHG emissions may by 2030 5 Sc Scenario 5 increase by 19% above the 2005 level [14]. Modeling with 6 Sc Scenario 6 linear programming optimization MARKAL, Latvia’s 7 Sc Scenario 7 model predicts that taking into account national economic 8 Sc Scenario 8 development forecasts comprising the existing GHG emission reduction policies and measures, emissions in 2030 may increase significantly (up to 26.5%) compared to Introduction 2005 [15]. Investigations [14–17] show that in the agri- cultural sector, it may be difficult or impossible to reduce In 2015, world leaders concluded an agreement to keep the non-ETS emissions below the 2005 level. It may mean that average global temperature increase well below the 2 C those emissions will rather increase, which means that preindustrial levels, with the aim to limit the increase to other sectors will need to achieve more substantial 1.5 C. According to the Paris Agreement, all parties have reductions. committed to making individual voluntary pledges to According to [18], the key focus on non-ETS emissions contribute to the global goal. The Paris Agreement came reduction should be placed on: (1) improving energy effi- into force on November 4, 2016. The EU is one of the ciency (for production, transmission, and end-use); (2) biggest greenhouse gas (GHG) emitters; however, the sit- reducing the peak load in the electricity distribution sys- uation differs in the 28 member states [1]. The initial tem; (3) increasing the use of biomass and biogas co- commitment of the EU is to reduce GHG emissions at least generation systems as well as biogas in the transport sector; 40% (from 1990 levels) by 2030. The EU energy climate (4) increasing the use of renewable energy in district and policy is considered to be a good example to follow [2, 3]. individual heating; and (5) a wider use of other renewable The EU has a comprehensive GHG reduction strategy sources, including wind energy. [4]. The EU emission trading system (ETS) is the key tool Different modeling approaches may be used for calcu- for reducing greenhouse gas emissions from large-scale lating the impact of climate mitigation measures and for a facilities of the power production and industrial sectors, as broader approach—green economy models including well as the aviation sector. The ETS covers about 45% of econometrics—measuring the relation between two or the EU’s GHG emissions. Non-ETS sectors account for more variables, running statistical analysis of historical some 55% of total EU emissions. These include housing, data and finding the correlation between specific selected non-ETS energy, agriculture, waste, and transport (ex- variables, optimization—generating a statement of the best cluding aviation). EU countries have taken on binding way to accomplish goals, and system dynamics—tools that annual targets to 2020 to cut emissions in these sectors (as provide information on what would happen, where a policy compared to 2005) according to the Effort Sharing Deci- is implemented at a specific point in time and within a sion [5] and draft binding targets for 2030. It is thought that specific context [19]. with early action, the EU can get comparative advantages District heating (DH) is one of the non-ETS emission with other countries that start the implementation process sources. It plays a significant role in countries, where it is later [6]. Modeling results show that the influence of widely used [20]. The fourth generation district heating reaching EU 2030 (as well as 2050) targets may be limited (4GDH) is a new paradigm of DH system moving to zero- regarding GDP and positive employment levels [7]. A new emission level [20]. investigation shows that the influence of anthropogenic In sum, the main aim of this investigation is to evaluate GHG emissions may be significantly greater than previ- how different mechanisms of CO emissions influence non- ously estimated [8]. If these results are confirmed, an ETS DH system development with the goal of moving additional urgent global action might be necessary. towards the zero-emission level and achieving 4GDH Bioenergy will pay a substantial role in reaching EU cli- system. Using the system dynamic (SD) model, policy mate—energy targets [9, 10]. instruments are integrated to determine their correlation to The Latvian non-ETS sector is large compared to other each other with a view to identifying the most efficient way EU countries—79.3% of total emissions (2014) [11]. That of moving towards a 4GDH system. Different scenarios are means—more action should be made at the national level. formed to identify the most efficient combinations of pol- The new draft regulation [12] requires a reduction of up to icy instruments. 