Energy Efficiency Scenarios Research Articles

Developing a systematic framework for integrating life cycle carbon emission assessment in urban building energy modeling

Extensive research has been conducted in recent years on the environmental evaluation of building lifecycles, spanning construction, usage, and end-of-life phases. This holistic approach shows promise in assisting cities to achieve decarbonization goals, given the significant carbon emissions attributed to buildings in urban areas. Prior efforts have successfully utilized Urban Building Energy Modeling (UBEM) tools to assess city-scale building energy consumption and explore energy efficiency scenarios. However, the integration of Life Cycle Assessment (LCA) with UBEM, a robust environmental assessment approach, remains underexplored. The combination of LCA and UBEM can provide a comprehensive understanding of a building's environmental impact, considering energy performance as well as broader considerations such as materials, construction processes, and waste management. Despite the evident benefits, there exists a research gap in fully harnessing the potential of integrating LCA and UBEM methodologies. Further investigation is thus warranted to unlock the synergies between these approaches, establishing an integrated framework that enhances sustainability assessments and informs decision-making in the built environment. A systematic approach is essential to address the challenges associated with the development of a city-scale environmental tool. This entails defining the challenges, identifying key components, establishing a structured framework, and testing and refining it through iterations. The defined framework should be implemented in modeling tools that offer a compatible environment.To address this need, this study proposes a systematic approach to the development of a framework for the integration of LCA into UBEM tools. Firstly, a structured procedure is defined to facilitate the systematic creation of the integrated framework. This procedure serves as a guide for developers to adopt a comprehensive and organized approach in addressing the challenges of combining LCA and UBEM methodologies. Subsequently, a workflow is designed to accommodate the implementation of any improvement practices within the defined systematic process of integrating LCA into UBEM tools.Applying the developed workflow in a real case study in Montreal, Canada, reveals that embodied carbon emissions can actually be higher than operational emissions, when the energy supply is renewable (hydropower). The outcomes of this research are expected to provide guidance for future studies on Urban Building Environmental Assessment (UBEA), facilitating a structured approach to advance and improve the field.

Renewable energy integration for lower emission planning and achieving regional energy independence: study in Yogyakarta

We propose and try to find a model renewable energy integration for lower emission and achieving regional energy independence. The research uses three stages. First, building an energy system model, to analyze the impact of implementing energy efficiency programs. The second stage is developing an integrated model from the energy demand and supply. Finally, arranging a mitigation scenarios to develop a low-carbon energy and achieving energy independence. Long-range Energy Alternative Planning (LEAP) software was utilized to simulate two scenarios are used to analyze energy demand and supply, namely Baseline scenarios and Mitigation scenarios. Mitigation Scenario is a combination of Energy Efficiency (EE) scenario, Fuel Substitution (FS), mode change scenario (MC), and efficient vehicle (EV) scenario and is used to analyze their impact on energy demand and GHG emissions. The results showed that mitigation scenarios can reduce energy demand by 22.86% when compared to the baseline scenario. In 2050, energy demand based on the mitigation scenario is 147.15 x 103 TJ, higher than based on the baseline scenario, which energy demand is 113.52 x 103 TJ. The cumulative of GHG emission reaches 1,369.37 x 103 Tons of CO2 Equivalent, based on the baseline scenario, can be reduced 13.54% by mitigation scenario. These results are robust. Overall, this research finding provides some possible steps to develop Yogyakarta energy independence and other regions.

Carbon Emission Influencing Factors and Scenario Prediction for Construction Industry in Beijing–Tianjin–Hebei

In this paper, the factors causing the change in carbon emissions from direct energy consumption in the construction industry in Beijing–Tianjin–Hebei are decomposed using the logarithmic mean divisia index (LMDI) method to analyze the effect values and contribution rates of each macrofactor. Based on the decomposition results and given relevant national policies, five scenarios are set up for each influencing factor, and a regression stochastic impact on population, affluence, and technology (STIRPAT) with ridge regression analysis is applied to each scenario combination for scenario prediction, forming a scientific and reasonable theoretical system to predict the future time of carbon peaking and carbon neutrality in the construction industry of Beijing–Tianjin–Hebei. The results show that (1) energy intensity and energy structure have a suppressive effect on direct energy consumption carbon emissions in the construction industry in Beijing–Tianjin–Hebei, and the industrial structure, economy, and population will promote an increase in carbon emissions. Energy intensity and the economy have a more significant effect on carbon emissions in the construction industry. (2) The peak year of carbon emissions varies with different scenarios, and the energy efficiency scenario achieves peak carbon in 2028, the earliest peak time, and the lowest peak, as it is the optimal emission reduction projection scenario.

