Energy Performance Certificates Research Articles

Impact of the EPBD on Changes in the Energy Performance of Multi-Apartment Buildings in Lithuania

As per general provisions of European Directive 2010/31/EU on the energy efficiency of buildings (recast), the Lithuanian government transposed the Directive into Lithuanian national law. In the process, the Lithuanian government prepared strategic documents in the field of energy performance and renewable energy that were integrated together through the National Energy and Climate Plan for 2021–2030 (NECP). To better understand the current situation vis-à-vis energy performance, the main characteristics of buildings pertaining to the Lithuanian multi-apartment building stock, classified according to their energy performance class, are analysed and discussed in this paper. Through the exploitation of data from the national Energy Performance Certificate (EPC) register, an overview of the energy performance of the existing Lithuanian residential building stock is presented along with an analysis of the unused potential energy savings pertinent to this building category. The results obtained from the analysed data of energy consumption in buildings shows that the policies adopted over the years were successful in improving the building stock, promoting the move towards the specifications required by a Class A++ (nearly zero energy buildings—NZEB) by 2021. The results show that this was primarily achieved by a significant reduction in the thermal energy used for space heating.

The Energy Efficiency Gap in the Rental Housing Market: It Takes Both Sides to Build a Bridge

We revisit the issue of the energy efficiency (EE) gap by explicitly acknowledging the two-sided nature of the rental housing market and two-sided asymmetries of information between tenants and landlords. Employing a theoretical matching model, we show that Energy Performance Certificates (EPCs) that signal a dwelling’s energy performance induce optimal EE investments by landlords only if tenants pay their energy expenditures in full. When landlords pay part of the energy expenditures, they seek tenants who will conserve energy. Our model shows that asymmetry of information over tenant characteristics results in suboptimally low investments in EE. This may even render EPCs counterproductive. As a remedy, we show that tenant-side signaling needs to be rolled out jointly with EPCs and may even be sufficient when contracts include energy expenditures. Data from an original survey provides support for these insights and suggests that information on the tenants’ side contributes to more EE investment.

Energy Efficiency Premium Issues and Revealing the Pure Label Effect

Following the European Union’s implementation of Energy Performance Certificates (EPCs) for buildings, the capitalization of energy efficiency in transaction prices and rents has been subject to much research. This paper uses different identification strategies for the Norwegian residential sales (N = 750,000) and rental (N = 670,000) markets to highlight the endogeneity and methodological limitations associated with assessing the price effects of energy efficiency and the signaling effect of label adoption. We find that the valuation of energy efficiency is subject to unobserved location and quality bias, that labeling has immediate, short-run, and long-run price effects and that different effects are observed in different submarkets. We provide evidence that sample selection issues related to location and time, with methodological and data limitations, are essential factors that must be considered when assessing the effects of the EPC implementation.

Estimating the Energy Demand of the Residential Real Estate Stock in Hungary Based on Energy Performance Certificate Data

In our study, estimates are made for the distribution of the Hungarian residential real estate stock in 2020 by energy characteristics. In our calculations, which are novel in Hungary, a new database has been compiled by combining the energy certificates issued since 2016, the 2016 Microcensus and the housing construction statistics of the HCSO. Energy performance certificate data are assigned to the dwellings included in the Microcensus and to the 68,000 new dwellings built in the period since then. A statistical relationship is established between the characteristics and the energy demand of dwellings, which is then extrapolated to the stock as a whole. This is processed to present the estimated calculated energy consumption per square metre of the Hungarian residential real estate stock and the characteristics of the estimate by area and real estate type. Our results can support sustainable mortgage lending in the financial system.

Energy performance estimation for large building portfolios with machine learning-based techniques

Building operation is responsible for 28% of the world’s carbon emissions. In this context, establishing priorities in refurbishment strategies at the scale of a city or a group of buildings is important. Such procedures are usually led by experts in energy performance and, therefore, they are rarely carried out due to their long and costly nature.This research aims at the estimation of building energy performance to pave the way towards finding near-optimal refurbishment strategies. Thanks to the identification of easily-accessible building characteristics, the method applies machine learning models to scan a building portfolio based on a low level of details. The results show good potential to identify low-performer buildings with simple machine learning methods. It also opens the door for further improvements through the inclusion of supplementary building features at the input of the predictive system.This work includes (a) the integration of a knowledge database thanks to the Swiss CECB energy performance certificates, referencing more than 70 000 buildings, (b) the preparation of a training data set through the selection of relevant physical characteristics of buildings (input) and the corresponding energy consumption labels (output), (c) the development of predictive models used in a supervised way, (d) their evaluation on an independent test set.