123 Int J Energy Environ Eng (2018) 9:111–121 113 Background information on case study The non-ETS DH system in Latvia The DH system is historically well-developed in Latvia; some 67% of the population is connected to the DH system [21]. The structure of DH consumption has not changed 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 significantly in recent years. Space heating consumes Renewable energy share, % Fosil fuel share share, % 65–70%, and hot water preparation 30–35% of the total thermal energy used. 71.1% of the total final consumption Fig. 1 Heat production share at DH system in Latvia [24] of district heating was directed to households; 25.6% for services; 2.2% for industry and construction, and 1.1% for apartment buildings yearly. According to this information, agriculture (2013) [22]. Historically, the vast majority of space heating consumption in 2016 is 152.04 kWh/m per DH operators are non-ETS sector participants (see Table 1) year. This represents a decrease from 157.67 kWh/m in [23]. 2014. There is a limited success in renovating older Only 20 Latvian operators are taking part in the apartment buildings because of legal problems and a 2013–2020 stage of the ETS. This means that the operators shortage of resources [25]. of the non-ETS DH systems will have reduced their Since the amount of energy produced in non-ETS DH emissions, and ideally, they should conform to the princi- systems is directly related to energy consumption in ples of the fourth generation of the DH system. The total buildings, it was assumed in this study that the rate of thermal energy produced in 2013 was 6.65 TWh, 1.79 TWh change of energy produced will be the same as the rate of of this was produced by heat only boiler (HOB) technology change of energy consumption in apartment buildings. [24]. The rate of change of energy consumption for heating Two fuels dominate in the Latvian DH system: natural apartment buildings was calculated based on the existing gas and biomass. The share of thermal energy produced energy consumption in apartment buildings and the from biomass has begun to increase only in last few years expected investments in building renovation. (see Fig. 1)[24]. It was assumed that apartment buildings will be reno- Systems operate within the second and third generation vated only if co-funding for renovation is received, con- temperature regimes, respectively, 110/70 or 90/60 (sup- sidering that there were no new renovation projects after ply/return). Heat production 1.79 TWh per year by pro- the middle of 2015 when EU co-funding became portion 30/70% (biomass/natural gas) was used as the unavailable. initial data for modeling. The available co-funding for apartment building reno- vation for the new EU funds programming period is 156.3 Energy consumption in buildings million EUR. On average, co-funding makes up 43% of total building renovation costs. This means that the total The main consumers for the DH non-ETS sector are investment for apartment building renovation can be apartment buildings. Pursuant to the Latvian regulation on assumed to be 363.5 million EUR. This investment must be building energy performance, the Ministry of Economics spent by 2022, which means a yearly investment of 60.6 updates certification space heating energy consumption in million EUR. The average cost of building renovation Table 1 Number of heat plants Heat capacity Heat plants, numbers Installed heat capacity, MW and installed capacity at DH system in Latvia [23] 2012 2013 2014 2012 2013 2014 Total 308 316 311 2135.3 1850.3 1834.2 B0.2 MW 57 66 69 7.7 8.9 9.2 0.2 \ P B 0.5 MW 42 45 45 13.9 14.8 15.0 0.5 \ P B 1 MW 41 41 39 31.6 31.8 29.8 1 \ P B 5 MW 104 109 104 265.0 280.3 268.9 5 \ P B 20 MW 44 38 37 409.3 364.3 375.1 20\ P B50 MW 11 10 10 333.5 299.4 276.1 \50 MW 9 7 7 1074.3 850.8 860.1 Heat production share, % 114 Int J Energy Environ Eng (2018) 9:111–121 (according to the results of previous EU funding period) is taken into account when developing a new model, to 2 2 120 EUR/m . This means that 505 thousand m of apart- acquire correct and reliable results [32]: ment buildings can be renovated each year. In total, there • stocks, flows and feedback loops; are 61 million m of households in Latvia [25]. It was • precisely defined boundaries of the system; assumed that after building renovation, the average space • causal relationships, not correlations. heating consumption will be 70 kWh/m , which is the level of heat consumption