A Study Using the Hybrid Fuzzy AHP&TOPSIS Method in the Conversion of a LEED-Certified Education Building into a Nearly Zero-Energy Building in a Cold Climate

In cold climate regions, it is essential to design and manufacture energy-efficient buildings for both economic benefits and the reduction of environmental effects by controlling energy consumption. This study aimed to increase the cost-effective energy performance and approach the nearly zero energy building (nZEB) by taking the leadership in energy and environmental design (LEED) in the cold climate region of Turkey as a model. The results of single and mixed scenarios that increase energy efficiency were determined by making energy modelling of the building. By applying single and mixed energy efficiency scenarios, a maximum saving of 85.60 % per year in terms of primary energy, an improvement of 83.6 % in terms of global costs and a reduction of 86.4 % in CO2 emissions were obtained compared to the reference building. The payback period of the scenarios is between 3.8 years and 14.53 years. The most suitable single and mixed scenario was determined by a systematic hybrid model, in which the fuzzy analytical hierarchy process (FAHP) and technique for order of preference by similarity to ideal solution (TOPSIS) methods among multi-criteria decision-making methods are used together. The results showed that economic criteria were decisive in determining the most suitable scenario for cold climate regions. The results of this study revealed that there can be a realistic decision-support model for the creation of energy-efficient buildings for countries without the need for foreign certification.

The Impacts of Energy Efficiency Modelling in Policy Making

Bottom-up energy system models have been used extensively to analyse future energy scenarios, addressing a wide variety of policy questions. This paper focuses on energy efficiency, a key energy, climate, and economic policy area where several examples of energy system model applications can be found in the literature. This paper analyses how different studies implement energy efficiency scenarios in energy system models and explains how the approach taken can affect the results significantly, potentially affecting policy decisions. This analysis contributes to understanding how this type of modelling framework considers energy efficiency policy issues and the extent of insight provided, or not, on different dimensions. With the aim of identifying ‘best practice’ in using energy system models to inform effective analysis of energy efficiency policy, the UK TIMES energy system model is used to implement five different energy efficiency scenarios for residential heating following different modelling approaches and replicating scenarios available in the literature. The study concludes that energy efficiency scenarios, implementing the same target, produce significantly different results in terms of technology mix, energy use, emissions and costs. Additionally, the outcomes show that there is no overall best energy efficiency scenario, as each impacts on different policy targets, which could come into conflict with each other.

The asymmetric and long-run effect of energy productivity on environmental degradation in the United Kingdom

Energy efficiency offers a vast, low-cost energy resource for the economy, but only if the nation can craft a comprehensive and innovative approach to unlock it. One of the most viable options is to invest in energy productivity. This study examines the asymmetric and long-run effects of energy productivity on environmental degradation while controlling for renewable energy, economic growth, and financial development, utilizing the nonlinear autoregressive distributive lag model and other robust econometric techniques in the United Kingdom during the period 1990Q1–2020Q4. The United Kingdom is one of the world's wealthiest countries; its environment is being severely strained and is challenged in boasting massive investments to reduce CO2emissions to 78% by 2035. Given the important scenarios for energy efficiency, this study outcome indicates that (a) energy productivity has been beneficial for reducing carbon emissions; (b) renewable energy sources significantly mitigate carbon emissions in the United Kingdom, and (c) economic growth and financial development cause to increase carbon emissions in the United Kingdom. Given the findings, some policy recommendations abound. The negative statistically significant coefficient of renewable energy is an indication that the expansion of renewable energy is essential to achieving clean and sustainable growth. Furthermore, policymakers should enact new environmental laws that prioritize R&D and embrace cleaner technologies.

