• Spatial Information Research
• /
• v.20 no.4
• /
• pp.29-36
• /
• 2012

The low-carbon freight transportation system was introduced due to increase traffic congestion cost and carbon-dioxide for global climate change according to expanding city logistics demands. It is necessary to create 3D-based road network map for representing realistic road geometry with consideration of fuel consumption and carbon emissions. This study propose that 3D road network model expressed to realistic topography and road structure within trunk road for intercity freight through overlaying 2D-based transport-related thematic map and 1m-resolution DEM. The 3D-based road network map for the experimental road sections(Pyeongtaek harbor-Uiwang IC) was verified by GPS/INS survey and fuel consumption simulation. The results corresponded to effectively reflect realistic road geometry (RMSE=0.87m) except some complex structure such as overpass, and also actual fuel consumption. We expect that Green-based freight route planning and navigation system reflected on 3D geometry of complex road structure will be developed for effectively resolving energy and environmental problems.

• Environmental and Resource Economics Review
• /
• v.24 no.1
• /
• pp.141-164
• /
• 2015

The concept of consumption-based greenhouse gas (GHG) inventory is directly related with the carbon content of international trade. Along the lines of the consumption-based GHG inventory, we investigate domestic and foreign carbon contents embodied in sectoral exports of Korea. In addition to the analysis of carbon content of exports, it is investigated how much share of responsibility for carbon emissions of Korea belongs to each major trading partner of Korea. We also compute the carbon intensities of Korean exports in carbon chain with other trading partners and find some characteristics revealed in Korea's carbon emissions embodied in its exports.

• Korean Journal of Remote Sensing
• /
• v.38 no.1
• /
• pp.1-20
• /
• 2022

As unmanned aerial vehicle (UAV) technology grew in popularity over the years, it was introduced for air quality monitoring. This can easily be used to estimate the sidewalk emission concentration by calculating road traffic emission factors of different vehicle types. These calculations require a simulation of the spread of pollutants from one or more sources given for estimation. For this purpose, a Gaussian plume dispersion model was developed based on the US EPA Motor Vehicle Emissions Simulator (MOVES), which provides an accurate estimate of fuel consumption and pollutant emissions from vehicles under a wide range of user-defined conditions. This paper describes a methodology for estimating emission concentration on the sidewalk emitted by different types of vehicles. This line source considers vehicle parameters, wind speed and direction, and pollutant concentration using a UAV equipped with a monocular camera. All were sampled over an hourly interval. In this article, the YOLOv5 deep learning model is developed, vehicle tracking is used through Deep SORT (Simple Online and Realtime Tracking), vehicle localization using a homography transformation matrix to locate each vehicle and calculate the parameters of speed and acceleration, and ultimately a Gaussian plume dispersion model was developed to estimate the CO, NOx concentrations at a sidewalk point. The results demonstrate that these estimated pollutants values are good to give a fast and reasonable indication for any near road receptor point using a cheap UAV without installing air monitoring stations along the road.

• Korean Journal of Construction Engineering and Management
• /
• v.18 no.1
• /
• pp.74-82
• /
• 2017

It is known that energy usage from Korean Universities was growing rapidly in the early 2000s. But since 2011, the change was caused by GHG emissions regulation enforced by the government. The purpose of this research was to find the characteristics and trends of greenhouse gas emissions from major universities in Korea according to the each university's data and information. The result shows that GHG emissions from University have increased steadily prior to enforcement by 4-5% annually, but the rate of increase marked 0.5~1% in 2011~2013 is the season of emission regulation and the total amount of emissions decreased 3%~5% in 2014~2015 while preparing an emissions trading scheme. Therefore we can say that the enforcement of GHG reduction such as energy target management system makes a visible effect at least in the University sector that level of GHG emissions is from $75kg/m^2$ to $58Kg/m^2$ for seven years. Another result says that the size of research fund is the main factor that affects the amount of GHG emissions before 2011, but the size of building area has been a new factor influencing the GHG emission since 2013. Thus we suggest that the criteria for evaluating the level of GHG emission from University is suitable if it is based on the building area intensity.

• Korean Journal of Artificial Intelligence
• /
• v.8 no.2
• /
• pp.17-22
• /
• 2020

Purpose: After entering the 21st century, a new industrial revolution, i.e. industrial revolution 4.0, which is characterized by intelligence, automation and networking, has opened the curtain of the "industry 4.0" era. In recent years, "low-carbon economy" has been a development goal that has been paid close attention to and adhered to at home and abroad. As a major economic province, Shandong Province has not only brought about rapid economic growth, but also caused rapid environmental deterioration due to its high energy consumption, high dependence and high environmental pollution. In this environment, low-carbon economy has become an inevitable trend in the development of foreign trade in Shandong Province. Based on the current situation of foreign trade in Shandong Province and various existing problems, this paper explores the relationship between low-carbon economy and foreign trade in Shandong Province under this strategic background. Research design, data and methodology: By selecting the data from 2008 to 2017, using the carbon emission coefficient method to measure the CO2 emissions in the past decade, analyzing the impact of ecological factors on trade, selecting the most representative GDP and total imports for regression analysis, it is proved that they have a real impact on CO2 emissions. The total GDP is positively correlated with carbon emissions, while the total import is negatively correlated with carbon emissions. Results:This paper discusses the impact of low-carbon economy on foreign trade of Shandong Province from the perspective of foreign trade. Especially in today's "low-carbon economy" background. Conclusions:it is helpful for relevant departments to formulate relevant policies and promote the sustainable development of foreign trade in Shandong Province.