in those apartment buildings in Latvia In the previous research of the authors [30], a conceptual that have already been renovated. It was assumed that after framework of the non-ETS DH model (Fig. 2) was devel- oped using the five main steps of SD—problem formula- 2022 when EU co-funding for apartment building renova- tion ends, energy consumption will decrease at the same tion, creating a dynamic hypothesis, model formulation and rate as during the period 2017–2022, assuming that other simulation, model testing, and scenario simulation. economical mechanisms promoting renovation will be Within this research, various scenarios for CO reduc- introduced. tion using different methods were examined: CO tax, The goal of the long-term energy strategy of Latvia is to subsidies for renewable energy technologies and grants for reduce specific heat consumption to 100 kWh/m per year conservation measures in buildings. These measures were by 2030 [26]. In this scenario, it is assumed that sufficient applied separately as well as in different combinations. In amount of economical mechanisms for reaching this goal this research, economic indicators in the context of the reduced CO emissions, which describes the sustainability will have been introduced. of the scenarios, were also examined. CO tax policy and other economical instruments The reduction of CO emissions (E, tCO per year), 2 2 2 in Latvia achieved by switching from fossil fuel technologies to renewable energy technologies in heat production, was Latvia has applied a carbon tax for non-ETS emissions in calculated using the following formula: the energy sector; the tax is relatively small, 2.85 EUR/t in E ¼ E : ð1Þ CO 2014, 3.5 EUR/t 2015–2016, and 4.50 EUR/t beginning in 2 2017. One of the arguments is that tax should not differ The 4GDH model was developed with the program significantly from the carbon price in the EU ETS system. ‘‘Powersim Studio 8’’. The model used in this research is Still, the Latvian non-ETS tax rate is one of the lowest in an extended version of the authors’ previous research [30]. comparison with other countries, including many EU The previous model was built to predict how the future DH countries [27]. There is an assumption that the price of system will look. Four different technologies were chosen carbon in the form of tax or ETS system should be at least for this purpose and they form the main stocks of the 20 EUR/t [28, 29]. The authors of [29] even discuss model—natural gas boilers and biomass boilers which are options about simultaneously applying a carbon tax and an already used in DH system as well as solar collectors with ETS system. According to [27], a realistic carbon tax of seasonal storage and heat pumps, which are taken as around 60 USD/t (55 EUR/t) may be set. plausible future technologies [30]. The initial share of The proceeds from the ETS auctions, according to the installed capacity of natural gas combustion technology in Latvian legislation, should be used for GHG mitigation SD model is 70% and the share of biomass combustion measures and for adapting to climate change. technology—30%. A share of the different resource tech- nologies depends on the capacity changes. Incoming investment flows and outgoing depreciation flows deter- Methodology mine the installed capacity stocks [33]. In general, the stock level at a particular time (t) is determined by dif- Overview of developed system dynamics model ferential equation [32]: CO tax policy and other economic instruments were Stock ¼ Flow  dt þ Stock : ð2Þ t ðt;tdtÞ ðtdtÞ integrated into the non-ETS system dynamics model made in previous research [30]. Then, the dynamics of the DH Technologies compete with each other. Economically system development towards the fourth generation system most profitable technologies are installed in the following were investigated. The aim of the system dynamics model years. is to analyze how complex systems change over time. To To determine which of these technologies will develop do so, the elements within the system as well as their in the future and how the DH system will look, heat tariffs mutual relationships have to be identified [31]. The SD of all four technologies are compared. Heat tariffs are taken theory is based on the three main concepts that should be 123 Int J Energy Environ Eng (2018) 9:111–121 115 Fig. 2 Conceptual framework CO2 tax policy and of applied methodology other economical Statistical Problem Results are instruments and technical formulation acceptable data Scenario Creating a Model Model Validation with formulation dynamic formulation and