Energy Consumption Index Research Articles

Artificial intelligence, household financial fragility and energy resources consumption: Impacts of digital disruption from a demand-based perspective

Ensuring access to affordable energy for all is laid out among the 17 Sustainable Development Goals and it remains an important open question as to how the popularity and widespread application of artificial intelligence (AI) in the workforce impact household energy resources consumption. To systematically investigate the impacts of the application of artificial intelligence in the workforce on household energy consumption in China, empirical analysis is conducted using data from the China General Social Survey (CGSS) from May to September 2022. The findings are as follows: (1) AI significantly reduces household energy consumption. Controlling other factors constant, for a one-standard-deviation increase in the impact of AI, household energy consumption drops by an average of 10.751%. Robustness and endogeneity tests, including dealing with missing values, using different energy consumption and AI indicators, as well as applying instrumental variable method, placebo test and penalized regressions, confirm this conclusion. (2) Mechanism analysis shows that AI reduces energy consumption by lowering household income and increasing their financial fragility. (3) AI's impacts on different types of energy consumption are heterogeneous. Its negative effects are mainly observed in the significant reduction of electricity and gas consumption. Furthermore, it increases the probability of using solid fuels such as honeycomb coal, coal lumps, traditional biomass, etc., thereby increasing the reliance on low-grade energy resources and raising the risk of energy poverty. (4) AI has greater negative effects on those who do not have access to energy subsidies and households with poor energy security and stability, lower income and inadequate social security. Besides, its impacts on regions with higher levels of technological development are more prominent. (5) Feasible pathways to mitigate the adverse effects of AI are explored. It is found that improving labor protection can help alleviate its adverse consequences on energy consumption. This paper provides evidence on the impacts of technological disruption from a demand-based perspective. It highlights the need for better policies on energy, social security, income distribution and labor protection to weaken AI's effects on household energy consumption and prevent them from falling into energy poverty.

Industrial Technologies for CO2 Reduction Applicable to Glass Furnaces

In recent years, the European Union’s legislation about sustainable development has promoted the gradual decarbonization of all industrial sectors, pushing towards the final goal of a carbon-neutral European glass industry in 2050. Moreover, the COVID-19 pandemic, the war in Ukraine and the consequent natural gas supply crisis have led to significant increases in the costs of traditional energy commodities and CO2 emission allowances. In this scenario, the European glass industry, which is both an energy-intensive sector and a large emitter of CO2, needs to reduce its specific energy consumption, change its energy sources and decarbonize its production process. In order to understand and support this metamorphosis of the glass industry, the follwing questions must be answered: are the technologies reported in scientific publications merely theoretical exercises, or can they be adopted by the industry? In what timeframe can they be adopted? The aim of this study is to review consolidated and emerging technologies applicable to the glass industry and investigate which ones can be implemented in the short or medium term to reduce energy consumption and CO2 emissions related to the glass production process. This study is based on a review of the literature, the materials presented in technical conferences and the opinions of interviewed experts. This study showed that the literature is not very substantial, lacking detailed information on technologies and their effects in terms of energy savings or emissions. More information can be found in the proceedings of selected specialist conferences. This study found that, on one hand, some technologies are mature and only adopted when economically viable, and appropriate boundary conditions are available; the state of the art regarding these technologies was already extensively covered in past publications (e.g., cullet pre-heating). On the other hand, there are many promising technologies in the research or testing phase (i.e., steam methane reforming, process electrification, use of hydrogen); in-depth studies about them are limited due to the novelty of the solutions that they propose or not available due to industrial secrecy issues. In addition to periodicals and specialized conferences, interviews were carried out with managers and technicians from industry, as well as technicians from the Italian glass research institute and industrial machinery producers (especially melting furnaces). The interviews represent added value of this publication, useful in helping us to truly understand the state of the art and degree of readiness of the technologies identified. In addition, the production values of the glass industry were studied: our research confirmed that the most important sub-sectors are flat and container glass, as well as the largest glass-producing nations/continents. Finally, a review of specific energy consumption and CO2 emissions indexes was carried out.

