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Regionalized LCA-Based Optimization of Building Energy Supply: Method and Case Study for a Swiss Municipality Dominik Saner, Carl Vadenbo, Bernhard Steubing,* and Stefanie Hellweg Group for Ecological Systems Design, Institute of Environmental Engineering, ETH Zurich, John-von-Neumann-Weg 9, 8093 Zurich, Switzerland S Supporting Information *

ABSTRACT: This paper presents a regionalized LCA-based multiobjective optimization model of building energy demand and supply for the case of a Swiss municipality for the minimization of greenhouse gas emissions and particulate matter formation. The results show that the environmental improvement potential is very large: in the optimal case, greenhouse gas emissions from energy supply could be reduced by more than 75% and particulate emissions by over 50% in the municipality. This scenario supposes a drastic shift of heat supply systems from a fossil fuel dominated portfolio to a portfolio consisting of mainly heat pump and woodchip incineration systems. In addition to a change in heat supply technologies, roofs, windows and walls would need to be refurbished in more than 65% of the municipality’s buildings. The full potential of the environmental impact reductions will hardly be achieved in reality, particularly in the short term, for example, because of financial constraints and social acceptance, which were not taken into account in this study. Nevertheless, the results of the optimization model can help policy makers to identify the most effective measures for improvement at the decision making level, for example, at the building level for refurbishment and selection of heating systems or at the municipal level for designing district heating networks. Therefore, this work represents a starting point for designing effective incentives to reduce the environmental impact of buildings. While the results of the optimization model are specific to the municipality studied, the model could readily be adapted to other regions.

1. INTRODUCTION Environmentally extended input-output studies (EE-IOA) have shown that housing energy demand, in particular heating, makes up a large share of the overall environmental footprint of households,1,2 exceeding 20% of global greenhouse gas emissions caused by households.1 These results illustrate the need to reduce the environmental impact, in particular climate change effects, of the housing sector. Switzerland is an example of a country where the housing sector contributes about 25% of the overall CO2-footprint, which corresponds to more than 3 tonnes per person and year.3 This exceeds the long-term goal of the so-called “1 tonne CO2-society” (i.e., reducing overall CO2-emissions to 1 t/capita/year)4 to combat climate change and hence calls for action to reduce this impact. While EE-IOA helps to target relevant consumption areas at larger scales such as nations, it does not usually contain enough detail to capture the situation at the level of municipalities or individual buildings. It is, however, exactly at these levels that many important decisions are taken, concerning, for example, building design and refurbishment, the choice of heating systems or the construction of district heating networks. Other local factors also play a role in reducing the environmental impact of the building stock, such as the climate and the availability of renewable energy sources. Therefore, in order to adequately © 2014 American Chemical Society

support decisions to lower the impact of the building stock, regionalized bottom-up studies are needed, which model the heat and electricity demand and supply for individual buildings and municipalities within their local context. A suitable method for such analyses is life cycle assessment (LCA).5 Several LCA studies have been conducted to analyze the environmental impacts of households or buildings. There seems to be a general consensus that the environmental performance of buildings is related to a number of interdependent factors, such as the construction typology, geographic location, and user behavior, and that the operational heat demand is usually the main driver of environmental impacts.6−12 Suggested measures to decrease the environmental impact of buildings relate mainly to lowering energy consumption, for example, through thermal insulation of the building envelope and reducing the impact of heat supply, for example, through efficiency measures or renewables.7,9,10 Since measures to reduce the energy demand of buildings are increasingly required by regulations and may come at the cost of higher embodied energy in building materials, Received: Revised: Accepted: Published: 7651

January 13, 2014 May 23, 2014 May 27, 2014 May 27, 2014 dx.doi.org/10.1021/es500151q | Environ. Sci. Technol. 2014, 48, 7651−7659

