• Energy Research
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• 11. Sustainability close
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Abstract The South Korean government has been actively promoting an educational-facility improvement program as part of its energy-saving efforts. This research seeks to develop a decision support model for selecting the facility expected to be effective in generating energy savings and making the facility improvement program more effective. In this research, project characteristics and electric-energy consumption data for the year 2009 were collected from 6282 elementary schools located in seven metropolitan cities in South Korea. In this research, the following were carried out: (i) a group of educational facilities was established based on electric-energy consumption, using a decision tree; (ii) a number of similar projects were retrieved from the same group of facilities, using case-based reasoning; and (iii) the accuracy of prediction was improved, using the combination of genetic algorithms, the artificial neural network, and multiple regression analysis. The results of this research can be useful for the following purposes: (i) preliminary research on the systematic and continuous management of educational facilities’ electric-energy consumption; (ii) basic research on electric-energy consumption prediction based on the project characteristics; and (iii) practical research for selecting an optimum facility that can more effectively apply an educational-facility improvement program as a decision support model.

Abstract Due to the various restrictions on the energy performance of public office buildings, it is essential to obtain occupancy information for not only evaluating but also regulating the building energy performance. There is still a lack of information and standard, however, for occupancy density due to the limitations on data collection and the lack of reliable data. Therefore, this study aimed to determine the optimal occupancy density for reducing the energy consumption in public office buildings. Towards this end, this study used various statistical methods, such as correlation analysis, decision tree, and Mann-Whitney U test, based on the actual occupancy data from public office buildings in South Korea. This study was conducted in three steps: (i) establishment of the database; (ii) determination of the optimal occupancy density using the statistical approach; and (iii) application of the proposed occupancy density using building energy policies. As a result, it was shown that buildings with an occupancy density above 31.41 m2/person could save up to 50.3% energy on average compared to those with an occupancy density below 31.41 m2/person. The analysis results showed that the proposed occupancy density could help in deciding the appropriate occupancy density for reducing the energy consumption of public office buildings.

Two types of methods are used to evaluate pedestrian comfort: pedestrian wind comfort and outdoor thermal comfort. To accurately ascertain the outdoor wind environment, wind speed is the only parameter considered. However, pedestrians may still feel discomfort when the perceived temperature is low, even though the wind comfort criterion has been satisfactorily fulfilled. The purpose of this study is, therefore, to investigate pedestrian comfort when the perceived temperature is low, especially in winter conditions. To achieve this, a pedestrian survey was conducted, and 588 respondents completed a questionnaire. The results show that pedestrians feel discomfort when the WCET (Wind Chill Equivalent Temperature) is low, with almost 40 percent of respondents answering that they feel discomfort in these conditions. In conclusion, the threshold wind speed of the winter season could be determined to be lower than that of the existing comfort criteria by applying the WCET.

This article aims to identify occupations at risk of climate change, and explore their spatial distribution by investigating their spatial concentration and cluster patterns in Korea. In order to identify professions vulnerable to climate change, we used three criteria: (1) exposure to the risks of job activities; (2) sensitivity to work environments; and (3) adaptive capacities based on the socioeconomic conditions of a person’s occupation. We identified 26 vocations as vulnerable, which corresponds to 16.5% of the total number of careers. Then, we used exploratory spatial data analysis (ESDA) techniques to examine the spatial distribution of jobs at risk of climate change effects. Endangered occupations show the extremely uneven spatial distributions across regions, indicating that there is a wide range of values for the proportion of endangered occupations across regions. The regions with high proportions of professions in jeopardy show clear spatial clustering based on the large geographic belt from the southwest to the northeast. Our results suggest that policymakers should consider the specific geographies of vulnerable occupations. The current work provides helpful hints on regional policy coordination to promote a virtuous circle of sustainable regional development.

