• Korean Journal of Soil Science and Fertilizer
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• v.43 no.5
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• pp.716-721
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• 2010

LCA (Life Cycle Assessment) carried out to estimate carbon footprint and to establish of LCI (Life Cycle Inventory) database of rice production system. The results of collecting data for establishing LCI D/B showed that organic fertilizer and chemical fertilizer input to 4.29E-01 kg $kg^{-1}$ rice and 2.30E-01 kg $kg^{-1}$ rice for rice cultivation. It was the highest value among input for rice cultivation. And direct field emission was 3.23E-02 kg $kg^{-1}$ during rice cropping. The results of LCI analysis focussed on greenhouse gas (GHG) was showed that carbon footprint was 8.70E-01 kg $CO_2$-eq. $kg^{-1}$ rice. Especially for 80% of $CO_2$ in the GHG and 7.02E-01 kg of its $CO_2$-eq. $kg^{-1}$ rice. Of the GHG emission $CH_4$, and $N_2O$ were estimated to be 13% and 5%, respectively. With LCIA (Life Cycle Impact Assessment) for rice cultivation system, it was observed that fertilizer process might be contributed to approximately 80% of GWP (global warming potential).

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.12
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• pp.1768-1774
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• 2012

This study was conducted to evaluate methane ($CH_4$) and nitrous oxide ($N_2O$) emissions from livestock agriculture in 16 local administrative districts of Korea from 1990 to 2030. National Inventory Report used 3 yr averaged livestock population but this study used 1 yr livestock population to find yearly emission fluctuations. Extrapolation of the livestock population from 1990 to 2009 was used to forecast future livestock population from 2010 to 2030. Past (yr 1990 to 2009) and forecasted (yr 2010 to 2030) averaged enteric $CH_4$ emissions and $CH_4$ and $N_2O$ emissions from manure treatment were estimated. In the section of enteric fermentation, forecasted average $CH_4$ emissions from 16 local administrative districts were estimated to increase by 4%-114% compared to that of the past except for Daejeon (-63%), Seoul (-36%) and Gyeonggi (-7%). As for manure treatment, forecasted average $CH_4$ emissions from the 16 local administrative districts were estimated to increase by 3%-124% compared to past average except for Daejeon (-77%), Busan (-60%), Gwangju (-48%) and Seoul (-8%). For manure treatment, forecasted average $N_2O$ emissions from the 16 local administrative districts were estimated to increase by 10%-153% compared to past average $CH_4$ emissions except for Daejeon (-60%), Seoul (-4.0%), and Gwangju (-0.2%). With the carbon dioxide equivalent emissions ($CO_2$-Eq), forecasted average $CO_2$-Eq from the 16 local administrative districts were estimated to increase by 31%-120% compared to past average $CH_4$ emissions except Daejeon (-65%), Seoul (-24%), Busan (-18%), Gwangju (-8%) and Gyeonggi (-1%). The decreased $CO_2$-Eq from 5 local administrative districts was only 34 kt, which was insignificantly small compared to increase of 2,809 kt from other 11 local administrative districts. Annual growth rates of enteric $CH_4$ emissions, $CH_4$ and $N_2O$ emissions from manure management in Korea from 1990 to 2009 were 1.7%, 2.6%, and 3.2%, respectively. The annual growth rate of total $CO_2$-Eq was 2.2%. Efforts by the local administrative offices to improve the accuracy of activity data are essential to improve GHG inventories. Direct measurements of GHG emissions from enteric fermentation and manure treatment systems will further enhance the accuracy of the GHG data.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1053-1065
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• 2019

Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.6
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• pp.1185-1194
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• 2011

This study was carried out to estimate carbon emission using LCA (Life Cycle Assessment) and to establish LCI (Life Cycle inventory) DB for lettuce production system in protected cultivation. The results of data collection for establishing LCI DB showed that the amount of fertilizer input for 1 kg lettuce production was the highest. The amounts of organic and chemical fertilizer input for 1 kg lettuce production were 7.85E-01 kg and 4.42E-02 kg, respectively. Both inputs of fertilizer and energy accounted for the largest share. The amount of field emission for $CO_2$, $CH_4$ and $N_2O$ for 1 kg lettuce production was 3.23E-02 kg. The result of LCI analysis focused on GHG (Greenhouse gas) showed that the emission value to produce 1 kg of lettuce was 8.65E-01 kg $CO_2$. The emission values of $CH_4$ and $N_2O$ to produce 1 kg of lettuce were 8.59E-03 kg $CH_4$ and 2.90E-04 kg $N_2O$, respectively. Fertilizer production process contributed most to GHG emission. Whereas, the amount of emitted nitrous oxide was the most during lettuce cropping stage due to nitrogen fertilization. When GHG was calculated in $CO_2$-equivalents, the carbon footprint from GHG was 1.14E-+00 kg $CO_2$-eq. $kg^{-1}$. Here, $CO_2$ accounted for 76% of the total GHG emissions from lettuce production system. Methane and nitrous oxide held 16%, 8% of it, respectively. The results of LCIA (Life Cycle Impact assessment) showed that GWP (Global Warming Potential) and POCP (Photochemical Ozon Creation Potential) were 1.14E+00 kg $CO_2$-eq. $kg^{-1}$ and 9.45E-05 kg $C_2H_4$-eq. $kg^{-1}$, respectively. Fertilizer production is the greatest contributor to the environmental impact, followed by energy production and agricultural material production.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.3
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• pp.439-446
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• 2014

