• International conference on construction engineering and project management
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• 2022.06a
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• pp.1256-1263
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• 2022

With the advent of the Fourth Industrial Revolution, the amount of energy used in buildings has been increasing due to changes in the energy use structure caused by the massive spread of information-oriented equipment, climate change and greenhouse gas emissions. For the efficient use of energy, it is necessary to have a plan that can predict and reduce the amount of energy use according to the type of energy source and the use of buildings. To address such issues, this study presents a model embedded in a digital twin that predicts energy use in buildings. The digital twin is a system that can support a solution of urban problems through the process of simulations and analyses based on the data collected via sensors in real-time. To develop the energy use prediction model, energy-related data such as actual room use, power use and gas use were collected. Factors that significantly affect energy use were identified through a correlation analysis and multiple regression analysis based on the collected data. The proof-of-concept prototype was developed with an exhibition facility for performance evaluation and validation. The test results confirm that the error rate of the energy consumption prediction model decreases, and the prediction performance improves as the data is accumulated by comparing the error rates of the model. The energy use prediction model thus predicts future energy use and supports formulating a systematic energy management plan in consideration of characteristics of building spaces such as the purpose and the occupancy time of each room. It is suggested to collect and analyze data from other facilities in the future to develop a general-purpose energy use prediction model.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1577-1589
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• 2023

Recently, the necessity of predicting changes for monitoring ocean is widely recognized. In this study, we performed a time series prediction of remote-sensing reflectance (Rrs), which can indicate changes in the ocean, using Geostationary Ocean Color Imager (GOCI) data. Using GOCI-I data, we trained a multi-scale Convolutional Long-Short-Term-Memory (ConvLSTM) which is proposed in this study. Validation was conducted using GOCI-II data acquired at different periods from GOCI-I. We compared model performance with the existing ConvLSTM models. The results showed that the proposed model, which considers both spatial and temporal features, outperformed other models in predicting temporal trends of Rrs. We checked the temporal trends of Rrs learned by the model through long-term prediction results. Consequently, we anticipate that it would be available in periodic change detection.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.11a
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• pp.68-69
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• 2023

Recently, decarbonization has been emphasized worldwide to cope with climate change, and carbon neutrality by 2050 has emerged as a global agenda. The domestic port authorities have been participating in the global agenda in line with the government regulations. Since 2010, when decarbonization has been regarded as an important assignment in ports, container terminal efficiency considering undesirable outputs such as Carbon dioxide has been analyzed. However, most previous studies measured carbon dioxide emissions according to the Tier 1 and it is a first time to analyze container terminal efficiency based on the Tier 3 presented in the IPCC guidelines. 17 domestic container terminal operators were considered as decision making units and DEA-SBM Model was used. Subsequently, Undesirable outputs model was conducted to calculate the environmental efficiency.

• Journal of the Korean Wood Science and Technology
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• v.51 no.6
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• pp.555-568
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• 2023

The diffusion strategies of wood culture were established using the analytic hierarchy process, to prepare a diffusion plan of wood culture and wood utilization in response to climate change due to global warming. 'Standardization of wood culture', 'Valuation of wood culture', and 'Habituation of wood culture' were set as three major implementation strategies and priorities were evaluated. As a result, it was analyzed in the following order: 'Development of systematic education programs for each age group for rational and efficient use of eco-friendly wood materials and development of wood education standard guidelines linked to the curriculum', 'Preparation of scientific basis data on human compatibility and eco-friendliness of wood to ensure the reliability of wood and wood products', and 'Establishment of monitoring and improvement plan through the designation as a model school'. Through this, it was determined that an educational environment, changes in public attitudes through publicity, and expanding opportunities to use wood and wood products were necessary for wood culture diffusion. The results of this study can be used as basic data to derive the diffusion strategies of wood culture and establish a roadmap and policy implementation strategy to revitalize wood culture.

• Nutrition Research and Practice
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• v.17 no.5
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• pp.1028-1041
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• 2023

BACKGROUND/OBJECTIVES: This study aimed to analyze the potential of school meals in South Korea as a sustainable tool to reduce carbon emissions by focusing on animal- vs. plant-based protein foods. MATERIALS/METHODS: By using a stratified proportional allocation method, 536 out of the 11,082 schools nationwide were selected including 21 kindergartens, 287 elementary-, 120 middle- and 108 high schools. A total of 2,680 meals served for 5 consecutive days (June 21-25, 2021) were collected. We analyzed the average serving amounts of protein foods (animal- vs. plant-based) per meal and then, calculated the estimated average amounts of carbon emission equivalents per meal by applying the conversion coefficients. The t-test and analysis of variance were used for statistical analyses (α = 0.05). RESULTS: The average serving amount of animal-based protein foods per meal was 12.5 g, which was approximately 3 times higher than that of plant-based ones (3.8 g) (P < 0.001); the Meat-group had the highest average amount of 17.0 g, followed by Egg-group (9.6 g), Fish-group (7.6 g), and Beans-and-Nuts-group (3.8 g) (P < 0.05). Specifically, pork (25.1 g) was ranked first, followed by poultry (19.6 g), processed meat products (18.0 g). The estimated average amount of carbon emission equivalents of animal-based protein foods per meal was 80.1 g CO₂e, which was approximately 31 times higher than that of plant-based ones (2.6 g CO₂e) (P < 0.001); the Meat-group had the highest average amount of 120.3 g CO₂e, followed by Fish-group (44.5 g CO₂e), Egg-group (25.9 g CO₂e), and Beans-and-Nuts-group (2.6 g CO₂e) (P < 0.05). Specifically, processed meat products (270.8 g CO₂e) were ranked first, followed by pork (91.7 g CO₂e), and processed fish products (86.6 g CO₂e). CONCLUSIONS: The results implied that school meals with plant-based alternatives could be a sustainable tool to improve carbon footprint.

