A Study on Applying Biomass Fraction for Greenhouse Gases Emission Estimation of a Sewage Sludge Incinerator in Korea: A Case Study

본 연구는 국가 온실가스 배출량 평가에서 향후 적용될 2006 IPCC 신규 가이드라인의 적용성을 검토하고자 해외사례를 분석하고, 기존 가이드라인과의 방법론 및 배출계수 차이점을 분석하여 실제 배출량을 산정해 보고자 수행하였다. 해외 ANNEX I 국가들의 적용성을 검토한 결과 우리나라와 농업여건이 비슷한 일본의 경우 일부는 2006 IPCC 가이드라인의 방법론과 배출계수를 적용하고 있었으며, 미국의 경우는 대부분이 2006 IPCC 가이드라인을 적용하여 배출량을 산정하였다. 1996 IPCC와 2006 IPCC 가이드라인의 방법론과 기본계수를 적용하여 경종부문 온실가스 배출량을 평가한 결과, 신규가이드라인을 적용했을 때 약 26~29%의 배출량이 감소되었다. 이러한 배출량 차이에 대한 주요 원인은 일부 배출원 항목에 대한 삭제 및 배출계수의 차이에 있음을 알 수 있었다. This study was conducted to compare of greenhouse gas emissions between 1996 and 2006 IPCC (Intergovernmental Panel on Climate Change) guidelines change. Greenhouse gas emissions were calculated separately by rice cultivation, agricultural soils and field burning of agricultural residues from 2000 to 2008 according to 1996 and 2006 IPCC guidelines. To calculate greenhouse gas emissions, emission factor and activity data were used IPCC default value and the food, agricultural, forestry and fisheries statistical yearbook of MIFAFF (Ministry for Food, Agriculture, Forestry, and Fisheries). The greenhouse emissions by 1996 IPCC guidelines were highest in rice cultivation as 4,008 <TEX>$CO_2$</TEX>-eq Gg of 2000 and 3,558 <TEX>$CO_2$</TEX>-eq Gg of 2008. The emissions by N-fixing crops, crop residues returned soils and field burning did not much affect the total emissions. <TEX>$CO_2$</TEX> emissions by urea and lime were calculated by adding in 2006 IPCC guidelines and its emissions were 157 and 82 <TEX>$CO_2$</TEX>-eq Gg in 2008 respectively. The emissions by N-fixing crops, crop residues returned to soils and field burning, in common with 1996 IPCC guidelines, did not have a significant impact on total emissions. The total emissions in agronomic sector was decreased continuously from 2000 to 2008 and annual emissions by 2006 IPCC guidelines were approximately 26-29% less than the 1996 IPCC guidelines.

Better information on greenhouse gas (GHG) emissions and mitigation potential in the agricultural sector is necessary to manage these emissions and identify responses that are consistent with the food security and economic development priorities of countries. Critical activity data (what crops or livestock are managed in what way) are poor or lacking for many agricultural systems, especially in developing countries. In addition, the currently available methods for quantifying emissions and mitigation are often too expensive or complex or not sufficiently user friendly for widespread use.The purpose of this focus issue is to capture the state of the art in quantifying greenhouse gases from agricultural systems, with the goal of better understanding our current capabilities and near-term potential for improvement, with particular attention to quantification issues relevant to smallholders in developing countries. This work is timely in light of international discussions and negotiations around how agriculture should be included in efforts to reduce and adapt to climate change impacts, and considering that significant climate financing to developing countries in post-2012 agreements may be linked to their increased ability to identify and report GHG emissions (Murphy et al 2010, CCAFS 2011, FAO 2011).

ABSTRACTIn this study, in order to understand accurate calculation of greenhouse gas emissions of urban solid waste incineration facilities, which are major waste incineration facilities, and problems likely to occur at this time, emissions were calculated by classifying calculation methods into 3 types. For the comparison of calculation methods, the waste characteristics ratio, dry substance content by waste characteristics, carbon content in dry substance, and 12C content were analyzed; and in particular, CO2 concentration in incineration gases and 12C content were analyzed together. In this study, 3 types of calculation methods were made through the assay value, and by using each calculation method, emissions of urban solid waste incineration facilities were calculated then compared. As a result of comparison, with Calculation Method A, which used the default value as presented in the IPCC guidelines, greenhouse gas emissions were calculated for the urban solid waste incineration facilities A and B at 244.43 ton CO2/day and 322.09 ton CO2/day, respectively. Hence, it showed a lot of difference from Calculation Methods B and C, which used the assay value of this study. It is determined that this was because the default value as presented in IPCC, as the world average value, could not reflect the characteristics of urban solid waste incineration facilities. Calculation Method B indicated 163.31 ton CO2/day and 230.34 ton CO2/day respectively for the urban solid waste incineration facilities A and B; also, Calculation Method C indicated 151.79 ton CO2/day and 218.99 ton CO2/day, respectively.Implications: This study intends to compare greenhouse gas emissions calculated using 12C content default value provided by the IPCC (Intergovernmental Panel on Climate Change) with greenhouse gas emissions calculated using 12C content and waste assay value that can reflect the characteristics of the target urban solid waste incineration facilities. Also, the concentration and 12C content were calculated by directly collecting incineration gases of the target urban solid waste incineration facilities, and greenhouse gas emissions of the target urban solid waste incineration facilities through this survey were compared with greenhouse gas emissions, which used the previously calculated assay value of solid waste.

