• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.107-108
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• 2020

The severity of the global climate crisis is increasing due to greenhouse gases caused by human activities. As a result, countries and industries are making efforts to reduce carbon dioxide emissions, the biggest cause of global warming. Many studies have been conducted to predict carbon emissions in the construction sector to reduce this, but they have not actually produced a highly usable formula in the field. Therefore, the two variables 'Curve Fitting' were performed based on the data of excavators and trucks measured at the field. As a result, we have obtained a carbon dioxide emission prediction model for construction equipment, and we would like to use it to help establish an eco-friendly process plan.

• Environmental Engineering Research
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• v.22 no.3
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• pp.302-311
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• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.

• Advances in Computational Design
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• v.8 no.4
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• pp.295-307
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• 2023

This paper presents an optimization of the reinforced concrete ribbed slab in terms of minimum CO₂ emissions and an economic justification of the final optimal design. The design variables are six geometry variables including the slab thickness, the ribs spacing, the rib width at the lower and toper end, the depth of the rib and the bar diameter of the reinforcement, and the seventh variable defines the concrete strength. The objective function is considered to be the minimum amount of carbon dioxide gas (CO₂) emission and at the same time, the optimal design is economical. Seven significant design constraints of American Concrete Institute's Standard were considered. A robust metaheuristic optimization method called improved dolphin echolocation and ant colony optimization (IDEACO) has been used to obtain the best possible answer. At optimal design, the three most important sources of CO₂ emissions include concrete, steel reinforcement, and formwork that the contribution of them are 63.72, 32.17, and 4.11 percent respectively. Formwork, concrete, steel reinforcement, and CO₂ are the four most important sources of cost with contributions of 67.56, 19.49, 12.44, and 0.51 percent respectively. Results obtained by IDEACO show that cost and CO₂ emissions are closely related, so the presented method is a practical solution that was able to reduce the cost and CO₂ emissions simultaneously.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.4
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• pp.1-11
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• 2006

The aim in this study is to develop the combined EGR system with a non-thermal plasma reactor for reducing exhaust emissions and improving fuel economy in turbo intercooler ECU common-rail diesel engines. In this study, the characteristics of soot, CO and $CO_2$ emissions under four kinds of engine loads are experimentally investigated by using a four-cycle, four-cylinder, direct injection type, water-cooled turbo intercooler ECU common-rail diesel engine with a combined plasma exhaust gas recirculation(EGR) system operating at three kinds of engine speeds. The EGR and non-thermal plasma reactor system are used to reduce $NO_x$ emissions, and the non-thermal plasma reactor and turbo intercooler system are used to reduce soot and THC emissions. The plasma system is a flat-to-flat type reactor operated by a plasma power supply. The fuel is sprayed by pilot and main injections at the variable injection timing between BTDC $15^{\circ}$ and ATDC $1^{\circ}$ according to experimental conditions. It is found that soot emissions with increasing EGR rate are increased, but are decreased as the applied electrical voltage of the non-thermal plasma reactor is elevated at the same engine speed and load. Results also show that CO and $CO_2$ emissions are increased as EGR rate is elevated, and CO emissions are increased, but $CO_2$ emissions are decreased as the applied electrical voltage of the non-thermal plasma reactor is elevated at the same engine speed and load.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.79-80
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• 2023

After the Second Industrial Revolution, as global warming caused by environmental issues has intensified, the CO₂ emissions from the cement industry have become an urgent challenge. Therefore, this study aimed to reduce and utilize CO₂ emissions by using CO₂-reducing Calcium Silicate Cement.

