Integrated carbon emission estimation method for construction operation and project scheduling

Low carbon construction is an important operation management goal because greenhouse gas (GHG) reduction has become a global concern. Major construction resources that contribute GHG, such as equipment and labour, are being targeted to achieve this goal. The GHG emissions produced by the resources vary with their operating conditions. It is commendable to provide a statistical GHG emission estimation method that models the transitory nature of resource states at micro-scale of construction operations. This paper proposes a computational method called Stochastic Carbon Emission Estimation (SCE2) that measures the variability of GHG emissions. It creates construction operation models consisting of atomic work tasks, utilizes hourly equipment fuel consumption and hourly labourer respiratory rates that change according to their operating conditions classified into five categories, and identifies an optimal resource combination by trading off eco-economic performance metrics such as the amount of GHG emissions, operation completion time, operation completion cost, and productivity. The study is of value to researchers because SCE2 fill in a gap to eco-economic operation modelling and analysis tool which considers operating conditions at micro-scale of construction operation having many stochastic work tasks. This study is also relevance to practitioners because it allows project managers to achieve eco-economic goals while honouring predefined constraints associated with time and cost.

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A Study on Sustainable Consumption of Fuel—An Estimation Method of Aircraft

Energies ◽

10.3390/en14227559 ◽

2021 ◽

Vol 14 (22) ◽

pp. 7559

Author(s):

Lisha Li ◽

Shuming Yuan ◽

Yue Teng ◽

Jing Shao

Keyword(s):

Neural Network ◽

Fuel Consumption ◽

Influencing Factors ◽

Carbon Emission ◽

Estimation Method ◽

Civil Aviation ◽

Accurate Estimation ◽

Low Carbon ◽

Fuel Price ◽

Aircraft Fuel

Though the development of China’s civil aviation and the improvement of control ability have strengthened the safety operation and support ability effectively, the airlines are under the pressure of operation costs due to the increase of aircraft fuel price. With the development of optimization controlling methods in flight management systems, it becomes increasingly challenging to cut down flight fuel consumption by control the flight status of the aircraft. Therefore, the airlines both at home and abroad mainly rely on the accurate estimation of aircraft fuel to reduce fuel consumption, and further reduce its carbon emission. The airlines have to take various potential factors into consideration and load more fuel to cope with possible negative situation during the flight. Therefore, the fuel for emergency use is called PBCF (Performance-Based Contingency Fuel). The existing PBCF forecasting method used by China Airlines is not accurate, which fails to take into account various influencing factors. This paper aims to find a method that could predict PBCF more accurately than the existing methods for China Airlines.This paper takes China Eastern Airlines as an example. The experimental data of flight fuel of China Eastern Airlines Co, Ltd. were collected to find out the relevant parameters affecting the fuel consumption, which is followed by the establishment of the LSTM neural network through the parameters and collected data. Finally, through the established neural network model, the PBCF addition required by the airline with different influencing factors is output. It can be seen from the results that the all the four models are available for the accurate prediction of fuel consumption. The amount of data of A319 is much larger than that of A320 and A330, which leads to higher accuracy of the model trained by A319. The study contributes to the calculation methods in the fuel-saving project, and helps the practitioners to learn about a particular fuel calculation method. The study brought insights for practitioners to achieve the goal of low carbon emission and further contributed to their progress towards circular economy.

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An extended Kalman-filter for regional scale inverse emission estimation

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-29195-2011 ◽

2011 ◽

Vol 11 (10) ◽

pp. 29195-29249 ◽

Cited By ~ 3

Author(s):

D. Brunner ◽

S. Henne ◽

C. A. Keller ◽

S. Reimann ◽

M. K. Vollmer ◽

...

Keyword(s):

Kalman Filter ◽

Dispersion Model ◽

Regional Scale ◽

Estimation Method ◽

Particle Dispersion ◽

Montreal Protocol ◽

Correlated Noise ◽

Temporal Correlations ◽

Emission Estimation ◽

Hfc 152A

Abstract. A Kalman-filter based inverse emission estimation method for long-lived trace gases is presented for use in conjunction with a Lagrangian particle dispersion model like FLEXPART. The sequential nature of the approach allows tracing slow seasonal and interannual changes rather than estimating a single period-mean emission field. Other important features include the estimation of a slowly varying concentration background at each measurement station, the possibility to constrain the solution to non-negative emissions, the quantification of uncertainties, the consideration of temporal correlations in the residuals, and the applicability to potentially large inversion problems. The method is first demonstrated for a set of synthetic observations created from a prescribed emission field with different levels of (correlated) noise, which closely mimics true observations. It is then applied to real observations of the three halocarbons HFC-125, HFC-152a and HCFC-141b at the remote research stations Jungfraujoch and Mace Head for the quantification of emissions in Western European countries from 2006 to 2010. Estimated HFC-125 emissions are mostly consistent with national totals reported to the Kyoto protocol and show a generally increasing trend over the considered period. Results for HFC-152a are much more variable with estimated emissions being both higher and lower in different countries. The highest emissions of the order of 1000 Mg yr−1 are estimated for Italy which so far does not report HFC-152a emissions. Emissions of HCFC-141b show a continuing strong decrease as expected due to its ban under the Montreal Protocol. Emissions from France, however, were still rather large (near 1000 Mg yr−1) in the years 2006 and 2007 but strongly declined thereafter.