Contributing to Low Emission Development through Regional Energy Planning in West Papua

A model of regional energy planning has been developed based for West Papua province. Regional energy planning had two major scenario which are baseline and mitigation scenario. Mitigation scenario consisted of energy efficiency and fuel switch scenarios. A baseline scenario has been used to reflect energy demand without any intervention of new energy policy in the province. Energy efficiency scenario describe the impact of more efficiency vehicle and appliance in energy consumption. In transportation sector, energy efficiency scenario included a mode change scenario. The use of renewable energy has been included in fuel switch scenario. In supply side planning, renewable energy sources have been accommodated to meet a portion of electricity demand. The model of regional energy planning has been implemented by Long-range Energy Alternative Planning software. The result indicated that the same goal of regional development program can be achieved with less emission. By the implementation of mitigation scenario, overall energy demand in the end of projection period can be reduced by 16.63 PJ compare to the baseline. As an impact, global warming potential is 15.89% less by mitigation scenario compare to baseline scenario. It can be concluded that the emission intensity by the implementation of mitigation scenario is 8.93 Thousand Ton CO2 Equivalent/Billion USD.

Coordinating to promote refrigerant transition and energy efficiency improvement of room air conditioners in China: Mitigation potential and costs

Implementing the Kigali Amendment to the Montreal Protocol has imposed certain restrictions on the production and consumption of hydrofluorocarbons (HFCs). Taking this opportunity to promote the alternatives of high global warming potential (GWP) HFC refrigerants in the room air conditioner (RAC) sector as well as improve the energy efficiency can bring double benefits. With the RAC sector as an example, a demand-emissions-cost model is developed to assess the potential and costs of emission reductions in different regions of China under different scenarios. The model includes three scenarios: a business as usual (BAU) scenario, a Kigali energy efficiency (KAE) scenario with simultaneous energy efficiency improvements following the Kigali amendment, and an accelerated transition energy efficiency (ATE) scenario with accelerated HFCs reduction and energy efficiency improvements. The results show that under the KAE and ATE scenario, the GHG emissions of the RAC sector will peak in 2025 at 389.8–393.9 Mt CO2-eq and 378.8–382.8 Mt CO2-eq in China. The main contribution to this result is the alternative of low GWP refrigerants. From 2021 to 2060, the cumulative direct emission reductions are about 6.4–7.4 Gt CO2-eq and 8.5–9.5 Gt CO2-eq in KAE and ATE, and the cumulative indirect emission reductions for both scenarios are 1.6–1.8 Gt CO2-eq. The cumulative abatement costs are $286–321 billion and $288–322 billion (prices in 2020). Under the ATE scenario, direct emissions from refrigerants in the RAC sector are near zero in 2060, and indirect emissions depend on the power system structure. The RAC sector's average abatement cost varies significantly in diverse climatic environments. Given the variation in average abatement cost, it is critical to tailor mitigation policies to local conditions to ensure maximum benefits.

The carbon emission comparison between hybrid manufacturing and conventional processes based on bionic computing of industrial metabolism

Hybrid Deposition and Micro Rolling (HDMR) is a hybrid technology combining in-situ rolling with Wire and Arc Additive Manufacturing (WAAM). With better performance in products than the conventional approach, the advantages in environmental contribution remain to be studied. Traditional methods in environmental evaluation are mostly based on Life Cycle Assessment (LCA), which can have further refinement in a short process like HDMR. This paper proposed a bionic computing method of industrial metabolism, proactively designed the ecological index, and comprehensively evaluated the environmental impact of manufacturing system. Material and energy flows in whole processes were tracked to obtain accurate quantitative data on the energy efficiency and carbon emission scenarios of long and short process manufacturing systems. This paper took the manufacture of a titanium alloy part as an example, the CO2 metabolism and energy metabolism produced by HDMR and Conventional Manufacturing (CM) with a long process were compared. The results show that CO2 emissions of the given part deposited by HDMR is 181.29kg, and that of the same part fabricated by CM is 2612.81kg. The CO2 emission intensities of the given part by HDMR and CM are 7.17kg/kg and 95.32kg/kg respectively. The total carbon emission and emission intensity of CM are 14.41 times and 13.29 times than that of HDMR respectively. In addition, the manufacturing time of HDMR is reduced by approximately 67% comparing with CM. The material utilization rate of HDMR is 2.6 times higher than that of CM. Therefore, it can be proved that HDMR has huge advantages over conventional manufacturing processes in terms of CO2 emission efficiency and emission intensity, specific energy consumption, energy efficiency, and material utilization. It is an energy-saving, emission-reducing, sustainable green manufacturing process.