and testing historical data hypothesis simulation simulation Change CO2 policy or other economical Modify Analysis of instruments the model Results are not results acceptable Previous study part Yes No Has the target Results been reached? This study part as the main driving force when making decisions about the total produced amount of heat, but by the amount of which of the four technologies to install after the old ones heat transmitted to the consumers. have reached the end of their technical lifetime. Tariffs are Variable costs concern the cost of fuel and the cost of compared at the time when decisions about the change of the production process required electricity as well as technologies should be made. The heat tariff calculation natural resources tax, income tax, and others. Fixed costs methodology based on the heat calculation methodology include maintenance and operating costs, which account developed by the Public Utility Commission of Latvia [34]. both labor and administration salaries, as well as repair Methodology determines the procedures by which suppli- and other additional expenses. One of the most important ers are calculated following the tariff-regulated thermal fixed costs is investments and the related loan repay- energy supply services: the production, distribution, and ments. The main input data for modeling are collected in sale. The single-part tariff is applied by research and cal- Table 2. culated so, as to cover both the fixed cost as well as the In addition, the efficiency of all provided technologies variable cost: will be gradually increased until 2030. This will increase the efficiency of biomass boilers from 0.8 to 0.95 [38]. T ¼ T þ T þ T : ð3Þ i prodi tri 3i Owing to the efficiency increase of the condenser Each part of the tariff is constituted by the values of economizer, the COP of the heat pump may be increased fixed costs (FC) and variable costs (VC): from 3.5 to 4.5, the efficiency of solar collectors—from 0.45 to 0.55, and the efficiency of gas boilers—from T ¼ðVC þ FC Þ=Q : ð4Þ prod R R prod 0.92 to 0.97 [38]. The initial price of natural gas was The distribution of the fixed and variable costs is 32 EUR/MWh, which is based on the average price in maintained also for the transmission and sales tariff; the year 2016 and the initial price of biomass was however, different from Eq. (4), costs are divided not by 12 EUR/MWh. Table 2 Main input data for modeling Parameters Technologies Natural gas combustion Biomass combustion Heat pump (large Solar collectors with seasonal (HOB) (boiler) scale) accumulation Fuel price, €/MWh 32 12 – – The pace of annual fuel price 3[35]5 – – increase, % Technology costs, M€/MW 0.1 [36] 0.25 [37] 0.6 [36] 240 [36] The pace of annual technology cost 0.33 [36] 1.25 [36]3[36]5[36] reduction, % a 2 Solar collectors price in €/m 123 116 Int J Energy Environ Eng (2018) 9:111–121 Creation of dynamic hypotheses different processes in society including: approved stan- dards and regulations, legislative requirements about The causal loop diagram consists from one reinforcing energy efficiency, and available funding to perform con- (R) and one balancing (B) loop. The reinforcing loop servation measures. describes a situation in which renewable technologies To weaken the balancing loop, a CO tax for fossil fuels replace fossil, or in this case, natural gas technologies, and was implemented in the model. A CO tax is already by so doing, attempt to decrease network temperature and applied in several EU countries (see ‘‘CO2 tax policy and increase the efficiency of the whole system. This con- other economical instruments in Latvia’’). tributes to even further development of renewable tech- nologies. The balancing loop tries to prevent renewable Scenarios showing the impact of economical technologies from being implemented thus slowing down mechanisms on CO emissions from non-ETS the transition. The heat tariff is the element that determines district heating in Latvia whether the reinforcing or the balancing loop will be the stronger; it actually determines which way the DH system Various policy instruments are used in different scenarios will develop—towards fossil technologies or towards to analyze the behavior of non-ETS DH systems during the 4GDH system. transition to the 4GDH (Table 3). The main dynamic hypothesis is that non-ETS DH The first two scenarios developed in this article are systems will be the first ones from this sector to shift to the relatedonlytoenergy-efficiencymeasuresinbuildings, fourth generation system, which is characterized by its high taking into account