Investigation of Energy Consumption via an Equivalent Thermal Resistance-Capacitance Model for a Northern Rural Residence

To achieve the goal of carbon peaking, it is crucial to reduce both carbon emissions and energy consumption during the operational stage of residential buildings. This paper proposed a method for assessing carbon emissions and energy consumption for an energy system utilized in a rural residence. First, an equivalent thermal resistance-capacitance model for a rural residence was established. The parameters of thermal resistance and capacitance were optimized based on the data collected from an operating air source heat pump heating system. On this basis, the energy consumption was derived, and it was compared with real consumption. Then, a method of estimating house energy consumption index per unit area under specified weather conditions was proposed. Finally, the carbon emissions of three heating types—heating driven by air source heat pump, gas boiler, and coal boiler—were compared. Results showed that the derived energy consumption index per unit area was 46.77 W/m2. The carbon emissions of the air source heat pump were 1406.1 kgCO2.

Efficient and precise docking trajectory optimization for the ship block assembly

The assembly of the ship block is an extremely important stage of the shipbuilding process. Nevertheless, currently, the manual assembly efficiency is low, the accuracy is poor, and collision is very easy to occur. Therefore, there is an urgent need to conduct technical research on the automatic docking of ship blocks. The core of the automated docking technology is the attitude estimation and the trajectory planning of the posturing equipment. However, current data measurement and point set matching methods lead to large attitude-estimation errors, and it is difficult to meet the accuracy requirements of the assembly. Moreover, the current ship block trajectory planning methods pay more attention to single metrics, for example, time or energy consumption, while omitting the shock degree. In response to the above, this study first proposes a high-precision matching method for measuring point sets, in order to estimate the exact attitude of the ship block. Subsequently, trajectory translation for the block is performed using the seventh-degree polynomial. On this basis, a nonlinear weighted improved particle swarm optimization (IPSO) method is proposed to optimize the time, energy consumption and shock degree in the ship block trajectory planning process. Finally, the accuracy of the matching optimization is validated by simulation analysis and it is concluded that the seventh-degree polynomial leads to less shock than other polynomials. Furthermore, the shock force does not change abruptly even when the ship block is poised in steps. Through IPSO, the energy consumption and shock degree performance indices are optimized by 37.07% and 50.06%, respectively, in the ship block translation process.

The Relationship between Renewable Energy Consumption, CO2 Emissions, Economic Growth, and Industrial Production Index: The Case of Kazakhstan

CO2 emission is an important parameter that indicates a country's development level and respect for nature. It's a well-known fact that a country's industrialization level and economic growth have a direct impact on CO2 emissions. We must prioritize the use of energy obtained from renewable sources and be mindful of our impact on the environment. This study analyses the industrial production index, economic growth, and the percentage of energy produced from renewable energy sources in energy consumption and CO2 emissions in Kazakhstan. The data are collected from the National Statistical Bureau of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan, Our World in Data, and the World Bank web pages. Research data were analyzed using the Johansen cointegration test, Vector Autoregressive (VAR) analysis, Granger causality analysis, and VECM model. In the study, we analyzed three key factors that impact CO2 emissions in Kazakhstan. Our findings revealed that these factors account for 16.1% of the variability in CO2 emissions, indicating the statistical accuracy of these variables. When deciding on renewable energy investments, it is very important to determine the causal relationship between renewable energy consumption and CO2 emissions. It seems that industrial development and economic growth can occur without any major concerns about CO2 emissions. This is based on the lack of statistical significance in the relationship between CO2 emissions and both the industrial production index and economic growth.

A compliance control strategy of lower‐limb exoskeletons for energy‐efficient stance‐squat transitions with a payload

AbstractThis paper proposes an admittance control algorithm for lower‐limb exoskeletons to assist able‐bodied wearers in performing energy‐efficient stance‐squat transitions while carrying a payload of up to 20 kg. A two‐degree‐of‐freedom (DOF) model dedicated to the stance‐squat transition is derived, which takes into account the wearer's torso inclination angle and the vertical displacement of the hip joint. The torso inclination angle is obtained from an inertial measurement unit (IMU) located at the backpack of the exoskeleton. In addition, the moment caused by the torso plus the payload with respect to the hip joint is estimated. Then a model‐based disturbance observer (DOB) is used to estimate the wearer's joint torque as an indication of the motion intention, and the exoskeleton complies with the intention by following the desired joint angular velocity generated from the estimated wearer's joint torque through an admittance function. Experiments with different admittance parameters are conducted, and the energy consumption of the wearer is evaluated in terms of the normalized energy consumption index (NECI) and the physiological cost index (PCI). Furthermore, experimental comparisons of energy consumption are made for a subject carrying a payload of up to 20 kg and performing repeated stance‐squat transitions with and without the exoskeleton. The results show that a 50% reduction of energy consumption (in terms of PCI) is achieved by the exoskeleton with the proposed admittance control strategy.