Many studies on methane ($CH_4$) and nitrous oxide ($N_2O$) emissions from livestock industries have revealed that livestock production directly contributes to greenhouse gas (GHG) emissions through enteric fermentation and manure management, which causes negative impacts on animal environment sustainability. In the present study, three essential values for GHG emission were measured; i.e., i) maximum $CH_4$ producing capacity at mesophilic temperature ($37^{\circ}C$) from anaerobically stored manure in livestock category ($B_{0,KM}$, Korean livestock manure for $B_0$), ii) $EF_{3(s)}$ value representing an emission factor for direct $N_2O$ emissions from manure management system S in the country, kg $N_2O-N$ kg $N^{-1}$, at mesophilic ($37^{\circ}C$) and thermophilic ($55^{\circ}C$) temperatures, and iii) $N_{ex(T)}$ emissions showing annual N excretion for livestock category T, kg N $animal^{-1}$ $yr^{-1}$, from different livestock manure. Static incubation with and without aeration was performed to obtain the $N_2O$ and $CH_4$ emissions from each sample, respectively. Chemical compositions of pre- and post- incubated manure were analyzed. Contents of total solids (% TS) and volatile solid (% VS), and the ratio of carbon to nitrogen (C/N) decrease significantly in all the samples by C-containing biogas generation, whereas moisture content (%) and pH increased after incubation. A big difference of total nitrogen content was not observed in pre- and post-incubation during $CH_4$ and $N_2O$ emissions. $CH_4$ emissions (g $CH_4$ kg VS-1) from all the three manures (sows, layers and Korean cattle) were different and high C/N ratio resulted in high $CH_4$ emission. Similarly, $N_2O$ emission was found to be affected by % VS, pH, and temperature. The $B_{0,KM}$ values for sows, layers, and Korean cattle obtained at $37^{\circ}C$ are 0.0579, 0.0006, and 0.0828 $m^3$ $CH_4$ kg $VS^{-1}$, respectively, which are much less than the default values in IPCC guideline (GL) except the value from Korean cattle. For sows and Korean cattle, $N_{ex(T)}$ values of 7.67 and 28.19 kg N $yr^{-1}$, respectively, are 2.5 fold less than those values in IPCC GL as well. However, $N_{ex(T)}$ value of layers 0.63 kg N $yr^{-1}$ is very similar to the default value of 0.6 kg N $yr^{-1}$ in IPCC GLs for National greenhouse gas inventories for countries such as South Korea/Asia. The $EF_{3(s)}$ value obtained at $37^{\circ}C$ and $55^{\circ}C$ were found to be far less than the default value.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.892-897
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• 2010

LCA (Life Cycle assessment) was carried out to estimate on carbon footprint and to establish of LCI (Life Cycle Inventory) database of sweetpotato production system. Based on collecting the data for operating LCI, it was shown that input of organic fertilizer was value of 3.26E-01 kg $kg^{-1}$ and it of mineral fertilizer was 1.02E-01 kg $kg^{-1}$ for sweetpotato production. It was the highest value among input for sweetpotato production. And direct field emission was 2.47E-02 kg $kg^{-1}$ during sweetpotato cropping. The result of LCI analysis focussed on greenhouse gas (GHG) was showed that carbon footprint was 4.05E-01 kg $CO_2$-eq. $kg^{-1}$ sweetpotato. Especially $CO_2$ for 71% of the GHG emission and the value was 2.88E-01 kg $CO_2$-eq. $kg^{-1}$ sweetpotato. Of the GHG emission $CH_4$, and $N_2O$ were estimated to be 18% and 11%, respectively. It might be due to emit from mainly fertilizer production (32%) and sweetpotato cultivation (28%) for sweetpotato production system. $N_2O$ emitted from sweetpotato cultivation for 90% of the GHG emission. With LCIA (Life Cycle Impact Assessment) for sweetpotato production system, it was observed that the process of fertilizer production might be contributed to approximately 90% of GWP (global warming potential). Characterization value of GWP and POCP were 4.05E-01 $CO_2$-eq. $kg^{-1}$ and 5.08E-05 kg $C_2H_4$-eq. $kg^{-1}$, respectively.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.898-903
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• 2010

