The urgency to mitigate climate change has led to a global focus on reducing greenhouse gas (GHG) emissions and thus energy consumption. Furthermore, the Intergovernmental Panel for Climate Change (IPCC) emphasizes that reductions in energy demand can increase energy security and reduce the need for raw materials and land in the energy transition [1]. One strategy to reduce energy demand is through improving energy efficiency, i.e., reducing the amount of energy needed per unit of goods or services produced [2]. For most European countries the building sector accounts for a significant portion of total energy use and GHG emissions. Refurbishing existing buildings with energy efficiency measures is widely regarded as a crucial step towards achieving national and international climate goals[3].

The reduction of heating energy consumption and the mitigation of GHG emissions in the building sector have been an ongoing quest, especially in countries where fossil fuel derived electricity or fossil fuels are used as the main heating solution in buildings. Numerous regulations are directed at increasing the building energy performance of existing buildings, however, there is a gap in the practical implementation of such regulations, especially in developing countries[4].

Several review articles present the status of life-cycle assessment (LCA) literature which compare various energy efficiency measures and associated GHG emissions . The most recent and comprehensive of these performed a scoping review resulting in 54 articles published between 2005 and 2022—which were then thoroughly reviewed and compared [5]. Their findings point to the majority of articles being of regional- or national case studies of specific building archetypes, typically age cohorts, covering product through construction plus operational energy respectively (A1‒A5 + B6), with a lifespan of 50 years. Several studies used Environmental Product Declaration (EPD) data, towards determining GWP (96%), though few (38%) combined these results with building costs—with the authors citing this as a weakness [6]. In a study of optimal energy performance packages at the lowest cost during the estimated economic lifecycle for a residential building located in three different locations in the Mediterranean climate, it was found that the optimum energy savings at the lowest cost are 23–39%. The study concludes that climatic characteristics is a very important parameter affecting the reference building definition procedure. Further, the methodology is highly sensitivity to national factors and utility rates and slightly sensitive to the discount rates, cost calculation period, and development price [6].

Since the Kyoto Protocol was adopted in 1997, international work has been conducted to identify the most environmentally- and cos- effective measures enabling European countries to meet their evolving targets on reductions in both energy demand and GHG emissions [7]. The Kyoto Pyramid was developed as a strategy for the design of low-energy buildings in Norway and has also served as a framework for energy demand reduction strategies for existing buildings. The key point of the Kyoto Pyramid is that one should start by reducing heat loss and thus heating demand, followed by reducing energy demand from appliances through energy-efficiency, before addressing the heating solution and overall energy supply [8]. The research field on energy efficiency measures for buildings has been developed over several decades and evolved from focusing on a measure’s effect on energy demand to broader scopes looking at cost, payback time and lately also GHG emissions related to the measure and its estimated energy savings. However, most existing studies include only two of these aspects in their analysis. The European Energy Performance of Buildings Directive (EPBD) requires that building codes are based upon a cost-efficiency approach. And because the requirements are driven by cost, there is no inherent balance between the GWP of the energy saved and the GWP investment in the measures that impact energy use [9]. The selection of cost-effective components of the building envelope plays a significant role in a sustainable building design solution. Therefore, in terms of effective decision-making, it is important to have a complete insight into construction and operating costs throughout the lifespan of the building [10]. In a study of apartment buildings in Norway, a multi- criteria assessment approach is presented where cost, GHG emissions, energy savings, as well as social aspects, are considered for several retrofit measures combined in packages. Such packages are highly relevant for larger buildings where comprehensive renovations are more common because they are more cost-effective, but for small, detached houses in the dwelling stock, such holistic approaches are rarely conducted because of house owners' exclusive ownership and responsibility depending solely on owners' capacity. For these buildings a longitudinal approach is practiced where measures are implemented when an opportunity arises because of maintenance, etc. It is therefore necessary to evaluate individual measures towards developing knowledge on which measures are most relevant to promote for these house owners [11].

In a study of a framework for evaluating the net present global warming potential (GWP) of an energy-saving measure, emissions from materials are weighed against energy savings over a period of 60 years as defined in the Norwegian standard for GHG calculations for buildings, NS 3720:2018 [9] . There are three crucial parameters in these considerations related to; the developments in the energy mix and thus GHG emissions from energy, the carbon footprint of the materials used, and perhaps most importantly, the temporal considerations of the measure. These three parameters do, however, not consider the pressing need to reduce energy demand to limit the need for new energy production which represents economic, environmental, and sociocultural costs. These considerations lie outside the system boundary of the calculations but should be considered. This argues for a more complex evaluation framework, spanning across sectors, that promotes low-carbon materials but favours a maximization of energy savings [9].