Strategic management of energy consumption and reduction of specific energy consumption using modern methods of artificial intelligence in an industrial plant

Given the importance of energy and its role in sustainable gas production, paying special attention to its consumption indicators in all industries, especially oil & gas is very important. The energy consumption index parameter, which equals the ratio of energy consumption to gas production, is one of the most significant indicators of the gas refinery. Artificial intelligence can be used to evaluate the effects of different parameters and find the factors influencing energy performance. Important variables such as inlet gas from various sources, the energy consumption of each part, and the amount of waste refinery gas are collected on different days of the year, then using the least square support vector machine method and networking and sensitivity analysis of the impact of each variable separately. The role of each in this parameter is determined so that it can be used to provide appropriate solutions to reduce energy consumption and the energy consumption index parameter for sustainable gas production. According to the results of this study, the steam unit has a 14 % impact on the energy consumption index, and the gas sweetening unit has an 18 % impact on gas consumption. So, more research on the optimization of these two units is necessary.

Development of a novel framework for agriculture simulation model for food-energy-water nexus analysis in watershed-scale

Agricultural crop production is the most vital sector of the food subsystem within the Food-Water-Energy (FEW) Nexus system, and many environmental and external factors affect it. Complex interactions between FEW subsystems and complex interrelations within each FEW subsystems affect each other dynamically. As a result, in this study, based on the dynamics between FEW subsystems and within FEW subsystems in agriculture, the Agriculture Simulation Model for the Food-Energy-Water nexus (ASM-FEW) is presented at the watershed scale. ASM-FEW includes modules for multi-crop yield modeling and calibration, energy consumption, indices related to FEW, and a comprehensive Water-Energy-Food Nexus Index (WEFNI) to assess crop production efficiency FEW Nexus approach. Modeling evaluation criteria demonstrate the precise crop yield modeling of ASM-FEW for the study area, the Maragheh-Bonab region in the Urmia Lake Basin, from 2005 to 2015. Model evaluation criteria demonstrate precise crop yield modeling. Results show that Potato and rainfed Chickpea exhibit the highest and lowest energy consumption indices, at 222,766 and 19,503 MJ/ha, respectively. Water economic productivity indices indicate irrigated Potato and Wheat as the highest and lowest, with values of 1.084 and 0.114 $/m3, respectively. Energy economic productivity indices reveal that irrigated Onion and rainfed Wheat are the highest and lowest, at 0.132 and 0.006 $/MJ, respectively. Lastly, according to the WEFNI index, irrigated Onion and irrigated Wheat are the most and least suitable crops for cultivation, with values of 0.66 and 0.264, respectively.

Ekolojik ayak izine çok etki eden faktörlerin yapay sinir ağı sınıflama özelliği kullanılarak belirlenmesi: Türkiye örneği

Since the end of the 20th century, ecological problems have become a priority problem due to industrialization, urbanization, technological developments and rapid population growth. The change in human living standards causes many ecological problems such as unconscious consumption of natural resources, extinction of forests and living species. Ecological Footprint is developed to measure the demand pressure that people exert on the environment. In study, Neural Network Fitting Model was used in MATLAB, for the development Artificial Neural Network (ANN) by using the data of 1996-2018 to estimate Turkey's ecological footprint. Urban Population, Renewable Energy Consumption, R&D Expenditures and Human Development Index were chosen as independent variables. The data were obtained from the database of “World Bank Group” and “Human Development Reports”. For the ANN, Levenberg-Marquardt algorithm was used to determine the appropriate hidden layer and hidden neurons in each layer. The data used to train an artificial neural network using feedforward and backpropagation were randomly divided into three groups for training, testing and validation purposes. R values for each stage, respectively; 0.999, 0.948, was obtained as 1. According to the results obtained, the independent variable with the greatest effect on the ecological footprint was found to be the Urban Population.