This study investigated lifecycle CO2 (LCCO2) emission reductions through application of double-window casement systems (DWCS) in residential units in Korea, compared with conventional single windows (SWs). The DWCS is a double window system that is energy-efficient, reducing energy consumption during operation. However, this system incorporates increased embodied CO2 emissions. We evaluated LCCO2 reductions associated with use of the DWCS by calculating CO2 emissions during space conditioning as well as the embodied CO2 emissions of the DWCS. The results showed that use of DWCS in a residential unit during the cooling season had 26.2 and 27.4 t CO2 fewer emissions than SWs in the natural ventilation and minimum ventilation modes, respectively. Although implementation of DWCS is expected to substantially reduce LCCO2 emissions, the large embodied CO2 emissions of the aluminum frame reduce the benefits of the DWCS.

Abstract The reliable benchmarks are required to evaluate building energy efficiency fairly. This study aims to develop the energy benchmarks and relevant process for a multi-family housing complex (MFHC), which is responsible for huge CO 2 emissions in South Korea. A database, including the information on building attributes and energy consumption of 503 MFHCs, was established. The database was classified into three groups based on average enclosed area per household (AEA) through data mining techniques. The benchmarks of site energy use intensity (EUI), source EUI, and CO 2 emission intensity (CEI) were developed from Groups 1, 2, and 3. Representatively, the developed benchmarks of CEI for Groups 1, 2, and 3 were 28.17, 24.16, and 20.96 kg-CO 2 /m 2 y, respectively. A comparative analysis using the operational rating identified that the developed benchmarks could solve the irrationality of the original benchmarks from overall database. In the case of the original benchmarks, 93% of small-AEA-groups and 16% of large-AEA-groups received lower grades. In the case of the developed benchmark, the upper and lower grades in Groups 1–3 were both adjusted to 50%. The proposed process for developing energy benchmark is applicable to evaluate the energy efficiency of other buildings, in other regions.

This study explores the role of community-led governance in innovation adoption by drawing determinants of RFID waste pricing system adoption. The Ministry of Environment of the Republic of Korea adopted an individual-incentive based RFID waste pricing system for apartment complexes in 2013 in an effort to reduce food waste. After invention and wide usage during World War Two, RFID has received lots of intention especially in public policy field. This innovative system using RFID machines and RFID tag-embedded cards showed effectiveness in waste reduction. Residents of each apartment complex decide whether and when to adopt the RFID system. Based on innovation diffusion theory, we would like to draw which determinant factors most affect RFID pricing system adoption. Although innovation diffusion is dependent on community level, many previous studies surveyed individuals and asked them about organizational adoption. We use non-perceptional data representing the characteristics of the level of apartment complex. This study, relying on ecological variables collected from 2018 apartment complexes across 25 autonomous districts located in Seoul City, explores significant factors related to the process of the RFID adoption. According to the empirical results, there were two important determinants: how much information the government provided, and how the apartment complex residents have opportunity to gather opinion and decide the RFID pricing system adoption. We emphasize the importance of community-led governance in adopting an innovative environmental system and how community-led governance contributes to sustainable innovation adoption.

This study examined the influence of heat recovery ventilators (HRVs) on energy savings and indoor air quality (IAQ) in high-rise residential buildings. Field measurements were performed in four residential units, which were validated by computer simulations and estimated the total annual energy consumption. The operation schedules for HRVs were determined by a survey of residents. Field measurement results indicate that HRVs could effectively improve IAQ and afford effective energy savings. The indoor concentrations of formaldehyde were reduced by 54.6% after HRVs were operated for 24 h. The initial concentration was reduced by 82% after 168 h. Toluene was the dominant volatile organic compounds (VOCs) in the indoor air. Its initial concentration was reduced by 50% and other VOCs were also reduced by 40.1% to 53.1% after HRVs were operated. Annual energy savings of up to 20.26% were predicted when HRVs were operated for 24 h continuously, exchanging sensible and latent heat. HRVs could save energy more effectively in winter than in summer due to the greater temperature difference between outdoor and indoor air. Based on the preferred operation schedules of homes surveyed, an annual energy savings could be as high as 8.52%.