the change in energy consumption. renewable energy share, low-temperature networks, and Scenario 1 is based on the available funding for apartment low specific heat consumption at end users. building renovation; while the high energy-efficiency To strengthen the reinforcing loop, even more, subsidies scenario 2 is based on reaching heat consumption of for solar collectors with seasonal storage at the amount of 100 kWh/m per year in 2030 (for government goal, see 25% were integrated into the model. Subsidies should help ‘‘Energy consumption in buildings’’). The consumption of to reduce the necessary investment for solar collectors and produced heat for non-ETS DH systems gradually thus promote solar’s faster introduction into the system by decreases from 1.75 to 1.64 TWh per year in scenario 1 replacing existing technical solutions. The availability of and from1.75to1.15 TWh peryearin scenario2(see solar subsidies depends on renewable energy policy and Fig. 4). available funding. Additional complications are caused by In scenarios 3–8, a CO tax for fossil fuel is introduced. the fact that use of solar technologies in Latvia is in its The CO tax is increased gradually from 4.5 EUR/tCO 2 2 early stages, which leads to additional costs (inconvenience (2016) to 20.0 EUR/tCO (2030) in scenarios 3, 5, and 7. A cost) (see Fig. 3). more rigorous policy is applied in scenarios 4, 6, and 8, The reduction of heat consumption at the end users where the CO tax is gradually increased from 4.5 EUR/ reduces the total energy required. It is associated with tCO (2016) to 55.0 EUR/tCO (2030). 2 2 The share of fossil fuel New technology Heat tariff of inconvenience cost technology renewable Access to Heat tariff of energy finance fossil fuel + Energy sources + conservation The share of Heat decrease measures Tax for fossil renewable energy consumption in buildings fuel sources R Standards and + B + normative Investment in fossil Energy efficiency Energy fuel technologies requirements in legislation conservation Heat losses in Investment in measures in network buildings renewable energy + Subsidies for solar + technologies collectors with Capacity of fossil acumulation fuel energy Capacity of Heat network sources renewable Subsidies for solar + + - + collectors with temperature energy sources + acumulation Access to Renewable finance source policy Renewable source New technology Energy + + policy Tax for fossil dependence inconvenience cost fuel Budget policy Emission limits + + Fig. 3 Causal loops diagram for non-ETS DH system 123 Int J Energy Environ Eng (2018) 9:111–121 117 Table 3 Description of scenarios Scenarios Energy-efficiency measures in buildings CO tax gradually increase for fossil Subsidies for investment for solar collectors Heat production gradually decrease until fuels until 2030, EUR/tCO with accumulation technologies 2030, TWh per year From 1.75 (2016) From 1.75 (2016) From 4.5(2016) From 4.5 (2016) 25% to 1.64 (2030) to 1.15 (2030) to 20.0 (2030) to 55.0 (2030) Scenario 110 0 0 0 (1 Sc) Scenario 201 0 0 0 (2 Sc) Scenario 310 1 0 0 (3 Sc) Scenario 410 0 1 0 (4 Sc) Scenario 501 1 0 0 (5 Sc) Scenario 601 0 1 0 (6 Sc) Scenario 701 1 0 1 (7 Sc) Scenario 801 0 1 1 (8 Sc) 1 policy is applied, 0 policy is not applied 110 40 2016 2018 2020 2022 2024 2026 2028 2030 1 Sc. 2 Sc. 3 Sc. 4 Sc. 5 Sc. 6 Sc. 7 Sc. 8 Sc. Projected specific heat consumption with existing funding Projected specific heat consumption with additional funding Share of CO2 emissions in 2030 compared to 2016 level Share of CO2 emissions in 2020 compared to 2016 level Projected specific heat consumption with government goal Fig. 5 Comparison of CO emission reductions from 2016 (100%) to Fig. 4 Projected specific heat consumption for scenarios 1 and 2 2020 and 2030, respectively Subsidies for investment in solar collectors with accu- The level of CO emissions in Fig. 5 is shown in relation mulation technologies are applied in scenarios 7 and 8. with the current state of DH systems (2016). In the short term, the emission reduction fluctuates from 11.6 to 35.0% in every scenario. A CO emission reduction to 11.6% (1 Sc) is expected in the worst-case scenario, which is if the Results country continues with its existing residential sector energy-efficiency policy, and if funding, which is the main Comparison of short- and long-term perspectives driving force of this process, is not increased. However, if of non-ETS CO emission mitigation in DH system the funding is increased and other economic incentives development implemented including, for example, the promotion of Energy service