• Journal of Advanced Technology Convergence
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• v.2 no.4
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• pp.43-48
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• 2023

Globally, there is a collaborative effort to achieve global carbon neutrality in response to climate change. In the case of South Korea, greenhouse gas emissions are rapidly increasing, presenting an urgent situation that requires resolution. In this context, this study developed a thermal energy collection device named a 'steam trap' and created an AI model capable of predicting future electricity usage by collecting energy usage data through steam traps. The average accuracy of electricity usage prediction with this AI model was 96.7%, demonstrating high precision. Consequently, the AI model enables the prediction and management of days with high electricity consumption and identifies which facilities contribute to elevated power usage. Future research aims to optimize energy consumption efficiency through efficient equipment operation using anomaly detection in steam traps and standardizing energy management systems, with the ultimate goal of reducing greenhouse gas emissions.

• Korean Journal of Environmental Agriculture
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• v.42 no.4
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• pp.435-443
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• 2023

Global concern over climate change, driven by greenhouse gas emissions, has prompted widespread interest in sustainable solutions. In the agricultural sector, biochar has emerged as a focal point for mitigating these emissions. This study investigated the impact of continuous biochar application on CO₂ and N₂O emissions during the spring cabbage cultivation period. Greenhouse gas emissions in the biochar treatment groups (soils treated with 1, 3, and 5 tons/ha of rice husk biochar) were compared to those in the control group without biochar. During the spring cabbage cultivation period in 2022, the total CO₂ emissions were in the range of 71.6-119.0 g/m² day, and in 2023, with continuous biochar application, they were in the range of 71.6-102.1 g/m² day. The total emissions of N₂O in 2022 and 2023 were in the range of 11.7-23.7 and 7.8-19.9 g/m² day, respectively. Overall, greenhouse gas emissions decreased after biochar treatment, confirming the positive influence of biochar on mitigating greenhouse gas release from the soil. Nevertheless, further research over an extended period exceeding five years is deemed essential to delve into the specific mechanisms behind these observed changes and to assess the long-term sustainability of biochar's impact on greenhouse gas dynamics in agricultural settings.

• Journal of Animal Science and Technology
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• v.65 no.4
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• pp.792-803
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• 2023

This study aimed to assess the impact of weather events on the sorghum-sudangrass hybrid (Sorghum bicolor L.) cultivar production trend in the central inland region of Korea during the monsoon season, using time series analysis. The sorghum-sudangrass production data collected between 1988 and 2013 were compiled along with the production year's weather data. The growing degree days (GDD), accumulated rainfall, and sunshine duration were used to assess their impacts on forage production (kg/ha) trend. Conversely, GDD and accumulated rainfall had positive and negative effects on the trend of forage production, respectively. Meanwhile, weather events such as heavy rainfall and typhoon were also collected based on weather warnings as weather events in the Korean monsoon season. The impact of weather events did not affect forage production, even with the increasing frequency and intensity of heavy rainfall. Therefore, the trend of forage production for the sorghum-sudangrass hybrid was forecasted to slightly increase until 2045. The predicted forage production in 2045 will be 14,926 ± 6,657 kg/ha. It is likely that the damage by heavy rainfall and typhoons can be reduced through more frequent harvest against short-term single damage and a deeper extension of the root system against soil erosion and lodging. Therefore, in an environment that is rapidly changing due to climate change and extreme/abnormal weather, the cultivation of the sorghum-sudangrass hybrid would be advantageous in securing stable and robust forage production. Through this study, we propose the cultivation of sorghum-sudangrass hybrid as one of the alternative summer forage options to achieve stable forage production during the dynamically changing monsoon, in spite of rather lower nutrient value than that of maize (Zea mays L.).

• Journal of Industrial Convergence
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• v.21 no.9
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• pp.133-142
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• 2023

The use of digital information technology has become important in order to effectively respond to changes in production conditions in Korean agriculture, which are continuously worsening due to a decrease in the rural population, deepening aging, and climate change. Accordingly, this study analyzed the factors affecting farmers' adoption of information devices use and the effect of information devices use on agricultural product sales using the propensity score matching method. As a result of the analysis, it was found that low-age farmers, high-education farmers, and leading farmers are highly likely to adopt use of information devices. For farms with similar characteristics such as age, management size, and farming type, it has been confirmed that farms that have adopted information devices use in agricultural management have higher sales of agricultural products. Therefore, increasing farmers' access to information and the ability to use information devices provides implications that farm income can be improved. The government's informatization support project in the agricultural and rural sectors is important so that farmers can have the ability to distribute informatization devices and utilize agricultural information, and active investment should also be made in information infrastructure.

• Journal of the Korean Institute of Gas
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• v.27 no.4
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• pp.92-101
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• 2023

To solve the problem of climate change, carbon neutrality has now become a necessity rather than an option. Hydrogen is not only a energy storage that can supplement the intermittent production of renewable energy, but is also considered a good alternative in the field of utilization as it does not emit carbon dioxide after reaction. In order to revitalize hydrogen vehicles, one of the fields of hydrogen utilization, the construction of hydrogen station infrastructure must be preceded. Prioritization of risk factors is necessary for efficient operation and risk assessment of hydrogen stations, but due to the short operation period of domestic hydrogen stations, there is a lack of frequency data on accidents and their reliability is low. In this study, we aim to identify the causes and consequences of deviations in hydrogen stations through HAZOP analysis. Additionally, we intend to analyze them using Fuzzy-AHP. Through this, we intend to derive the decision values for the causes of deviations in hydrogen stations and apply them to hydrogen accident cases and risk assessments to confirm the reliability and utility of the data.