Comprehensive competitiveness assessment of ammonia-hydrogen fuel cell electric vehicles and their competitive routes

Recently, green ammonia has attracted growing attention due to its high energy density, environmental friendliness, and safety, and is gradually being tested or demonstrated in vehicles. However, the knowledge and understanding of the strengths and weaknesses of fuel cell electric vehicle with on-board ammonia cracking for hydrogen production and other commercially available vehicles are insufficient.This study evaluated 12 technical routes (Electricity, Gasoline, Hydrogen, Ammonia) from technical, environmental and economic perspectives and compared the comprehensive competitiveness by coupling carbon emissions, energy consumption, and costs indicators using the entropy weighting method.The results show that although the renewable energy-based technical routes reduce carbon emissions and energy consumption, they have relatively high economic costs. Considering energy consumption, carbon emissions and total costs, battery electric vehicles have the best comprehensive competitiveness at room temperature. However, ammonia-hydrogen fuel cell electric vehicles can be applied in special scenarios. Their comprehensive competitiveness is essentially equal to that of battery electric vehicles when the electric consumption of battery electric vehicles increases by more than 35 % under low temperature conditions. In addition, large-scale, long-distance transportation of ammonia is economically advantageous compared to hydrogen transportation, especially when hydrogen/ammonia is transported over distances of more than 500 km.

Pareto multi-objective optimization of metro train energy-saving operation using improved NSGA-II algorithms

Quantitative analysis of the factors affecting the energy consumption of metro trains and finding out the breakthrough points of energy conservation is of great practical significance for reducing transport costs and improving energy utilization. Train operation curve optimization is one of the main methods to reduce the energy consumption of train operation. This paper introduces the metro train energy-saving operation optimization model with fixed travel time. Firstly, taking the energy consumption index and punctuality index as the objectives, a train energy-saving operation optimization model is established. Then, the solution method based on the improved NSGA-II algorithm is developed. Finally, the developed optimization model is applied to Guangzhou Metro Line 7 to verify the performance. The results show that the energy consumption of the developed operation strategy is 24.4 % less than that of the minimum travel time strategy, and the total operation time meets the punctuality requirements. Meanwhile, the improved NSGA-II algorithm has less energy consumption than GA and PSO algorithms in the application of Guangzhou Metro Line 7.

Study on the Effectiveness of Green GDP Accounting System in Mitigating Climate Change- Based on ARIMA-LSTM Model

At present, the most widely used composite index to measure economic growth in the world is GDP and its growth rate, but this composite index does not sufficiently emphasize the importance of climate environment. In order to emphasize the greenness of development, it is necessary to establish a green GDP accounting system and test whether the green gdp system can mitigate climate change. In this paper, we first select seven direct climate impact indicators, including energy consumption and indirect climate impact indicators, and assign weights to the direct climate impact indicators by entropy weighting method to establish a green GDP accounting system. Then the climate prediction model based on ARMIMA-LSTM is established with CO2 emission and temperature change as evaluation indicators, and the model is trained to obtain the climate change under the GGDP system. This paper is important for measuring green GDP.

The Influence of Government Ideology on Renewable Energy Consumption in the European Union Countries

By utilizing the panel quantile regression method and employing unbalanced panel data covering the years between 1972 and 2020, this study examines the impact of government ideology on renewable energy consumption for 26 EU member countries. The estimation results indicated that an increase in the relative power position of right-wing parties in government was correlated with a decrease in renewable energy consumption, whereas a rise in the relative power position of left-wing or center-wing parties in government was associated with an increase in renewable energy consumption across all quantiles evaluated. The estimation results were robust to different indicators of renewable energy consumption and government ideology. The estimation results of this study also indicated statistically significant positive associations between the share of seats in parliament for political parties classified as green and renewable energy consumption, and between the total Christian democratic governing party seats as a percentage of all legislative seats and renewable energy consumption. This result suggests that green parties and Christian democratic parties may be the key driving forces behind the positive coefficients for variables representing left-wing and center-wing parties in our study.