• Journal of Information Technology Services
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• v.12 no.2
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• pp.73-84
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• 2013

The development of the information on the internet brought a lot of changes in the real estate market. Because the real estate has local distinctiveness and individuality household who want to move must to visit place for housing information. But now household use internet real estate information at every decision-moving step and that is able to reduce not only the cost of real estate information but also social benefit like $CO_2$ emissions reduction effect. In this study, I analyzed the effect of $CO_2$ emissions reduction with Seoul household residential mobility data when household take informations from internet real estate site. As increasing a single family who is good at internet service, the effect of $CO_2$ reduction from the development of the Internet real estate information has more increased.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.508-520
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• 2020

Dam reservoirs have been reported to contribute significantly to global carbon emissions, but unlike natural lakes, there is considerable uncertainty in calculating carbon emissions due to the complex of emission pathways. In particular, the method of calculating carbon dioxide (CO2) net atmospheric flux (NAF) based on a simple gas exchange theory from sporadic data has limitations in explaining the spatiotemporal variations in the CO2 flux in stratified reservoirs. This study was aimed to analyze the spatial and temporal CO2 distribution and mass balance in Daecheong Reservoir, located in the mid-latitude monsoon climate zone, by applying a two-dimensional hydrodynamic and water quality model (CE-QUAL-W2). Simulation results showed that the Daecheong Reservoir is a heterotrophic system in which CO2 is supersaturated as a whole and releases CO2 to the atmosphere. Spatially, CO2 emissions were greater in the lacustrine zone than in the riverine and transition zones. In terms of time, CO2 emissions changed dynamically according to the temporal stratification structure of the reservoir and temporal variations of algae biomass. CO2 emissions were greater at night than during the day and were seasonally greatest in winter. The CO2 NAF calculated by the CE-QUAL-W2 model and the gas exchange theory showed a similar range, but there was a difference in the point of occurrence of the peak value. The findings provide useful information to improve the quantification of CO2 emissions from reservoirs. In order to reduce the uncertainty in the estimation of reservoir carbon emissions, more precise monitoring in time and space is required.

• Journal of Institute of Convergence Technology
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• v.11 no.1
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• pp.13-18
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• 2021

It is widely accepted that the prevention of global warming requires significant reductions in greenhouse gases, particularly CO₂ emissions. Although fossil fuel-based power plants account for the majority of CO₂ emissions, it is urgent to reduce CO₂ emissions in industries that emit large amounts of CO₂ such as steel, petrochemical, and oil refining. This paper examines the current status of CO₂ emission in the domestic oil refining industry and CO₂ emission sources in each unit process in the oil refining industry. Focusing on the previously developed CO₂ capture process, cases and applicability of greenhouse gas reduction in FCC and hydrogen manufacturing processes, which are major processes constituting the oil refining industry, are reviewed.

• Journal of the Korea Institute of Building Construction
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• v.10 no.3
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• pp.83-90
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• 2010

The carbon dioxide($CO_2$) emissions that result from construction are one of the main factors causing a global warming problem. It is therefore necessary to make efforts to reduce $CO_2$ emissions in the construction industry. Some researchers have studied $CO_2$ emissions in the industry ; however, there has been a lack of study on $CO_2$ emissions cost. Therefore, in this study, the construction costs, including the $CO_2$ emission cost, of masonry wall type, which is a common brick wall, concrete brick wall, and fired brick wall, were examined. The purpose of this study is to compare the construction costs of masonry wall types, including $CO_2$ emission costs. The study found that the $CO_2$ emission costs were highest for the fired brick wall, followed by the concrete brick wall. This research could provide basic information that can be used in other engineering methods to convert $CO_2$ emissions to $CO_2$ emission cost.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.2
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• pp.96-111
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• 2016

Effort to reduce energy consumptions or CO2 emissions is global trend. To follow this trend, spatial studies related to characteristics affecting energy consumptions or CO2 emissions have been conducted, but only with the focus on spatial dependence, not on spatial heterogeneity. The aim of this study is to investigate spatial heterogeneity patterns of CO2 emission based on socio-economic factors, land-use characteristics and traffic infrastructure of Seoul city. Geographically Weighted Regression (GWR) analysis was performed with 423 administrative district data in Seoul. The results suggest that population and employment densities, road density and railway length in most districts are found to have positive impact on the CO2 emissions. Residential and green area densities also have the highest positive impact on CO2 emissions in most districts of Gangnam-gu. The resulting model can be used to identify the spatial patterns of CO2 emissions at district level in Seoul. Eventually it can contribute to local energy policy and planning of metropolitan area.