Despite the consensus on the importance of these measures, there is a lack of comprehensive analysis that places energy savings in context with both economic costs and GHG emissions. This paper provides an extensive evaluation methodology of energy efficiency measures where potential energy savings, GHG emissions, and related costs for the most prominent measures are presented, aiming to answer the following research questions:

1. How can different energy efficiency measures be weighted based on the measures’ costs, energy use reduction and GHG emissions?

2. How should energy efficiency policies change to achieve higher and more realistic energy reduction?

The case study presented in this paper analyses energy efficiency measures for detached houses in Norway. Towards developing a better understanding of the case study, and its results, a short description of the Norwegian context is provided next.

Historically, Norway has enjoyed an abundance of energy from hydropower which is considered a renewable energy source. The transition towards a low-carbon society in 2050 entails a shift from fossil fuels to renewable energy sources in all sectors. This transition can ultimately lead to an increase in electricity demand, and Norway is headed for an energy deficit in 2027. Because of hydropower, Norway has had low electricity costs and energy security, leading to building an energy supply consisting of 85% electricity [12]. Buildings account for 40% of the national energy demand, where approximately 60% of this demand is used for heating [12]. The Norwegian building regulations have since the early 2000s sharpened demands for energy-efficient buildings and renewable energy supply, and a ban on oil burners in buildings from 2020 has led to the decarbonization of the operation phase of buildings in Norway. Building regulations have now reached a “nearly zero energy”- level where stricter energy efficiency measures will have to be considered against the increase in materials and their consequences on both the environment and costs [13]. A total of 76.4% of the inhabitants own their dwelling in Norway. It is the individual homeowner who is responsible for the maintenance, adaptation, and upgrading of their dwelling [14]. A market-driven development in the dwelling sector since the 1980s has led to a lack of focus on long-term physical qualities such as energy-efficiency improvements in the dwelling stock [15]. An important challenge in housing policy therefore lies in ensuring the development of the existing housing stock so that the dwellings can satisfy new and future needs [16].

Reducing electricity demand in buildings is crucial for achieving the energy transition whilst limiting the need to establish new energy production that represents economic, environmental, and sociocultural costs [11]. In addition, a properly implemented energy upgrade of residential buildings will provide a better indoor climate and comfort, prolong building life, and reduce energy costs [17]. There have been policies promoting energy efficiency improvements in Norwegian homes ever since the energy crisis in the 1970s, and the new-built housing stock has gradually become more energy efficient [18]. The two most important contributions to this development are the stricter technical regulations for new buildings [19] and the ECO design standard, which together have reduced the energy consumption of domestic technical equipment[20]. Since the early 2000s, there has been a widespread uptake of heat pump solutions in the dwelling stock, partly due to the need for new heating solutions in preparation for the oil ban in 2020, but also motivated by comfort improvements at low cost [21]. These measures have therefore had large rebound effects and do not comply with the logic of the Kyoto Pyramid of reducing the heat loss through building envelope improvements before selecting heating solutions and energy supply. Today the most effective measures for improving energy efficiency in the Norwegian dwelling stock are considered to be insulation of the building envelope and smart control of energy use [19]. These conclusions are made solely on potential reduction of energy demand and have rarely been subjected to cost and LCA-considerations.

The energy calculations are performed using the energy simulation programme SIMIEN [27] , which is the most common energy simulation programme used in the building industry in Norway today. SIMIEN is a Norwegian-developed energy calculation programme that has been validated according to EN 15265 and harmonized to NS 3031. The European standard specifies a set of assumptions, requirements and validation tests for procedures used for calculating the annual energy needs for space heating and cooling of a room in a building where the calculations are done with a time step of one hour or less. The programme performs dynamic simulations of energy needs, validates indoor climate, and sizes heating systems, ventilation systems and room cooling. The values applicable to the various technical regulations are applied to the building models in SIMIEN for the various building categories.

Simulations are then carried out to obtain a theoretical potential for energy savings. The building models used are the same as those that form the basis of the framework for national energy requirements for buildings in Norway. In the model, the total window/door area makes up 20% of the usable indoor floor area. The model has windows on all facades.

Inputs into the building models are based on the Norwegian building regulations from 1969 (TEK69) and the building model represents a house built according to these requirements and the construction methods of the time. The variables, e.g., U–values (W/m²K), have been calculated thereafter. There were no requirements for air leakage in TEK69. Here, the leakage figure has been chosen after assessments concerning the construction method of the time for the individual building category. For thermal bridge values, standard values from NS3031 Table A.4 have been used, according to the typical construction method for the individual building category. For the solar factor for glass, figures based on expert judgement for the type of glass that is typical for windows that satisfy the U–value requirements of the time are used. For ventilation air volumes, a conversion has been made to what the text of the regulations corresponds to m3/(hm2) based on estimates for the number of rooms that are specially ventilated (kitchen, bathroom, laundry room, etc.) and the size of the building models. Natural ventilation is used for detached houses built according to TEK69. Cooling is not included in any of the calculations. The specific fan power factor (SFP factor) is estimated based on a typical ventilation system. As input data for power and energy requirements for lighting, equipment and hot water, as well as internal heating contribution, , figures from Tables A.1 and A.2 in Norwegian standard NS 3031 are used. These data are fixed in all calculations.