Energy Performance Aspects of Non-Residential Buildings in Latvia

Abstract Increase of energy efficiency is Latvia’s national priority and policy instrument that allows reducing consumption of energy and costs, increasing the safety level of energy supply and reducing dependence on import. One of the trends for reducing energy consumption is to increase energy performance of buildings. In compliance with the EU and Latvian political statements, it is required to promote renovation of residential and non-residential buildings. Studies regarding energy performance aspects in the sector of non-residential buildings in Latvia are comparatively rare; however, non-residential buildings form a considerable part of the building stock, and the increase of their energy performance can bring a significant contribution in achieving the national targets. The research analyses the sector of non-residential buildings, their characteristic statistical data, energy consumption and requirements of energy performance standards. To characterise this sector more comprehensively, information on energy performance certificates of buildings issued during the period from 2016 to 2021 has been evaluated, allowing to make general conclusions on the energy performance level of different non-residential buildings and provide proposals for trends to increase their energy efficiency.

Doweled cross Laminated Timber (DCLT) Building Air Tightness and Energy Efficiency Measurements: Case Study in Poland

A contemporary challenge for the construction industry is to develop a technology based on natural building materials which at the same time provides high energy efficiency. This paper presents the results of an airtightness test and a thermal imaging study of a detached house built with technology using cross laminated dowelled timber panels. The thermal conductivity coefficients of the wood wool used to insulate the walls and ceiling of the building have also been measured, the linear heat transfer coefficients of the structural nodes have been numerically determined, and calculations have been made regarding the energy efficiency of the building. On the basis of the research, it was found that the air exchange rate in the analyzed building n50 is at the level of 4.77 h−1. Air leaks were also observed in the places of connection of longitudinal walls with the roof and at the junction of window frames with external walls. The experimentally determined thermal conductivity coefficient of the wood wool was ~10% higher than that declared by the manufacturer. Calculations for the energy performance certificate showed that an increase of ~10% in the thermal conductivity coefficient of the wood wool used to insulate the building results in a heating demand increase of 2.1%. It was also found that changing the value of the parameter n50 from 1.0 h−1 to 4.77 h−1 leads to a 40.1% increase in heat demand for heating the building. At the same time, the indicators for final energy demand EK and non-renewable primary energy demand EP increase by 18.1%.

Air Cavity Building Walls: A Discussion on the Opportunity of Filling Insulation to Support Energy Performance Improvement Strategies

The paper intends to discuss the use of cavity wall insulation in existing buildings for energy improvement. Cavity walls are widely spread throughout the EU, typically in buildings from the 20th century, which are recognized as the subject of urgent and deep energy renovation interventions. Their main characteristic is an empty air cavity between two layers of building materials (typically brick, stone, or concrete). Filling the empty air cavity with insulating materials reduces energy loss by transmission through opaque envelope surfaces, and therefore, it represents a cheap solution to obtain a significant reduction in building energy consumption. Various aspects should be carefully evaluated for the effectiveness of this type of intervention: The first step is knowing the materials’ behavior depending on their thermal and hygrometric parameters (conductivity and transmittance, thermal bridge transmittance, vapor permeability, and resistance to vapor diffusion). Some indications on limits or reference/target values are usually given by national/regional laws and EU Directives. The filling insulation could lead to collateral problems (thermal bridges, risk of vapor condensation inside walls, etc.). To help understand the relevance and the impact of this insulation technique, an example of the assessments of a common cavity wall type is presented. The analyses highlight the usefulness of an organized and systematic database on building envelope features to set targeted energy-saving actions. Public administrations could base their strategies on this information to promote the reduction of energy consumption on a large scale with efficient and economically sustainable interventions. In the absence of more specific databases, the Energy Performance Certificates (EPC) data consulting could help develop thermal insulation strategies at a regional/national level. In particular, the diffusion of cavity walls can be obtained as a function of wall surface and cavity thickness for estimating the energy saving potential, economic impact, and general feasibility of systematic insulation-filling actions. An example of this kind of database analysis is discussed and carried out on a regional scale to explain how it could represent a useful approach to evaluate potential energy-saving interventions. The sample building stock provides suggestions on the possibility to extend the considerations on a larger scale to help set systematic energy-saving strategies.