Blockchain based energy efficient multi-tasking optimistic scenario for mobile edge computing

Mobile edge computational power faces the difficulty of balancing the energy consumption of many devices and workloads as science and technology advance. Most related research focuses on exploiting edge server computing performance to reduce mobile device energy consumption and task execution time during task processing. Existing research, however, shows that there is no adequate answer to the energy consumption balances between multi-device and multitasking. The present edge computing system model has been updated to address this energy consumption balance problem. We present a blockchain-based analytical method for the energy utilization balance optimization problem of multi-mobile devices and multitasking and an optimistic scenario on this foundation. An investigation of the corresponding approximation ratio is performed. Compared to the total energy demand optimization method and the random algorithm, many simulation studies have been carried out. Compared to the random process, the testing findings demonstrate that the suggested greedy algorithm can improve average performance by 66.59 percent in terms of energy balance. Furthermore, when the minimum transmission power of the mobile device is between five and six dBm, the greedy algorithm nearly achieves the best solution when compared to the brute force technique under the classical task topology.

DISCOVERING CLUSTERING PATTERNS FROM E-COUNTER DATA STREAMS

Abstract. Clustering data streams has gained popularity in recent years due to their potential of generating relevant information for planning building automation, evaluating energy efficiency scenarios, and simulating emergency protocols in indoor spaces. In this paper, a Data Stream Affinity Propagation (DSAP) clustering algorithm is proposed for analyzing indoor localization data generated from e-counters systems. The data streams are a sequence of potentially infinite and non-stationary data points, arriving continuously where random access to the data is not feasible and storing all the arriving data is impractical. The DSAP model is developed based on a two-phase approach (i.e., online and offline clustering phases) using the landmark time window model. It is non-parametric in the sense of not requiring any prior knowledge about the number of clusters and their respective labels. The validation and performance of the DSAP model is evaluated using real-world data streams from a behavioural experiment aimed at finding new spatio-temporal patterns in stair usage due to an educational intervention campaign.

On the Use of Quantum Reinforcement Learning in Energy-Efficiency Scenarios

In the last few years, deep reinforcement learning has been proposed as a method to perform online learning in energy-efficiency scenarios such as HVAC control, electric car energy management, or building energy management, just to mention a few. On the other hand, quantum machine learning was born during the last decade to extend classic machine learning to a quantum level. In this work, we propose to study the benefits and limitations of quantum reinforcement learning to solve energy-efficiency scenarios. As a testbed, we use existing energy-efficiency-based reinforcement learning simulators and compare classic algorithms with the quantum proposal. Results in HVAC control, electric vehicle fuel consumption, and profit optimization of electrical charging stations applications suggest that quantum neural networks are able to solve problems in reinforcement learning scenarios with better accuracy than their classical counterpart, obtaining a better cumulative reward with fewer parameters to be learned.

Improving residential building energy simulations through occupancy data derived from commercial off-the-shelf Wi-Fi sensing technology

One of the main required actions for moving forward in zero-carbon cities’ mission is to increase building energy efficiency. Building energy simulations are key tools for HVAC control, building energy management, testing different building energy efficiency scenarios, and providing feedback to the occupants. Unfortunately, these tools have a significant weakness in occupant behavior modeling and prediction. Several recent studies used different occupancy measurements and methods to predict occupant behavior. However, they suffer from threatening the occupant's privacy and the high cost of their infrastructure and deployment. This paper aims to leverage passive WiFi sensing methods for occupant behavior estimation using commercial off-the-shelf WiFi devices, for which the infrastructure is already available in many buildings. Passive and device-free WiFi sensing does not threaten the occupant's privacy, and its deployment cost is low. Therefore, this method of occupancy measurement has the potential to be used on an urban scale and to play an essential role in zero carbon and smart cities in the future.Different machine learning algorithms have been applied to the collected, denoised, and preprocessed Channel State Information (CSI) data to estimate the occupant behavior. Five schedules of occupancy, occupant activity, lighting, electrical equipment usage, and thermostat set point temperature could be extracted and estimated from CSI data. In the last step, all estimated schedules were fed into EnergyPlus, an energy simulation software, along with other required data for accurate energy demand calculation to estimate heating and cooling demand, which can be used for HVAC control and occupancy feedback.