Environmental Science & Technology

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households in 1332 buildings. Geo-referenced energy demands for individual households for the reference year 2010 were calculated in a previous study.32 For the case study presented here, these household demands were aggregated to the building level. In order to quantify potential savings in environmental impacts by refurbishment and thus a reduction of heating demand, four building renovation measures (floor, roof, wall, and window refurbishments) were included in the model. The actual renovation status of each building was available from Saner et al.32 The space heat demand was calculated for each individual building with and without each refurbishment measure (i.e., refurbished windows, refurbished roof, etc.), assuming that all refurbishments would meet best available standards. The difference corresponded to the amount of space heat that could be saved (i.e., supplied) from each renovation measure. Obviously, for buildings that had recently been renovated the potential savings were close to zero, while they were substantial for old buildings with poor insulation. The environmental impact from the production of insulation materials for the refurbishment were calculated per year (considering the lifetime of the material) as reported in the Supporting Information (SI) (section S1.2.2) of Saner et al.32 2.2. Energy Supply. The possible space heat and hot water supply technologies were oil, gas, and wood heaters (chips, logs, pellets), heat pumps (brine−water, air−water), district heat (woodchips), and polymer electrolyte membrane (PEM) fuel cell systems (cogeneration of heat and electricity fueled by gas). Hot water could additionally be partly supplied by solar collector panels. Electricity demand could be supplied by electricity from local photovoltaic (PV) panels (ribbon-Si) mounted on roof tops, PEM fuel cells or electricity from the Swiss grid. For electricity from PV, it was assumed that 50% of the rooftops in the case study municipality could be equipped with panels. It was assumed that PV panels could only occupy the residual roof area after subtracting the area of the solar collectors from the total roof area. PEM fuel cells could only be run with natural gas or purified biogas and thus only in buildings with a connection to the gas network. Ground source heat pump systems were only allowed for buildings situated in designated areas where the risk of groundwater contamination by leaking borehole heat exchangers was small. A certain area was suitable for a district heat grid, and all buildings situated in this area had the possibility to choose district heat as a supply option. The supply of locally available woodchips within the municipality was 10 000 m3/year35 and that of wood logs 2500 m3/year with shares of 28% hard wood and 72% soft wood. Both chips and logs were assumed to be residues from round wood production from sustainably managed forests. Potential long-term effects of removing wood from the forest,36 for example, on soil fertility, were not assessed in this study, partly because there is not yet a consistent methodology implemented in standard life cycle impact assessment methods.37 The supply with wood from outside the municipality was assumed to be zero as other regions may need their local renewable energy sources for themselves. All background life cycle inventories except for ground source heat pumps38 were taken from the ecoinvent database (v2.2).39 The complete list of life cycle inventories for the included energy technologies is provided in Table S4 in the SI.

several authors emphasize that the whole life cycle should be considered when designing or retrofitting buildings.7,8,10−12 While analyses and comparisons of alternative scenarios are commonly performed in LCA studies, this does not ensure that optimal solutions are identified, especially when there is a large number of possible scenarios resulting from different technology combinations and, for example, building-specific and local constraints. To overcome this problem, LCA has been combined with mathematical optimization techniques already in the mid1990s.13,14 Since then, studies have addressed environmental optimization problems related to scheduling and process design15−18 as well as planning of supply chain networks19−25 (see also26 for a review of the integration of process optimization techniques with the LCA methodology). Several studies have combined LCA and optimization approaches on a building level, for example, to optimize building envelopes27 or building energy supply.28,29 Other authors have presented approaches on how to quantify, with high geographic resolution, the energy demand30 and CO2 emissions31 of cities. However, to the best of our knowledge, an LCA-based optimization of building energy supplies within the context of real settlements and for an entire municipality has not been presented previously. This paper presents an LCA based optimization approach with the goal to minimize building related environmental impacts by optimizing the energy supply through alternative technologies and refurbishment measures. For the case of a Swiss municipality,32 two geographical scales are considered simultaneously: the level of individual buildings and the level of the municipality. This disaggregated system perspective enables consideration of system-wide constraints such as capacity limits and resourcesupply constraints, as well as site-specific factors such as the possibility to join a district heating network, to exploit groundwater heat pumps or to install solar panels or collectors.