Benchmarking building energy performance: Accuracy by involving occupants in collecting data - A case study in Germany

Energy performance certificates (EPC) aim to provide transparency about building energy performance (BEP) and benchmark buildings. Despite having qualified auditors examining buildings through on-site visits, BEP accuracy in EPCs is frequently criticized. Qualified auditors are often bound to engineering-based energy quantification methods. However, recent studies have revealed data-driven methods to be more accurate regarding benchmarking. Unlike engineering methods, data-driven methods can learn from data that non-experts might collect. This raises the question of whether data-driven methods allow for simplified data collection while still achieving the same accuracy as prescribed engineering-based methods. This study presents a method for selecting building variables, which even occupants can reliably collect and which at the same time contribute most to a data-driven method's predictive power. The method is tested and validated in a case study on a real-world data set containing 25,000 German single-family houses. Having all data collected by non-experts, results show that the data-driven method achieves about 35% higher accuracy than the currently used engineering method by qualified auditors. Our study proposes a stepwise method to design data-driven EPCs, outlines design recommendations, and derives policy implications.

Energy Performance Certificate Classes Rating Methods Tested with Data: How Does the Application of Minimum Energy Performance Standards to Worst-Performing Buildings Affect Renovation Rates, Costs, Emissions, Energy Consumption?

Energy renovations of the building stock are a paramount objective of the European Union (EU) to combat climate change. A tool for renovation progress monitoring is energy performance certificate (EPC) labelling. The present study tested the effect of different EPC label classifications on a national database, which comprises ~25,000 EPC values from apartment buildings, detached houses, office buildings, and educational, commercial, and service buildings. Analysing the EPC classes labelling resulting from four different EU methods, we estimated the annual renovation rates, costs, energy savings, and CO2 emissions reduction that would affect the national building stock if each of them was adopted, to fulfil the European Climate Target Plan by the year 2033. The ISO 52003-1:2017 two-point and one-point methods determined a very uneven distribution of renovation rates, from 0.45% to ~9%. Conversely, the Directive 15% recently proposed in COM/2021/802 with uniform rates determined smaller differences and standard deviation, not pushing renovations above 3.70%, namely a rate that once fine-tuned can stimulate realistic, yet effective renovation campaigns. The major differences in renovation rates provided by the studied methods show the need for a harmonized strategy such as the Directive proposal to enable achievement of European targets.

Regulatory barriers to implementing sustainable buildings in Kosovo

PurposeThe purpose of this paper is to highlight the regulatory barriers to achieving sustainable buildings in Kosovo. The present paper focuses on regulatory barriers viewed from the perspective of construction industry experts in achieving sustainable buildings.Design/methodology/approachThe present study uses a qualitative research method and semi-structured interviews as a research instrument. The present study interviews around 20 experts in construction and property management, property development, spatial planning and energy management.FindingsThe study finds that Kosovo building laws and regulations provide for the materials assessment criteria, but the materials assessment criteria are only for mechanic strength. The study further finds that the sustainability concept is not included and incorporated in Kosovo's urban planning laws and regulations. The study also finds that despite specific clauses mentioning energy performance certificates in the Law on Energy Performance of Buildings in Kosovo, energy performance certificates appears to be not enforced and the nature of the barrier is more organizational rather than regulatory. Finally, the study finds that Kosovo laws are silent as far as green labeling of building materials is concerned.Practical implicationsThe implication of the present finding is that policymakers in Kosovo not only should include clear sustainable materials assessment criteria in the law, but also enforce those criteria through testing and inspection mechanisms included in the law and implemented in practice through funding and organizational support. Nonetheless, policymakers in Kosovo should contemplate amending the urban planning laws in Kosovo and include both the term of sustainability at the planning level and conformity guidelines for sustainable design that can be done at the administrative directive level. Further, the clauses in the law do not suffice if the clauses are not accompanied by specific systemic and organizational support in the issuance of energy performance certificates. Policymakers in Kosovo should be proactive in designing clauses that specify green labeling standards for materials; however, these labeling standards should not adversely affect the cost of construction and reduce the demand for real estate.Originality/valueThe study is the first qualitative study about the perception of construction professionals in Kosovo, regarding the regulatory barriers of sustainable buildings in Kosovo.