Energy efficiency in residential and non-residential buildings: Short-term macroeconomic implications

Investments in energy efficiency are vital for reducing greenhouse gas emissions by 2030 given that changes in the structure of the main energy sources cannot be expected in the short to medium term. The greatest potential for energy savings and cutting emissions lies in buildings due to their significant carbon intensity and rapid growth rate. However, gaps remain in what is known about integrating decarbonisation of the built environment into the economy-wide system. The paper addresses such gaps by examining the socio-macroeconomic implications of different scenarios related to building stock investments in a small open economy. The effectiveness of the measures implemented for various building types and the possibility of a rebound effect are also considered. The study is valuable for the peculiarities of the recursive dynamic computable general equilibrium model, GreenMod Slovenia, which was established to analyse the effects of decarbonisation. Two major contributions emerge: (1) projected energy efficiency in buildings is presented separately for commercial services, public services and households in two investment scenarios, namely, the business-as-usual scenario and the energy efficiency scenario with additional investments; and (2) the disaggregation of households into income quintiles to project their consumption of energy inputs. The study reveals vital macroeconomic benefits flowing from the energy efficiency scenario, including higher GDP and employment. Second, energy efficiency improvements in commercial services might encourage higher energy consumption. Finally, low-income households reduced the consumption of energy products the least. In this quintile group, energy efficiency improvement can lead to greater energy consumption, denoting policy failure.

THE AMERICAN CONTINENT HYDROPOWER DEVELOPMENT AND THE SUSTAINABILITY: A REVIEW

The present review compares and takes the main ideas around hydropower development in eight countries of the American continent, identifying its advantages and disadvantages, showing a vision concerning sustainability. It is conclusive that there are impacts for each megawatt produced with hydropower, and the generation structure that uses the water resource of natural currents is not highly clean. Moreover, there is the mistaken criterion for developing a renewable hydropower project related to sustainability, a wrong approach, as demonstrated with the review. The examination in the eight countries of America some analyzes and the most concludes that, before considering a construction with thousands of dollars of investment and water contained in dams, the social and environmental analysis must respond to the restrictions on building new hydropower projects, promoting other unconventional renewable energy sources development. It is recommended to determine an objective quantitative approach of hydropower combining hydrology, energy efficiency, and interaction scenarios of future climate change to know the best energy grids diversifying balanced renewable and no renewable sources for each country.

Domestic Energy Efficiency Scenarios for Northern Ireland

Building fabric retrofitting is an important first step in improving building energy efficiency. The United Kingdom’s (UK) housing stock is one of the most inefficient in Europe, and Northern Ireland has the second-highest level of fuel poverty in the UK. This Northern Irish case study developed three fabric retrofit scenarios that estimate potential demand reductions, CO2 emissions removals and retrofit costs. The first scenario reduces domestic demand by 10% and removes 6% of domestic emissions. The second scenario is more ambitious than the first, and results in an 18% reduction in demand and 12% of emissions removed. The third scenario proposes fabric retrofitting to PassivHaus standard and results in a 42% reduction in demand and 27% of emissions removed. Furthermore, retrofit schemes can provide up to approximately 350,000 jobs annually between 2022 and 2050 for the Northern Irish population. This study demonstrates how fabric retrofit scenarios can be streamlined to the unique features of a housing stock. It shows that fabric retrofit research is important for the formulation of energy efficiency policy and emphasises that domestic sector retrofitting will yield socioeconomic and environmental benefits locally and internationally.

Exploring pathways to deep de-carbonization and the associated environmental impact in China’s ammonia industry

China is the largest producer of synthetic ammonia, accounting for one-third of the world’s total production. Ammonia is mainly used to produce fertilizer and is also considered as a potential fuel and new energy carrier for the future. Concomitantly, the ammonia industry is the largest energy consumer and CO2 emitter in China’s chemical industry. In this study, we developed the MESSAGEix-ammonia model with detailed process descriptions to evaluate the energy-saving and emission reduction potential that can be generated by energy efficiency (EE) improvement, as well as the transition path and emission characteristics in the context of deep emission reduction. Results show that the cost-effective EE measures implemented under the EE scenario could reduce fresh water, fuel coal, and electricity consumptions by 7%, 25%, and 16%, as well as reduce CO2, PM2.5, SO2, and NOx emissions by 33%, 24%, 24%, and 24%, respectively, by 2060. Regarding the exploration of the deep de-carbonization path, carbon capture and storage technology (CCS) increases the CO2 reduction potential to 62%, but it requires additional electricity. Meanwhile, electrolysis technology not only saves additional fresh water and fuel coal, but also reduces CO2, PM2.5, SO2, and NOx by 80%, 84%, 86%, and 84%, respectively. Furthermore, the integration of electrolysis technology and CCS can bring 98% carbon emission reduction, which is close to net-zero emission status. With the development of renewable electricity, sufficient, clean, and affordable electricity can be provided for electrolysis devices. Our recommendation to policy makers is that electrolysis of water to produce ammonia using renewable electricity is a feasible deep de-carbonization pathway.