2. GOAL AND SCOPE DEFINITION The aim of the study was to identify the theoretic environmental improvement potential that can be achieved by changing energy supply systems for space heat, hot water, and electricity and by refurbishing the building stock in a Swiss municipality. As a case study we chose the municipality of Wattwil because another recent study already estimated current household energy demands and supplies for the building stock of this municipality,32 which was an ideal point of departure for the present paper. We assumed that the population of the municipality would not grow (as projected for the specific municipality studied), the building park would remain constant (since there were no new building areas planned) and that the demand for per-capita living space would remain constant. The results of the study should serve as an overall benchmark and to pinpoint specific measures for the reduction of environmental impact. The functional unit was defined as providing a heated living space as well as hot water and electricity for each building in the municipality. Environmental impacts were assessed for two midpoint categories from the ReCiPe method33 with particular relevance for energy supply systems: climate change (kg CO2-eq ) and particulate matter formation (kg PM10-eq ). While climate change is currently the environmental dimension receiving the highest political attention and an indicator of the fossil fuel intensity of products, we chose particulate matter formation as a second indicator as we expected trade-offs with climate change, especially in the context of using wood energy.34 2.1. Energy Demand. The case study municipality is situated in the eastern part of Switzerland. It consists of 3328

3. OPTIMIZATION MODEL 3.1. Distinction of Foreground and Background Systems through Matrix Extension. Since the building energy supply and refurbishment decisions involved are made on 7652

dx.doi.org/10.1021/es500151q | Environ. Sci. Technol. 2014, 48, 7651−7659


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a local level, an explicit distinction was made between a local foreground system, which could be influenced by the decision makers, and the background system, which consisted of those upstream processes that could not be influenced by the decision makers.40 In mathematical terms, this was implemented by using two distinct technology matrices: a square A matrix representing the background system (the ecoinvent technology matrix39) and a rectangular A’ matrix representing the (underdetermined) foreground system. The foreground system matrix defined the competing technologies, energy carriers and refurbishment options used in the municipality as well as interactions between these. A’ can be considered an extension of the A matrix. A detailed description of the implementation and an example are provided in the SI. 3.2. Mathematical Formulation. Equations 1−18 describe the mathematical formulation of the optimization (the nomenclature used is summarized in Table 1 and Table 2). To

Table 2. Parameters and Variables Used in the Optimization Parameter

i j k l t e c iU iL iLheating iLhotwater iheating ihotwater iremovation iil iit iilt

description full set of processes economic flows in the background system economic flows in the foreground system (space heat, hot water, electricity, natural gas or biogas, woodchips, and logs) set of buildings time steps (months) environmental exchanges characterization factors of life impact assessment methods subset of i for processes with an upper limit on total supply subset of i for processes for which a minimum (threshold) demand has to be reached, i.e. heat and hot water supplied over a district network subset of iL representing heat supply over a district heating network subset of iL representing hot water supply over a district water network subset of i for processes providing space heat subset of i for processes providing hot water subset of i for renovation measures subset of i for processes with limited spatial availability subset of i for processes with limited temporal availability subset of i for processes with limited spatial and temporal availability

technology matrix element of the inverted background technology matrix Ă element of the foreground technology matrix Ă element of the environmental intervention matrix B element of the total final demand matrix F element of the total final demand matrix F̆ element of the lower bound characterization result vector hL element of the upper bound characterization result vector hU element of the renovation characterization result matrix hren sufficiently large constant used to control the scaling vector based on the binary decision variables element of the time-specific constraint matrix pdyn element of the building-specific constraint matrix pind element of the building- and time-specific constraint matrix pind,dyn element of the process-specific constraint matrix psys element of the characterization matrix Q Description

pdyn pind pind,dyn psys q Variable

Table 1. Indices and Subsets of Indices Used in the Optimization index

Description

A â ă b f f̆ hL hU hren m

λ number of elements

auxiliary scalar variable describing degree of satisfaction (to be maximized) in the fuzzy LP formulation element of the background scaling matrix S element of the foreground scaling matrix S̆ element of the binary decision variable for decisions on the level of individual buildings Binary decision variable for decisions on system level, i.e. district heating network

s s̆ pind

4243 4243 6

psys

1332 12 4373 2

below. The building-specific scaling vector s̆ of the foreground system and the resulting global scaling vector s represent two of the decision variables to be optimized in order to supply the final demand f while minimizing the environmental impacts from a systems perspective. We also introduce two binary decision variables (uind and usys) in order to reflect investment decisions of individual buildings and the municipality, respectively. This addition converted the problem into a mixed-integer program (MIP). The optimization was performed for the total supply with energy (i.e., space heat, hot water, and electricity) over one year. The year was divided into 12 time steps t, that is, months, to be able to account for temporal constraints (e.g., unusable surplus heat from solar collector systems in summer). The mathematical formulation of the optimization problem is outlined below and presented in detail in Section S1.4 in the SI. The optimization was performed using the General Algebraic Modeling System (GAMS).41