"In recent years, it has become increasingly necessary to reduce CO2 emissions as a countermeasure to global warming. Paris Agreement sets out a global framework to limit global warming to well below 2degC, which needs an extremely huge reduction of CO2 emissions. Therefore, it is critical to deal with the use of automobiles that has a big impact on this problem. On the other hand, since it is difficult to dramatically improve individual vehicle efficiency anymore, new approaches are required to achieve the goal. Because of this background, we, DENSO, are proceeding with development to reduce CO2 emissions not only by performance improvement of individual vehicles but also by operations management or cooperative control of multiple vehicles. In this paper, a rideshare operations management technology reducing the total fuel spend is introduced, where several users going to the common destination share a ride in the same vehicle. In general, it is well known that whole CO2 emissions from multiple vehicles can be reduced if the use of vehicles is reduced with ridesharing. Meanwhile, there is room for an additional reduction of CO2 emissions because, in existing researches, only simple cost functions such as the sum of route costs are minimized without considering vehicle efficiency. Besides, since the vehicles used for rideshare are often heterogeneous with various kinds of powertrain systems, CO2 emissions can be reduced further by taking these characteristics. Therefore, a simulation model including each powertrain characteristics for rideshare operations management is proposed to calculate CO2 emission from each vehicle for each driving route precisely. Then, an optimization problem is formulated by designing a cost function described with the total amount of CO2 emissions calculated with the simulator. Since this problem is complicated, where not only a destination but also users going to the destination must be assigned to each vehicle, mixed-integer linear programming (MILP) is not an appropriate methodology to find the optimal solution. Therefore, an operations management technology has been developed to find the optimal destination and user allocation by incorporating this problem into a constraint satisfaction problem (CSP) for minimizing the total amount of CO2 emissions from multiple vehicles. The effect of this technology has been validated with simulation, showing more than 30% CO2 emissions reductions in some cases comparing with conventional rideshare technologies, where the amount of used vehicles is minimized."

The paper present the results for the influence of investment costs into biogas station on the amount of emissions from the agricultural sector. For the evaluation is applied structural analysis of major factors affecting the level of CO2 emissions from agriculture. Among these factors are: the number of animals (converted to livestock units), cost of investment in biogas plants, the quantity of nitrogen fertilizers and the total amount of CO2 emissions from agriculture. The results show that the investment costs haven t significant influence despite the correct direction of effect. Significant impact on CO2 emissions from agriculture have the numbers of animals (respectively cattle units). In the case of applications reviewed model from the Czech Republic to selected countries of the EU shows that the highest investment costs and also decrease CO2 equivalent emissions from agricultural biogas plants is in Germany. The high number of agricultural biogas plants is also evident in Italy and the United Kingdom. Investment costs are in these two countries in the range of 115 to 144 mld. CZK. Furthermore, it is evident that the significant investment costs are incurred by the smaller countries (Czech Republic, Slovakia, Belgium). Investment costs in this case are in the range 10-33 mld. CZK.

Based on Intergovernmental Panel on Climate Change (IPCC) assessment report at 2007, it is likely that there has been a substantial anthropogenic contribution to global warming. Carbon dioxide (CO2) is a major anthropogenic greenhouse gas and its increase is thought to give rise to the recent global warming. Although studies suggested the impact of population growth on carbon dioxide increase, much attention has not been paid. In this study population was plotted as compared to the atmospheric CO2 concentration. A quite linear relationship was observed between population and CO2 concentration at both before and after 1970, after which the global temperature rapidly increased. In addition, direct and indirect human-derived CO2 emission appeared to contribute much to the total amount of CO2 emission in developing countries and as the economy grow fossilfuel-derived CO2 emission increased more as compared to human-derived emission. These findings indicate that population growth especially in developing countries is a critical factor for manipulation of global CO2 increase.