A Framework for CO2 Emission Reduction in Manufacturing Industries: A Steel Industry Case

Rising carbon emissions are linked to the increase in global temperature, because of increasing human activities and increasing greenhouse gas emissions. Since manufacturing is one of the most carbon intensive sectors, it is vital to suggest solutions that lead to carbon emission reduction in all sectors, among which is the steelmaking manufacturing sector. The present study focuses on presenting a framework for energy intensive industries introducing digitalization and energy efficient equipment in the production line. The current framework proposes different metrics, both from carbon emissions and cost viewpoints. A secondary steelmaking industry was used as a case study, showcasing the impact of digitalization and energy efficient equipment towards the reduction of carbon emissions. In addition, different metrics were calculated with energy efficient scenarios providing the lowest energy consumption metric, but their high purchasing costs make these scenarios less attractive. However, if carbon emission reduction per cost is the metric, a combination of digital tools and energy efficient equipment is the answer to the company’s needs. Applying the concepts of innovation absorption and digitalization, the number of alternatives is kept low, however, the impact on the line is quite large. The introduction of new technologies is also supported by training of the workforce, aligning the framework with current industrial trends.

Feasibility Study for Off-Grid Hybrid Power Systems Considering an Energy Efficiency Initiative for an Island in Ecuador

This paper shows the technical–economic, operational and environmental feasibility of four off-grid hybrid power systems to supply energy to the Cerrito de los Morreños community in Ecuador. These configurations consist of combinations of diesel generators, solar photovoltaic systems, and battery energy storage systems. Each configuration was simulated and the results were analyzed for two different load conditions: (1) the existing load profile and (2) a reduced load profile by incorporating an energy efficiency initiative. Homer Pro software was used to perform the simulations. The planning horizon for the simulations was selected to be 15 years. The results showed the diesel/photovoltaic/battery configuration with energy efficiency showed the best performance, which was achieved with a photovoltaic system of 160 kWp, the existing generator of 165 kW and a storage system of 283 kWh. The stand-alone diesel generator and photovoltaic/diesel configurations showed higher Net Present Costs, instability problems and higher CO2 emissions. Additionally, the configurations without energy efficiency had increases of between 15% and 40% higher costs compared to their respective energy-efficient scenarios. The information in this work could be useful for some organizations in Ecuador who are interested in investing in rural electrification projects with renewable energy to reduce and/or compensate their CO2 emissions.

A pre-diagnosis model for radon potential evaluation in buildings: A tool for balancing ventilation, indoor air quality and energy efficiency

In a very early stage of implementation of a comprehensive experimental campaign for indoor radon assessment, a pre-evaluation selection of the variables that play a leading role in influencing expected results must be insightfully assessed, by stimulating compatibility between Indoor Air Quality (IAQ) and energy efficiency. Hence, a practical methodology for variable selection based on an analysis of historic data plays a key role concerning radon potential assessment, while never losing sight of the building energy performance. Given the circumstances, this work is focused on the design of a qualitative pre-diagnosis model for the evaluation of radon potential in indoor environments, for different energy efficiency scenarios, by considering a set of relevant variables carefully selected to characterize occupants’ risk exposure. A prior survey was done to identify all relevant characteristics that most affect both Indoor Air Quality (IAQ) and building energy efficiency, mainly concerning local geology, thermal insulation, ventilation schemes, energy equipment, renewable energy assets, and occupancy schedules. The selected parameters will be afterward weighted and combined into performance indicators through an evidence-based literature review. In the current early stage, the requirements to drive the software development are presented, together with a software architecture proposal. Finally, it is expected that this pre-diagnosis model will allow a more refined sample selection for indoor radon assessment, by choosing the most susceptible variables that influence radon potential in a given scenario and making it compatible with the building energy efficiency.