9 2 1 1 13 13 4 32 3

(1)

maximize λ

2

Subject to

∑ qc ,e ·∑ ∑ be ,j ·sj ,l + ∑ ∑ uiind

renovation

be able to minimize several life cycle impact categories at once in a multiobjective optimization, we apply the fuzzy LP extension of the general matrix-based LCA developed by Tan et al.25 Compared to general LP formulation of matrix-based LCA (as described in SI), the original objective function (minimize h = QBs, SI eq S.2) is replaced by eqs 1−3. Equation 4 represents the inequality for which the final demand needs to be satisfied by the product of the (rectangular) technology matrix and the scaling vector, which should satisfy the unique final demand vector of each building (As ≥ f, SI eq S.3). Considering the entire building stock results in a scaling matrix where the number of columns is equal to the total number of buildings in the municipality. Equations 5−6 replace SI eq S.4 (s = A−1 (s ̃ + f)), and eqs 7−18 represent the constraints imposed on the system (SI eq S.5). These constraints are described in more detail in the sections

e

j

i renovation

l

≤ hcU − λ(hcU − hcL) ∀ c 0≤λ≤1

A−1 = Â

∑ aĵ ,i ·∑ (sĭ ,l ,t + fi ,l ,t ) ∀ j , l

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t

(4) (5)

t

∑ ∑ sĭ ,l ,t ≤ pisys l

(2)

∀ k, l, t

i

i

·hcren , i renovation , l

(3)

∑ ak̆ ,i ,t ·sĭ ,l ,t ≥ fk̆ ,l ,t

sj , l =

,l

l

(6)

∀ i ∈ iU (7)



∑ sĭ ,l ,t ≤ pidyn ,t

(8)

∀ i ∈ i il , t (9)

t

sĭ , l , t ≤ piind,dyn ∀ i ∈ i ilt , l , t ,l ,t

(10)

uiind , l = {0; 1} ∀ i , l

(11)

uisys = {0; 1} ∀ i

(12)

heating ,l ∑ sĭ ,l ,t ≤ m·uiind ,l ∀ i ∈ i t

uiind ,l ≤ 1 ∀ l

∑

calculated individually for each building according to Saner et al.32 These impacts are accounted for in the second term on the lefthand side of eq 2. The binary variable uind reflects the decision of whether a possible renovation measure is chosen for each building considered. 3.2.2. System Constraints. Some of the systems constraints in eqs 7−18 are related and can be divided into blocks of equations that impose limitations on the decision space for the optimal solution. Equations 7−10 represent different types of capacity constraints in terms of process availability based on spatial and/or temporal aspects. The subsets of the process index i (summarized in Table 1) were used to address constraints specific for different processes. In eq 7, iU is used to introduce an upper limit (psys) for the system-wide supply by processes that are neither building- nor time-specific (e.g., the amount of locally available hard and soft woodchips: 10 000 m3). Other subsets were defined to address process constraints that are only timespecific (pdyn) in eq 8 (subset iit, e.g., constraint for the temporal availability of electricity from photovoltaic panels), specific to the individual buildings (pind) as in eq 9 (subset iil, e.g., constraint for the spatial restriction for the use of ground source heat), or building- and time-specific (pind,dyn) as in eq 10 (subset iit, e.g., constraint for the spatial and temporal availability of heat from solar collectors). The constraints in eqs 11−15 relate to process selection (based on binary decision variables) for the individual buildings and for the municipality. Buildings usually only have one major hydronic energy supply system. It is, for example, almost never the case that energy is supplied by both an oil boiler and by a natural gas furnace. Thus, the decision space is restricted to only one major energy system by introducing the binary variable uind. However, there is still the possibility that part of the heat for hot water could be delivered by an auxiliary device (i.e., solar collectors) (see eqs 13−15). The parameter m is a sufficiently large constant, which allows the binary decision variable uind to control the corresponding element š of the foreground system scaling matrix. The dependencies between the demand and availability of the district heating or hot water network are reflected in eqs 16 and 17. The index iL is used in eq 16 to represent processes with a threshold demand (psys) that had to be achieved to make a specific energy supply option available for use (e.g., threshold for energy supply via a district heat system, assumed to be 1.8 GWh per year). The subsets iLheating and iLhotwater include processes that provide energy for space heat and hot water, respectively, over a district heat network in eq 17. In eq 16, uind are controlled by usys, that is, whether a district heating network is made available in the municipality or not, and eq 17 enforces the energy demand threshold constraints upon the decision to invest in a district heating system. The binary variable usys describes whether a district heating system is an optimal option for the municipality.