• Journal of Internet of Things and Convergence
• /
• v.10 no.2
• /
• pp.43-50
• /
• 2024

This manuscript proposes an artificial intelligence-based(AI) energy platform system that efficiently use existing energy than creating new energy than creating new energy sources. To this end, it collects public information data portal and statistics data portal and data emissions, including energy usage and greenhouse gas emissions, including energy consumption and greenhouse gas emissions.In addition, it provides strong security and personal information protection functions to overcome the limit of existing energy platform. Through the built energy platform, improving power supply and user convenience of users and users to contribute to global warming issues.In this paper, the contents to implement the contents of the system, and improvement direction from the future completion and improvement direction.

• Journal of Korean Society of Rural Planning
• /
• v.19 no.2
• /
• pp.121-128
• /
• 2013

The greenhouse gas emission according to the energy consumption is the cause of global warming. With various climates, it is occurs the direct problems to ecosystem. The various studies are being to reduce the carbon dioxide, which accounts for more than 80% of the total greenhouse gas emissions. In this study, estimate the carbon usage using potential biomass extracted from forest type map according to land-use by satellite image, and estimate the amount of carbon dioxide, according to the energy consumption of urban area. The $CO_2$ adsorption is extracted by the amount of forest based on the direct absorption of tree, the other used investigated value. The $CO_2$ emission in Jecheon was 3,985,900 $TCO_2$ by energy consumption. At the land cover classification, the forest is analyzed as 624,085ha and the farmland is 148,700ha. The carbon dioxide absorption was estimated at 1,834,850 Tons from analyzed forest. In case of farmland, it was also estimated at 706,658 Tons.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.8
• /
• pp.928-941
• /
• 2016

Energy consumption of today's wired data networks is driven by access networks. Today, green networking has become a issue to reduce energy wastes and $CO_2$ emission by adding energy managing mechanism to wired data networks. However, energy consumption and environmental impacts of wired access networks are largely unknown. In addition, there is a lack of general and quantitative valuation basis of energy use of large-scale access networks and $CO_2$ emissions from them. This paper compared and analyzed limits of existing models estimating energy consumption of access networks and it proposed a model to estimate energy consumption of large-scale access networks by top-down approach. In addition, this work presented models that assess environmental(anti-greening) impacts of access networks using results from our models. The performance evaluation of the proposed models are achieved by comparing with previous models based on existing investigated materials and actual measured values in accordance with real cases.

• Journal of Advanced Navigation Technology
• /
• v.27 no.5
• /
• pp.649-657
• /
• 2023

ICAO adopted the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) at the 39th General Assembly in 2016, and 115 countries, including South Korea, expressed their intention to participate in CORSIA as of January 1, 2023. Since carbon generated in the aviation industry is mainly caused by greenhouse gases emitted from aircraft engines, fuel consumption must be reduced to reduce carbon emissions. Prior research, such as simulation, is essential to predict the effectiveness of each plan and to make decisions about its implementation. High-quality data is needed to derive accurate results, but it has been difficult to secure actual fuel consumption data, as they are considered to be classified airline data. Therefore, in this paper, after establishing a model that estimates fuel consumption based on actual fuel consumption data, the model is to be advanced to improve its accuracy.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.26 no.5
• /
• pp.335-342
• /
• 2013

Since the seriousness of environmental pollution came to the fore recently, various efforts have been made globally for the reduction of the environmental load. In particular, in the field of construction, an industry responsible for a considerable amount of pollution, studies have been actively conducted to reduce $CO_2$ emissions and energy consumption. However, most conventional research about pollution as it relates to construction is focused on the maintenance stages where $CO_2$ emissions are the greatest. Research related to the design stage is in its infancy, as it has only been conducted thus far on steel buildings and RC buildings. In fact, in order to achieve environmentally friendly construction considering the Life Cycle Assessment(LCA), the building design should be derived to reduce the $CO_2$ emissions from the early building design stage, and structural engineers should be able to suggest a design plan considering its environmental friendliness. In this study, optimal structural design method for steel reinforced concrete(SRC) columns considering $CO_2$ emissions is presented. The trends of $CO_2$ emissions in SRC columns according to the variations of steel shapes, concrete strengths and loads are investigated.