This study was carried out to estimate carbon emission using LCA (Life Cycle Assessment) and to establish LCI (Life Cycle Inventory) database of soybean production system. Based on collecting the data for operating LCI, it was shown that input of organic fertilizer was value of 3.10E+00 kg $kg^{-1}$ soybean and it of mineral fertilizer was 4.57E-01 kg $kg^{-1}$ soybean for soybean cultivation. It was the highest value among input for soybean production. And direct field emission was 1.48E-01 kg $kg^{-1}$ soybean during soybean cropping. The result of LCI analysis focussed on greenhouse gas (GHG) was showed that carbon footprint was 3.36E+00 kg $CO_2$-eq $kg^{-1}$ soybean. Especially $CO_2$ for 71% of the GHG emission. Also of the GHG emission $CH_4$, and $N_2O$ were estimated to be 18% and 11%, respectively. It might be due to emit from mainly fertilizer production (92%) and soybean cultivation (7%) for soybean production system. $N_2O$ was emitted from soybean cropping for 67% of the GHG emission. In $CO_2$-eq. value, $CO_2$ and $N_2O$ were 2.36E+00 kg $CO_2$-eq. $kg^{-1}$ soybean and 3.50E-01 kg $CO_2$-eq. $kg^{-1}$ soybean, respectively. With LCIA (Life Cycle Impact Assessment) for soybean production system, it was observed that the process of fertilizer production might be contributed to approximately 90% of GWP (global warming potential). Characterization value of GWP was 3.36E+00 kg $CO_2$-eq $kg^{-1}$.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.6
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• pp.502-509
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• 2013

To estimate greenhouse gas (GHG) emission, we established inventory of conventional rice cultivation from farmers in Gunsan and Iksan, Jeonbuk province in 2011~2012. This study was to calculate carbon footprint and to analyse the major factor of GHGs. We carried out a sensitivity analysis using the analyzed main factors of GHGs and estimated the mitigation potential of GHGs. Also we tried to suggest agricultural methods to reduce GHGs that farmers of this case study can apply. Carbon footprint of rice production unit of 1 kg was 2.21 kg $CO_2.-eq.kg^{-1}$. Although amount of $CO_2$ emissions is largest among GHGs, methane had the highest contribution of carbon footprint on rice production system after methane was converted to carbon dioxide equivalent ($CO_2$-eq.) multiplied by the global warming potential (GWP). Source of $CO_2$ in the cultivation of rice farming is incomplete combustion of fossil fuels used by agricultural machinery. Most of the $CH_4$ emitted during rice cultivation and major factor of $CH_4$ emission is flooded paddy field in anaerobic condition. Most of the $N_2O$ emitted from rice cultivation process and major sources of $N_2O$ emission is application of fertilizer such as compound fertilizer, urea, orgainc fertilizer, etc. As a result of sensitivity analysis due to the variation in energy consumption, diesel had the highest sensitivity among the energies inputs. If diesel consumption is reduced by 10%, it could be estimated that $CO_2$ potential reduction is about 2.5%. When application rate of compound fertilizer reduces by 10%, the potential reduction is calculated to be approximately 1% for $CO_2$ and approximately 1.8% for $N_2O$. When drainage duration is decreased until 10 days, methane emissions is reduced by approximately 4.5%. That is to say drainage days, tillage, and reducing diesel consumption were the main sources having the largest effect of GHG reduction due to changing amount of inputs. Accordingly, proposed methods to decrease GHG emissions were no-tillage, midsummer drainage, etc.

• Environmental and Resource Economics Review
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• v.19 no.4
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• pp.963-1001
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• 2010

The national accounting matrix including environmental accounts(NAMEA) includes informations on environmental pressures in relation to economic activities as developed in the national accounts. In the NAMEA, conventional national accounts tables have been extended with accounts in physical units. This paper describes the background for work carried out on environmental accounts in Korea and explains how the NAMEA-air is developed. The Korea's NAMEA-air provided here serves several purposes; (1) a consistent and systemic feature of the interrelationship between the economy and the environment; (2) a basis for environmental economic analysis; and (3) the vital information for climate and air policy planning.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1215-1224
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• 2013

Great efforts have been made worldwide to reduce the Green House Gas (GHG) emission following the "Kyoto Protocol" declared during the United Nations Framework Convention on Climate Change in 1997. Many industries have restructured to meet the standard set by the Protocol. However, no clear guidance has been established for the purpose of reducing the GHG emission in construction industry. In addition, no significant effort has been made to conserve the energy during construction activities. For more effective energy saving in construction industry, it is essential to collect data about energy consumption, quantity of environmental emissions and costs. However, most studies on sustainable construction have been concentrated on the use of equipment, maintenance and repair works during construction due to the difficulties of collecting such data. This study suggests a method to select the most environmentally friendly equipment combination for earthwork with comparing environmental loads and costs using the database of Life Cycle Inventory in the Ministry of Knowledge Economy and Ministry of Environment of Korea.