The Effect of HPGR and Conventional Crushing on the Extent of Micro-Cracks, Milling Energy Requirements and the Degree of Liberation: A Case Study of UG2 Platinum Ore

Comparative high pressure grinding rolls (HPGR) and cone crusher pilot-scale tests were conducted using Upper Group 2 (UG2) platinum-bearing ore in order to determine the impact of micro-cracks in HPGR products toward energy requirements in ball mills and the degree of liberation. The ball mill was fed with HPGR and cone crusher products of similar particle size distributions (PSDs). Qualitative analysis of the degree of micro-cracking on the HPGR and cone crusher products performed using scanning electron microscope (SEM) and image analysis software showed that an HPGR product had more micro-cracks than the equivalent cone crusher product. Milling energy requirements were evaluated using size-specific energy consumption indices calculated based on three grind sizes of 300 µm, 150 µm and 75 µm. The effect of residual micro-cracks in the products of HPGR and cone crusher on the milling size-specific energy requirement is inconclusive. The kinetic parameter k in the cumulative rate kinetic model for ball milling cone crusher products and for ball milling HPGR products were similar. Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN) was used to determine the degree of liberation of various mineral phases in the mill products. At a coarser grind size (P80 of 300 µm), the sulfides in the mill products pre-crushed using the cone crusher have consistently poorer liberation than in the equivalent HPGR pre-crushed sample. However, at a finer grind size (P80 of 75 µm), the sulfides in the mill products pre-crushed using the cone crusher and using an HPGR showed similar liberation.

Energy Consumption, Economic Growth, and Human Development Index Evidence from SAARC Economies

The majority of energy used globally is attributed to human activities. To enumerate exactly how energy consumption affects the process of human development, however, may be necessary. There is much work to be done in this area of inquiry. The association between energy consumption, economic growth, and the HDI is therefore explored in this study. For this purpose, we used the panel data of SAARC countries from 1971 to 2019. Data is taken from the World Bank and human development reports. This purpose of study is to investigate the connections among energy use, economic growth, HDI, trade openness, environmental variables, and urbanization in the context of SAARC economies. In this work, we utilized the Housman test, the random effect model and the fixed effect model. This study shows that energy consumption has a negative but significant impact on the human development index.

Research on axial compressive behavior of steel-fiber-reinforced recycled concrete-filled square steel tube slender columns

This paper presents an experimental and analytical study of the axial behavior of steel-fiber-reinforced concrete-filled square steel tube slender columns. A total of 25 axially compressive column specimens are manufactured to investigate the effects of the mass replacement percentage of recycled coarse aggregates (Rp), tube wall thickness (Ts), length-to-width ratio of columns (Lc), and steel fiber content (Vsf) on the performance of slender columns. The results show that the effects of Lc and Ts on the compressive behavior of the column specimens are significant owing to changes in the confinement effect and instability. In terms of the bearing capacity, the compressive strength increases by 12.45%–23.19% as Ts increases from 3 to 5 mm but decreases by 4.35%–10.65% as Lc increases from 6 to 12. However, the incorporation of steel fibers improves the post-peak load mechanical behavior of the slender columns by performing anchoring and bridging effects, and when Vsf reaches 1.2%, it tends to compensate for the reduction caused by changes in Lc and Ts. The synergistic action of the column specimens under axial compression is quantified based on an energy consumption indicator. In addition, predictions of the bearing capacity of the slender columns specified in the Eurocode 4, GB 50936-2014, and Han model are compared and discussed. An incremental iteration model is proposed, and the predicted values are similar to the experiment results with reasonable safety margins.

CO2 emission analysis of metakaolin and alccofine replaced cement in M40 grade concrete.

Among the largest CO2 emission industries, the cement industry is ranked in 2nd place. A large volume of CO2 is emitted at the clinker production, which is a cement manufacturing intermediate product. Countries around the world were having difficulty reducing atmospheric emissions of greenhouse gases (GHG). Concrete is still being used more and more as the nation's infrastructure advances. The amount of CO2 emitted by concrete can be decreased by using less cement or substituting other materials for cement. In this study, the CO2 emission analysis is made on M40 grade, which is that metakaolin (MK) and alccofine (AL) are replaced to the cement in the manufacturing of concrete and is compared with the conventional concrete. The optimum cement replacement of MK and AL is 10% in the production of M40 grade concrete. MK and AL concrete have advantages and disadvantages. If proper safety precautions are taken during the manufacturing process, the toxicity level can be reduced, as well as the amount of CO2 released by the cement during the production of concrete. The LCA (life cycle analysis) is made for the concrete specimens, and the results were interpreted to know which concrete sample emits less and more carbon dioxide. The LCA study provided insights into the environmental aspects of metakaolin and alccofine concrete, including potential reductions in CO2 emissions, energy consumption and other environmental indicators. It helps identify areas of improvement and informs decision-making processes regarding sustainable material choices and construction practices. In M40 grade concrete, a 10% cement replacement with metakaolin and alccofine was found to be ideal. These results could also help in identifying the major cause of CO2 emission, and they can be used for further research purposes.