Deep multimodal learning for residential building energy prediction

The residential sector has become the second-largest energy consumer since 1987 in the UK. Approximately 24 million existing dwellings in England made up over 32% of the overall energy consumption in 2020. A robust understanding of existing buildings’ energy performance is therefore critical in guiding proper home retrofit measures to accelerate towards meeting the UK’s climate targets. A substantial number of predictions at a city scale rely on available data, e.g., Energy Performance Certificates (EPCs) and GIS products, to develop statistical and machine learning models to estimate energy consumption. However, issues with existing data are not negligible. This work adopted the idea of deep multimodal learning to study the potential for using Google Street View (GSV) images as an additional input for residential building energy prediction. 20,031 GSV images of 5,933 residential buildings in central Barnsley, UK, have been selected for a case study. All images were pre-processed using a state-of-the-art object detection algorithm to minimise the noise caused by other elements that may appear nearby. Building specifications that cannot be easily determined by the appearance are extracted from existing EPC information as text-based inputs for prediction. A multimodal model was designed to jointly take images and texts as inputs. These inputs are first propagated through a convolutional neural network and multi-layer perceptron, respectively, before being combined into a connected network for final energy prediction. The multi-input model was trained and tested on the case study area and predicted an annual energy consumption with a mean absolute difference of 0.01kWh/m2 per annum on average compared with what is recorded in the EPC. The difference between the predicted results and the EPC may also provide some hints on the bias the certificates potentially contain.

Who lives in overcrowded households in north-east London? Cross-sectional study of linked electronic health records and Energy Performance Certificate register data.

ObjectivesHousehold overcrowding is associated with adverse health outcomes, including increased risk of infectious diseases, mental health problems, and poor educational attainment. We investigated inequalities in overcrowding in an urban, ethnically diverse, and disadvantaged London population by pseudonymously linking electronic health records (EHR) to Energy Performance Certificates (EPC) data. ApproachWe used pseudonymised Unique Property Reference Numbers to link EHRs for 1,066,156 currently registered patients from 321,318 households in north-east London to EPC data. We measured household occupancy and derived the bedroom standard overcrowding definition (number of rooms relative to occupants’ sex and ages) to estimate overcrowding prevalence. We examined associations with: household composition (adults only, single adult+children, ≥2 working-age adults+children, ≥1 retirement-age adults+children, three-generational household); ethnic background (White, South Asian, Black, Mixed, Other, missing); and Index of Multiple Deprivation (IMD) quintile. We used multivariable logistic regression to estimate the adjusted odds (aOR) and 95% Confidence Intervals (CI) of overcrowding. ResultsOverall, 243,793 (22.9%) people were overcrowded. People living in households with children, or three-generational households were more likely (aOR [95% CI] 3.79 [3.74 - 3.84]; 6.53 [6.41 - 6.66] respectively), and single adults or retirement age adults with children less likely (0.36 [0.35 - 0.38]; 0.36 [0.23 - 0.57] respectively), to be overcrowded. Overcrowding was more likely among people from Asian or Black ethnic backgrounds (1.24 [1.22 - 1.25] and 1.17 [1.15 - 1.19] respectively). There was a dose-response relationship between IMD quintile and overcrowding: OR 0.20 [0.20 - 0.21] in the least deprived compared to most deprived quintile. ConclusionOne in five people in north-east London live in overcrowded households with marked inequalities by ethnicity, household generational composition, and deprivation. Up-to-date estimates of household overcrowding can be derived from linked housing and health records and used to evaluate the impact of economic policies on health and housing inequalities.

Determining realistic U-values to substitute default U-values in EPC database to make more representative; a case-study in Ireland

Energy Performance Certificates (EPCs) are the foremost source of information on the energy performance of the EU’s building stock. Inherent in all EPC methodologies are trade-offs between reproducibility, accuracy, assessor expertise and costs. During an assessment, where accurate building data acquisition would be excessively invasive or costly, nationally specified default values are used.Default values are necessarily pessimistic to; avoid a better-than-merited rating, enable homeowners to know the advantage of energetic refurbishment, encourage homeowners to record upgrades informing EPCs, and propel assessors to seek-out information to provide an accurate rating.This work reviews default U-value use across Europe and the UK before focusing on Ireland’s EPC methodology, finding 1 in 3 entries in the Irish EPC dataset to be characterised on default U-values in 2020, leading to the dataset presenting an overly pessimistic view of the stock, thus lacking validity.To mitigate the thermal energy performance gap between theoretical rated energy consumption and actual or likely energy consumption arising from the selection of unrealistic default values for parameters; this work reviews the literature to identify and catalogue predominant/prevalent construction characteristics over time and calculates associated U-values that can be substituted for unrealistic defaults, thereby making the EPC dataset more representative and resultant national building energy stock models more robust.A total of 38 wall (8 predominant) and 4 predominant roof types were characterised finding differences between default and realistic U-values to be as high as 187 %. A generalisable find-and-replace methodology for the Irish EPC database is also proposed.