∀ i ∈ i it , l

l

∑ sĭ ,l ,t ≤ piind ,l

Article

(13)

(14)

i ∈ i heating

∑ sĭ ,l ,t − ∑ sĭ ′ ,l ,t ≥ 0 ∀ i ∈ i hotwater , i′ ∈ i heating , i = i′, l t

t

(15) sys L uiind , l ≤ ui , ∀ i ∈ i , l

(16)

∑ ∑ sĭ ,l ,t + ∑ ∑ sĭ ′,l ,t l

t

≥

pisys ·uisys

l

t

∀ i ∈ i L heating , i ∈ i L hotwater , i = i′

renovation ,l ∑ sĭ ,l ,t ≤ m·uiind ,l ∀ i ∈ i t

(17)

(18)

A list of the indices and subsets used in eqs 1−18 is provided in Table 1, and the parameters and variables used are summarized in Table 2. 3.2.1. Objective Functions. There are a wide range of different methods available to solve multiobjective or multicriteria optimization problems.42 The solution to these problems typically consists of a set of Pareto-optimal alternatives that represent the efficient trade-offs between the objectives considered.43 The concept of Pareto-optimality implies that it is not feasible to improve the performance in one objective without compromising another objective. An alternative strategy is offered by the fuzzy LP extension for matrix-based LCA developed by Tan et al.25 Following this approach several impact categories are linearly related to a scalar denoted λ. It expresses the degree of satisfaction based on the estimated upper (hUc ) and lower bounds (hLc ) for the objectives. The upper and lower bounds might, for example, represent conservative and ambitious environmental targets respectively for each objective. The optimization problem is thus converted to the maximization of the degree of satisfaction that can be obtained for all objectives simultaneously. In the case study, the lower bound for each impact category (global warming potential and particulate emissions) was determined by solving the optimization problem separately for each objective. During the single-objective optimizations, the impact results for the other impact categories were simultaneously calculated. The maximum value for each category over all generated solutions provided the upper bounds, that is, any constraints in terms of the required performance level of the heating technologies were disregarded for this study. For the minimization of the impact results, λ was maximized (see eqs 1−3). The environmental impacts for the four renovation measures hren (i.e., floor, roof, walls, and windows refurbishment) were

4. RESULTS Figure 1 shows two maps of the case study municipality, depicting the annual life cycle greenhouse gas (GHG) emissions per hectare grid cell and year (including indirect emissions that occur, that is, during the production and end-of-life of energy carriers and technologies). Impact results are aggregated per hectare (100 × 100 m) to ensure data privacy of single buildings. The left map shows the impact results for the reference case, whereas the right map shows the results of the multiobjective optimization. The scale ranges from less than 0.5 to more than 32 tons CO2-eq per hectare (colors encode quintiles). 7654



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Figure 1. Results for the energy supply of buildings in a Swiss municipality for the reference case (left) and the multiobjective optimal case (right), aggregated per hectare. Top: annual life cycle GHG emissions. Middle: distribution of hydronic energy supply systems. Bottom: solar collectors as auxiliary hot water supply devices (In the gray shaded areas only air-sourced heat pumps are allowed, borehole heat exchangers are prohibited due to groundwater protection).