Carbon footprint is the total amount of CO2 emissions by particular product or service system in it full life cycle, or, it is the total amount of direct and indirect CO2 emissions by activity principals. There are significant differences of provincial total carbon footprint result from the different energy efficiency, final demand and input-output relationship of intermediate products. Based on the Structure Decomposition Analysis and input-output model, the differences of carbon footprint between Beijing and Tianjin are analyzed in this paper. The results show that the total carbon footprint is higher in Beijing than that in Tianjin. The effect of carbon emission intensity on carbon footprint in Beijing is lower than Tianjin by 0.008 billion tons CO2; according to the complicated relationship between industries in Beijing, there is 0.029 billion tons CO2 more the carbon footprint than Tianjin, The demand scale and structure is higher than Tianjin, So in the factors of final requirements on carbon footprint, the carbon footprint of Beijing is higher than Tianjin by 0.058 billion tons CO2.

Methane emissions along biomethane and biogas supply chains are underestimated

Many studies have evaluated CO2 emission from batteries. However, the impact of Li-ion battery (LiB) degradation on the CO2 emissions from the material through operation phases has not been sufficiently examined. This study aims to clarify the dominant CO2 emission phase and the impact of the degradation of general industrial LiBs from repetitive cycle applications. We developed a model common to general LiB composition and calculated CO2 emissions by the LCA method using the IDEA database. Our model simplifies the degradation process, including capacity decrease and internal resistance increase. We used it in a sensitivity analysis of the carbon intensity of electricity charged to a LiB. The loss mechanism was determined by experimental data for an electric bus with an industrial LiB. The results illustrate that the carbon intensity of electricity affects CO2 emissions dominance, the operation phase for mix (71.3%), and the material phase for renewables (70.9%), and that battery degradation over six years increases the total amount of CO2 emissions by 11.8% for mix and 3.9% for renewables equivalent. Although there are limitations regarding the assumed conditions, the present results will contribute to building a method for monitoring emissions and to standardizing degradation calculations.

The issues of energy consumption and CO2 emissions of major ironmaking processes, including several new technologies, are assessed. These two issues are interconnected in that the production and use of fuels to generate energy add to the total amount of CO2 emissions and the efforts to sequester or convert CO2 require energy. The amounts of emissions and energy consumption in alternate ironmaking processes are compared with those for the blast furnace, currently the dominant ironmaking process. Although more than 90% of iron production is currently through the blast furnace, intense efforts are devoted to developing alternative technologies. Recent developments in alternate ironmaking processes, which are largely driven by the needs to decrease CO2 emissions and energy consumption, are discussed in this article. This discussion will include the description of the recently developed novel flash ironmaking technology. This technology bypasses the cokemaking and pelletization/sintering steps, which are pollution prone and energy intensive, by using iron ore concentrate. This transformational technology renders large energy saving and decreased CO2 emissions compared with the blast furnace process. Economic analysis indicated that this new technology, when operated using natural gas, would be economically feasible. As a related topic, we will also discuss different methods for computing process energy and total energy requirements in ironmaking.

In this study, greenhouse gas (GHG) differences due to the application of biomass content are compared at a sewage sludge incinerator. The result of the comparison shows that the differences between the methods of GHG emission estimation based on biomass fraction analysis (sewage sludge analysis and sewage sludge flue gas analysis) were not substantial. On the other hand, the GHG emission estimated from the method in this study showed a difference of 8–9 ton CO2eq/day from the currently used method in Korea. This implies that the latter underestimates the GHG emissions because CO2 emission was not taken into account upon estimating the GHG emission from sewage sludge. Therefore, it has been determined that, from now on, emissions due to CO2 should be reflected in the estimation of GHG emission from sewage sludge.

In this study, the fossil carbon contents of the two facilities were analyzed using 10 or more samples for each facility from June 2013 to March 2015. In addition, the optimal measurement period was calculated from the analyzed fossil carbon contents using a statistical method. As a result of the analysis, the fossil carbon contents were found to be less than 35%, indicating that the biomass content of sewage sludge was not 100%. The fossil carbon content could be representative of using yearly period measurements value. When calculating Green house gas (GHG) emissions from waste incineration, South Korea has been calculating only Non-CO2 emissions because it regarded the CO2 emitted in GHGs from sewage sludge (SS) incineration facilities as originating from biomass. However, biomass of the sewage sludge incineration facility is not 100%, so it is necessary to estimate the greenhouse gas emissions considering the fossil carbon content. Therefore, there is a need to increase the reliability of the greenhouse gas inventory by conducting further studies (such as CO2 concentration analysis) related to the calculation of CO2 emissions for the relevant facilities (sewage sludge incinerator).