Building stock characteristics of residential buildings in Salzburg, Austria based on a structured analysis of energy performance certificates

Heating and cooling of residential buildings is accountable for a significant share of the EU’s final energy consumption. In order to determine the current condition of the building stock and to implement appropriate measures and requirements to improve energy efficiency of buildings, a detailed analysis of existing buildings is essential. This paper aims at describing characteristics of the building stock of Salzburg by analysing energy performance certificates. Therefore, the building stock is grouped into archetypes based on the construction period, building condition (not-retrofitted and retrofitted) and building type (single-family house and multi-family house) and then analysed regarding energy need for space heating, energy carrier, heat emission system, share and U-values of building elements and window to wall ratio. The analysis of the data demonstrates that the structure and naming conventions within the EPC-database pose hurdles for a correct investigation of the data. A comprehensible and distinct notation as well as versioning is inevitable for the sake of avoiding a loss of information and enabling international comparability. An analysis of energy carriers of the archetypes reveals, that a significant share of buildings still uses fossil fuels like oil and gas for energy supply, although electricity (heat pumps) and district heating are on the rise for buildings constructed in recent periods as well as for retrofitted buildings, with heat pumps being the most important energy carrier for new single-family houses amounting to a share of 65% and district heating for new multi-family houses amounting to a share of 46%. A detailed observation of the thermal performance of buildings and building elements reveals high potential and practicability not only for energy efficient new buildings, but also retrofitted buildings throughout all construction periods and for both, single-family houses and multi-family houses (e.g. median U-values for retrofitted ceilings/roofs range from 0.17 to 0.26 W/(m2K) compared to a median of 0.14 W/(m2K) for not-retrofitted ceilings/roofs from the most recent construction period). The conducted analysis provides detailed statistical information on the Salzburg building stock and hence a robust basis for further studies regarding the thermal performance of the building stock and future scenarios by means of urban building energy modelling approaches. Furthermore, based on this information, policies for energy efficient buildings can be improved and adapted according to current circumstances.

Smart Estimation Model for Energy Performance Certificates of Residential Buildings

The wasted amount of energy has become a critical problem for many countries due to limited energy sources and energy production costs. These forced countries to increase energy usage awareness by making regulations in the construction sector, which might dramatically decrease energy consumption cause of the size of the domain. One of them is standardizing heating and cooling loads (HL/CL) to avoid energy waste. HL and CL need an advanced engineering process because of different parameters such as the thermal characteristics of the building, hot water supply, passive solar systems, etc. Hence, it can only be carried out by expert engineers in calculations. In this paper, a classification model as the decision support system is proposed for predicting the energy consumption of residents which is an efficient indicator of architectural features of the construction about energy consumption concept. The data is collected from architectural projects and energy performance certificates. Multilayer Perceptrons, Bagging, and Random subspace are used to predict the energy class of buildings. Based on findings, the most accurate results were achieved by Bagging. Moreover, main input features affecting the prediction performance of HL were revealed and classification success was observed.

An enhanced framework for next‐generation operational buildings energy performance certificates