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Seasonal Urban Carbon Emission Estimation Using Spatial Micro Big Data

Sustainability ◽

10.3390/su10124472 ◽

2018 ◽

Vol 10 (12) ◽

pp. 4472 ◽

Cited By ~ 5

Author(s):

Yoshiki Yamagata ◽

Takahiro Yoshida ◽

Daisuke Murakami ◽

Tomoko Matsui ◽

Yuki Akiyama

Keyword(s):

Big Data ◽

Hot Spots ◽

Carbon Emission ◽

Energy Use ◽

Three Dimensional ◽

3D Mapping ◽

Transportation Energy ◽

Transportation Emissions ◽

Emission Estimation ◽

First Time

The objective of this study is to map direct and indirect seasonal urban carbon emissions using spatial micro Big Data, regarding building and transportation energy-use activities in Sumida, Tokyo. Building emissions were estimated by considering the number of stories, composition of use (e.g., residence and retail), and other factors associated with individual buildings. Transportation emissions were estimated through dynamic transportation behaviour modelling, which was obtained using person-trip surveys. Spatial seasonal emissions were evaluated and visualized using three-dimensional (3D) mapping. The results suggest the usefulness of spatial micro Big Data for seasonal urban carbon emission mapping; a process which combines both the building and transportation sectors for the first time with 3D mapping, to detect emission hot spots and to support community-level carbon management in the future.

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Development of Carbon Emission Estimation System in Port

10.14257/astl.2015.98.16 ◽

2015 ◽

Author(s):

Hyung Rim Choi ◽

Sung Il Byeon ◽

Chang Hyun Park

Keyword(s):

Carbon Emission ◽

Emission Estimation ◽

Estimation System

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CARBON EMISSION ESTIMATION DUE TO LAND COVER CHANGE IN THE TROPICAL FOREST LANDSCAPE IN JAMBI PROVINCE

Jurnal Sains dan Teknologi Mitigasi Bencana ◽

10.29122/jstmb.v14i1.3561 ◽

2019 ◽

Vol 14 (1) ◽

pp. 27-34

Author(s):

Dian Nuraini Melati

Keyword(s):

Land Cover ◽

Greenhouse Gases ◽

Land Cover Change ◽

Carbon Emission ◽

Forest Cover ◽

Secondary Forest ◽

Anthropogenic Activities ◽

Global Environmental Change ◽

Dryland Agriculture ◽

Emission Estimation

Land use land cover change and forestry play an important role in the global environmental change. Anthropogenic activities in changing the land have caused earth surface change. This change has a role to increase the change of global greenhouse gases in the atmosphere which also causes the increase greenhouse gases emission. Land cover change and forestry are sectors which cause high carbon emission. Therefore, a study in land cover change and estimation of carbon emission becomes important. This study took place in Jambi Province where deforestation has been in a high pace. In 2009 and 2011, the dominant area is dryland agriculture mixed with bush followed by secondary forest, i.e. 25% and 18.6%, respectively (in 2009); and 37.1% and 18.9%, respectively (in 2011). For the secondary forest, the gain was caused by the conversion of dryland agriculture mixed with bush and shrub into secondary forest. The loss of secondary forest is the highest among other forest cover at around 87,765 Ha due to the conversion into bare land and dryland agriculture mixed with bush. Due to land cover change in Jambi Province, the estimation of nett emission in the period of 2009-2011 is 4.8 Mt CO2-eq/year.

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Estimation of Air Pollutant Emissions in Eastern Indonesia from Non-Oil and Gas Sources (Case Study in Sulawesi and Papua)

JKPK (Jurnal Kimia dan Pendidikan Kimia) ◽

10.20961/jkpk.v3i3.24754 ◽

2018 ◽

Vol 3 (3) ◽

pp. 152

Author(s):

Dessy Gusnita ◽

Dita Fatria

Keyword(s):

Air Pollutants ◽

Oil And Gas ◽

Estimation Method ◽

Air Pollutant ◽

Pollutant Emissions ◽

Pollutant Emission ◽

Eastern Indonesia ◽

South Sulawesi ◽

Emission Estimation ◽

Papua Barat

Estimation of air pollutant emissions from non-oil and gas sources in eastern Indonesia, namely Sulawesi and Papua provinces during the period 2014 – 2016 was conducted. This paper intended to estimate the emission of three air pollutants namely NOx, SO2 and CO2. The aim was to find out the amount of pollutant and greenhouse gas (GHG) emissions in the Sulawesi and Papua regions. The method used was the emission estimation method based on statistical data of Gross Regional Domestic Income (GRDP) in the Papua and Sulawesi regions. The results from estimation of pollutant emissions was then carried out for pollutant emissions mapping. The pollutant emission estimation showed the emission of air pollutants in Sulawesi region was higher than Papua. The mapping of emissions in Sulawesi were consisted of four provinces, namely north, central, south and southeast Sulawesi. The Papua region were consisted of Papua and west Papua provinces. The highest emission in Sulawesi region was south Sulawesi. The CO2 emission in Sulawesi was increase about 23% with the detail value; 84.4 tons in 2014; 94.3 tons in 2015; and 103.7 tons in 2016. The emission of NOx during 2014 until 2016 are 0.53, 0.58 and 0.64 tons, there was an increasing in the emission of NOx around 21%. In addition, SO2 emission of south Sulawesi are 0.42 tons in 2014, 0.47 tons in 2015 and 0.51 tons in 2016, increased about 21 % during the year 2014 - 2016. In the Papua region, the emission in Papua was higher than Papua Barat province. CO2 emissions in Papua during 2014 -2016 were 112, 124.8 and 144.99 tons, it means the CO2 was increased 29%. The emission of NOx during 2014-2016 were 0.70, 0.77 and 0.89 tons, increased around 27%. In addition, SO2 emission was increase 26% with the detail value; 0.56 tons in 2014; 0.61 tons in 2015 and 0.71 tons in 2016.

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