Energy supply utilities release significant amounts of greenhouse gases (GHGs) into the atmosphere. It is essential to accurately estimate GHG emissions with their uncertainties, for reducing GHG emissions and mitigating climate change. GHG emissions can be calculated by an activity-based method (i.e., fuel consumption) and continuous emission measurement (CEM). In this study, GHG emissions such as CO2, CH4, and N2O are estimated for a heat generation utility, which uses bituminous coal as fuel, by applying both the activity-based method and CEM. CO2 emissions by the activity-based method are 12–19% less than that by the CEM, while N2O and CH4 emissions by the activity-based method are two orders of magnitude and 60% less than those by the CEM, respectively. Comparing GHG emissions (as CO2 equivalent) from both methods, total GHG emissions by the activity-based methods are 12–27% lower than that by the CEM, as CO2 and N2O emissions are lower than those by the CEM. Results from uncertainty estimation show that uncertainties in the GHG emissions by the activity-based methods range from 3.4% to about 20%, from 67% to 900%, and from about 70% to about 200% for CO2, N2O, and CH4, respectively, while uncertainties in the GHG emissions by the CEM range from 4% to 4.5%. For the activity-based methods, an uncertainty in the Intergovernmental Panel on Climate Change (IPCC) default net calorific value (NCV) is the major uncertainty contributor to CO2 emissions, while an uncertainty in the IPCC default emission factor is the major uncertainty contributor to CH4 and N2O emissions. For the CEM, an uncertainty in volumetric flow measurement, especially for the distribution of the volumetric flow rate in a stack, is the major uncertainty contributor to all GHG emissions, while uncertainties in concentration measurements contribute a little to uncertainties in the GHG emissions.Implications:Energy supply utilities contribute a significant portion of the global greenhouse gas (GHG) emissions. It is important to accurately estimate GHG emissions with their uncertainties for reducing GHG emissions and mitigating climate change. GHG emissions can be estimated by an activity-based method and by continuous emission measurement (CEM), yet little study has been done to calculate GHG emissions with uncertainty analysis. This study estimates GHG emissions and their uncertainties, and also identifies major uncertainty contributors for each method.

There are many technical challenges and opportunities ahead as sustainability in the built environment becomes a major economic force in relation to both building construction and energy supply. Climate change, the depletion of fossil fuel energy sources, and the security of future energy supplies are becoming a major concern for all countries. Materials and water are also becoming short in supply. All these issues are a clear indication that we are living in unsustainable ways. Many countries are developing at a rapid pace and the global rate of building construction is unprecedented, generally with little attention given to the impact that the construction and operation of these building will have on the environment, both locally and from a global perspective. In 2007, the Intergovernmental Panel on Climate Change (IPCC) reported a 90% certainty that global warming is caused by human activity associated with greenhouse gas emissions. The increased CO2 level in the global atmosphere is now a major concern. The reports states that if we are to avoid catastrophic climate change, average temperature rises must be maintained within 28C which equates to carbon dioxide (CO2) levels in the atmosphere staying below 450 ppm. Perhaps the most alarming aspect of this is that we must take action within 10 years from 2007. That means we have only 8 years, not just to plan, but to act on the sustainability issues concerning the built environment. In the longer term the planet needs to achieve carbon neutrality. If we take the view of climate change modellers that we have a finite amount of CO2 that we can put into the atmosphere before the 28C limit is exceeded then we must budget this over a period of time. We might take the view that the carbon neutrality target is to be achieved by the end of this century and that the amount of emissions must be agreed globally, with some countries allowed to continue to increase their emissions, whilst other more developed countries begin to reduce sooner. At some stage all countries would emit the same per capita and then reduce to zero carbon. This is called the contraction and convergence principle. So what role should the built environment play in this? In a global context the built environment is said to be responsible for about 50% of emissions and 70% if we include transportation associated with mobility within the built environment. It is convenient to divide the built environment into new build, existing buildings and supporting infrastructures (for transport, water/sewage, waste and energy supply). Probably the easiest sector to deal with first is new build. As they are likely to be around for some time it is important that they perform well in relation to CO2 emissions. Many governments worldwide are developing policies to reduce CO2 emissions. In the UK, the government has set a target for all new houses to be ‘‘zero carbon’’ by 2016, and in some regions of the UK, for example Wales, the regional government has set a target for all new buildings to be zero carbonby2011.Thedefinitionofazerocarbonbuilding in its simplest form is that it has a reduced energy demand for thermal energy and power and that the supply is from renewable energy sources, integrated into the building,

Forecasting of carbon dioxide emissions from power plants in Kuwait using United States Environmental Protection Agency, Intergovernmental panel on climate change, and machine learning methods