The improvement of the energy performance of buildings is identified as one of the core challenges toward achieving a carbon-neutral built environment. In the 2018 recast of the Energy Performance of Buildings Directive, the European Commission has emphasized the need for improved schemes, to ensure the best possible evaluation of the actual energy performance of buildings, taking into consideration all the parameters related both to their construction and operation. Significant research efforts have been designated in this area, to identifying the additional information required to not only improve the energy performance certification process but also to provide more thorough reports to end-users. To increase comprehension, awareness, and thus genuine involvement, cutting-edge digital technologies are expected to be used. The research project entitled “Next-generation Dynamic Digital EPCs for Enhanced Quality and User Awareness” (D^2EPC GA 892984) introduces a comprehensive approach to next-generation Energy Performance Certificates that addresses the main challenges and gaps in buildings' energy assessment process, by introducing additional layers of information for the assessor and the user. A non-exhaustive list of the novel set of Energy Performance Certificates indicators, proposed in D^2EPC includes energy, smart readiness, wellbeing, comfort, financial, and sustainability related indicators. In this study, aspects of employing advanced digital solutions, like Building Information Modelling and Geographical Information Systems for the certification process are also demonstrated, through a well structured, high-level, detailed representation of the next-generation Energy Performance Certificates system architecture. This framework elaborates on individual components and their interaction, toward delivering the envisioned final enriched cloud-based platform, that will enable dynamic Energy Performance Certificates based on (near) real-time field data. This study aspires to initiate the discussion within the scientific community of buildings' energy assessment on the required practices for digitizing and enriching the certification process of buildings, in compliance with Industry 4.0 practices.

Understanding building energy efficiency with administrative and emerging urban big data by deep learning in Glasgow

With buildings consuming nearly 40% of energy in developed countries, it is important to accurately estimate and understand the building energy efficiency in a city. A better understanding of building energy efficiency is beneficial for reducing overall household energy use and providing guidance for future housing improvement and retrofit. In this research, we propose a deep learning-based multi-source data fusion framework to estimate building energy efficiency. We consider the traditional factors associated with the building energy efficiency from the Energy Performance Certificate (EPC) for 160,000 properties (30,000 buildings) in Glasgow, UK (e.g., property structural attributes and morphological attributes), as well as the Google Street View (GSV) building façade images as a complement. We compare the performance improvements between our data-fusion framework with traditional morphological attributes and image-only models. The results show that including the building façade images from GSV, the overall model accuracy increases from 79.7% to 86.8%. A further investigation and explanation of the deep learning model are conducted to understand the relationships between building features and building energy efficiency by using SHapley Additive exPlanations (SHAP). Our research demonstrates the potential of using multi-source data in building energy efficiency prediction with high accuracy and short inference time. Our paper also helps understand building energy efficiency at the city level to help achieve the net-zero target by 2050.

Analysis of domestic hot water in residential buildings in Spain

Energy analysis in residential buildings considers three main areas of consumption: heating, air conditioning and domestic hot water. Traditionally, the former, heating, has been the most significant area of consumption in Spain but, due to modern technological advances and both the national and European regulations in force, this is no longer the case. The objective of this research is to define some strong, verified consumption patterns which make it possible to compare systems with one another and evaluate their influence on the energy efficiency of new and existing buildings, thereby, making progress toward improving generation systems. Real domestic hot water consumption measurements were collected over a one-year period for more than 500 Spanish properties in five different buildings located in both public and private residential developments. Moreover, a comparative study of the indicators set out in Spanish and other European regulations was carried out, evaluating their impact on energy performance certification. Finally, the suitability of the current indicators, both in quantitative and qualitative terms, was evaluated, concluding that the unit of measurement used in the Spanish regulations is not the most appropriate, and confirming that both seasonal impact and residential development type must be considered due to their influence on domestic hot water consumption.

On the surplus accuracy of data-driven energy quantification methods in the residential sector

Increasing trust in energy performance certificates (EPCs) and drawing meaningful conclusions requires a robust and accurate determination of building energy performance (BEP). However, existing and by law prescribed engineering methods, relying on physical principles, are under debate for being error-prone in practice and ultimately inaccurate. Research has heralded data-driven methods, mostly machine learning algorithms, to be promising alternatives: various studies compare engineering and data-driven methods with a clear advantage for data-driven methods in terms of prediction accuracy for BEP. While previous studies only investigated the prediction accuracy for BEP, it yet remains unclear which reasons and cause–effect relationships lead to the surplus prediction accuracy of data-driven methods. In this study, we develop and discuss a theory on how data collection, the type of auditor, the energy quantification method, and its accuracy relate to one another. First, we introduce cause–effect relationships for quantifying BEP method-agnostically and investigate the influence of several design parameters, such as the expertise of the auditor issuing the EPC, to develop our theory. Second, we evaluate and discuss our theory with literature. We find that data-driven methods positively influence cause–effect relationships, compensating for deficits due to auditors’ lack of expertise, leading to high prediction accuracy. We provide recommendations for future research and practice to enable the informed use of data-driven methods.