companies (ESCO), to achieve the gov- A comparison of CO emission decrease for all eight sce- ernment’s energy-efficiency goal for 2030 (2 Sc), then it is narios described in this article for both the short-term possible to reach a 20% reduction in the short term. The (2020) and long-term perspectives (2030) is shown in difference between 1 Sc and 2 Sc is only 8.4% in the short Fig. 5. Specific heat consumption for heating, kWh/m per year Specific share of CO2 emissions, % 118 Int J Energy Environ Eng (2018) 9:111–121 1 Sc.2 Sc. 3 Sc. 4 Sc.5 Sc.6 Sc.7 Sc.8 Sc. Share of natural gas production Share of biomass production Fig. 6 Technologies share in 2030. The share of heat pump production is negligible and not included in the figure term, but in the long term, it doubles and reaches 15.6%. required total amount of subsidies is 185.4 million EUR The financial support necessary for implementing the res- until 2030 in 7 Sc and 195.3 million EUR in 8 Sc. idential sector housing renovation processes is 60.6 million EUR per year in the case of 1 Sc. By 2030, the total costs Heat production technologies shares: a comparison will be 848 million EUR. From the standpoint of energy of short- and long-term perspectives for non-ETS efficiency, the more sustainable 2 Sc goal will cost 363.6 DH system million EUR per year, and by 2030, the total costs are estimated to be up to 4643 million EUR (5.48 times more Figure 6 shows the distribution of technical solutions in the than 1 Sc). short term (2020). The share of natural gas-fired tech- The implementation of a CO tax and its gradual nologies will decrease from 10% (1 Sc) to 24.6% (8 Sc) increase (from 4.5 to 20 EUR/tCO ) reduces emissions (70% share at the start of modeling). The share of biomass- an additional 5% (1 Sc compared to 3 Sc) in the short fired equipment increases from 9.9% (1 Sc) to 23.9% (8 Sc) term and 9.9% in the long term. In addition, it will (30% share at the start of modeling). increase Government revenues by 28.3 million EUR (3 Unfortunately, the use of solar collectors with accumu- Sc) to 2030 which 50% more than in 1 Sc. Where a lation technologies does not exceed 1% in all scenarios in more rigorous CO policy is applied, the emissions will the short term (2020). The share of solar collectors and heat be reduced by 7% (1 Sc compared to 4 Sc) in the short pump productions is negligible and not included in Fig. 6. term and 14% in the long term when the CO tax is Solar collectors, as well as heat pump usage, are not pro- increased from 4.5 to 55 EUR/tCO . 48.9 million EUR cesses that can evolve without the application of additional will be collected in the country’s budget by 2030 (4 Sc). financial aid [30]. We can conclude that a more stringent CO tax policy A better situation for solar technologies can be achieved gives an additional 4.1% emission reduction (3 Sc by 2030 (Fig. 7). However, even taking into account compared to 4 Sc in the long term). A similar result (a technology developments without the subsidies, we cannot 3.8% emission reduction) is achieved if we apply this increase the use of solar collectors by more than 10% (1, 2, CO tax policy to the advanced energy-efficiency sce- 3, 4, 5, and 6 Sc). The share of solar collector production narios for apartment buildings (5 Sc compared to 6 Sc in increase in scenarios 7 and 8 in the long term (2030) is the long term). Using this CO tax policy augmented based on the reductions dynamics of solar technology cost. with subsidies for solar technologies (7 Sc compared to The heat tariff of the solar technology remains competitive 8 Sc), the result is similar—4% additional emission from the 2023 year. On the other hand, in all other sce- reduction. Subsidies for solar technology installations narios (1, 2, 3, 4, 5, and 6 Sc), it happens only in result in the largest additional emission reductions. A 2027–2028, which does not allow installed solar tech- subsidy policy together with a moderate CO tax nologies to compete with biomass combustion technology. (4.5–20 EUR/tCO ) results in a decrease in emissions by However, thanks to the additional subsidies, which speeds 11.4% in the short term and by 18.9% in the long term. up the tariff reduction, solar technology installation (2 Sc compared to 7 Sc). Where subsidies are added to increases significantly in scenarios 7 and 8 (Fig. 6). The the more stringent CO taxpolicy(from 4.5to55EUR/ rate of solar collectors with accumulation technologies tCO ), emissions are reduced by 15% in the short term usage can increase to about 50% if subsidies of 25% above and by 22.9% in long term (2 Sc compared