In both cases, the highest GHG emissions are in the center of the municipality due to the higher population density and bigger buildings. Additionally, in the reference system, the heating

systems of the buildings in the city center rely mainly on fossil fuels, whereas buildings in the outskirts of the municipality are mainly heated with wood. In the optimal case, GHG emissions 7655



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41% decentralized woodchip incineration and 6% decentralized wood log incineration. On the contrary, in the case of particulate matter formation minimization, refurbishment rates are high (91% for roofs, 95% for walls, 89% for windows, and 51% for floors). The heat supply consists in this case of a mixture of heat pump systems (21% ground sourced and 52% air sourced) and PEM fuel cells (27%). A trade-off therefore exists between the two optimization objectives: a minimization of GHG emissions by installing wood heating systems can only be realized at the cost of higher particulate matter emissions. The single-objective optimization results are provided in the SI.

are distributed more equally than in the reference case. GHG emissions above 32 tons CO2-eq per hectare are rare. Locally increased GHG emissions can be explained by a switch of the energy supply system from a purely wood based system with low GHG emissions to another system (e.g., heat pumps) in order to achieve a better trade-off between GHG and particulate matter emissions. In the optimal case, GHG emissions are reduced by more than 75% compared to the reference case, from 11 824 to 2664 tons CO2-eq. Also, particulate matter emissions are reduced by more than 50% from 10.7 to 5.12 tons PM10-eq This means that due to the changing structure in energy supply, the impacts from both impacts categories decrease. The multiobjective optimization applied here searches for the best consensus between the two objectives and therefore leads to a fundamentally different portfolio of hydronic energy supply systems in the municipality than in the reference case. Figure 1 shows the distribution of energy supply systems, that is, space heat and hot water supply systems (middle) and auxiliary hot water supply with solar collectors (bottom) in the case study municipality both for the reference case (left) and the optimal case (right). Each pixel depicts the most frequent system per hectare. The gray area describes where borehole heat exchangers and thus brine-water heat pumps are prohibited due to protection of groundwater. In the reference situation, the most common supply systems in the city center are oil boilers and gas furnaces, thereby 1156 t of light fuel oil and 1418 Nm3 of gas are used per year. In the outskirts, wood incineration is predominant (11 m3 of woodchips and 4583 m3 wood logs). Only in some cases the most frequent systems are direct electric boilers and heat pumps. The total electricity use for housing purposes is 4.76 GWh per year. Solar collector panel installations, delivering auxiliary heat for hot water generation, are very rare in the municipality (Figure 1). In the optimal case, however, solar collectors are installed to the fullest possible extent. The distribution of the space heat supply systems is rather homogeneous and heat pump systems (brinewater or air−water) as well as woodchips are preferred. Heat pumps are predominant in the areas where drilling boreholes for heat exchanger tubes is allowed. Woodchip incineration systems are predominant in the other areas. In total, 8452 m3 of woodchips and only 418 m3 of wood logs are used. The total electricity demand for housing purposes almost quadruples due to the installation of heat pump systems and amounts to 16.4 GWh per year (50% electricity from PV and 50% from the grid delivered from outside the municipality). Figure 2 shows whether each of the four possible refurbishment measures, respectively, is optimal for the majority of the buildings located within a given hectare cell. The refurbishment rate is the number of buildings that are refurbished divided by the total number of buildings in the municipality. The multiobjective optimization suggests that optimal refurbishment rates are 67% for roofs, 81% for walls and 68% for windows, and only 5% for floors. However, refurbishment rates in the single-objective optimizations are quite different: if only GHG emissions are minimized, refurbishment rates are low for all measures (8% for roofs, 28% for walls, 3% for windows, and 0% for floors). This is because GHG emissions released during the production of building parts are higher than those of the additional heat requirements. Due to the rural setting of the municipality with surrounding forests, the wood supply is sufficient to provide heat and hot water to all buildings. Therefore, in the case of GHG emissions, heat is supplied entirely by wood-based systems: 53% centralized woodchip incineration with district heat delivery,