Improving the Energy Performance of a Household Using Solar Energy: A Case Study

In the global context of energy transition from fossil fuels to renewable sources of energy, solar energy plays a key role in electricity generation, having the highest annual growth in the last years. This case study focuses on improving the overall energy efficiency of a household through thermal retrofitting, harnessing solar energy with photovoltaic (PV) systems and using air-source (ASHP) or ground-source (GSHP) heat pumps. The electricity generated by the PV systems is used to power the heat pumps and all other electricals of the household. The simulations for the ASHP and GSHP systems were conducted with GeoT*SOL software, while for electricity generation by PV systems, PV*SOL Premium software was used. The comparative results show: a decrease in the heating load to about 51.56%; an annual heating requirement of 53.88% of the normed one; an energy consumption index of about 58.34 kWh/m2·year; an annual energy consumption reduction of 88% for ASHP and 91% for GSHP, compared with the current gas heating system; 34% of the household consumption was covered by the PV system in the case of using ASHP and 36% for GSHP; and lower operating costs by 47% for the PV system with ASHP and 53% for the PV system with GSHP.

Exploring the link between green energy, CO2 emissions, exchange rate and economic growth: Perspective from emerging South Asian countries

This paper investigates the nexus between renewable energy use, CO2 emissions, exchange rate, and economic development within emerging South Asian nations, namely Bangladesh, India, Pakistan, and Sri Lanka, employing the Autoregressive Distributed Lag (ARDL) framework. It examines annual data spanning from 1990 to 2019, examining key indicators of renewable energy consumption, CO2 emissions, exchange rate, and economic development. The ARDL bounds test results demonstrate the existence of co-integration among the variables in the long run. The empirical result finds that the renewable energy consumption, CO2 emissions, and exchange rate have a significant impact on economic growth in Bangladesh, Pakistan, and Sri Lanka in the long run. In India no significant relationship found in the long run. In short run assessment, Bangladesh, India, and Sri Lanka also found same relationship with economic growth and renewable energy consumption, CO2 emissions, and exchange rate. Interestingly, In Pakistan no significant relationship has found in short run estimation analysis. Furthermore, study tried to determine the causality direction by using the Toda Yamamoto granger causality approach, which reveals bidirectional causation between exchange rate and CO2 emission in India. In Pakistan, study also found bi-directional causality among the variables renewable energy consumption, CO2 emissions, and economic growth. Finally, this paper emphasizes developing the policy as well as making a concrete decision regarding the renewable energy consumption, CO2 emissions, exchange rate, and economic development for ensuring sustainable economic growth in South Asian region. Future research could extend this work by including different dimensional data, additional countries, or using alternative or supplementary modeling techniques.

Energy efficiency strategies for Brazilian social housing considering a life cycle perspective: Optimisation between thermal autonomy, energy consumption and costs

This study aims to establish optimal cases for energy-efficient social housing projects in Brazil, considering a life cycle cost-benefit analysis. Energy efficiency measures for the building envelope were evaluated in representative single-family and multifamily typology projects for the lower-income sector, considering thermal autonomy, energy consumption and cost indicators. The measures estimated in terms of macrocomponents allowed for comparative evaluation and association with costs. Cases were simulated in EnergyPlus to obtain the building’s expected operational consumption. With the macrocomponents and simulation data, Python routines were used to compute results for each proposed combination. Optimal cases that present high thermal performance in the national standard (NBR 15575) with cost-effectiveness, offering guidelines for projects in the 8 Brazilian bioclimatic zones, were established. Results presented in more detail for bioclimatic zone 8 showed optimal cases, with the increase in the percentage of hours of thermal autonomy reaching 65% to 71%, whereas that of a typical building was 30%, while life cycle cost related to the envelope showed reductions of up to 22%, even with higher initial costs. This study served as a basis for the new Brazilian policy proposed by the National Housing Office (SNH) to develop new social housing projects.