It is important to highlight that the calculated energy savings (kWh) for the various measures and their rankings apply only to the first energy measure the building owner chooses to implement. Once this measure has been implemented, the effect and ranking of the remaining measures will be affected. New calculations must then be made to find the secondbest measure for energy savings, and so on. Costs and GHG emissions for the various measures are independent of this procedure.

Costs have been calculated at component level. Figures from similar projects and cost figures from external cost databases, for example "Holte" [28], are used. The total cost for the component is calculated by multiplying the unit price (total including rigging and operation) by the area or volume of the component. This calculation gives the total costs for the measure. The costs shown in the figures and tables are construction costs, including Norwegian VAT. Costs for planning/design are not included. Some of the measure costs are based on numbers from calculations in 2013 [23] adjusted for the consumer price index (increase of 38% from 2013 to 2023). Others are based on cost data from 2023.

Also, a profitability indicator that gives the total cost of a measure divided by the energy saving per year is calculated. A low value indicates the best profitability. This indicator should not be confused with the payback period, although it has similarities in its calculation methodology, though lifetimes are not taken into account. This indicator still gives a picture of which measures pay off the most.

An environmental life cycle assessment (E–LCA) has been conducted to determine the climate change impact or GWP—measured in kilograms carbon dioxide equivalent (kg CO2 eq.) for those measures outlined previously in this section (GWP_T), including comparison. This assessment has been conducted in accordance with ISO 14040 series [29] standard and the General Guide for Life Cycle Assessment (ILCD Handbook [30]. A cradle-to-installation assessment was conducted, i.e., emissions and consequent impacts originating during the use-and end-of-life stages are not included. Within the bounds of this paper, decision context A, i.e., micro-level decision support, and subsequent application of attributional life-cycle inventory (LCI) modelling principles have been applied. Furthermore, it was assumed that all foreground processes are uni-functional; information pertaining to the handling of multifunctionality in background processes can be found in their respective resources (Appendix 1). These impact-, method- and assumption-related decisions can limit the interpretation of results.

Primary foreground data constituting the final demand are adopted from previous cost- and energy calculations. Regarding secondary background data and life cycle impact assessment (LCIA), an important distinction has been made between passive- and active building measures. Here, passive measures are defined as those which do not require/produce energy during their use phase, and conversely active measures as those which do require/produce energy during their use phase. For passive measures (i.e., 1–6; see Table 1) secondarybackground GWP impact data are based on Environmental Product Declarations (EPD) which have been determined in accordance with EN 15804 ([31]; see Appendix 1). For these passive measures (i.e., 1–6), and because of limitations in scope, emission/sequestration of biogenic CO2 in wood-based products from cradle-to-installation (A1–A5; [31]) are not included in the results. For active measures (i.e., 7–10; see Table 1) SimaPro v9 and accompanying EcoInvent v3 database were used. For these active measures, GWP was determined using the ReCiPe (H) midpoint method [32]; see Appendix 1).

Additionally, a simple GWP payback time calculation was performed using Equation 1.

Where:

E_TA is adopted from the previous energy calculations. For GWPel. SimaPro v9 and accompanying EcoInvent v3 were applied to determine the GWP intensity per kWh for low-voltage electricity at the consumer level in both Norway and the EU–27, 0.026 kg CO₂ eq./kWhel. and 0.400 kg CO₂ eq./kWhel. respectively.

The study is focusing only on Norwegian detached houses. Similar calculations are conducted for dwellings and office buildings, though not presented in this paper. The methodology and study should be expanded to include more regions/countries with different climates. This expansion could build a more robust model for generalizing the results. The study is a fully theoretical study based on simulations and thus neglects the important but challenging factor of user behaviour. There are also several parameters that involve high uncertainty, e.g., energy prices, energy mix and corresponding GHG emission, GHG emissions from materials, and general cost developments for both labour and materials. Further work should focus on integrating energy efficiency measures into general maintenance practices and promote these solutions through existing channels advising house owners. A further discussion on system boundaries for analyses like this is also needed. For this discussion, the neighbourhood level represents a promising scale for energy optimization, in addition to a more holistic approach on a regional or national scale.