5. DISCUSSION The aim of this study was to analyze the potential of reducing environmental impacts related to the energy supply of 1332 buildings in a Swiss municipality based on spatially explicit energy demand and infrastructure data, life cycle inventories for energy technologies, and different constraints. Compared to a standard LCA study, a major advantage of an optimization approach is that it is possible to take into account individual supply constraints (e.g., prohibition of ground source heat pump systems in certain areas) at the same time as systemic constraints that concern the energy supply of the whole municipality. For instance, district heating was only an option for individual buildings if the overall demand for a district heating system was high enough (above threshold). In a standard LCA, all of these constraints and local conditions would have to be addressed and compared in a (unfeasibly large) number of alternative scenarios. In a linear programming extension of LCA, only the constraints have to be defined while an algorithm looks for the best solution among all possible scenarios allowing for system-wide analyses to be performed. The study highlights trade-offs between two different environmental impact categories with particular relevance for residential heat systems. Policies aimed at minimizing only one indicator, for example, GHG emissions, may lead to considerable impacts in other categories, for example, PM emissions. Policies should therefore rely on studies that identify and present solutions for existing trade-offs from a system perspective. An important finding of the study is that approximately 75% of the municipality’s GHG emissions and 50% of the PM emissions could be reduced simultaneously. A comparison to a recent study by Heeren et al.,44 who calculated a GHG reduction potential of 85% for the city of Zurich, suggests that the magnitude of these potential impact reductions is reasonable. At the same time, the municipality could drastically minimize its dependency on fossil energy resources to supply the energy demand of its building stock. Although these figures are very encouraging in view of sustainable future energy supplies, they should be seen as longterm targets for environmental improvement potentials rather than realistic goals in the near future. Our underlying model remains theoretical in the sense that neither economic aspects nor the willingness of households to take action regarding the installation of new technologies and the refurbishment of their homes were taken into consideration. Further research is therefore necessary to take these factors into account and investigate, for example, cost-optimal options to reduce environmental impacts. Nevertheless, from an environmental perspective the study showed that energy technology replacement and high rates of refurbishment are desirable in spite of the environmental impacts associated with their production. The high reduction potential of GHG and PM emissions should be seen in the context of a rural Swiss setting where wood 7656



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Figure 2. Optimal decisions for four possible refurbishment options: roof, wall, floor, and window refurbishments.

living space. The results of the model can serve as a benchmark for policy makers, quantifying maximum reduction potentials in environmental impact. They also pinpoint specific decisions that can make a difference and thus help to design incentive systems for sustainable urban energy systems. Finally, the results can be helpful to identify pathways to meet political goals, such as the energy turnaround in Switzerland (switching to a low-impact energy supply without nuclear power).

heating has a tradition and is, due to the used technology, the source of a considerable share of total PM concentration.34 More stringent emission limits for PM-emissions would reduce the feasible decision space in the optimization problem and presumably constrain the reliance on wood heating or require a shift toward “cleaner” wood furnaces. In an urban setting, biomass availability can be assumed to be a limiting factor. Moreover, PM emissions should be evaluated differently as the population density is higher.45 Some of the results are therefore specific to the case study. However, given the availability of data, an adaptation of the model to other regions and constraints should be possible. Further model improvements are possible, for example, considering future technology development, including cost data, taking into account building park renewal rates and changes in population number, and changed demand in, for example,

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ASSOCIATED CONTENT

S Supporting Information *

Supporting Information describes the implementation of the optimization extension including an illustrative example and provides additional results for the single-objective optimizations of the case study. This material is available free of charge via the Internet at http://pubs.acs.org/. 7657



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AUTHOR INFORMATION

Corresponding Author

*Phone: +41 44 633 76 39; e-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Dominik Saner was funded by the Competence Center for Energy & Mobility (CCEM) and swisselectric Research within the THELMA project (www.thelma-emobility.net). Carl Vadenbo and Bernhard Steubing are grateful for the financial support from the Swiss Competence Center for Energy Research (SCCER) “Efficient Technologies and Systems for Mobility”. We thank Catherine Raptis and Justin Boucher for the English proofreading and Alejandro Alonso for the design of the TOC art. The comments from two anonymous reviewers are gratefully acknowledged.

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NOTE ADDED AFTER ASAP PUBLICATION This paper was published ASAP on June 13, 2014. Due to production error, not all of the author’s corrections were incorporated. The corrected version was reposted on June 17, 2014.

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