to 8 Sc). The the amounts invested are included. A substantial reduction Technologies share in 2020, % Int J Energy Environ Eng (2018) 9:111–121 119 Fig. 7 Technologies share in 2020. The share of solar collectors and heat pump productions is negligible and not included in the figure 1 Sc. 2 Sc. 3 Sc. 4 Sc. 5 Sc. 6 Sc. 7 Sc. 8 Sc. Share of Natural gas production Share of biomass production Share of solar collectors production in the use of natural gas technologies can be achieved. A 1.75 to 1.64 TWh per year in scenario 1 and from 1.75 reduction of 31.1% (1 Sc) in the use of natural gas tech- to 1.15 TWh per year in scenario 2. Implementation of both scenarios requires large capital investments. The nologies is projected. Usage could fall to 14.5% (8 Sc) from 70% at the start of modeling. In scenarios 1–6, the investment for scenario 2, which is more sustainable, is estimated up to 4643 million euro (5.48 times more main alternative for natural gas-fired technologies is bio- mass-fired technologies. Only by adding the subsidies for than 1 Sc). Energy-efficiency measures implementa- tion by consumers’ side allows reducing CO emis- solar technology, we can increase its usage to 43.8–49.4% at the same time reducing the usage of natural gas tech- sions by 62% (2 Sc). nologies from 18.8 to 14.5%. This will also have to effect 2. Moreover, increasing of CO tax is added to energy- of not allowing biomass-fired technologies to become the efficiency measures by renovating in buildings. dominant technology. Biomass-fired technology will have Increased CO tax can be implemented in two ways: an important role in Latvia’s future 4GDH system, but we gradually from 4.5 EUR/tCO (2016) to 20.0 EUR/ should take into account that we can produce different tCO (2030) and from 4.5 EUR/tCO (2016) to 2 2 55.0 EUR/tCO (2030). This paper provides four sce- products with higher added value from biomass rather than burning it in DH systems [39]. Still, biomass has an narios, which combine these two mechanisms with to energy-efficiency measures by renovating in buildings. important role in stabilizing daily and seasonal fluctuations that happen with solar energy sources. It will also make DH A more rigorous CO policy allows collecting systems more flexible. This means that scenarios 7 and 8 48.9 million euro in the country’s budget by 2030 (4 are optimal to encourage technology diversification. Sc). Unfortunately, it does not cover the necessary investment for solar technology which is up to 136 million euro. The emissions will be reduced by Conclusions 55.5% (1 Sc compared to 4 Sc) in the long term (2030) when the CO tax is increased from 4.5 to 55 EUR/ 1. According to the Paris Agreement, all EU countries tCO . 3. In addition, 25% subsidies for investment for solar have committed to making individual voluntary pled- ges to reduce GHG emissions. The article has inves- collectors with accumulation technology were included in scenario 7 and scenario 8. The CO emission tigated different means for reducing CO emissions, 2 2 from further development of non-ETS DH systems, to reduces at 76% for scenario 7 and at 80% for scenario developing 4GDH systems that move towards zero 8. In that case, scenarios 7 and 8 are the most emission levels: increasing CO tax, the energy-effi- sustainable, because they achieve the highest emission ciency measures by heat consumers’ side, and solar reduction from natural gas and the highest share of technology subsidies. This paper provides eight dif- solar collectors while keeping the share of biomass ferent scenarios that combine these three mechanisms. combustion technologies at a sustainable 30–40% level. It can be concluded that for achieving emission The first two scenarios examine the reduction in CO emissions due to energy-efficiency measures by reno- reductions in the non-ETS sector as well as energy- efficiency goals and taking into account biodiversity vating in buildings. The consumption of produced heat for non-ETS DH systems gradually decreases from goals, Government should as much as possible apply a Technologies share in 2030, % 120 Int J Energy Environ Eng (2018) 9:111–121 12. COM (2016) 482 final 2016/0231 (COD) https://ec.europa.eu/ policy mix that includes taxation, solar technology transparency/regdoc/rep/1/2016/EN/1-2016-482-EN-F1-1.PDF. subsidies, and promotion of energy efficiency in Accessed 1 Nov 2016 buildings. 13. SWD (2016) 82 final COMMISSION STAFF WORKING DOCUMENT Country Report Latvia 2016 http://ec.europa.eu/ europe2020/pdf/csr2016/cr2016_latvia_en.pdf. 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